Insights

When Healthcare Data Analysts Fulfill the Data Detective Role

There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:

  1. They are inquisitive and relentless with their questions.
  2. They let the data inform.
  3. They drive to the heart of what matters.
Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.

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The Six Care Management Challenges Healthcare Must Overcome

Most health systems struggle to succeed in care management. Whether it’s the frustrating, manual, fragmented processes or the ubiquitous lack of standardization in care management, health systems aren’t alone when it comes to the six care management challenges they struggle to overcome:

  1. Fragmentation
  2. Limited data access.
  3. Poor data quality.
  4. Limited involvement in IT and data governance.
  5. Lack of standardization.
  6. Limited visibility and transparency for program evaluation.
The consequences of these challenges are widespread, ranging from wasting valuable staff time to delaying patient entry into the right care management program. Although far from easy, overcoming these challenges is a must for the industry to achieve the Triple Aim. Fortunately, the future of care management—automated, streamlined, and patient centric—is bright.

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How Healthcare AI Makes Machine Learning Accessible to Everyone in Healthcare

Before the introduction of healthcare.ai, an open source, healthcare-specific machine learning software, only a small subset of healthcare staff (primarily data scientists) had the ability to leverage predictive analytics to improve outcomes. Healthcare.ai will democratize machine learning by empowering everyone in healthcare with the appropriate technical skills (BI developers, project managers, data architects, etc.) to download the healthcare.ai tools (packages for R and Python), request features, ask questions, and contribute code. What sets healthcare.ai apart from other machine learning tools is its healthcare-specific functionality:

  • Pays attention to longitudinal questions.
  • Offers an easy way to do risk-adjusted comparisons.
  • Provides easy connections and deployment to databases.
Healthcare.ai will do more than just democratize machine learning—it will transform healthcare.

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Patient-Centric Care Management Is a Strong Strategy

Care management is an important field in healthcare that ensures cost-effective, timely, and personalized care. Essentially, it gets the right care to the right patients at the right time. An effective care management system is defined by three components:

  1. The fundamental of patient-centered care: understanding each patient’s individual needs, developing relationships with them, and providing tailored care.
  2. The technology to deliver real-time data and support the workflows and processes of care management teams.
  3. A culture of continuous improvement integrated throughout the organization. A care management platform must be supported by best practices, analytics, and adoption to lead and sustain outcomes improvement.
This article explores the principles of good care management through these components, and through the eyes of a physician who understands the powerful impact of treating patients with one-to-one care.

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Understanding Risk Stratification, Comorbidities, and the Future of Healthcare

Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.

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The Case for Care Management: Arline’s Story

Patients with complex care needs, like Arline in this real-life story, account for the highest percentage of costs. Yet, they aren’t necessarily receiving the best care. A care management program for these patients can make all the difference by helping patients and caregivers more effectively manage their health conditions. It takes time, effort, and the implementation of new care delivery models and support systems to realize those benefits, however.

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The Best Way Hospitals Can Engage Physicians, Nurses, and Staff

A big key to improving quality and patient care is engaging physicians and nurses. As many healthcare systems begin to implement improvement initiatives, they must ensure their clinicians are supportive and engaged in order to achieve success. Senior-level executives need to understand the challenges their clinical staff are facing in feeling overwhelmed, having too little time, as well as not really understanding new risk-based payment models. Knowing what motivates physicians and nurses to engage (and what doesn’t) ensures process improvements become tangible, sustainable, while at the same time building trust between clinicians and the healthcare organization.

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Introducing the Breakthrough Health Catalyst Care Management Product Suite

Health systems are faced with the challenge of doing more than just reducing costs and improving quality of care—they must maximize their Return on Engagement by identifying and working with the patients they’ll impact the most. Health Catalyst’s Care Management Suite promises to help systems identify and improve the outcomes for these patients by delivering a comprehensive population health approach that addresses the five critical parts of any successful care management program:

  1. Data Integration
  2. Patient Stratification and Intake
  3. Care Coordination
  4. Patient Engagement
  5. Performance Measurement
What’s unique about Care Management Suite is it’s innovative, multi-pronged approach. It’s a mobile-first, patient-centric, end-to-end solution designed to help healthcare organizations succeed in a value-based world.

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5 Reasons the Practice of Evidence-Based Medicine Is a Hot Topic

Evidence-based medicine is an important model of care because it offers health systems a way to achieve the goals of the Triple Aim. It also offers health systems an opportunity to thrive in this era of value-based care. In specific, there are five reasons the industry is interested in the practice of evidence-based medicine: (1) With the explosion of scientific knowledge being published, it’s difficult for clinicians to stay current on the latest best practices. (2) Improved technology enables healthcare workers to have better access to data and knowledge. (3) Payers, employers, and patients are driving the need for the industry to show transparency, accountability, and value. (4) There is broad evidence that Americans often do not get the care they need. (5) Evidence-based medicine works. While the practice of evidence-based medicine is growing in popularity, moving an entire organization to a new model of care presents challenges. First, clinicians need to change how they were taught to practice. Second, providers are already busy with increasingly larger and larger workloads. Using a five-step framework, though, enables clinicians to begin to incorporate evidence-based medicine into their practices. The five steps include (1) Asking a clinical question to identify a key problem. (2) Acquiring the best evidence possible. (3) Appraising the evidence and making sure it’s applicable to the population and the question being asked. (4) Applying the evidence to daily clinical practice. (5) Assessing performance.

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The Top Seven Quick Wins You Get with a Healthcare Data Warehouse

In an industry known for its complex challenges that can take years to overcome, health systems can leverage healthcare data warehouses to generate seven quick wins—reporting and analytics efficiencies that empower healthcare organizations to thrive in a value-based world:

  1. Provides significantly faster access to data.
  2. Improves data-driven decision making.
  3. Enables a data-driven culture.
  4. Provides world class report automation.
  5. Significantly improves data quality and accuracy.
  6. Provides significantly faster product implementation.
  7. Improves data categorization and organization.
Health systems that leverage healthcare data warehouses position themselves to do more than just survive the transition to value-based care; they empower themselves to achieve and sustain long-term outcomes improvement by enabling data-driven decision making based on high quality data.

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Improve Patient Engagement with Five Public Health-Inspired Principles

Patient engagement is critical as we move toward population health—as patients who engage in their own care by following medical recommendations and making healthy nutrition and lifestyle choices will have better outcomes and experiences. There isn’t, however, a clear path to successful patient engagement. Fortunately, public health can lend several established principles that may help us better involve patients in their own care:

  1. Using systematic, population-level solutions that require less individual effort.
  2. Engaging patients on interpersonal and community levels as well as personal.
  3. Identifying root-cause, assessing and capitalizing on strengths, and engaging stakeholders.
  4. Using strategies from behavioral economics to help individuals make good choices.
  5. Anticipating failure and learning from it.

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The Four Balancing Acts Involved with Healthcare Data Security Frameworks

There’s a lot at stake for healthcare organizations when it comes to securing data. A primary concern is to protect privacy and avoid costly breaches or leaks, but at the same time, data must be accessible if it’s to be used for actionable insights. This executive report introduces four balancing acts that organizations must maintain to build an ideal data security framework:

  1. Monitoring
  2. Data de-identification
  3. Cloud environments
  4. User access
This can be a tug-of-war between IT and security, two groups that often have divergent interests, however well-meaning they may be. Healthcare systems that build bridges between these interests and strike the crucial balance between data utilization and security can dial in on long-term goals, like better care at a lower cost and overall outcomes improvement.

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Healthcare Information Systems: A Look at the Past, Present, and Future

Healthcare information systems are integral to hospital operations and clinical care for patients. In the 1960s healthcare was driven by Medicare and Medicaid and HIT developed shared hospital accounting systems. In the 1970s communication between departments and individual transactional systems became important. DRGs drove healthcare in the 1980s and HIT needed to find ways to pull both clinical and financial data in order for reimbursements. The 1990s saw competition and consolidation drive technology to create IDN-like integration. In the 2000s outcomes-based reimbursement became the drive behind developing real-time clinical decision support. For the future, ACOs and value-based purchasing means that CIOs will need to implement data warehouses and analytics application to provide the insights to drive performance improvement necessary for hospital survival.  

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Eight Reasons Why Chief Data Officers Will Help Healthcare Organizations Thrive in the Future

The state of healthcare information technology and analytics has evolved to the point where a revised executive structure is advisable in the C-suite. This new structure calls for a Chief Data Officer (CDO) to focus on extracting data from systems and on mining value from that data, rather than getting data into systems, which is the responsibility of the CIO. This article makes the case for the CDO, explains how the need for this emerging role evolved, outlines its responsibilities, advises on how to recruit and budget for this position, and details its domain in eight critical business areas:

  1. Governance and standards
  2. Managing risk
  3. Reducing costs
  4. Driving innovation
  5. Data architecture and technology
  6. Data analytics
  7. Meeting regulatory demand
  8. Creating business value

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Healthcare Dashboards vs. Scorecards: Use Both to Improve Outcomes

Healthcare IT leaders tend to debate over which tool is best for measuring and sustaining outcomes improvement goals: healthcare dashboards or scorecards. But using both tools is the most effective approach. “Scoreboards” take advantage of the high-level, strategic capacity of scorecards and the real-time, operational functionality of dashboards. But using both effectively requires a thorough understanding of the who, what, when, and how of each tool.

  • Who: Scorecards are for leaders; dashboards are for the frontline.
  • What: Scorecards are strategic; dashboards are operational.
  • When: Scorecards are daily, weekly, or monthly reports; dashboards are real-time or near real-time.
  • How: Scorecards enforce accountability and provide actionable data; dashboards provide drill-down capability and inform root cause.
Despite the different but equally important aspects of each tool, they best support outcomes improvement when used together.

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The Top Seven Analytics-Driven Approaches for Reducing Diagnostic Error and Improving Patient Safety

From a wrong diagnosis to a delayed one, diagnostic error is a growing concern in the industry. Diagnostic error consequences are severe—they are responsible for 17 percent of preventable deaths (according to a Harvard Medical Practice study) and account for the highest portion of total payments (32.5 percent), according to a 1986-2010 analysis of malpractice claims. Patient safety depends heavily on getting the diagnosis right the first time. Health systems know reducing diagnostic error to improve patient safety is a top priority, but knowing where to start is a challenge. Systems can start by implementing the top seven analytics-driven approaches for reducing diagnostic error:

  1. Use KPA to Target Improvement Areas
  2. Always Consider Delayed Diagnosis
  3. Diagnose Earlier Using Data
  4. Use the Choosing Wisely Initiative as a Guide
  5. Understand Patient Populations Using Data
  6. Collaborate with Improvement Teams
  7. Include Patients and Their Families

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Evaluating an EHR-Centric vs Data Warehouse-Centric Analytics Strategy: Seven Points to Ponder

Too much is at stake in value-based healthcare and the technology needed to provide it. When it comes to investing in the best healthcare analytics tools for delivering data-driven care management and outcomes improvement, executives should compare these seven points to determine whether an electronic health record or an enterprise data warehouse should be the foundation of their analytics platform:

  1. Incorporating data from a wide range of sources
  2. Ease of reporting
  3. The data mart concept
  4. Relevance of each to value-based care
  5. Relevance of each to managing population health
  6. Surfacing results of sophisticated analysis for physicians at the right time
  7. Ability to combine best practices, data, and technology tools into a system of improvement
This executive report starts by examining the origin of EHRs and EDWs, then dives into the value derived from both in terms of their contributions to the major issues impacting healthcare delivery today.

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The Key to Transitioning from Fee-for-Service to Value-Based Reimbursement

The shift from fee-for-service to value-based reimbursements has good and bad consequences for healthcare. While the shift will ultimately help health systems provide higher quality lower cost care, the transition may be financially disastrous for some. In addition, the shifting revenue mix from commercial payers to Medicare and Medicaid is creating its own set of challenges. There are, however, three keys to surviving the transition: 1) Effectively manage shared savings programs to maximize reimbursement. 2) Improve operating costs. 3) Increase patient volumes. With an analytics foundation, health systems will be able to meet and survive today’s healthcare challenges.

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The 6 Critical Components of Population Health

This article examines how to define population health through a review of the top analytics research firms. It lands on a single theme, but in the process it uncovers six common categories of IT capabilities required to successfully manage population health:

  1. Data Aggregation
  2. Patient Stratification
  3. Care Coordination
  4. Patient Engagement
  5. Performance Reporting
  6. Administrative/Business
These six strategic components define the population health ecosystem, and successful organizations must multitask across these domains, working with an enterprise data warehouse, if they hope to thrive in value-based healthcare and become true partners and assets in their respective communities.

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Aiding Analytics Adoption Via Metadata-Driven Architecture: If You Build It, They Will Come

A key feature of effective analytics infrastructure in healthcare is a metadata-driven architecture. In this article, three best practice scenarios are discussed:

  • Automating ETL processes so data analysts have more time to listen and help end users
  • Using a metadata repository to enhance data literacy among users and improve trust in data, thus enabling data governance policies
  • Improving turnaround time for data analysts who support frontline staff who, in turn, monitor interventions based on evidence-based medicine that is constantly changing
The article unravels the components of the metadata-driven architecture as part of an overall analytics platform. Learn the methodology for creating faster data results, generating speed to value, and realizing systemwide analytics adoption.

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HAS 16 Metrics: The Who, What, Why…and 80’s Nostalgia

The HAS 16 Metrics have been compiled into this infographic. See what 1,045 attendees from 281 organizations think about the future of value-based care and analytics. Seventy-four percent of attendees identify as healthcare providers, and 95 percent say the role of analyst within their organization is important or very important. Most participants (80 percent) think that value-based reimbursement will make the quality of care better or somewhat better. And, in reference to the 80’s Night held at HAS 16, most attendees picked Super Mario Brothers as their favorite 80’s video game, narrowly edging out Pac-Man.

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Quality Data Is Essential for Doctors Concerned with Patient Engagement

It might be a bit of a leap to associate quality data with improving the patient experience. But the pathway is apparent when you consider that physicians need data to track patient diagnoses, treatments, progress, and outcomes. The data must be high quality (easily accessible, standardized, comprehensive) so it simplifies, rather than complicates, the physician’s job. This becomes even more important in the pursuit of population health, as care teams need to easily identify at-risk patients in need of preventive or follow-up care. Patients engaged in their own care via portals and personal peripherals contribute to the volume and quality of data and feel empowered in the process. This physician and patient engagement leads to improved care and outcomes, and, ultimately, an improved patient experience.

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How to Create Change Agents Who Will Sustain those Hard-earned Healthcare Improvements

Establishing a healthcare improvement initiative is just the first step toward transformation. The real work of improvement lies in sustaining it, which is why qualified change agent are essential to meaningful progress. Change agents are trained to lead organizations in:

  • Case for change
  • Data management
  • Change management concepts
  • Cost Benefit Analysis
  • Integration
Health Catalyst’s Accelerated Practices Program gives change agents adaptive leadership training to guide systemwide change within their organizations. They are prepared to meet technical adaptive challenges while keeping teams engaged and productive, and, importantly, to use data analysis to improve quality, cost, and patient satisfaction outcomes.

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The Powerhouse Data Analytics and Visualization Tool That Excels

There are many advanced tools that come to mind when considering healthcare data analytics and visualization. Microsoft Excel may not necessarily make the list, but it has distinct advantages, the least of which are that it’s already installed on your system and that you already know how to use it. Healthcare finance folks already know the capabilities of Excel when it comes to quantitative analysis. Excel also deserves a place on the podium when it comes to pulling data from the warehouse and from various source marts. Excel pivot tables are extraordinary for providing ad hoc analysis. And when preceded by dimensional modeling—with the help of Health Catalyst’s data architects—Excel can easily transform large datasets. This article summarizes all of the surprising features that Excel brings to the data analytics and visualization table.

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Thursday Recap: 2016 Healthcare Analytics Summit Finale

Thursday at the 2016 Healthcare Analytics Summit started with a moving presentation from Dr. Don Berwick. Keynotes from Liz Wiseman, Dr. David Torchiana, and Dr. Strauss rounded out the general session. Breakouts included the word debut of a card game designed to teach outcomes improvement governance principles, a comprehensive lesson in running a successful care management program, and much more.

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Wednesday Recap: 2016 Healthcare Analytics Summit Kicks Off

On Wednesday during the 2016 Healthcare Analytics Summit, attendees heard from Anne Milgram on how criminal justice can provide insights for healthcare, from Dr. Bishoff on how Intermountain Healthcare improved care, from Dale Sanders and Dr. Freier on how a small town in Minnesota improved the lives of its citizens, and how a game of the Price Is Right by Tom Burton can teach us all a thing or two about surviving in a value-based world. This was in addition to 12 outstand breakout sessions from healthcare systems and experts from all over the nation.

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The Real Reason Healthcare Costs Are Elusive

These past few years have seen a lot of coverage on healthcare costs. But a majority of these articles just confuse the issue. Some of the reasons healthcare costs are elusive do not include: 1. Hospitals are hiding something. Or 2. There isn’t enough data. Instead, the real reasons behind the difficulty are: 1. Healthcare is complex. 2. Fragmentation. And, most importantly and pervasively, 3. Data governance. Until data governance becomes a priority, healthcare organizations will not be able to get clear answers for their healthcare costs.

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Winning Big with Your Healthcare Analytics Knowledge: HAS 16 The Price Is Right

It’s one thing to learn more about healthcare analytics and outcomes improvement, but quite another to put your knowledge to work in front of an audience for a chance to win great prizes. Selected participants at the HAS 16 game session, The Price Is Right, will take the stage to use their data expertise in activities modeled after the game show and redesigned around healthcare analytics principles. With real substance, the highly interactive session is designed to be as thorough a learning opportunity as it fun. Programming will cover:

  1. At-risk contracting
  2. Matching patients to programs
  3. Engaging all stakeholders
  4. The Necessity of the Three Systems

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Here’s What’s New with the Mobile App for the 2016 Healthcare Analytics Summit

Combine a registration site, networking and social media capabilities, a voting booth, an audience measurement device, a map, an audience participation tool, a QR code reader, a help desk, a calendar, and analytics software all into a single program and you get the Healthcare Analytics Summit app. For 2016, the HAS app holds a few new updates in store for attendees looking to enrich their conference experience. This article outlines what to expect from the new app and how it will help bring HAS 16 to life.

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“Moneyballing” Criminal Justice: Anne Milgram Is Fighting Crime with Data Science

Considering the impact that criminal justice has on society (from the cost of incarceration to our everyday safety), optimal decision making is paramount. To make the best choices, judges and law enforcement need data science. Without analytics, they’re left relying on instinct and experience, which, while highly valuable, are best employed together with research-based instruments. Having recognized this clear need in the criminal justice system, Anne Milgram has led the development of data-driven tools to support better decision making when it comes to differentiating between high- and low-risk criminals. This helps ensure that more dangerous individuals stay behind bars while their less threatening counterparts are freed (thereby freeing governments from the expense of unnecessary incarceration).

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HAS 16 Answers Calls for Intensive, Interactive Learning with New Deep Dives

Since HAS launched in 2014, the annual conference has conveyed and generated a lot of knowledge and also inspired an appetite for more—more in-depth explorations of complex topics, more time with substantive experts, and more opportunities for interaction among attendees. In 2016 the summit responds with an addition to the programming: three two-hour sessions that allow for expansive expert presentations and guided dialogue within the audience. Topics for these “deep dives” at HAS 16 include:

  • Partners’ Care Management Strategy: A10-Year Journey
  • Improving Outcomes in a Value-Based Environment: Holistic Care Management for Complex Medical Conditions
  • Leading Adaptive Change to Create Value

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Technical Sessions at This Year’s Healthcare Analytics Summit Offer New Advanced Topics

There’s something for everyone at the Healthcare Analytics Summit, but to stay ahead of the curriculum curve, Health Catalyst surveys attendees to learn how to improve. The surveys said there is a desire for higher level, hands-on technical training. This year, in addition to case study and educational breakout sessions, technical sessions will be offered for the first time. This new track features five sessions:

  1. FHIR’d up about Clinical Data Intelligence: Cleveland Clinic’s Real-Time Decision Support System
  2. Powerful Ways to Use Hadoop in your Healthcare Big Data Strategy
  3. Deploying Predictive Analytics: A Practitioner’s Guide
  4. Security Frameworks in Data Warehousing and Their Interplay with Healthcare Analytics
  5. Text Analytics: You Need More than NLP
Read more about what a technical session comprises, who should attend, and how the Healthcare Analytics Summit curriculum is more relevant than ever.

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Dr. Jay Bishoff to Speak About Trimming Waste in Healthcare at HAS 16

Many healthcare organizations find they are behind in quality improvement efforts and awash in a sea of wasteful spending. Dr. Jay Bishoff of Intermountain Health has many great talking points on how to deal with this, chief among them the need to get started now. In this article, and as a keynote speaker during the 2016 Healthcare Analytics Summit, he will elaborate on these seven principles of a quality improvement program:

  1. Explain the why behind any process improvement effort.
  2. Training may be needed to articulate the why, even for those who see and understand the problems.
  3. Pick something to improve and get started. Don’t overcomplicate this step.
  4. Gather data on the process being improved.
  5. Get everybody who’s involved in the process involved in the process improvement. Then diagram the people and processes.
  6. Develop leverage points where interventions can be inserted and from which changes in data can be measured.
  7. Adjust, measure the data again, and then move on to the next thing.

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The Who, What, and How of Health Outcome Measures

Even though thousands of health outcome measures have the potential to impact the work we do every day, how well do we really understand them? In this article, we take a close look at the definitions, origins, and characteristics of health outcome measures. We break down the financial relevance of certain measures, the relationship between outcome measures and ACOs, and which measures impede, rather than enhance, a typical healthcare system. We review the role of an enterprise data warehouse and analytics, and we touch on the future of health outcome measures, all in an effort to provide deeper insight into some of the mechanics behind outcomes improvement.

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The Top Two Reasons to Attend the Analytics Walkabout

There are several reasons to attend the Analytics Walkabout, back by popular demand at the 2016 Healthcare Analytics Summit, but there are two in particular that make this a not-to-miss event: #1: The rare chance to have one-on-one conversations with outcomes improvement team members. Analytics Walkabout attendees have the opportunity to meet, talk to, and learn from outcomes improvement teams from a wide range of healthcare organizations, from children’s hospitals to ACOs, about the strategies behind their successes. #2: More than 30 Clinical, Financial, Analytic, and Operational Outcomes Improvement Successes. The Analytics Walkabout showcases a variety of outcomes improvement focus areas, from clinical (improving sepsis) to financial (managing costs in an at-risk environment). Analytics Walkabout participants will come away with how-to strategies they can apply to their own improvement projects.

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The Top Five Recommendations for Improving the Patient Experience

Improving patient satisfaction scores and the overall patient experience of care is a top priority for health systems. It’s a key quality domain in the CMS Hospital Value-Based Purchasing (VBP) Program (25 percent) and it’s an integral part of the IHI Triple Aim. But, despite the fact that health systems realize the importance of improving the patient experience of care, they often use patient satisfaction as a driver for outcomes. This article challenges this notion, instead recommending that they use patient satisfaction as a balance measure; one of five key recommendations for improving the patient experience:

  1. Use patient satisfaction as a balance measure—not a driver for outcomes.
  2. Evaluate entire care teams—not individual providers.
  3. Use healthcare analytics to understand and act on data.
  4. Leverage innovative technology.
  5. Improve employee engagement.
This article also explains why patient experience is so closely tied to quality of care, and why it’s a prime indicator of a healthcare organization’s overall health.

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How to Prepare for Value-based Purchasing in 4 Steps

I spent many years of my career as a healthcare finance executive in the state of California, where I had the opportunity to participate in several groundbreaking pay-for-performance (P4P) initiatives. However today, things have grown much more complex and intimidating. Health systems have to report on Medicare quality measures that determine important incentives and penalties. Add to this the host of private payers who have established their own value-based programs, all of which require different metrics. In this post, I discuss 4 lessons learned for success in VBP: 1) assessing your performance 2) education programs, 3) your analytics strategy, and identifying areas for clinical quality and cost improvement.

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A Population Health Success Story: From Rural Action to Global Vision

Every 43 seconds, someone in the United States has a heart attack. They are the leading cause of death for both men and women, yet heart attacks are largely preventable through healthier lifestyles. So the community of New Ulm, Minnesota set out to combat this national trend in its own small-town way. This is the story behind the documentary film, Hearts Beat Back: The Heart of New Ulm Project, screening at the 2016 Healthcare Analytics Summit. Dale Sanders, along with New Ulm Medical Center President, Toby Freier, will lead a presentation titled, Population Health: Lessons from One of the Nation’s Most Innovative Rural Community Models, followed by the documentary film. It’s a look inside the 10-year journey that has spurred an entire community toward improved health with remarkable results. It’s destined to be an inspiration for all HAS attendees, as well as healthcare populations all across America.

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Engaging Physicians to Be Good Financial Stewards

This article, first published by in July 2016 by hfma, outlines how hospitals can get physicians to understand the financial impact of their clinical decisions and become actively engaged in improving the value of care. Texas Children’s Hospital was successful through recognizing the need for cultural transformation and ensuring quality came first. The organization engaged clinicians with financial data, including educating them on key financial principles, linking quality improvement training with financial accountability, and accompanying financial choices with clinical choices.

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Going Beyond Genomics in Precision Medicine: What’s Next

Precision medicine processes, while involving genomics, are not confined to working with data about an individual’s genes, environment, and lifestyle. Precision medicine also means putting patients on the right path of care, taking into consideration other individual tolerances, such as participation and cost. Precision medicine processes incorporate data beyond the individual, pulling in socio-economic data, as well as relevant internal and external data, to create an entire patient data ecosystem. With reusable data modules, this information is processed within a closed-loop analytics framework to facilitate clinical decision making at the point of care. This optimizes clinical workflow, thus leading to more precise medicine.

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9 Ways Your Outcomes Improvement Program Could Be Delayed

A health system’s outcomes improvement program is an expensive undertaking. It’s worth the results, but there’s no need to make it even more expensive through unforeseen and unnecessary delays. We outline the three phases of managing outcomes improvement programs, from hardware and software acquisition and configuration to resource management to sustaining and scaling the gains. We also examine the nine potential pitfalls that can undermine success in each of these phases:

  1. Hardware and software acquisition delays
  2. Environment readiness
  3. Source system access
  4. Lack of resource capacity
  5. Lack of analytic and technical skills
  6. Data quality paralysis
  7. Lack of clinical or operational engagement
  8. Punitive culture: data used as a weapon
  9. No CEO, no go

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How Physicians Can Prepare for the Financial Impact of MACRA

If all goes according to plan, the first performance period for the new Medicare Access and Chip Reauthorization Act (MACRA) is just around the calendar corner. It’s a complicated reimbursement structure with multiple tracks that are guaranteed to reward with bonuses or inflict pain through penalties in CMS’s new zero sum game. To the physicians and practices that adopt this new program early and position themselves for the best fiscal outcomes, go the spoils. But for many smaller practices and those that consistently underperform, the outlook may be glum regardless. Here are some highlights of the new program and the financial impact it will have on clinicians and practices.

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How Rookie-style Leadership Can Help Transform Healthcare, According to HAS 2016 Keynote Liz Wiseman

As we define critical goals in healthcare transformation (improved outcomes and control of cost, for example), it is just as important that we identify the type of leadership best suited for these objectives. And, much like we’re disrupting our approach to healthcare, we’re also disrupting some common notions about effective leadership. We may have traditionally valued leaders as absolute experts and indispensable parts of an organization. Effective leadership today, however, may be more rooted in what leaders don’t know—in other words, how they ask questions and capitalize on the knowledge around them. In answer to today’s leadership challenges, Liz Wiseman proposes what she calls “rookie smarts.” She argues that the real power lies in what we don’t know and that asking good questions outweighs knowing all the answers.

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The 7 Best Ways to Prepare for MACRA Today

The Medicare Access and CHIP Reauthorization Act (MACRA) was signed into law in 2015, with major impacts starting in 2019. MACRA attempts to prioritize quality over quantity by letting providers choose between two value-based payment tracks: MIPs and APMs. Providers won’t have to choose until 2019; until then, they will receive a .5 percent annual increase. The industry is conflicted about MACRA. On the one hand, many believe it is part of the overall shift to value-based healthcare. On the other hand, many say the administrative burden will overwhelm providers. Another area of MACRA controversy has to do with meaningful use which, contrary to what the CMS Acting Administrator said in 2016, isn’t going away with the introduction of MACRA. Although it seems a ways away, MACRA’s base year will likely be 2017. Armed with the seven best ways to start preparing for MACRA today, and an EDW that provides clinicians with the self-service tools to monitor their performance, health systems can be ready to tackle MACRA when it finally goes into effect.

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The Happy Marriage of Hospital Finance and Frontline Operations

The hospital finance department typically acts as administrator and controller over hospital operations, at least in the eyes of frontline clinicians. Additionally, finance is burdened with the day-today tasks of balancing the books. And all too often, finance thinks they know what their customers want, but customers think that finance is isolated, secretive, and bureaucratic. The hospital finance department needs a makeover. To transition into the role of valued business partner and financial expert, finance needs to reinvent itself by:

  1. Simplifying the flow of, and expand access to, information
  2. Repositioning financial analysts as experts
  3. Understanding what customers value
Learn how these straightforward business practices can support operations in their outcomes improvement efforts, and ultimately benefit the entire healthcare organization.

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From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More than Technology

The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:

  1. Installing an analytics platform
  2. Gaining adoption
  3. Implementing best practices
The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.

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Leading Adaptive Change to Create Value in Healthcare

In pursuit of the Triple Aim, healthcare leaders work hard to improve care, reduce costs, and improve the patient experience. But accomplishing these goals requires an engaged staff that makes progress, day in and day out. Adaptive Leadership (AL) principles help leaders understand human behavior to mobilize change and overcome work avoidance, which happens when staff operate above or below the productive zone of tension. By understanding what adaptive work actually is (and that adaptive problems can’t be solved with technical fixes), and why work avoidance happens (because people are overwhelmed; the heat is too high), leaders can keep their teams engaged by using influence and leadership—not authority—to “lower the heat” on their people:

  • Validate the difficulty of the situation.
  • Simplify/clarify the work.
  • Provide additional resources (time, training, etc.)
Dr. Ulstad has worked with healthcare leaders and teams for the last 20 years to help them understand behaviors triggered by rapid, high-volume change, and apply AL principles to guide the changes critical to their organizations’ success.

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Moving Healthcare Outcomes Improvement Projects to the Finish Line

There are many ways to approach outcomes improvement in healthcare. Health Catalyst advocates the three-systems methodology whose individual components remain firm: best practices, adoption, and analytics. There are also various ways to interpret the three systems and this article uncovers nuances in how they are defined. With this unique perspective, organizations may be better able to understand how to develop outcomes improvement projects that not only launch with enthusiasm, but sustain energy over the long-term. Furthermore, outcomes improvement done right is scalable so that small firms, those with fewer resources, can adapt the methodology to improve their performance.

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Finding and Compiling Salient Population Health Data

Given the variety of payment contractors and models, ACOs have their hands full when it comes to assessing risk and managing population health. EMRs are one source of data for painting a partial picture of the population; claims and HIE data are limited, and the promise of FHIR is still a work in progress. To complete the picture, external data sources are necessary. Those are available in a variety of ways, including demographic analysis, and external EMRs from physician practices independent of the ACO. There are many challenges to stratifying risk, but there also many creative ways to pull in the data for accurately identifying the patient population, improving their health, and reducing costs.

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The Top Five Essentials for Outcomes Improvement

Outcomes improvement is complicated, but we’re beginning to understand what successful quality improvement programs have in common:

  1. Adaptive leadership, culture, and governance
  2. Analytics
  3. Evidence- and consensus-based best practices
  4. Adoption
  5. Financial alignment
Although understanding the top five essentials for quality improvement in healthcare is key, it’s equally important to understand the most useful definitions and key considerations. For example, how different service delivery models (telemedicine, ACO, etc.) impact quality improvement programs and how quality improvement starts with an organization’s underlying systems of care. This executive report takes an in-depth look at quality improvement with the goal of providing health systems with not only the top five essentials but also a more comprehensive understanding of the topic so they’re in a better position to improve quality and, ultimately, transform healthcare.

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Questions You Should Ask When Selecting a Healthcare Analytics Platform

As vice president of technology for a healthcare IT company, I’m often asked what should be considered when selecting a solution for healthcare analytics. Healthcare organizations have many choices when selecting a healthcare data warehouse and analytics platforms. I advise them to consider the following fundamental criteria: 1) time-to-value (measured in months, not years), 2) experience as a predictor of future success, and 3) extensibility to meet your needs.

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Healthcare Interoperability: New Tactics and Technology

Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.

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The Top Six Early Detection and Action Must-Haves for Improving Outcomes

Given the industry’s shift toward value-based, outcomes-based healthcare, organizations are working to improve outcomes. One of their top outcomes improvement priorities should be early detection and action, which can significantly improve clinical, financial, and patient experience outcomes. Through early detection and action, systems embrace a proactive approach to healthcare that aims to prevent illness; the earlier a condition is detected, the better the outcome. But, as with most things in healthcare, improving early detection is easier said than done. This executive report provides helpful, actionable guidance about overcoming common barriers (logistical, cultural, and technical) and improving early detection and action by integrating six must-haves:

  • Multidisciplinary teams
  • Analytics
  • Leadership-driven culture change
  • Creative customization
  • Proof-of-concept pilot projects
  • Health Catalyst tools (knowledge briefs, outcomes improvement packets and worksheets, and care process improvement maps).
The report features a Thibodaux Regional Medical Center sepsis success story that demonstrates how creative customization, when paired with evidence-based standardization, can improve early detection and action efforts, as well as clinical, financial, and patient outcomes.

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No More Excuses: We Need Disruptive Innovation in Healthcare Now

U.S. healthcare is one of the most technologically advanced industries in the world, yet it has such a difficult time transforming some of its most mundane problems (cost, quality, and service). With these problems, we are not so different from many other industries, so we should be able to learn from the individuals and industries that have succeeded in finding answers. At the same time, we need to recognize that healthcare is incredibly complex, so we need to search within for barriers that prevent disruption and innovation. The future of healthcare lies in technology, but more importantly, in our ability to pave the way for its implementation starting right now.

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The Formula for Optimizing the Value-Based Healthcare Equation

Two variables are required in the value-based healthcare equation if it is to add up to a profitable contract. One variable, optimizing the care for the patient population, is commonly included and is a focus for most healthcare systems involved in managing population health. However, a second variable, getting the right dollars in order to care for that population, is often overlooked. And yet this variable is easier to attain. It’s a matter of appropriately assessing the risk of the population by addressing inaccurate diagnoses coding. Here, we offer four methods for solving this variable: identifying high-risk gaps over time, persistent diagnosis tracking, identifying code adequacy, and identifying likely diagnoses.

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The Top Success Factors for Making the Switch to Outcomes-Based Healthcare

Transitioning to outcomes-based healthcare is an industry wide goal. While some health systems, such as Texas Children’s Hospital, are in the process of making the switch (and doing it successfully), many systems don’t even know where to begin. Despite the challenges of achieving outcomes-based healthcare, it is essential for surviving the transition from fee-for-service (FFS) to value-based care. Systems can overcome the top three challenges associated with making the switch (lack of analytics, lack of access to information, and inappropriate organizational structure) by focusing on the most important success factors:

  1. Analytics
  2. Multidisciplinary Teams
Armed with an enterprise data warehouse (EDW) to make data-driven decisions about the best outcomes improvement goals to pursue, and permanent multidisciplinary teams responsible for continuously improving care, systems can start making the switch to outcomes-based healthcare.

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Why a Patient-centric Approach Is Best: Stories from a Physician

Good patient care means patient-centric care. Relying on good mentors during residency training, physicians can learn how to put patients first. For example, during one rotation of mine, I saw a mentor consistently use humor and expertise with patients to connect with them and help them change their environments at home. I was also part of patient-centered teams that worked together to identify potentially life-threatening conditions, and intervene to save lives. We can put people before projects and be patient-centric.

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7 Features of Highly Effective Outcomes Improvement Projects

There’s a formula for success when putting together outcomes improvement projects and organizing the teams that make them prosper. Too often, critically strategic projects launch without the proper planning, structure, and people in place to ensure viability and long-term sustainability. They never achieve the critical mass required to realize substantial improvements, or they do, but then the project fades away and the former state returns. The formula for enduring success follows seven simple steps:

  1. Take an Outcomes Versus Accountability Focus
  2. Define Your Goal and Aim Statements Early and Stick to Them
  3. Assign an Owner of the Analytics (Report or Application) Up Front
  4. Get End Users Involved In the Process
  5. Design to Make Doing the Right Thing Easy
  6. Don’t Underestimate the Power of 1:1 Training
  7. Get the Champion Involved

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The Surprising Benefits of Bad Healthcare Data

Bad healthcare data is inevitable. Whether it happens as a result of human input error or an incorrect rule, bad healthcare data will happen. And rather than ignoring it, hiding it, or scrubbing it, health systems need to take a more transparent approach. Bad healthcare data, when approached correctly, has four surprising benefits:

  1. Provides valuable feedback to application users/data consumers.
  2. Inspires an improvement culture.
  3. Creates a Snowball Effect of Success.
  4. Improves Data Accuracy.
It’s not easy to make the shift from fearing bad data to embracing it, but there are several steps systems can take to start creating a data transparency culture:
  1. Empower: encourage data consumers to provide feedback.
  2. Share: Provide a mechanism for sharing feedback.
  3. Act: dedicate time and resources to respond and act.
Health systems prepared and willing to fix bad data will ultimately improve data quality.

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Integrating Data Across Systems of Care: Four Perspectives from Industry Leaders

How to integrate data across systems of care depends on the organization’s perspective. In this report from the Scottsdale Institute, learn how leaders from Health Catalyst, Cerner, Geisinger, and CHI have tackled issues such as population health, HIEs, value-based payments, and data governance. Ultimately the starting point isn’t really how to integrate the data, but why the data needs to be integrated in the first place. The approach changes, for example, when an organization needs to combine data for a regulatory report versus using data for real-time patient-physician interaction.

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The Top 100 Hospitals Improve Outcomes and Reduce Costs

Truven recently conducted its annual 100 Top Hospitals Study. Using objective and independent research, and publicly available information, Truven determines the top-performers based on a variety of performance measures, from mortality to readmissions. For this first time in the study’s 23-year history, the top-performing hospitals were able to simultaneously improve outcomes and reduce costs. When interviewed about the reason behind this unprecedented trend, Truven’s Senior VP of Performance Improvement credited “uniformity and consistency.” But the report reveals other commonalities among the winners:

  • Data and Analytics
  • Evidence-Based Practices
  • Standardization
This curation summarizes the study’s high-level findings, including specific achievements.

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Why We Need to Shift Healthcare Quality Measures from Volume to Value

Healthcare quality reporting is integral to achieving the Triple Aim and improving outcomes. But the sheer volume of quality measures has become as much a part of healthcare as healing and prevention. Recently, CMS and AHIP took the unprecedented step of aligning and consolidating measures in seven care categories. This will go a long way toward reducing the amount of time physicians and staff spend every week on quality reporting, but it’s only a beginning. Healthcare’s focus needs to shift from volume to value of quality measures, such as those that concentrate on quality of life and patient-reported outcomes. The International Consortium for Health Outcomes Measurement is setting the right example for quality measures designed to actually improve outcomes rather than just processes.

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Standardized Care vs. Personalization: Can They Coexist?

Perceptions of standardization and personalization vary widely by healthcare industry role. Advocates of standardized care say it improves efficiency, outcomes, and patient safety. Advocates of personalization, however, don’t want to see a one-size-fits-all approach become the norm. They want to see a healthcare system in which physicians treat patients like unique individuals. But what if standardization and personalization didn’t have to be mutually exclusive? What if these historically competitive approaches to care improvement could work together to improve care? Dr. Corbett describes how health systems can prioritize standardization and personalization using data to bridge the gap. Data enables informed decision making, customized treatment plans, and patient engagement. It supports both standardization and personalization approaches in the ultimate quest for care delivery improvement.

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The Top 4 Ways De-Identified Data Improves Research

Level 1 of the Healthcare Analytics Adoption Model is providing de-identified data marts and self-service tools. Researchers navigating the complex research process can use de-identified data in each step of the process to increase their chances of having more successful research projects. Using de-identified data not only removes research roadblocks, but also enables researchers to navigate the four fundamental research steps with more ease. There are four specific ways de-identified data improves research:

  1. Enables early discovery and exploration to test the validity of the hypothesis before committing time and resources.
  2. Enables researchers to explore and refine their cohorts and determine whether the patient population can support the scope of the project.
  3. Enables researchers to put together strong grant applications without having to tax the resources of enterprise data analysts—and without having to wait for analysts to answer relatively straightforward questions.
  4. Enables researchers to come to the IRB with a strong, fully supported application. A data-driven research process ensures that both researchers and IRB reviewers don’t have to waste their time on projects that may not be viable.

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The Best Way to Optimize Physician Workflow

Optimize physician workflow and you’ll contribute to optimizing patient care. But what is it physicians look for to improve diagnoses, decision-making, patient care, and ultimately, outcomes? To answer this, consider what constitutes ideal working conditions in any industry: the right tools, training, and information to maximize productivity and deliver results. Physicians need analytics integrated into the EHR to maximize their efficiency, a common quest among the chronically overworked. And by flowing the universe of global, local, and individual data back into an enterprise data warehouse, a healthcare system can close the analytics loop, and begin to realize true precision medicine.

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The Top 8 Skills Every Healthcare Process Improvement Leader Must Have

Healthcare process improvement leaders not only have to be a jack-of-all-trades, but they need to be a master, as well. This is one of the most important leadership roles in the healthcare system with responsibilities that can ultimately end up saving lives, improving the patient experience, improving caregiver job satisfaction, and reducing costs. Although there are many others, these eight skills are the most critical for the efficient, and ultimately, successful process improvement leader:

  1. Communication
  2. Trust Building
  3. Coaching
  4. Understanding Process Management
  5. Understanding Care Management Personnel
  6. Constructive Accountability and Constructive Conflict
  7. Resiliency and Persistency
  8. Seeing the Big Picture
Along with the right training, education, and sponsorship, it’s easy to see why this role blends many elements of art and science.

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5 IT Must-Haves for Quality Initiatives in Healthcare

IT teams are the guardians of health system data. Their role in quality initiatives in healthcare is undeniable. Yet maximizing IT contributions to quality initiatives requires a shift in IT’s traditional role. Traditionally supporters of data governance, security, privacy, and access—all important for achieving quality initiatives—IT teams need to do more. They need to integrate five must-haves:

  1. Collaboration
  2. Real-time feedback
  3. Interoperable infrastructure
  4. Data best practices
  5. Engaged frontline staff
The industry is up against expanding regulatory requirements that will place high demands on IT teams, including ONC’s goal to reduce the collection and reporting burden on providers. IT teams that embrace these five must-haves are best positioned to create user-centric tools and processes that reduce this burden.

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Population Health in Three Paragraphs

Population health strategy can borrow a lot from public health. However, health systems haven’t had to deal with patient socioeconomic issues and need to build new skills and use new data. The skills can be adapted from the public health sphere, with hospitals developing health interventions alongside law enforcement, community-based social support, etc. The most important data are patient-reported outcomes data, social determinants of health data, and activity-based costing data. With this approach, the fundamental equation for population health would be Return on Engagement, that is the clinical outcome achieved divided by the total patient investment.

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New Health Plan Shows Commitment to Healthcare Quality

As the healthcare industry continues to transition to value-based models, more health systems are becoming insurers. Intermountain Healthcare’s health plan divisions, SelectHealth, has been around for 30 years. Intermountain has a long history of striving to improve patient care and reduce unnecessary expenses. Now Intermountain is passing its cost savings onto customers through its new health plan, SelectHealth Share, which locks in yearly rate increases at approximately four percent. There are numerous financial risks associated with this plan, but Intermountain has the systems and infrastructure in place to manage these risks. The new plan requires close collaboration from all stakeholders: unaffiliated providers must adhere to Intermountain’s changes, like using an EMR; employers must pay at least 70 percent of average premiums; and employees need to proactively manage their health using online tools. Intermountain’s attempts to make coverage a “predictable expense” deserve recognition.

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Outcomes Improvement Readiness: Learn Where Your Organization Stands

Healthcare organizations need to make lasting, systemwide improvements to make the transition to value-based care models. Starting this work is tough, but a new tool from Health Catalyst will show the way. This 25-question assessment based on an integrate literature review of outcomes improvement research, will show how organizations are performing in five main categories:

  • Adaptive leadership and culture
  • Analytics
  • Best Practices
  • Adoption
  • Financial Alignment
With this information, organizations can start on the path to sustainable outcomes improvement.

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The Top 7 Outcome Measures and 3 Measurement Essentials

Outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this blog adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples:

  1. Mortality
  2. Readmissions
  3. Safety of care
  4. Effectiveness of care
  5. Patient experience
  6. Timeliness of care
  7. Efficient use of medical imaging
CMS used these exact seven outcome measures to calculate overall hospital quality and arrive at its 2016 hospital star ratings. This blog also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement:
  1. Transparency
  2. Integrated care
  3. Interoperability

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Why Process Measures Are Often More Important Than Outcome Measures in Healthcare

The healthcare industry is currently obsessed with outcome measures — and for good reason. But tracking outcome measures alone is insufficient to reach the goals of better quality and reduced costs. Instead, health systems must get more granular with their data by tracking process measures. Process measures make it possible to identify the root cause of a health system’s failures. They’re the checklists of systematically guaranteeing that the right care will be delivered to every patient, every time. By using these checklists, organizations will be able to improve quality and cost by reducing the amount of variation in care delivery.

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Improving Outcomes That Matter Most to Patients

Patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs) have been used in healthcare since the 1970s. But the industry hasn’t had meaningful, consistent PROs and PROMs definitions until ICHOM developed one. ICHOM, a pioneer in outcomes measurement and improvement, demonstrates that healthcare organizations focused on improving patient outcomes that patients actually care about are the ones most likely to transform healthcare.
PROs and PROMs complement clinical indicators in understanding the quality of healthcare a team is delivering. For example, an improvement program for prostate cancer patients that only focuses on improving blood loss or length of stay in the hospital completely misses a patient’s biggest fears: will they need to wear pads for the rest of their life? Will their relationship with their partner be the same as it was?
By focusing on outcomes that matter most to patients, health systems will be more successful at improving outcomes. ICHOM describes five strategies for getting started with PROs and PROMs:
  1. Find the Believers (Identify Clinician Champions)
  2. Organize a Cross-Functional Team (with Appropriate Governance)
  3. Invest Time and Resources
  4. Celebrate Progress Along the Way
  5. Use Early Successes to Scale and Spread

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Factoids Reveal Healthcare Trends in Analytics and Technology

We hand-picked the most interesting, useful, credible factoids from 2015 (including the plethora of facts that came out of the 2nd Healthcare Analytics Summit) to create an easy-to-share presentation. The 32 factoids included in this presentation revealed several interesting healthcare trends:

  • Trend #1: Healthcare analytics continue to improve outcomes and save money. For example, OSF’s predictive readmission model reduced its all-cause readmission rate to less than 10%.
  • Trend #2: New technologies are improving patient engagement. For example, 73 percent of health executives surveyed see positive ROI from personalization technologies, and 76 percent of doctors say patient use of wearable health devices improves engagement.
  • Trend #3: Patients and providers agree on data is useful but have security and interoperability concerns. For example, 83 percent of patients don’t trust EHR safety and security, and 83 percent of physicians are frustrated by EHR interoperability.
Although a majority of healthcare leaders understand the importance of using analytics to improve outcomes and reduce costs, only 15% of hospitals use predictive analytics. We hope to see analytics use increase in 2016, and we’re excited to see how technology will continue to engage patients and lead to better health outcomes.

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4 Ways Healthcare Data Analysts Can Provide Their Full Value

Analysts are most effective when they have the right tools. In healthcare, that means providing data analysts with a means of accessing and testing ALL of the available data and using it to discover more insights. To do this, analysts need guidance more than they need a detailed set of instructions. And, equally as important, they need a data warehouse and access to a testing environment and data discovery tools, so they can truly do the work they were hired to do: analyze.

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How to Measure Health Outcomes that Matter to Everyone

To measure health outcomes that matter to everyone, it’s important to ask several questions before starting out:

  • How do regulatory requirements differ from outcomes improvement?
  • Do the measurements align with organizational goals and values?
  • Are the measurements worth the resources required to document them?
  • Will the metrics actually be applied to outcomes improvement?
  • Who are the beneficiaries of the outcomes improvement initiative?
The answers to these questions help save time and resources, sustain and expand the improvement effort, refine the list of measures to those that truly improve outcomes, and most of all, help avoid the outcomes measures graveyard.

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Improving Patient Safety and Quality through Culture, Clinical Analytics, Evidence-Based Practices, and Adoption

According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents.  Learn key best practices in patient safety and quality including:  patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.

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Why Pioneer ACOs Are Disappearing and 3 Trends to Expect from the Exodus

Over half the Pioneer ACOs have dropped from the program in the last four years, despite achieving $304 million in savings, and fifty percent of the participating ACOs receiving shared savings reimbursements. Why the exodus? Overutilization and inconsistent performance benchmarking and attribution hindered the ability of many participants to achieve success. The overall impact of the program, however, has been a positive one for value-based care. In the next 3-5 years, providers and health systems will bear more of the financial risk of the populations they serve. The proliferation of data, and the tools to analyze and exchange it, will be critical to the long-term success of value-based care.

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Physician Reporting: The Secret to Useable, Engaging Reports

While working as an internist at an outpatient clinic, I would see physician performance reports that would tell me little more than if I was doing “good” or “bad.” There was no way to know how I compared to others. My colleagues, who also received these reports, and I didn’t trust the numbers either. In short, the reports were useless. Then, I discovered creating reports with a data warehouse. This addresses issues in six ways: 1. There is a cleaner data set and physicians don’t need to worry about fixing the data. 2. It addresses the “but my patients are different” argument. 3. The information is up-to-date. 4. The data is granular and detailed. 5. Physicians take ownership of the data because they are involved in the process. 6. Finally, it saves valuable time. When reports are created this way, physicians can make real change in their behavior and improve patient outcomes.

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The Best Way to Use Data to Determine Clinical Interventions

One of the most important aspects of managing clinical interventions is how you measure an intervention to determine if it is effective. A run chart is a very important tool for measuring improvement, but it doesn’t give you all the information you need to assess the effectiveness of your process change. The next step in maturation of your measurement process is creating a statistical process control (SPC) chart. An SPC chart shows you if your intervention is changing the process in a significant way or whether changes in the data just represent random variation.

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Improving Healthcare Outcomes: Keep the Triple Aim in Mind

The battle cry for healthcare organizations throughout the United States? Improve outcomes! However, as organizations begin to measure outcomes they realize not all outcomes are created equal and the question of what constitutes an improvement becomes more challenging. Healthcare leaders would be wise to keep the Triple Aim in mind when creating a strategy for optimizing outcomes. Achieving the appropriate balance among the three dimensions of the Triple Aim is critical to driving real, long-term change in healthcare delivery outcomes.

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How Patient-Centered Care Turns Patients Into Leaders

Patient engagement is the cornerstone of effective patient-centered care teams. But what if care teams could do more than engage patients? What if they could turn these patients into care team leaders? Patient-centered care teams can transform patients into proactive leaders who have the knowledge and motivation to take ownership of their health by following four key recommendations:

  • Make decisions using data.
  • Make it personal.
  • Make it easy to share information.
  • Leverage new technology when appropriate.
In what often feels like a fragmented system, patient-centered care teams provide a personalized environment and care continuity. And while patient engagement is a top priority, leveraging predictive analytics to move beyond engagement to empowerment will lead to better health outcomes for individuals and entire patient populations.

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Five Deming Principles That Help Healthcare Process Improvement

Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare.

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Why the Data Steward’s Role Is Critical to Sustained Outcomes Improvement in Healthcare

The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:

  1. Reinforces the global data governance principles.
  2. Helps develop and refine details of local data governance practices.
  3. Is the eyes and ears of the organization with respect to data governance and the governance committee.
  4. Provides direction to peers regarding appropriate data definitions, usage, and access.
  5. Anticipates local consequences of global changes
For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.

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The Real Opportunity of Precision Medicine and How to Not Miss Out

Precision medicine, defined as a new model of patient-powered research that will give clinicians the ability to select the best treatment for an individual patient, holds the key that will allow health IT to merge advances in genomics research with new methods for managing and analyzing large data sets. This will accelerate research and biomedical discoveries. However, clinical improvements are often designed to reduce variation. So, how do systems balance tailoring medicine to each patient with standardizing care? The answer is precise registries. For example, using registries that can account for the most accurate, specific patients and disease, clinicians can use gene variant knowledge bases to provide personalized care.

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Clinical Data Management: 3 Improvement Strategies

Most health systems suffer from data clutter and efficient problems.  As a result, analysts spend most of their time searching for data, not performing high value work.  There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.

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Employee Wellness: A Combination of Personal Accountability and Corporate Responsibility

A strong employee wellness program is the first step to encouraging better health and creating meaningful, positive change in the lives of employees and their families. A well-designed healthcare insurance plan, a comprehensive wellness program, and creating a culture of personal accountability for wellness can optimize healthcare spending and improve employee health. It can also bolster the understanding and shared accountability for healthcare costs between the employees and the company.

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How to Avoid the 8 Most Common Pain Points in Becoming a Data Driven Healthcare Organization

What does it mean to be a data-driven organization? What are the advantages of achieving this status? How can my organization get there? These are all great questions, but they are outnumbered by the eight common pain points every healthcare system encounters along the way:

  1. Conflicting versions of the truth
  2. Lacking a culture that supports data transparency
  3. A lack of trust in the data
  4. Data volume and overload
  5. Struggling to find an effective system
  6. “We already tried that”
  7. A dearth of data-savvy staff
  8. Lack of executive sponsorship
Anticipating and absorbing those pain points is part of the secret to success. This article explores them further, as well as the advantages of letting data guide critical healthcare business decisions.

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Turn Research Into Care Delivery Improvements Using the Research Analytics Adoption Model

Research is a complex yet vital component of improving care delivery, and it can be hindered by a variety of organizational and technical roadblocks:

  • Insufficient tools and processes
  • Poor infrastructure
  • No single source of truth for data
Health systems can overcome these common research roadblocks and turn analytics-powered research into care delivery improvements by using the Research Analytics Adoption model as a strategic roadmap. The model consists of 8 levels designed to align operations and research priorities:
  1. De-identified tools and data marts
  2. Delivery of customized data sets
  3. EDW-facilitated study recruitment
  4. Centralized, research-specific data collection
  5. Automated research operations reporting
  6. Biobank/genomic data integration
  7. Multi-site data sharing
  8. Translational Analytics

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Top 7 Financial Healthcare Trends and Challenges for 2016

Healthcare financial leaders will encounter a myriad of challenges and improvement opportunities in 2016. 2016 will force health system financial leadership to focus and prioritize, with challenges including increased healthcare spending, continued momentum toward value-based care, and the need to reexamine the revenue cycle after years of focusing so intently on ICD-10. But 2016’s financial healthcare trends include more than just challenges; exciting opportunities abound, from using technology to engage patients to a national focus on population health. Engaged healthcare financial leaders—particularly those with the characteristics of effective leaders (resilient, collaborative, and inspirational)—are positioned to stay ahead of the curve in 2016.

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The Dangers of Data Shopping: The Mad Scramble for Information

The phrase “data shopping” should conjure up images of crowded stores, out-of-stock items, long lines, and cranky sales clerks. This scenario is similar to that of your data users and analysts when they are trying to operate without a strict data management policy and without a unified data platform. Many healthcare institutions attempt to operate with data stored in multiple locations, accessible in different ways. Too much time is spent by users looking for the one source of truth and too much time is spent by analysts attempting to gather data to fulfill user requests. Not enough time is spent analyzing data and generating improvements. Data shopping is dangerous and organizations caught up in the spree need to consider a cleanup on aisle 9 (that’s analytic-speak for “consider an enterprise data warehouse”)

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Analytics in Community Hospitals: Embracing Data to Thrive in the New Era of Value-Based Care

Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.

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Prospective Analytics: The Next Thing in Healthcare Analytics

Retrospective and predictive analytics are familiar terms for practitioners of clinical outcomes improvement, but the new kid on the block is prospective analytics. This is the next level that uses findings from its predecessors to not only identify the best clinical routes, but also what the results might be of each choice. Prospective analytics gives bedside clinicians an expanded, branching view of operational and clinical options in a type of decision support that can lead to not only improving surgical and medical outcomes, but to making a positive financial contribution, as well. But, as expected with any new process or new way of thinking, prospective analytics requires careful introduction and stewardship to help drive its adoption within the organization.

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Motivating Employees Toward Wellness: There’s an App—and a Program—That Really Works

Insurance premiums increase for employers and productivity decreases for employees when the workforce is unhealthy. Absenteeism increases, morale decreases, but research proves that a healthy workforce positively impacts each of these measures. An employee wellness program is a proven method for improving employee health, but the challenge is in elevating wellness program participation. Well, there’s an app for that. It’s called Get Fit Stay Fit and it was developed by Health Catalyst to motivate their employees toward healthier lifestyles. It’s actually much more than an app and the results have been so impressive, we are looking for other organizations to step up to the challenge.

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What Do You Get With a Clinical Improvement Application from Health Catalyst?

Transforming healthcare takes more than just dashboards and data. It takes an entirely new approach combining best practices, analytics, and adoption of the improvement program throughout the entire organization. Which is why Health Catalyst Clinical Improvement Applications offer tools to help organizations with all three of those systems. The applications contain starter content (best practices), which includes a knowledge brief, a care process improvement map, and an outcomes improvement packet. Of course, analytics is also part of the applications in the form of precise patient registries, outcomes and process metrics, and visualizations. And finally, Health Catalyst includes deployment services to drive adoption of improvement work. This includes engagement with health system teams and sharing of insights based on work from a variety of healthcare organizations across the country and the world. Armed with a Clinical Improvement Application, a health system is in a better position to make real, meaning changes resulting in outcomes improvement for patients and itself.

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Why Adding External Data to Your EDW Is Critical to Driving Outcomes Improvement

Many healthcare organizations are entering, or are planning to enter, into some type of at-risk contract, be it a bundled payment program, a Medicare Advantage plan, or an ACO. In order to manage these contracts most effectively, integrating external and internal claims data in to the EDW is critical. Aggregating data in to an EDW from internal, disparate, clinical, administrative, and financial systems is the first critical step to identify opportunities for quality improvement. However, external data from organizations such as CMS and commercial payers, along with benchmarking and consumer and demographic data, also has the potential to improve the quality of care, increase patient satisfaction, and lower costs. In the new world of at-risk contracts, integrating external and internal data enables leaders to successfully oversee, manage, and strategically plan for future at-risk arrangements.

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Healthcare Visualizations: Are You Getting the Entire Story?

The emergence of powerful and user-friendly healthcare data visualization programs has transformed analytical reporting. The amount of information conveyed by all types of graphs, symbols, sizes, and colors is staggering. The ability to “drill down” in real-time with increasing levels of granularity enables all manner of analyses. The downside of this data hunger is the creation of simplified, context-free visualizations which may inadvertently lead to misinterpretations, most often in the form of a false positive (believing a change has occurred that really hasn’t). This often leads to knee-jerk reactions to correct the “change” and unnecessary actions being taken that waste time, effort, and money. Avoiding the most common pitfalls will ensure your organization has the most complete picture to drive meaningful change.

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Advanced Care Management: Healing the Whole Patient

In the new world of value-based care, success is defined as consistently delivering excellent, value-based care, and improving patient outcomes, long-term. Making the vision of value-based care a reality will require a cross-functional team, focused on healing the entire patient according to the principles of advanced care management. Recognizing the needs of the whole patient, instead of only focusing on the specific condition, especially in a shared-risk arrangement, can have a profound effect on everyone, most importantly the patient. To achieve success the entire team, including physicians, nurses, social workers, psychiatrists, and social workers,   must work together and coordinate their efforts. Building a genuine and trusting relationship between the patient and care team is the cornerstone of ACM programs. Helping a patient feel better can inspire them to be an active part of their treatment plan. A successful ACM strategy can inspire the care team to drive long-term, tangible change, leading to improving outcomes for the organization and the patient.

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Population Health Outcomes: 3 Keys to Systematic Improvement

Population Health can mean many different things depending on whom you ask or what you read. The one common element among all the definitions is the focus on outcomes. These outcomes can be related to quality (successfully treating the patient), experience (the patient’s satisfaction with the care that was provided), or cost outcomes (reducing errors and a decline in length of stay). In the end it means delivering the highest quality care for patients at the lowest possible cost over and over again. To ensure these outcomes are tangible, sustainable, and transferable a three-system approach is necessary: a best practices system (to determine what should be done), an analytic system (to tell the organization how it’s doing versus the goal), and an adoption system (to report the results to the organization).

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A Data-Driven Culture: Making Data a Part of Everyday Decision Making

Healthcare organizations are establishing data-driven improvement processes to improve the quality of care at a lower cost. Implementing an analytics infrastructure, clinical content, and deployment processes required to achieve success can be a challenge. Surprisingly, building the technology infrastructure is the relatively easy part. Ensuring clinicians are utilizing the data in every day decision making and creating a data-driven culture is more difficult. Senior leadership engagement is crucial, driving the organization to undergo a purposeful change, and making analytics and improvement everyone’s responsibility.

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Health Catalyst Named a Best Place to Work for Women in Utah

Career Contessa (a blog for professional women) selected Health Catalyst as one of Salt Lake City’s top 5 companies with the best benefits for women thanks to a culture that prioritizes work-life balance and offers numerous benefits:

  1. Unlimited paid time off
  2. Onsite gym
  3. Wellness program
  4. Breakrooms with free food and drink
  5. Flexible work hours
  6. Treadmill and stand-up desks
  7. Above average compensation

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Deloitte Report Reveals Why Health Systems Lack Integrated Healthcare Analytics Strategy; Recommends Adoption Framework

A 2015 Deloitte Report investigates why health systems lack integrated healthcare analytics strategies (despite acknowledging the myriad of benefits analytics-driven insights offer):

  • Lack clarity on current analytics spending.
  • Culture, operating models, and fragmented oversight.
  • Lack of access to funding and skilled resources.
  • Numerous confusing vendor product offerings.
  • Inconsistent industry definitions of analytics.
The report concludes by recommending analytics adoption guidelines, from engaging committed leaders across the enterprise and implementing a structured data governance model to emphasizing data and technology standards to promote interoperability.

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Analytics in Medical Practice: How to Get Physicians On Board and Engaged

The Affordable Care Act has changed the landscape for physicians. They are under pressure to care for patients more effectively and efficiently. However, the significant increase in diagnostic testing and treatments have made it challenging for physicians to decide on an optimal diagnosis and treatment plan. Meaningful, actionable data to measure the effectiveness of these treatments has only recently become available, delaying the adoption and use of analytics among physicians. Integration and use analytics in medical practices is critical to improve outcomes, shorten the timeline for translating best practices into clinical practice, and ultimately improve the overall health of individual and populations of patients.

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Healthcare Analytics Careers: New Roles for the Brave, New World of Value-based Care

Job titles can be leading indicators of the direction an industry is moving and the same holds true for healthcare. The new healthcare economic model—from fee-for-service (FFS) to value-based—is driving a change in roles and responsibilities for professionals seeking healthcare analytics careers. Motivated by CMS and commercial payers, healthcare organizations are realizing the need to find and hire new types of healthcare professionals, a Chief Population Health Officer or Vice President of Clinical Informatics, who are focused on value. Senior leaders are seeking to build teams that have the ability to bring together analytics, best-practice clinical content, and process improvement to create long-term, sustainable change across their healthcare systems.

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The Case for Healthcare Data Literacy: It’s Not About Big Data

While many people are looking to Big Data to solve a lot of healthcare’s data problems, Big Data won’t offer a lot of solutions for a while to come. For one, healthcare doesn’t have “Big” data; there just isn’t the volume, velocity, or variety seen in other industries such as banking where Big Data has been used successfully. For another, Big Data seems to be the answer to almost every question from cancer to Alzheimer’s, and that’s blinding us to the reality of healthcare analytics. A big way toward answering healthcare’s problems would be to improve data literacy among not only consumers, but physicians and administrators as well. Learning to ask the right questions about the data and learning how to read data correctly will get us further down the road to improvement than the latest buzzword (in this case, “Big Data”) ever will.

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How Organic Valley Established Business-driven Data Governance (And Why it Matters in Healthcare)

Volume doesn’t equal quality in the world of healthcare data governance. This case study from the dairy industry shows that a large data governance committee doesn’t necessarily add up to trustworthy data or effective decision-making. Why the dairy industry? Prior to Organic Valley, the author worked as a data architect in healthcare data warehousing for one of the largest healthcare systems in the Midwest. This article shows how valuable a strong data governance model is, regardless of industry, at aligning IT and business decision-making personnel from across the enterprise to bring about a greater appreciation of the importance of data and its role in guiding the business. A best-practice governance framework also can result in more trustworthy data and a faster track to developing the enterprise data warehouse (EDW) model.

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The Value of Claims Versus EHR Data in Care Management and Population Health Analytics Strategies

If EHR data have the breadth and depth of a pond, then claims data are just lily pads on the surface. In other words, the volume of EHR data is far more substantial than that of claims data. Population health strategists should adopt the AND-BOTH approach, rather than the EITHER-OR, when it comes to working with both types of data. This best-of-both-worlds tactic offers data that is standardized, accessible, discrete, AND real-time, detailed, and physician based. Given what some might view as an overwhelming volume of data when working with both, it’s wise to develop a data governance framework supported by some type of flexible data management platform, such as an enterprise data warehouse (EDW).

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Why Precise, Tailored Patient Registries Lead to Cost-Effective Care Management Programs

Early this year, CMS began a per member per month reimbursement for Medicare beneficiaries with two or more chronic conditions. It immediately validated the need for care management programs. Three models are used to measure the savings of an effective care management program:

  1. Historical or intent-to-treat design
  2. Matching comparison design
  3. Randomized control design
All three place a heavy reliance on data and precise, tailored patient registries. Reliable patient registries are one of the most valuable tools in the care management toolbox. And the means to that reliability is an enterprise data warehouse, which essentially gives program managers an all-access pass to stratifying patient risk and leads to a more successful population health initiative.

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How to Assess the ROI of Your Population Health Initiative

In the brave new world of value-based healthcare, investing in population health management (PHM) is a requirement for success. Defining PHM isn’t easy, but there is one common term that appears among all the diverse interpretations—outcomes. Assessing the potential ROI for investments in PHM using a clear, understandable framework, can help organizations methodically identify and prioritize their PHM investments. While not every PHM intervention makes sense for every situation, it is important to determine which programs provide the most benefit, as well as determining when the investment will begin paying dividends, to achieve success in the era of PHM.

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When Choosing Agile or Waterfall Development for Healthcare, Take a Pragmatic Approach

IT project development usually proceeds down one of two development paths: Agile or Waterfall. But those involved with developing process improvement and project management understand that taking a more pragmatic approach is required when determining which path is best. It’s not a single-path environment where Agile has replaced Waterfall, nor is Waterfall the one-and-only legacy option. Depending on the project, both may be viable at different points along the timeline. The speed to value of Agile is attractive to organizations seeking quicker returns on the work in progress. It also minimizes documentation. Waterfall is valuable when risks must be minimized and when the development path and end product are familiar. Sometimes, a blending of Agile and Waterfall is appropriate for different stages in the development process. Ultimately, organizations should remain open regarding both approaches and apply the one that will work best for any given circumstance.

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2015 Healthcare Analytics Summit: Thursday Recap

The final day of the 2015 Healthcare Analytics Summit started with presentations from on value-based care delivery, population health outcomes, and analytics from keynote speakers Dr. Caleb Stowell (ICHOM), Dr. Timothy Ferris (Partners HealthCare), and Dr. Timothy Sielaff (Allina Health). There were some outstanding breakout sessions. And it finished with a bang-up presentation from Ed Catmull, president of Pixar.

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2015 Healthcare Analytics Summit: Wednesday Recap

Wednesday at the 2015 Healthcare Analytics Summit was a full day of learning and fun. The day included keynote presentations from Daryl Morey, General Manager of the Houston Rockets, Jim Collins, best-selling author of Good to Great, and Amir Dan Rubin, President and CEO of Stanford Health Care.

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Patient Engagement And Outcomes Improvement from the Patient’s Perspective

Usually, when we think of the phrase “patient engagement,” we think of what providers and healthcare systems are doing to involve patients in their own care. Patient engagement is often defined as providing access to a patient portal or reaching out to patients through social media channels or via an organization’s website. But it’s also about patients proactively becoming involved in their own care, in partnership with their healthcare providers. Call it “DIY” or “personalized” medicine, but it can reduce preventable admissions and shorten lengths of stay. It can also significantly improve an individual’s outcomes and always creates better awareness of one’s symptoms and how they are changing. With proper tracking, patients can create a view of their personal data that enhances what’s conventionally available to their providers. This is one motivated patient’s account through an episode of personalized medicine.

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Two-Midnight Rule: Ready For the Clock to Strike 12?

CMS’s proposed changes to the controversial two-midnight rule that governs short hospital stays, has been met with strong opposition by the healthcare community. While the core of the rule is fairly straightforward, implementation could be anything but. Being classified as an outpatient or inpatient can have a substantial financial impact the patient and the hospital. Adding to the confusion, CMS has also stated this policy won’t override a physician’s judgment. Unfortunately, CMS failed to provide details on what the physician must provide in order to justify their decision. The good news is there is still time to provide feedback to CMS. Take action, understand the new rules, let your voice be heard, and most importantly, be prepared for the new rule in 2016.

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Why Data Governance Requires a Henry Kissinger

The number of partnerships and collaboratives in healthcare continues to climb. One of the many complications of these deals involve integrating and governing data. In fact, 100% of the 2014 Pioneer ACOs reported that they had difficulties with data integration, which had a major and negative impact on performance. Right now, data governance in healthcare is in a transitionary stage not unlike the U.S. in the 1980s. Leaders who manage the data governance in these partnerships must be like a data-savvy version of Henry Kissinger, able to bring the data of loosely affiliated organizations together for the benefit of all.

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Lean Principles in Healthcare: 2 Important Tools Organizations Must Have

The transition from fee-for-service to value-based reimbursement is driving many healthcare systems to fine-tune processes and work waste out of the system. In the search for quality improvement tools there has been much buzz surrounding lean, touted for its ability to remove waste from processes. Many have tried lean and, failing to achieve any sustainable benefit, are learning that applying lean principles to healthcare can be quite difficult. The lean approach isn’t a magic potion. Sustainable change will never become real without a committed organization dedicated to making it a reality. Lean, or any quality improvement tool, works in healthcare only when it is part of a larger initiative driving real cultural change.

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Dr. Timothy Sielaff: Coordinated Care At the Bedside

Dr. Timothy Sielaff, a liver and pancreas surgeon, learned early in his career of the need to treat the entire patient, not just the illness, and, it takes a village to make it work. At the center of it all are nurse care coordinators whose role as advocate, communicator, and troubleshooter, allows patients to focus on being well, getting well, and getting back home to their families. Coordinating care across the spectrum of oncology treatments produced such remarkable results for the patient and their families that today; every new cancer patient has access to a nurse care coordinator. Dr. Sielaff believes clinicians can apply the knowledge gleaned from caring for individual patients to entire populations of patients and promote health in the communities they serve.

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5 Keys to Improving Hospital Labor Productivity

The shift to value-based payments and a greater focus outcomes and cost reduction has hospital leaders seeking new ways to work more efficiently and improve patient satisfaction. Monitoring and analyzing productivity more effectively is crucial to ensure healthcare organizations are aligned with this goal. Getting overtime and labor productivity under control isn’t an easy task, but it’s not impossible. A few best practices can shorten the learning curve. These include 1) secure leadership commitment, 2) implement data governance, 3) ensure financial targets are defined, 4) create transparency, and 5) keep productivity metric balanced with quality goals.

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PHI Security and Auditing: Reducing Risk and Ensuring Compliance with a Data Warehouse

EHRs and other electronic health information has created many benefits for patients and the healthcare industry, from improved treatment to reduced duplication of services. However, electronic data also increases the risks associated with PHI. Ensuring compliance with PHI security, including auditing trails, is more important than ever. Breaches constitute a violation of HIPAA and can result in stiff financial penalties and tarnished reputations. Health Catalyst addresses security compliance using a multifaceted solution that ensures the right balance between having enough granularity of information available to the people who need it and securing that information from people who don’t need that level of detail.

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From Afterthought to Safety Net: Mayo Clinic Prioritizes And Standardizes Emergency Services

Three years ago, Mayo Clinic’s emergency services—22 emergency care sites serving Minnesota, Wisconsin, and Iowa—were coming up short of the flagship hospital’s reputation as a world leader in health care. What you’d normally associate as problems with a more dysfunctional system—mismatched talent acquisition, excessive patient transfers, financial waste, a dissatisfied workforce—unfortunately defined the Division of Community Emergency Medicine at Mayo. The outcomes of this non-standardized system were apparent in the dissatisfied and mistrustful patients, as well. Fortunately, value-based purchasing and capitated payments came along under the Affordable Care Act in 2011 and Mayo leadership began the move toward integration and standardization. Leading the way was Dr. Christopher Russi, Chairman of the Division of Community Emergency Medicine at Mayo. Under his leadership, the turnaround has been nothing short of spectacular, with the emergency clinics in all 21 communities providing trusted safety nets and gateways into the larger healthcare system. Dr. Russi will present this outcomes improvement case study during the 2015 Healthcare Analytics Summit in Salt Lake City.

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Patient-Centered Healthcare: Why Health Systems Need to Move Beyond Sick Care

Our current healthcare system is designed to provide “sick care”—care intended to help patients return to their previously healthy state after experiencing an illness or injury. But sick care is costly and introduces the risk of further patient harm. A new model of care, patient-centered care, aims to improve cost and quality by shifting the focus of care to preventative measures. Moving to patient-centered care requires organizations to provide the following for their patients: respect for preferences, values, and desire to stay informed; emotional support; physical comfort; information and education about conditions; continuity of care and transitional assistance after discharge; care coordination and integration care providers; access to care whenever care is needed; and the inclusion of family and friends as caregivers and decision makers.

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A Guide to Governing Healthcare Claims Data Successfully: Lessons from OSF HealthCare

OSF HealthCare has committed that 75 percent of its primary care patient will be part of a value-based program by 2020. The organization’s leaders knew that success depended on how well they managed their data and decided to build a data warehouse in-house. They recognized that beneficiary claims data was essential to understanding their population better. To get that claims data, however, was no easy task. This required patient matching through master data management and getting buy-in from leaders and physicians throughout the health system. Then, they prioritize where to start efforts using the 80/20 rule and using that as a guide, loaded the claims data.

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At Cincinnati Children’s, Standardization is an Individual Art

It’s not the easiest of tasks to standardize the myriad processes and drive out waste from an organization the size of Cincinnati Children’s Hospital, but this has been accomplished behind the determined efforts of Dr. Frederick Ryckman and his dedicated team as they have saved more than $50 million over the past two years. They’ve also decreased hospital-acquired infections by more than 50%. All this adds up to doing what is best for patients: improving their quality of care, improving safety of that care, and decreasing their costs. It’s required a top-down approach, starting with leadership focus and prioritization, but the entire organization is vested in the mantra “the best at getting better.” Dr. Ryckman is a presenting his Outcomes Improvement Case Study in a breakout session during the 2015 Healthcare Analytics Summit.

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Dr. Timothy Ferris: One Doctor’s Journey from Nepal to Boston

A year spent studying the public healthcare systems in Nepal and Indonesia, and watching patient suffering firsthand, had a profound impact on Dr. Timothy Ferris, Senior Vice President for Population Health Management at Partners Healthcare. Having always been interested in understanding how to reduce suffering by optimizing scarce resources, Dr. Ferris was impressed at how Nepal and Indonesia prioritized their scant resources to deliver care, especially to rural patients. Could the same be done in the U.S.? Over the last 20 years, Dr. Ferris and his team have dedicated themselves to applying the lessons learned in Indonesia and Nepal; utilizing resources efficiently and effectively to ease suffering. “I’ve been part of a developing community of highly skilled, dedicated people who are committed to improving healthcare delivery,” Dr. Ferris says. In the future, he predicts the new triple threat will be understanding not only how to best care for populations of patients, but also the systems that support care delivery, and the data that informs the assessment of opportunities, care redesign, and evaluation. While the last several years have seen growing excitement for Population Health Management, Dr. Ferris predicts that over the next few years the process will be one of one-step forward, two-steps back. “People will see the approach takes longer to implement than they expected and it’s much harder in practice than in theory,” he said. “A deep commitment is a necessity to weather the inevitable setbacks that will arise.”

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5 Ways to Mitigate ACO Risk Using Analytics

Many healthcare organizations seem to have been in perpetual pilot stage while experimenting with value-based payment models. Healthcare organizations are focusing their efforts in two primary areas: developing the skills to successfully manage at-risk contracts and, preparing for the considerable business and care delivery transformation necessary for true population health management. But what are the foundational competencies needed to take on risk?  Healthcare organizations should consider the following 5 key areas:  1) at-risk contract management, 2) network management, 3) care management, 4) performance monitoring, and 5) improvement prioritization.  The value of analytics in each of these competency areas is to prioritize limited resources on the highest impact area.

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Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for Quality Improvement Initiatives

Business intelligence may not sound like something that belongs in a healthcare setting. After all, what role can it possibly play in medical excellence and compassionate care? But federal mandates that require cost and care improvement and reporting on those improvement metrics, are driving the need for business intelligence tools. For healthcare, this means an enterprise data warehouse with the processing power and architecture to handle the vast volumes of data, analytics applications that will effectively unlock the data, and data visualization tools to easily illustrate areas of opportunity.

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Delivering Excellence: How Stanford Health Care Uses Analytics to Improve Outcomes

Many healthcare organizations deliver care that’s based on science or compassion, but usually not both. Amir Dan Rubin, CEO and President of Stanford Health Care, along with his leadership team, has made it a priority to deliver the best patient experience anywhere by combining breakthrough medicine with innovative patient care programs. Stanford’s consistent ranking among the top healthcare organizations in the nation, its roster of Nobel Prize winners, and its list of medical technology achievements, are proof that this strategy is working well.

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Dr. Bridget Duffy: Combining Empathy, Data Analytics and The Human Story

During her 20-year journey to humanize healthcare, Dr. Bridget Duffy has listened to the voice of patients, families, nurses, physicians and other care team members to determine the elements of an optimal healing and working environment. Her experiences led her to become the Chief Experience Officer (CXO) of the Cleveland Clinic–the first senior position of its kind in healthcare in the nation. Undeterred by the naysayers, who called it “foo-foo medicine”, Dr. Duffy is leading the charge for the next generation of communication and collaboration solutions. Her goal? Teach hospitals to use data to hardwire empathy and get physicians and nurses back to the patients bedside.

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Use Well-Crafted Aim Statements To Achieve Clinical Quality Improvements

Too often, hospitals and health systems stop at developing broad clinical quality improvement statements that come up short of achieving their desired goals. What’s missing are clearly defined improvement objectives in the form of aim statements that take into account the effects on other areas of the organization: patient safety and satisfaction, physician engagement, and financial contribution. Aim statements help articulate the problems that add value for patients and the organization, but good data, and the analytics tools required to understand the data, are essential to illuminating high-value problem areas. Additionally, aim statements must stick to the SMART guidelines: Specific, Measureable, Achievable, Relevant, and Time-bound.

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Daryl Morey: The Poster Boy for Data Analytics Has a Message for Healthcare

Daryl Morey’s road to becoming the face of data analytics in the NBA has not been an easy one. Sports, like healthcare, have been late adopters in the use of data to drive decision-making. Morey was charged with not only creating a data-driven culture in the Houston Rocket’s front office, but also convincing the players and coaches that relevant, timely statistics, when combined with their expertise and experience, would have a positive impact on both the individual player and the team as a whole. Today, Morey believes the argument on whether objective data drives better decisions is over. Morey believes the next innovations in data analytics will be created by medicine, what he calls “the most venerable of professions.” Throughout his HAS keynote, Morey will share his insights for creating a data-driven culture, and how to ensure teams stay engaged, motivated, and inspired, especially in times of change. As Morey stated in a December 2013 Men’s Journal article, “How The Geeks Took Over The NBA”, “…the science shows over and over that if you use objective data and analysis in decision-making, you make better decisions.’”

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A Sneak Peek at the New HAS 15 Event Application

One of my favorite tools we are developing for the Summit is our HAS analytics app. We are developing a cool upgraded app for the Healthcare Analytics Summit. This app, which premiered to rave reviews at the 2014 Summit, will enable participants to metaphorically breathe, touch, and feel analytics throughout the summit. We are enhancing almost every feature that we originally introduced in the 2014 version. The new, improved app will have innovative real-time polling, question-and-answer, social engagement, gamification, and interactions based on analytics developed using beacon location-sensing technology. Read more to get a sneak peek at a few of the cool experiences we’re creating.

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5 Principles of Adaptive Leadership and Why It’s a Critical Skill for Healthcare Leaders

Adaptive leadership is a leadership language and conceptual framework developed by Ronald Heifetz, MD, as a way to help hardworking leaders bring about change at their organizations. By applying adaptive leadership principles, leaders can enhance their ability to work with others by seeing human behavior differently and making sense of the behaviors triggered by rapid, high-volume change. The following five principles form the framework for adaptive leadership: (1) There are two types of challenges: technical and adaptive. (2) People need a certain amount of tension to do their best work, but the amount of tension needs to be productive. (3) There is a difference between the role of authority and the exercise of leadership. (4) Work avoidance (resistance) means that people are outside the productive range of tension. (5) Reflect in action by spending time on the balcony and the dance floor.

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The HAS15 Analytics Walkabout: “Speed Learning” for Outcomes Improvement

The 2015 Healthcare Analytics Summit promises a deep agenda of educational, entertaining, and engaging offerings. One session that covers all three types of activities is the Analytics Walkabout, almost four hours of face-to-face “speed learning” over the first two days. This is where attendees will be able to meet with dozens of experts on topics critical to their outcomes improvement journeys. It promises to be an informal and relaxing environment…that’s fun, too. Our new app and GPS-based “treasure hunt” game will keep everyone learning with some amusing competition thrown in to keep things really interesting.

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Health Catalyst Jobs: Growing and Innovating with Help from Utah

The State of Utah is helping Health Catalyst grow and add jobs through tax incentives. This will mean almost 300 potential new jobs located in Utah over the next five years. We consider this to be a kind of investment Utah is making in Health Catalyst. Utah is experiencing booming growth and as Health Catalyst achieves success, we add to this growth by contributing jobs and providing economic stimulus to support industries around us. We are grateful to Utah for the vote of confidence in allocating its scare and important public resources. It’s an honor to work and live here.

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Outcomes Improvement: What You Get When You Mix Good Data with Physician Engagement

The prescription for improving healthcare outcomes is pretty straightforward: improve quality by working with good data that’s based on patient perceptions of quality, as well as functional health outcomes. Then make that data accessible and actionable among your physicians and give them the leeway they need to reduce variation and, ultimately, improve outcomes. As simple as this may seem, it’s been complicated by an inefficient data infrastructure with non-standardized components (EHRs) and the inability to distribute analyses and visualizations where they are needed most (at the point of care). Dale Sanders explains these issues in detail and outlines solutions in this article published in the April 2015 edition of BMJ Outcomes.

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Jim Collins: Leading in Times of Turbulence

We can all agree the healthcare industry is facing turbulent times. Clinicians have been impacted by the changes-and not always for the better. Many have experienced shifts in autonomy, and sometimes income and social stature. The move to value-based care and the pressure to improve quality while providing better care has left clinicians feeling overwhelmed and ill equipped. Engaging physicians, nurses, and staff in improvement efforts is essential; it ensures improvements become sustainable and improvement becomes a part of the culture. However, getting that engagement is tricky, especially in times of uncertainty and change. Leaders who understand what motivates people to engage (and what doesn’t) will set apart the merely good healthcare organizations from the great ones. But what distinguishes the great leaders from the merely good? How do they react when hit by unforeseen events that are impossible to control? What distinguishes those who perform exceptionally well from those who don’t? I am excited to have an award-winning business and management consultant, Jim Collins, join us for the 2015 Healthcare Analytics Summit. In his speech is entitled “Greatness in Turbulent Times,” Collins will share the timeless fundamentals that enable organizations to not only survive, but also thrive in this era of transformation.

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Why Most Analytic Applications Will Never Be Able to Significantly Improve Healthcare Outcomes

The availability of healthcare IT solutions can be overwhelming and all promise to solve an organization’s most pressing issues. While typical data and analytic applications are excellent at exposing opportunities for improvement that are impacting the bottom line, most are not effective at helping the organization determine what to do to address them and improve outcomes. However, a new approach to creating analytics applications is emerging. Analytics applications that incorporate best practices clinical content along with the best practices visualizations help everyone understand the problem and the solution. These applications also enable clinicians to better understand, adopt, roll out, and execute outcome improvement initiatives with healthcare systems. Health Catalyst has deliberately created a comprehensive, dynamic suite of applications that integrate clinical content and facilitate the orderly implementation of action plans.

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What do Pixar and creativity have to do with healthcare?

We don’t often use the terms “creativity” and “healthcare” in the same breath, but it’s a requirement for executives who wish to lead their organizations into the 21st century. Now, more than ever, healthcare leaders need to keep their organizations ahead of the reform curve via pragmatic innovation and creative management. Ed Catmull, the president of Pixar and Walt Disney Animation Studios, and an inspirational force of the entertainment industry for the past three decades, will share his thoughts and insights on these leadership qualities and how creativity can apply to healthcare, and any other organization. Catmull is the closing keynote speaker for the 2015 Healthcare Analytics Summit coming up in September. Here are some highlights of his leadership philosophy:

  • Give a good idea to a mediocre team, and they will screw it up. But give a mediocre idea to a great team, and they will either fix it or come up with something better.
  • It’s not the manager’s job to prevent risks. It’s the manager’s job to make it safe for others to take them.
  • A company’s communication structure should not mirror its organizational structure. Everybody should be able to talk to anybody.

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Introducing the Accelerated Practices (AP) Program: An Innovative Way to Help Health Systems Accelerate and Sustain Outcomes Improvement

We are excited to announce the launch of Health Catalyst University’s Accelerated Practices (AP) Program. This program is a highly immersive, project-based learning experience that healthcare industry experts have spent a lot of time developing. The goal of the program is for participants to leave with the tools and knowledge they need to achieve significant improvements in a short amount of time for their organizations. They will also learn how to communicate the need for change in this new value-based care environment by using data and proven leadership principles.

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2015 Sustainable Growth Rate Repeal: The Future is Here

The bill repealing SGR and replacing it with a more reliable payment schedule will alleviate physicians having to wonder whether their Medicare reimbursement rate will be slashed each year. However, it has also accelerated the need for providers to adopt value-based reimbursement (VBR). While the transformation won’t happen overnight healthcare organizations can, and should, begin preparing now. The following steps will make the transition easier: 1. Get educated and involved; 2. Understand the new law; and 3. Most importantly, providers should consider ways to take advantage of the upcoming 5 percent bonus. Providers that are already participating in VBR arrangements should consider how to increase the volume of their VBR business. Those that have not yet embraced VBR practices need to start immediately. Having a solid roadmap by the end of 2015 will ensure for a successful transition to a value-based system.

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The 4 Clinical Teams Needed to Drive Sustainable Improvement

As the healthcare industry shifts from a fee-for-service to pay-for-performance and accountable care organizations are under greater pressure to make improvements to their clinical, financial and operational outcomes. As clinical quality improvement efforts grow systematically improving and sustaining care across the organization becomes more challenging. In order to ensure sustainable, long-term change a cross-functional, team-based approach that accelerates the implementation of change throughout the organization is necessary. This is the adoption system. Without an adoption system, improvement initiatives become a series of one off projects that may have a temporary positive impact, but soon return to the baseline level.

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Healthcare Dashboards: 3 Keys for Creating Effective and Insightful Executive Dashboards

As the use of data-driven Key Performance Indicators (KPIs) increases, healthcare organizations are adopting Executive Dashboards to track organizational performance. While dashboards deliver insight and identify areas for improvement, they fail to make the data actionable and the value is often offset by the unproductive fire drills and churn they create. There are three keys to create and deploy insightful and effective dashboards successfully:

  1. Aggregation of underlying dashboards to create the executive dashboard
  2. Establishment of clear ownership and accountability
  3. Sustainable process

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Value-based Healthcare and Effective Analytics Outlined in New Gartner Report

The need for healthcare analytics has expanded greatly over the last few years, especially with the rise of value-based healthcare. CIOs are finding they must adapt quickly to keep up with the transition. A report from Gartner entitled “Transitioning to Value-Based Healthcare: Building Blocks for Effective Analytics” recommends that organizations view analytics as a program, not a project, and defines the Logical Data Warehouse (LDW) as the most important concept a CIO must understand.

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The 3 Challenges of Translational and Clinical Research Data Management and a Strategy to Succeed

Researchers are facing problems with clinical research data management. These challenges include: 1. Accessing healthcare data due to technology barriers, regulatory barriers, and organizational barriers; 2. Inefficient use of time and resources when working with the data because of poor study recruitment, data cobbling with Excel and Access databases, and materials waste when samples can’t be found.; and 3. Translating research discovery into clinical practice because systems aren’t in place to move new best practices into everyday clinical care.

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Healthcare Reporting: Centralized vs. Decentralized

The purpose of analytics in a healthcare organization is to gain insights to improve a process to address an issue such as, improving clinical quality and patient safety or improving the health of a particular patient population. Analysts are responsible for gathering disparate data from different functional areas and develop a narrative so those driving change can take the information and make it actionable. Organizations generally build one of two analytic reporting structures. One is a centralized model, where the analytics group is its own entity, independent of any particular group. The second is a decentralized model where the analysts work directly for the different groups or departments. In this way, the group does not have to compete for the attention of the analysts and the analysts’ sole focus is to serve those “customers” well. There is a third way, as well, that optimizes the strengths of both centralized and decentralized.

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Value-Based Purchasing: Why Your Timeline Just Got Shorter

CMS has announced a new, more aggressive timeline for implementing Medicare fee-for-service (FFS) payments to value-based reimbursement. While this transition is needed gaps in several areas of the VBP models are causing angst among provider organizations. Healthcare organizations can begin to improve quality and lower costs today as CMS works to address the challenges associated with these models. An EDW is key in enabling organizations perform the sophisticated analysis and tracking to meet the requirements set by CMS successfully.

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Michael Porter and Others Show How to Deliver Better Care in Value-based Healthcare Documentary

Healthcare organizations from Hamburg to Gothenburg to Boston are realizing the future of care delivery through a value-based approach, as portrayed in this video documentary featuring professor Michael Porter of the Harvard Business School. Measured Outcomes: A Future View of Value-Based Healthcare explains how value-based care is a methodology that involves standardizing outcome measurements, tracking them over the long term, and putting clinical teams in place with the longevity needed to build a sustainable program. More importantly, it is healthcare that matters most to patients because they report and track their own quality measurements, giving them a say in their own healthcare experience. Providers are winning, patients are winning, and the results for the organizations showcased in this video are remarkable, such as an 88 percent prostatectomy success rate for the Martini-Klinik in Hamburg, Germany, compared to a 32.8 percent rate for the rest of the country. And with just 10 surgeons on staff, they are doing more volume than any other facility in the world, by far, all attributable to their value-based approach.

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HIMSS 2015 Finale: Days Three and Four

The HIMSS 2015 finale featured presentations from Marshfield Clinic, the Military Health System, and MultiCare Connected Health. Kori Krueger, MD and Kate Konitzer, MMI from Marshfield Clinic started the day talking about population health management. They explained the many stages to PHM and the challenges within each. Next, Colonel Bonnema, MD from the Information Delivery Division in the Defense Health Agency showed the Department of Defense’s healthcare management systems modernization project, which includes an $11B contract to upgrade to a single EHR. Finally, Christopher Kodama, MD, president of MultiCare Connect Health, showed how MultiCare developed an ACO and CIN in response to health reform and market-driven opportunities.

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CMS at HIMSS 2015: Day Two’s Presentations

Lots of great presentations during Day Two at HIMSS 2015, including a fantastic one about CMS. The day started an educational session entitled “The Intersection of Quality and Innovation at CMS” from Dr. Patrick Conway, the CMS Deputy Administrator for Innovation and Quality and the Chief Medical Officer. CMS sums up the vision for improving healthcare delivery in three words: better, smarter, and healthier. Next, Sarah Kadish from the Dana Farber Cancer Institute at Harvard University, presented on transforming process improvements through real-time locating systems (RTLS) data. Using this data patient clinic flows and staff workflows can be carefully mapped and analyzed. And last, Ken Congdon, Editor in Chief at Health IT Outcomes, spoke about driving health IT ROI with data analytics. Ken argued that the ultimate end game of healthcare IT initiatives would be predictive analytics and personalized care.

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HIMSS 2015 Day 1: The State of Healthcare Analytics

Day One at HIMSS 2015 had several interesting educational sessions. The first was a fantastic and touching presentation from Texas Children’s Hospital covering managing population health with science, analytics, and quality improvement. Next, Susquehanna Health showed how it uses perioperative analytics to reach the triple aim, and the results were amazing (for example, a new patient flow model eliminated 90 minutes of waiting time). Advocate Health Care presented on data-enabled strategic resource allocation in ACOs. As a 13-hosptial system, Advocate operates the largest ACO in the country. Finally, we heard from the Cleveland Clinic and Healthcore/Anthem on leveraging clinical data for risk adjusting bundled payments. The model they used seemed to be quite accurate. Looking forward to Day Two.

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Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s Going

Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.

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Improve Patient Satisfaction: 5 Things Healthcare Organizations Can Learn From Disney

Patient satisfaction metrics are being put in the spotlight and are becoming more important as healthcare organizations transition from fee-for-service reimbursements to alternative payment models. While healthcare and the entertainment industry may seem disparate on the surface, there is much organizations can learn about improving the patient experience from companies like Disney who utilize data to understand their customers’ wants and needs in order to provide a superior guest experience. Disney creates the idea guest experience in 5 ways: 1. Understanding the guest; 2. Everyone is a performer; 3. Seeking out interactions; 4. Owning the guest; and 5. Accountability

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Pragmatic Innovation in Healthcare: Taking Risk and Establishing Partnerships

Investment and innovation in healthcare is driven by health system providers partnering with entrepreneurs. During my time at venture capital companies, I saw how sharing risk could marry the concept of innovation with pragmatism. Health Catalyst uses Pragmatic Innovation as an operating principle. This is evident on a company-level and in the risks we take with our client-partners, such as Allina Health. Earlier this year, Health Catalyst and Allina Health announced an exciting innovation in healthcare: a true partnership to improve outcomes. Each party took a risk, and each will share in the improvements derived.

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Hospital Readmissions Reduction Program: Keys to Success

Avoidable readmissions are a major financial major problem for the healthcare industry, especially for government payers. To tackle this problem, CMS launched the Hospital Readmissions Reduction Program (HRRP). While some hospitals may be able to absorb the financial penalties under HRRP, they still need to track increasingly complex reporting metrics. Most tracking solutions are inadequate for today’s complicated reporting needs. A healthcare enterprise data warehouse and analytics applications, however, are designed to solve the numerous reporting burdens. When used together, they also deliver a robust solution that enables hospitals to track and drive real cost and quality improvement initiatives, all without the need for users to be technical experts.

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How Health Catalyst Ensures HIPAA Security Compliance: 2 Key Components

National awareness for the privacy and security of patient electronic health information is currently at an all-time high. Yet providing HIPAA-compliant solutions has been an ongoing priority since the founding of Health Catalyst. While our handling of PHI isn’t as extensive as that of a payer or healthcare provider, we are committed to complete compliance with HIPAA and ensuring the privacy and security of our clients’ PHI. This is possible because of our culture and advanced technology. Technology features include tracking and audit trails, physical security of the data, limited user access to data during deployment, role-based security features, protection of sensitive subsets of PHI, and ongoing control of user access regardless of the hosting environment.

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New Ranking Helps Prioritize Your Healthcare Cost Reduction Efforts With Surprising Results

When deciding to prioritize your clinical improvement or cost reduction efforts, it’s helpful to use clinical program costs as a key input. The idea is to start with the first three or four in the first year, then work down the list.  Most health systems prioritize using inpatient costs because they do not have access to outpatient data.  However as accountable care and population health efforts increase, looking at costs in silos will not be sufficient.  Under the leadership of Dr. Burton, our Health Catalyst team has been collecting, analyzing, and compiling a macro, industry-level view of inpatient and outpatient costs to serve as a guide for healthcare organizations who lack access to either of those views.  The following ranking is the first of its kind, combining months of detailed analysis of several California health systems to show a combined total of both outpatient and inpatient costs.  The results are surprising.  Adding outpatient costs significantly changes the cost rankings of many of the traditional top health system care processes.

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6 Essential Data Analyst Skills for Your Healthcare Organization

Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.

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How Great Patient Satisfaction Metrics Can Be Achieved in a Regional Medical Center

With the major shift to value-based care sweeping across the U.S. landscape, healthcare organizations are looking for ways to significantly improve their patient satisfaction metrics. Thibodaux Regional Medical Center has done just that by creating a culture of patient-centered excellence. Their formula includes three key promises that have become the heart and soul of their operations: (1) provide great clinical care, (2) provide great emotional care, and (3) invest in great technology and processes.

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Interactive Healthcare Dashboards Are Gaining Momentum

Workers in today’s healthcare systems need dashboards with more power, interactivity, and visual feedback than traditional static reports are able to provide. Users also need to understand how and where to make improvements based on the dashboard’s information. To provide such deep insight to the data, a healthcare dashboard should have the following characteristics: be easily accessible, display reliable data, contain relevant data, be up-to-date for the task at hand, and include trends and/or benchmarks. When the right type of dashboard is combined with a late-binding data warehouse, users will gain access to the knowledge their data holds to drive lasting and effective improvement initiatives.

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6 Proven Strategies for Engaging Physicians—and 4 Ways to Fail

For healthcare organizations to be successful with their quality and cost improvement initiatives, physicians must be engaged with the proposed changes. But many physicians are not engaged because their morale is suffering. While some strategies to encourage buy-in for improvement initiatives don’t work, there are six strategies that have proven to be effective: (1) discover a common purpose, (2) adopt an engaging style, (3) turn physicians into partners, not customers, (4) segment the engagement plan, (5) use “engaging” improvement methods, and (6) provide them with backup—all the way to the board. Once the organization has their trust, physicians will gain enthusiasm to move forward with improvement efforts that will benefit everyone.

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The Clinical Integration Hierarchy: A Primer on the Backbone of Data-driven Quality and Cost Improvement

Healthcare delivery is typically siloed into departments and care settings. But accountable care and value-based payment models require organizations to coordinate care across the continuum. To accomplish this, the Clinical Integration Hierarchy groups healthcare into work process that reflect how care is actually delivered. At the most granular level are care processes such as AMI and Cardiac Rehab (some of which are further divided into sub-care processes such as when AMI is divided into PCI and CABG). Next, care process families form the link between care processes through common pathologic conditions. Finally, the care process families comprise clinical programs such as Cardiovascular and Behavioral Health. The Clinical Integration Hierarchy forms the foundation for systematically tackling quality and cost improvement.

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Improving Outcomes for Sepsis Patients: 3 Key Solutions Proven to Help

There are a number of reasons why hospitals are looking for improvement opportunities to reduce sepsis rates: sepsis costs lives, sepsis adds significant expense to care, and high sepsis rates lead to financial penalties. The three key solutions include an enterprise data warehouse, sophisticated healthcare analytics applications and dashboards, and permanent cross-functional teams for sustainable sepsis improvements.

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Early- vs. Late-Binding Speed: Which Is the Faster Data Warehouse?

Which is a faster data-warehousing model, early-binding or late-binding? Skipping the suspense, the late-binding approach is the speedier option. Binding has to do with how data is modeled within the EDW. Early-binding requires business rules to be set up early in the analysis process. This means early-binding isn’t very flexible or adaptable to changes. On the other hand, the late-binding approach is all about speed and flexibility. Business rule decisions can happen at the last moment, right when the analysis takes place.

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Healthcare Analytics Software Solutions: A Definitive …

There’s a new trend in the healthcare industry to adopt analytics software solutions to help organizations achieve clinical and financial success. Because of the high demand for analytics, there are many players touting their ability to delivery comprehensive solutions. With so many options available, health systems need to be able to cut through the marketing hype to find tools that provide the best value for their needs. Key solutions include an enterprise data warehouse and analytics software applications (from foundational to discovery to advanced). Other considerations include the organization’s readiness for cultural change, the total cost of ownership required, and the viability of the company providing the technology.

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Data Lake vs. Data Warehouse: Which is Right for Healthcare?

The data lake style of a data warehouse architecture is a flexible alternative to a traditional data warehouse. It allows for unstructured data. When a warehousing approach requires that the data be in a structured format, there are constraints on the analyses that can be performed because not all of the data can be structured early. The data lake concept is very similar to our Late-Binding approach in that data lakes are our source marts. We increase the efficiency and effectiveness of these through: 1. Metadata, 2. Source Mart Designer, and 3. Subject Area Mart Designer.

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How to Develop a Robust Clinical Content System

On average it takes seventeen years for medical research findings to make their way into standard clinical practice. Healthcare organizations are being challenged to find much faster ways to implement evidence-based best practices into everyday care delivery. Successful implementation of a robust content system is critical for hospitals to drive clinical integration more quickly and efficiently across the organization to achieve improved, sustainable outcomes. There are 5 major steps including:. 1. Performing key process analysis; 2. Understanding the population; 3. Understanding best practices and current state; 4. Identifying key metrics; and 5. Defining specific goals

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Top 7 Healthcare Trends and Challenges from Our Financial Expert

As the healthcare industry moves closer to value-based care, there are a lot of projections about the changes that will occur in the near future. This article discusses seven of the top trends the industry is focused on: (1) physicians start to feel the financial impact of CMS’s rules; (2) the use of technology in healthcare is exploding; (3) financial viability is a key concern for CEOs; (4) reducing exposure to risk performance is becoming more important; (5) interest in population health management continues to grow; (6) outcomes improvements will continue to increase; and (7) collaboration between providers and payers will increase.

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Understanding the Culture and Core of Health Catalyst: One Man’s Perspective

Typical venture capital meetings with prospective entrepreneurial businesses usually cover routine information about how the company in question will achieve growth and financial success. But imagine a meeting so exceptionally different from traditional venture capital meetings that one of the original investors feels inspired to join the company as a team member. This is exactly what happened after Kyle Salyers listened to Health Catalyst’s management team explain the company’s goal of creating business value by first focusing on its cultural attributes and operating principles.

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Patient Flight Path Analytics: From Airline Operations to Healthcare Outcomes

We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.

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2015 and Beyond: 6 Predictions for Healthcare and Population Health

Healthcare will undergo a number of changes in 2015, particularly as organizations look to manage population health. Dr. David A. Burton outlines what he believes will happen in terms of at-risk contracting, risk evaluation, network optimization, quality and safety, cost reduction, and infrastructure, and how 2015 can develop into opportunity for all.

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Streamlining the Risk Management Process in Healthcare to Improve Workflow and Increase Patient Safety

Managing risk in a healthcare system is a tough job. And in this era of quality measures and reimbursement reform, the job of not just managing risk, but also proactively improving patient safety is critical. That’s because improving patient safety improves overall patient outcomes, a goal that’s being driven by declining reimbursements and a potential loss of reputation if organizations aren’t able to demonstrate compliance with the Affordable Care Act‘s mandates — two situations that could put a health system out of business. To reduce the current inefficiencies in the risk management process, two solutions are necessary: an EDW and a simple data entry application tool. These solutions will give risk managers — and everyone else involved in the process — an easy-to-use tool to review the risk event in near real time and in one location. This approach eliminates the current tedious process of using emails and meetings to collaborate on each event.

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The Practical Use of the Healthcare Analytics Adoption Model

When an analyst from another health system asked our resident analytics expert about the practical value of the Analytics Adoption Model, our expert had a lot to say. Specifically, he elaborated on the results the organization would realize, especially if they used the Adoption Model as a roadmap on their journey to become data driven. But first, they would need to adopt a late-binding data warehouse and analytics applications. With both solutions, they would be able to confidently deliver evidence-based care.

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Is That Data Valid? Getting Accurate Financial Data in Healthcare

A consolidated EDW is not a replacement or threat to the individual financial systems and reporting tools employed for general ledger, billing, payroll, or supply management. On the contrary, each of those systems is designed with sophisticated functionality that drives organizational efficiency. But alone, these systems realize only a portion of their true return on investment for the enterprise. As a consolidated data resource, these systems provide untold potential to address the underlying challenges to efficient, cost-effective health care.

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Improving Population Health Outcomes: From Airplanes to Doctors’ Offices

A staple of inflight magazines, the “Best Doctors” ad showcases individual doctors for specialties in healthcare. Yet, there are no “Best Pilots” ads. That’s because healthcare functions as a craftsmanship practice, while aviation operates using a standard of production. The craftsmanship mentality in medicine leads to a wide variation in results for patients, even those facing the same diagnoses. To improve population health systematically, three systems are required: 1. The Best Practice System (including best practices identified and agreed upon), 2. The Adoption System (meaning how those practices are used across the enterprise), and 3. The Analytics System (in part, measuring how well those best practices are being implemented). Taken together, these systems will move healthcare toward an effective system of production and improve outcomes for patients.

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The Truth About Changing Healthcare Reimbursements: A Q&A with Dale Sanders

We sat down with Senior Vice President of Strategy, Dale Sanders, and asked him about healthcare reimbursements, risk models, and how physicians are handling these changes. Dale explains that reimbursements aren’t changing very fast. And in today’s risk models, there isn’t a lot of risk for providers or insurance companies. Good data and a strong culture around change are the best predictors of success. Federal ACOs have invested far more than they’ve recovered and few are willing to re-enroll in the ACO program unless major changes are made. As for looking at high-risk patients, most of the high-risk interventions have focused on preventable readmissions, motivated by CMS penalties. There seem to be two root categories for interventions: provider-centric (better discharge planning; scheduling follow-up visits at the time of discharge) and patient-centric (the socio-economic factors like transportation to care and lifestyle challenges). Finally, when data is introduced into a physician’s practice, most are surprised by how little they actually use evidence-based best practices.

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How to Improve Clinical Programs by Breaking the Cycle of Waste in Healthcare

To succeed with value-based care, health systems must demonstrate to CMS they operate more effectively, efficiently, and safely. This requires organizations to identify and improve three types of waste commonly found in clinical programs: ordering waste, workflow and operational variations waste, and defect waste. Finding these areas, however, requires three critical solutions: an EDW, a KPA Application, and organizational readiness assessments.

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What is the ROI of investing in a healthcare data analyst? A real example of saving millions

Making the most of a healthcare data analyst’s knowledge is a key component to getting the best ROI from a hospital improvement project. But all too often, analysts serve merely as data validators — they justify the data that leadership wants validated. Because analysts aren’t decision makers, they don’t have the authority to ask the questions that can save a health system millions. Empowering analysts, however, enables them to ask the right questions — and find the right answers — that will lead to significant savings.

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5 Strategies to Use Regardless of What Happens to the ACA

Our guest commentator, Dr. Tommy Prewitt from HORNE LLC, believes that the ACA is likely to fail. He has 5 strategies he suggests that hospitals should use, regardless of what happens with the ACA. 1. Rethink your model of care by eliminating waste. 2. Allocate 20% of your financial resources toward care innovation. 3. Get serious about data analytics; in fact, this is a good place to spend your 20%. 4. Avoid buyer’s remorse and be cautious when buying smaller hospitals. 5. Win the race to the bottom; post fixed charges for episodes of care. 

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Healthcare analytics applications to the rescue — or not?

Better analytics technology alone will not achieve the healthcare industry’s desired improvements in life, efficiency, effectiveness, or provider and patient satisfaction. That all will change, however, when the technology is combined with a deployment system. With such a system, health systems learn from experienced healthcare experts about how to use the information from their analytics applications to transform from the old world to the new.

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My Personal Experience with Dr. Shetty’s Healthcare Mission: An $800 Heart Surgery

I spent a few weeks this summer creating a documentary of the work of Dr. Devi Shetty. One of the most memorable moments of this experience was when I talked with Dr. Shetty himself while he was performing a heart operation on an 18-month-old boy. It was an enlightening and amazing time, where I fully learned why Dr. Shetty deserves the title bestowed on him by the WSJ, the “Henry Ford of healthcare.” His ultimate goal is to reduce the cost of heart surgery from $3,000 to $800 by evaluating each cost component. He has opened hospitals all across the world offering low-cost, high-quality care. Could this model work for the U.S.? It’s a wonderful thing to hope for.

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Outsourced vs. In-house Healthcare Analytics: Pros and Cons

Healthcare analytics are essential for organizations to thrive in the new healthcare environment. Using analytics, systems can evaluate efficiency, effectiveness, and find improvement opportunities. There are two principal approaches: outsourcing the analytics function to benchmarking companies and providers of software-as-a-service; and doing analytics in-house with a system’s own data warehouse. The pros of outsourcing include gaining benchmarking access to how health system peers are performing. The cons to outsourcing include focusing too much high-level outcomes with no insight in how to effect change. The pros of in-house analytics include having quick access to fine-grained details of the data and being able to include clinicians in the implementation and development of the analytics process. A con is that in-house analytics can require significant resources - an investment in the right personnel and right technology.  

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The Best Healthcare Analytics Application for Prioritizing Improvement Programs

Healthcare organizations are looking to analytics applications that will help them identify and prioritize the best areas for improvement projects. Even once they choose an analytics solution, though, it’s difficult to know where to start because of all of the data health systems have stored. Two solutions will help eliminate the guesswork: a healthcare enterprise data warehouse and sophisticated analytics applications, such as the Key Process Analysis (KPA) Application. The KPA application uses the Pareto principle to find areas with highest variation and highest resource consumption. This valuable information gives health systems a starting point as they begin their journey to improve the delivery of care and reduce costs.

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A New GIS-driven Approach to Optimize Service Area Boundaries for ACOs

While many organizations use patient registries from EMRs to determine their patient population, there is a better way. Using GIS location technology, a health system can identify its care population based on geography and drive times. Health Catalyst uses Dartmouth Atlas hospital referral regions, a hierarchy of facility levels with appropriate drive time isochrones, and medical specialties-based central place theory to develop a more comprehensive view of a health system’s minimum bounding geometry. Using this method, ACOs derive a better understanding of their enrolled patients and eligible payer groups resulting a better basis for strategy and decision making.

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The Changing Role of Healthcare Data Analysts—How Our Most Successful Clients Are Embracing Healthcare Transformation (Executive Report)

The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.

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Innovation in Healthcare: Why It’s Needed and Where It’s Going

Healthcare organizations are facing unprecedented challenges to improve quality and reduce waste. The traditional encounter-based delivery model is overwhelmed due to aging Baby Boomers and the increasing prevalence of chronic disease. To tackle these challenges, more disruptive innovation is needed in healthcare. We already have development of new diagnostic procedures, therapies, drugs, and medical devices, but healthcare needs more innovation around prevention and personalized care. Sensors, wearable technology, and big data offer ways for healthcare to start exploring new possibility and opportunities in this realm.

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Successfully Rolling Out Healthcare Improvement Initiatives with Key Influencers

Rolling out a healthcare improvement initiative and getting buy-in from clinicians can be tricky. Paul Revere’s midnight ride shows how finding and using key influencers can help prompt action. This knowledge can be used to drive success of hospital data-driven improvement initiatives too. Achieving buy-in of new best practices and process across an organization is not easy, but it is imperative to success. Picking the right people for these teams means picking the innovators and early adopters in the organization, as the Paul Revere example illustrates. Once these people are identified, putting them into three key teams (the Guidance team, the Workgroup, and the Clinical Implementation team) will get the organization the right deployment system to drive successful improvement initiative on a system-wide basis.

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From Ski Industry Management to a Career in Healthcare Information Technology: How Improving Healthcare Became a Skier’s Passion

Not all healthcare information technology experts know what they want to be when they grow up. Some detour down other career paths first. That’s what happened with Greg Miller. But after fortuitously meeting with a healthcare CIO to explore career opportunities, he discovered a passion for healthcare transformation. Then he went on to find a company that made his dreams possible.

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Three Ways Doctors Can Use Patient Data to Get Better Results

Vast new pools of patient data will become available to physicians over the next few years. This data will change our understanding of health and disease, providing a rich new resource to improve clinical care and maximize patient health and well-being. Three ways physician will use this data to drive transformation include: 1. Efficient and effective operations (reducing wasteful spending); 2. Manage population health; and 3. New technology-enabled care and personalized medicine. The impact of new data on healthcare costs will be immense.

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Cut Through the Confusion: The 5 Types of Healthcare Analytics Solutions

As healthcare leaders look into ways to bend the cost curve in today’s value-based reimbursement environment, they are faced with many vendors offering analytics solutions. The key is to determine which product best fits the organization’s needs, not just now but for the long term. This article discusses the five primary options available and the pros and cons of each one. The five analytics options include the following: (1) Buy from a hosted analytics service provider. (2) Buy from a large non-healthcare-specific technology vendor. (3) Buy “best of breed” point solutions. (4) Buy from the EMR vendor. (5) Buy and build an analytics solution from a healthcare data warehouse platform vendor.

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Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better for You?

Most industries use enterprise data warehouses (EDWs) to create meaningful analytics on their operations and processes. Healthcare has long struggled with implementing and maintaining EDWs. One reason for this is that a lot of the data healthcare uses is unstructured, meaning there are few to no restrictions on it. And this unstructured data can exist in several systems within the organization. Additionally, health systems must pull data from many sources, such as EMRs, financial systems, and patient satisfaction data. The early-binding approach to data warehousing makes the binding decisions early in the process and, thus, lacks the agility healthcare needs to respond to ever-changing business rules and requirements. This approach can also take a long time to implement. Late-binding data warehousing has a much faster time-to-value and allows users to create analytics based on what-if scenarios. Plus, it can change to reflect the always-moving world of healthcare analytics needs.

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How Technology-enabled Care Models Can Help Fix Healthcare’s Greatest Challenges

The U.S. healthcare system faces a multitude of challenges, from an unprecedented growth in demand to widespread waste to serious quality issues. The healthcare resource consumption rate is expected to rise to almost 20% of the national GDP by 2022, threatening the nation’s economy. These realities mean that healthcare must emphasize value production and use more efficient care models to complement the traditional encounter-based care model. New technology-enabled care models are allowing care providers to remotely manage health problems and create better outcomes for patients with chronic conditions. Wearable sensors can collect information about a patient’s physiological condition, activity, behaviors, and environment and transmit it to providers, who can act proactively on health concerns before they become a bigger problem. All of this new type of data will require health systems to build analysis of Big Data into their core competencies.

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The Three Systems Critical to Healthcare Analytics Success

Health systems looking to improve the lives of patients through analytics often face problems that prevent them from making the improvements they desire. But by using the three systems: Analytics, Best Practice, and Adoption, organizations can be successful. The analytics system ensures that data is aggregated, easy-to-access, and distributed efficiently by implementing a data warehouse. The best practice system provides the framework for best practices and baselines, which provide context and actionable insight to metrics provided by the analytics system. Finally, the adoption system consists of a permanent, multidisciplinary team to enact those actionable insights from the best practice system. All three systems together form the base for organizations to make the journey to data-driven improvement successful.

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Demystifying Healthcare Data Governance: An Executive Report

Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy. The Triple Aim of data governance is: 1) ensuring data quality, 2) building data literacy, and 3) maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee. Data governance committees need to be sponsored at the executive board and leadership level, with supporting roles defined for data stewards, data architects, database and systems administrators, and data analysts. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape. Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance including: 1) establishing the tone for becoming a data-driven organization, 2) providing access to data, 3) establishing data stewards, 4) establishing a data quality program, 5) exploiting data for the benefit of the organization, 6) the strategic acquisition of data to benefit the organization. As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.

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Genomic Medicine: Personalized Care for Just Pennies

In April 2003, the Human Genome Project was completed and scientists gained the ability to read the entire genetic blueprint for human beings. Since that time, the cost of gene sequencing has fallen from $100 million to $1,000. By 2020, the cost is expected to be mere pennies. Using the power of genomes scientists have found genomic defects for more than 5,000 inherited diseases and are on track to uncover 4,000 more. The implications for treatment of disease are also vast. In the future, clinicians will be able to use genomic-powered personalized medicine to treat patients on an individual basis knowing exactly how their genes will react to treatments and what the best course of action will be.

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Healthcare Performance Improvement Readiness Assessment: The Key to Achieving the IHI Triple Aim

Enhanced patient experience, clinical quality improvements, and the ability to provide cost-effective care are all necessary to meet the IHI’s Triple Aim objectives. To prepare for these types of improvement initiatives, healthcare organizations need to assess their readiness to change and also to determine which of the competing priorities to choose from. We’ve found that starting with a performance improvement readiness assessment is the first step to success because the readiness assessment digs into the three systems necessary for a successful performance improvement initiative — the best practice system, the adoption system, and the analytics system. Then the readiness assessment provides an overall roadmap to ensure success with the organization’s specific improvement goals.

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How to Improve Patient Satisfaction Scores By Using Data

Cleveland Clinic wanted to improve their patient satisfaction scores, so they put the patients first and at the center of everything they do. Health systems may believe they’re already prioritizing their patients’ needs, but in reality, they may not be addressing the real issues. To uncover this information, health systems must dig into their patient satisfaction scores and analyze all of the data, including patient comments, anecdotes, and verbatims. Armed with these insights, they’ll be able to drive effective improvement initiatives and improve their scores, just as Cleveland Clinic did.

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Knowledge Management in Healthcare: It’s More Important Than You Realize

Healthcare is becoming an increasingly complex environment that requires organizations to have access to knowledge from both inside and outside its walls. An organized and effective knowledge management strategy can help organizations achieve operational excellence, foster innovation, and provide the best possible care for its patients. Three reasons for its important are: It facilitate decision-making capabilities. It build learning organizations by making learning routing (and this fosters innovation). And it stimulates cultural change. Using IT systems to enable knowledge management is possible today thanks to the widespread adoption of EHRs and advanced analytical tools.

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The Story Behind the Inspirational Video About Healthcare Transformation, From the Heart

Through a series of happy coincidences, when Dale Sanders was looking for the next move in his career, he ended up at the Cayman Islands Health Services Authority, and started on the path of an amazing story that we’ve decided to share through a documentary. You see, six months after starting as the CEO, he met Dr. Devi Shetty and his team from Narayana Health System in India. They had a unique proposition: built a 2,000-bed hospital, medical school, and long-term care facility in the Cayman Islands, based on the ground-breaking ideas that had worked in India, that could meet the need for high-quality, low-cost healthcare . In spring of 2014, Dr. Shetty and his team, in collaboration with the Cayman Islands government, Ascension Health in the U.S., and prominent businessman, Gene Thompson, opened the doors on the first phase of the project, a 200-bed hospital specializing in cardiothoracic and orthopedic surgery. The documentary describes how they are using many innovative approaches and data to drive out significant costs while maintaining high standards of quality, leading to what many are calling a truly “breakthrough” model for healthcare. Our mission at Health Catalyst is to transform healthcare, hence we wanted to share the amazing work Dr. Shetty and his team do in India, and now, in the Cayman Islands. Health Catalyst funded, produced, and directed this documentary, From the Heart, with no financial relationship and nothing to gain. We simply believe it is a story that must be told.

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How to Survive CMS’s Most Recent 3% Hospital Readmissions Penalties Increase

Hospital readmissions rates are now at 3 percent, which means that health systems are feeling the financial burden of decreased payments from Medicare. They also need to track two more 30-day readmission rates. While there aren’t any new penalty measures planned for 2016, coronary artery bypass grafts will be added as yet another measure to track in 2017. By using three strategies to reduce readmission rates, health systems will experience better outcomes and decreased penalties. The three strategies include the following: (1) implementing a data warehouse that provides a single source of truth; (2) engaging a multidisciplinary team to lead the improvement efforts; (3) installing a sophisticated analytics platform.

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Health Data Stewardship and Its Importance in Healthcare Analytics

Health data stewards are keepers of tribal knowledge, and they’re invaluable when a health system launches or expands a healthcare data analytics initiative. Their intimate and expansive knowledge of how data is collected to represent workflow across different systems can save days’ worth of time (and cost) in the development process while improving the accuracy of the analytics output. But getting anything more than a few spare moments of their time can be difficult because health data stewardship isn’t part of their job description. While it may seem difficult to justify at first, organizations need to formalize the role of the health data steward. The investment will ultimately return many times its value as the organization realizes the advantage of the analytics.

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Disease Surveillance: Monitoring and Reacting to Outbreaks (like Ebola) with an Enterprise Data Warehouse

The current options for monitoring data to help identify disease outbreaks like Ebola are not great. These are: 1) Monitoring chief complaint/reason for admission data in ADT data streams. Although this is a real-time approach, the data is not codified and would require some degree of NLP. 2) Monitoring coded data collected in EHRs. The most precise option available, but the data is not available until after the patient encounter is closed, which would be too late in most cases. And 3) Monitoring billing data. This approach has the same problems as the two listed above, but it’s better than nothing in the absence of an EMR. All of these weaknesses can be solved with the use of a data warehouse.

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Is the Health Sensor Revolution About to Dramatically Change Healthcare?

Technology is growing at an exponential rate and while healthcare has experienced some effects of this, nowhere is this more evident than in the field of health sensors. The Internet of Things is the idea of an ecosystem of devices that connect to software giving feedback and analytics on the person or object using the device. Clinically, this can mean improved monitoring of vital signs, infusions, etc. Outpatient care is also affects by sensors and has the potential to lower the annual costs of managing chronic diseases.

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Self-Service Hospital Reporting Possibilities: Enabling Clinicians to Make Faster and More Informed Decisions

Self-service capabilities are growing in our society and it’s starting to make its way into hospital reporting. Traditionally, analytics and reports have fallen under the purview of the IT department. However, this approach takes more time and is ineffective when trying to make care improvements. With self-service analytics tools, clinicians and other users can access and analyze data on their own, leaving IT to do the more complex analytical tasks and function at the top of their education.

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Healthcare Factoids to Power Your Thinking

We spent time collecting healthcare factoids that show key trends driving the need for data in healthcare. And now, we’ve put it into an easy-to-view, shareable, memorable presentation to use as you see fit. You can use these factoids to help you make a case for reducing healthcare waste or get pointers for your next IT project. You can even use a few of them to predict the future of healthcare.

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Master Data Management in Healthcare: 3 Approaches

Master data management is key for healthcare organizations looks to integrate different systems. The two types of master data are identity data and reference data. Master data management is the process of linking identity data and reference data. MDM is important for mergers and acquisitions and health information exchanges. The three approaches for MDM are: IT system consolidation, Upstream MDM implementation, and Downstream master data reconciliation in an enterprise data warehouse.

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The Rising Healthcare Revolution: The Future Is Already Here

We are now experiencing the beginning of a true data-driven improvement revolution in healthcare. From germ theory in the 1870s to the use of randomized control trails in the 1970s, approximately every 50 years healthcare undergoes a revolution that fundamentally changes patient care and the way physicians practice. We are overdue to another revolution, but it’s starting with the trend toward the use of advanced analytics and the systemization of care.

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6 Reasons Why Healthcare Data Warehouses Fail

It’s no secret that the failure rate of data warehouses across all industries is high – Gartner once estimated as many as 50 percent of data warehouse projects would have only limited acceptance or fail entirely. So what makes the difference between a healthcare data warehouse project that fails and one that succeeds? As a former co-founder of HDWA, Steve details six common reasons: 1) a solid business imperative is missing, 2) executive sponsorship and engagement is weak or non-existent., 3) frontline healthcare information users are not involved from start to finish, 4) boil-the-ocean syndrome takes over, 5) the ideal trumps reality, and 6) worrying about getting governance “perfect” immobilizes the project.

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Healthcare Data Stewardship: The Key to Going from Information Poor to Information Rich

Poor healthcare data stewardship is part of the problem for health systems that feel like they are “data rich and information poor.” But this can be fixed two ways: implementing a data warehouse and improving data stewardship. Without appropriate healthcare data stewardship, even the best infrastructures become underutilized and poorly understood by knowledge workers who could be generating value with the data every day. Data stewards will become critical partners to the data warehouse team in creating a thriving user base. They are the data librarians who advise and guide users, and help them get the most value out of the enterprise data warehouse.

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Linking Clinical and Financial Data: The Key to Real Quality and Cost Outcomes

Since accountable care took the healthcare industry by a storm in 2010, health systems have had to move from their predictable revenue streams based on volume to a model that includes quality measures. While the switch will ultimately improve both quality and cost outcomes, health systems now need the capability of tracking and analyzing the data from both clinical and financial systems. A late-binding enterprise data warehouse provides the flexible architecture that makes it possible to liberate both kinds of data to link it together to provide a full picture of trends and opportunities.

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Data Management and Healthcare: Why Databases and EMRs Don’t Make the Cut on Their Own

Healthcare organizations preparing for the value-based payment model shift have found their internal resources pushed to the limit. Often, in an attempt to address regulatory timetables, systems will use point solutions rather than move toward a long-term strategy of developing robust clinical analytics. If an organization is using their EHR for analytics, they will soon discover that these built-in analytics packages cannot help them identify opportunities for cost effectiveness and clinical best practices. Sophisticated data management and healthcare analytics solutions, however, can provide leaders with the integrated clinical, financial, and patient satisfaction data they need to transform their systems into data-driven enterprises.

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Planning for Healthcare Improvement: A Goal Without a Plan Is Just a Wish

While our current health system has accomplished some great things, the complexity is producing serious quality issues. Healthcare improvement will require that the system changes how it is organized and operated. The best approach to this kind of change and improvement will be the develop a systematic plan composed of the Analytic System (where organizations unlock their data), the Deployment System (organizational, team-based structures), and the Content System (knowledge and best-practices management).

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3 Phases of Healthcare Data Governance in Analytics

Healthcare data governance is a broad topic and covers more than data stewardship, storage, and technical roles and responsibilities. And it’s not easy to implement. It’s necessary, though, for health systems that are entering the world of analytics because the governance structure will enable the organizations to drive higher-quality, low cost care. In order for healthcare data governance to be most effective however, it needs to be adaptive because real healthcare data governance is much more fluid than any plan laid out on paper. Typically there are three phases that characterize successful analytics implementations: the early stage, the mid-term stage, and the steady state. As health systems begin to determine the effectiveness of their data governance strategy, it’s important to look at key metrics from their analytics implementations that will either trend up, remain solid, or trend down.

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Healthcare Analytics Summit Day Two Recap

The second day of the 2014 Healthcare Analytics Summit on September 25, 2014 was another day packed full learning. It started with an analytical recap of the previous day. Then, Dr. Penny Wheeler shared her experiences as President and Chief Clinical Officer of Allina Health and how the organization got to be ranked in the top three for quality metrics in the country according to Modern Healthcare. Next up was Dr. Macias, Chief Clinical Integration Officer at Texas Children’s Hospital. He told some amazing stories about successful clinical improvement projects at the hospital. Then, Dr. David A Burton talked about an accountable care transformation framework and how population health management should fit into the conversation. The breakout sessions included Stanford Health Care’s Yohan Vetteth and Dr. Bohman sharing how their organization uses analytics to improve outcomes in population health and HF readmissions reduction; Health Catalyst’s Tom Burton with his hands-on presentation on how to drive clinical improvement that gets results; Dr. Sreekanth Chaguturu from Partners HealthCare presenting on key principles and approaches to population health management; and, John Henderson from Texas Children’s Hospital and Bobbi Brown and Leslie Falk from Health Catalyst showing how to get ROI out of your healthcare analytics projects. The next keynote was from Health Catalyst’s Dale Sanders on predictive analytics. This was followed by a documentary film about Health City Cayman Islands entitled “From the Heart: Healthcare Transformation from India to the Cayman Islands.”

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Healthcare Analytics Summit Day One Recap

The 2014 Healthcare Analytics Summit kicked off September 25, 2014 in Salt Lake City. Billy Beane was the first keynote speaker, sharing his insights on using analytics in baseball, as documented in the movie Moneyball.  Dr. Glenn Steele then shared his insight into Geisinger’s history and experience with data. Next keynote speaker up was Dr. James Merlino from Cleveland Clinic, who shared his experience and learnings as the Chief Experience Officer for one of the largest, most respected healthcare systems in the nation. The breakout sessions included a Healthcare Analytics Adoption Model survey from Dale Sanders, a lesson in making healthcare waste reduction actionable from Drs. David Burton and Greg Stock, a presentation on organizing for analytics success by Steve Barlow and Holly Rimmasch, a hands-on workshop in getting the most out of your data analyst by John Wadsworth, and finally, a user group kickoff/product roadmap review by Holly Rimmasch, Tom Burton, and Steve Barlow posing (literally) as the Three Amigos. The day ended with two fascinating keynote presentations. First, former Utah Governor Mike Leavitt discussed the future of healthcare reform and government’s role in the process. Then, Google futurist, Ray Kurweil shared how the acceleration of technology will drive healthcare to places we can only image.

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3 Frequent Mistakes in Healthcare Data Analytics

Healthcare organizations are recognizing the value of healthcare analytics, especially in their Big Data, population health management, or accountable care initiatives. This is good because without analytics it is difficult to impossible to run these programs successfully. That said, analytics are not the magic bullet and proper process must be in place. The three most common mistakes health systems makes with their healthcare analytics are: 1. Analytics Whiplash- when the analytics goes from one project to another without being able to fully understand the data and what it’s saying. 2. Coloring the Truth- When analysts don’t feel like they can be completely forthcoming with information and only give leadership the news they want to hear. 3. Deceitful Visualizations- Manipulating charts, graphs, and the like to reflect what the analyst or leadership wants the data to say, rather than what it actually says.

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How to Avoid the 3 Most Common Healthcare Analytics Pitfalls and Related Inefficiencies

Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.

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How Clinical Analytics Will Improve the Cost and Quality of Healthcare Delivery

There’s never been a more critical time in the history of U.S. healthcare for providers to turn to clinical analytics to help them survive and thrive amidst healthcare reform. Clinical analytics can enable health systems with the following improvements as they shift from fee-for-service reimbursements to value-based purchasing: pinpoint waste reduction opportunities, identify specific margin improvement opportunities, systematically identify performance improvement opportunities, automate the tracking and reporting of quality measures, and understand the cost structure at a granular level. Clinical analytics can also help ACOs unlock the data from their EMR investment and provide real-time data for providers and payers to work together to provide value-based insurance design.

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Data-Driven Healthcare That Works: a Physician Group Perspective

A health system shares their story about using proven strategies to care for patients in an accountable care model by using data to drive those strategies. Gregory A. Spencer, MD, FACP, CMO, and CMIO at Crystal Run Healthcare also discusses why Crystal Run Healthcare moved towards analytics and data warehousing as well as the 6 requirements their health system had as they searched for a partner: 1) The solution needed to hit the ground running. 2) The solution needed to provide quick, actionable data. 3) There needed to be a library of analytical applications. 4) The healthcare data model needed to be able to evolve. 5) They needed to be taught how to fish for the data. 6) A long-term relationship with the vendor was important.

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Hadoop in Healthcare: A No-nonsense Q and A

Many industries, especially those using huge amounts of data like Facebook, are using Hadoop for their processing needs. So, what exactly is Big Data and Hadoop and what are its implications for healthcare? Hadoop is a distributed processing and storage platform. The use of Hadoop is rare in the healthcare industry, but healthcare analytics hasn't necessarily been stalled because of this. In fact, the quality of data healthcare produces doesn’t justify Hadoop-level of processing power.  This article answers questions such as what is Hadoop, what are the drivers of this platform in other industries, how might it affect healthcare analytics, how would clinicians use data sources outside their environment, and what drawbacks currently exist for further adoption.

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Healthcare Analytics Applications: Why You Need an Out of Box Solution with Customizability

Providers throughout the U.S. are facing difficult choices for their healthcare analytics applications: should they use an out-of-the-box solution or put in the extra time, effort, and expense to develop a customized solution? Out-of-the-box healthcare analytics applications are just that — they’re applications most health systems can use as-is because the application is designed to work well with the popular source systems in the marketplace. To really gain a deep understanding of the organization and its patients, though, customization of the analytics application is necessary. Customization enables analysts to dig much deeper into the data — and not just after the initial implementation. Instead, the best type of customized healthcare analytics applications solutions can accommodate endless customizations time after time based on new definitions and rules. By selecting customized applications, health systems will get made-to-order analytics that will provide a return on investment — now and in the future.

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An Updated Version of the Anatomy of Healthcare Delivery Model

Read about the recent refinements to this breakthrough model and framework, developed and refined by Dr. David Burton during his 25 years of executive healthcare experience. With revisions to the descriptions and visual elements, the new and enhanced diagram makes it easy to understand the scientific flow of care as understood by physicians and advanced practice clinicians.

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Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse

When creating a healthcare data warehouse, typically time-to-value will take one to two years. But using our data warehouse tools, we’ve reduced that time to months. Usually a lot of manual labor goes into extracting data from EHRs or other sources systems. Metadata mapping helps by indicated where data is located in each system. However, that mapping process is also typically time-consuming and onerous. Using Health Catalyst’s Source Mart Designer, the mapping is automated and ETL scripts become a cinch. Then we use our Atlas tool to make search for specific data easier and more intuitive.

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Intuition-Based Healthcare or Data-Informed Healthcare — Which Would You Prefer? (Guest Contributor: Jim Adams, Advisory Board)

Healthcare has come a long way, but there’s still a long way to go because of the many challenges healthcare leaders are facing. Some of the challenges include knowing which improvements to make without risking the financial health of the organization, implementing advanced analytics solutions, setting up good data governance programs, and changing the culture to be more enterprise focused and data informed. Despite the challenges, there are also great opportunities for healthcare to improve. But first, healthcare leaders need to be able to better understand, monitor, and manage their businesses by knowing what data to use or capture by the use of sophisticated analytics

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Healthcare Databases: Purpose, Strengths, Weaknesses

Most healthcare data is now electronic and with that comes electronic healthcare databases. A healthcare database replaces the old paper documents, file folders, and filing cabinets. They take the form of EHRs, practice management systems, costing systems, patient satisfaction programs, and more. The benefits of databases are equal to the benefits that run on them. They can be stored externally and backed up in a secure place. However, healthcare databases have two big problems: There is an overwhelming amount of raw data with little ability to gain targeted, actionable knowledge from that data. And databases are siloed preventing the across-the-organization insight that is needed in today’s world of value-based care. The answer is a data warehouse, which sits on top of all the other databases providing sophisticated analysis.

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Using Clinical Metrics the Right Way: 5 Considerations Every Hospital Should Know

The ability to analyze data is more important than ever as the healthcare industry shifts from fee-for-service to a pay-for-performance model. And the wealth of clinical data now available is larger than ever. But just having that data isn’t enough. The data, clinical, financial, operational, etc., must be combined to form the powerful foundation that will drive quality improvement. The destination is higher quality care for improved patient outcomes. The route is having the right clinical metrics.

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5 Ways to Use the ICD-10 Delay to Create a Competitive Advantage for Your Health System

In April 2014, Congress gave the entire healthcare industry in the U.S. a reprieve — a one-year ICD-10 delay before providers will be required to document care using ICD-10 codes. The new deadline is October 1, 2015. The question now is, what will health systems do with an extra year to make the transition to using ICD-10 codes? My recommendation is to proceed as though the ICD-10 deadline will still happen this year for five reasons: (1) Coders need time to learn how to code. (2) Dual coding early rewriting of reports and the discovery problems. (3) ICD-10 codes can be used to improve analytics and provide a 2014-2015 comparison of data. (4) Health systems will be able to more accurately project the financial impact of the transition. (5) Payers are learning too, opening up new opportunities for improved relationships with providers.

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How Healthcare Visualizations Can Improve Organizational Buy-In

Healthcare visualizations can be used as a spark to help the leaders of health systems move from a passive understanding of the data to active support of data-driven quality improvement recommendations. But simply showing the visualizations won’t be enough. Instead, those who are using the healthcare visualizations need to be taught, shown, and involved to fully understand their value and drive organizational change. We use a three-tiered approach to help health systems gain better buy-in for their data-driven quality improvement recommendations:  1) we form teams and teach how to overcome organizational barriers, 2)  we show healthcare visualizations to better understand the data, and 3) we involve the teams and answer their questions. It works like this...

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Healthcare Data Should Help, Not Hinder, the Human Endeavor

The patient-provider relationship stands to suffer if we’re not careful about how we implement and use healthcare data and technology. For example, a young woman with a rash on her hands visits two different doctors. The first doctor barely looks up from his computer long enough to write her a prescription. The second doctor using the computer and asks a series of questions to figure out that the patient actually has a latex allergy from the gloves she uses to serve food at her work. And while data is not new, actionable data and analytics are opening new opportunities for physicians to improve care and reduce waste.

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A Time for Revolutionary Thinking: Three Things Clinicians Can Do to Shape the Debate on Healthcare Reform

Politicians and policy makers should not be leading the charge for change in the healthcare industry. Healthcare costs have spiraled out of control, and the healthcare mantra “first do no harm” doesn’t seem to apply anymore when we consider that there is, actually, an acceptable level of harm in the care we provide today. Clinicians need to lead the charge to build a new system that is well-resourced but affordable and efficient. This challenge is too important to be left to politicians and policymakers. The new system must be data-driven, outcomes-focused, and deliver the appropriate level and type of care, no more, no less. If we view this challenge as an opportunity, there’s no telling what we can accomplish.

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Data Collection Tools in Healthcare: What You Need

Data collection tools in healthcare are supposed to make analyzing data from disparate sources easier. But in the real world, we often see stop-gap solutions we call spreadmarts—that conglomerate of one-off Access databases and Excel spreadsheet. There are three big problems with spreadmarts: Data quality (keeping accurate, consistent data); Collaboration (the spreadmarts become yet another silo of data); Security (it’s a challenge to ensure security on free-flowing spreadsheets). The solution is the Instant Data Entry Application (IDEA). With this tool there is no opportunity for manual data entry errors. The application is on a central server enabling collaboration. And security is much more controllable because it sits on the secure server behind a firewall.

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Why You Need to Understand Value-Based Reimbursement and How to Survive It

There are clear signs the healthcare industry is in the midst of a shift to value-based reimbursement. The most noticeable signs are the recent and proposed 2015 rulings from CMS. There are four areas in value-based reimbursement that will be impacted by the end of 2015: the physician payment structure, bundled payments, Inpatient Prospective Payment Systems regulations, and commercial payers. To survive the shift to value-based reimbursement, it’s important for providers and payers to take three steps: provide access to rich data, share knowledge and learn from each other, develop strategies by doing assessments.

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Best Solution to Aggregate Healthcare Data Including Clinical, Financial, Research, Population Health, and More

Health systems generate and collect enormous amounts of healthcare data. Health systems also need to analyze the data for many different needs, such as quality improvement, operations, research, and financial analytics. The best solution an organization can use to aggregate all of this data is an enterprise data warehouse with the following five qualities: new source data feeds that can be developed quickly, a flexible architecture, data definitions that match their context, a single source of truth to support all use cases, and customized data access.

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Preventable Medical Errors: The Future is Calling Us

Healthcare’s journey to improving care and reducing preventable medical errors is a difficult one. But those who embrace the changes are finding new, exciting opportunities. Some of the new realities are reflected in the American Board of Medical Specialties Maintenance of Certification program: Professionalism, Patient care, Medical knowledge, Practice-based learning and improvement, Interpersonal and communication skills, and Systems-based practice. While this has created considerable friction, it is possible to make this shift as part of an integrated practice, like Mayo Clinic and M.D. Anderson Cancer Center have done. Healthcare needs an environment to better manage complexity, not add to it. This is possible and it is happening today.

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My Wake-Up Call: How Data Saves Lives

Have you ever had one of those "wake up moments" where you literally learn a lesson that impacts and changes the trajectory of your life?   Read this personal story by Dr. Bryan Oshiro of his "wake up" call where he learned the importance of data to save lives.  He learned this first-hand when he saw rows of babies on ventilators in the neonatal unit and realized that they had all been electively delivered before 39 weeks. But he didn’t have the data compiled to make a compelling case to his physicians to stop elective pre-39 week deliveries. Working with his technology team, he gathered the data, analyzed it, and successfully engaged his physician team in a quality improvement project to reduce these elective deliveries.

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The Power of Geo-Analytics (and Maps) to Improve Predictive Analytics in Healthcare

As far back as the 1840s, clinicians have been using maps to inform them about population health trends. Today, the geo-analytics industry is well-developed in almost every application, with the exception of healthcare and medicine. There is potential to use mapping technologies to show patient disease burden in geographic form, map locations of health care facilities, and a plethora of accountable care population health initiatives would benefit from geo-analysis. Health Catalyst is working to integrate inputs into analysis like maps that can show geographic care boundaries, population health demographics, and more.

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What is Population Health and How Does it Compare to Public Health?

The shift from volume-based to value-based purchasing and the emergence of accountable care organizations are creating a focus on comprehensive management of the health and well-being of patient populations. This doesn’t mean that individual patient care processes are unimportant or unnecessary. It does mean that healthcare systems need to improve both individual patient care processes, while at the same time learning how to manage the entire population of patients they serve. Learn more about population health management and how it is interrelated, but different than public health.

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The Unintended Consequences of Electronic Clinical Quality Measures

Many quality reporting programs are being to shift to the use of electronic clinical quality measures (e-measures).  There are good reasons and benefit for accelerating this shift and reducing the labor and effort associated with previous manual approaches.  However, there are some potential unintended consequences to this shift.  Healthcare providers must pay even more attention to reviewing the integrity of their clinical data.  Failure to do so could result in inaccurate reporting and create financial risk.  By tightly combing quality reporting efforts with strong data governance practices, however, healthcare organizations will not just survive, but also benefit from, the move to electronic quality measure reporting.

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10 Trends in Healthcare Data Warehousing That Every Health System Needs to Know

What are the hottest trends in healthcare data warehousing and analytics?  Read about them from Dale Sanders, one of the industry’s foremost experts. Dale has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry and is frequently introduced as the leading authority on healthcare data warehousing and business intelligence in the United States.

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A Blueprint for the Future of U.S. Healthcare? An Inspiring, Real-Life Story to be Featured at 2014 Healthcare Analytics Summit

For our closing session in the Healthcare Analytics Summit, we will show a unique Health Catalyst filmed documentary highlighting an inspiring affordable healthcare collaboration between the Cayman Islands and Narayana Health, a multi-specialty hospital chain in India. This story starts with Dr. Devi Shetty, an Indian cardiac surgeon who first came to wide public notice when he operated on Mother Theresa after she experienced a heart attack and then went on to become her personal surgeon.  Dr. Shetty, who the Wall Street Journal has since called "The Henry Ford of heart surgery", then founded Narayana Health, an organization focused on bringing affordable, high quality healthcare to the poor in India. After achieving widespread success in India, Dr. Shetty looked to expand the Health City concept past the borders of India. He particularly wanted to target the rapidly growing population of aging patients in the U.S., especially those who needed complex procedures such as heart surgery but couldn’t afford the high cost. After reviewing his options he realized he couldn’t develop a Health City inside the U.S. but looked instead for a location that was close enough to the U.S. to be easily reached but with a business-friendly government that encouraged innovation.  And that was the Cayman Islands.  This is the story of how Health City Cayman Islands came to be.

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3 Ways to Decrease Nosocomial Infections in Hospitals

Health systems waste enormous amounts of money on treating and tracking nosocomial infections. Instead of waiting for hospital-acquired infections to surface, health systems should proactively work to prevent such easily preventable infections. These three suggestions will help health systems reduce the rate of nosocomial infections: 1) Use an EDW to reduce wasted tracking time. 2) Create a data-driven culture of commitment. 3) Manage the data on an enterprise level.

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What Healthcare Executives Can Learn from Military Decision Making

Dale Sanders, SVP at Health Catalyst, gave a presentation covering  healthcare analytics strategy at the recent Plante Moran executive Healthcare Summit. He covered similarities between his former career in assessing nuclear threats for the US Air Force and NSA, and his role today in advising effective use of healthcare analytics. His session covered the Healthcare Analytics Adoption Model and how organizations need to take a systematic, strategic approach to implementing new software solutions. He also covered the importance of establishing a healthcare data acquisition plan, in light of the coming patient-driven sources of data, such as wearable devices.

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Clinical Data Repository Versus a Data Warehouse — Which Do You Need?

It can be confusing to know whether or not your health system needs to add a data warehouse unless you understand how it’s different from a clinical data repository. A clinical data repository consolidates data from various clinical sources, such as an EMR, to provide a clinical view of patients. A data warehouse, in comparison, provides a single source of truth for all types of data pulled in from the many source systems across the enterprise. The data warehouse also has these benefits: a faster time to value, flexible architecture to make easy adjustments, reduction in waste and inefficiencies, reduced errors, standardized reports, decreased wait times for reports, data governance and security.

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Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates

Heart failure patients are adding an enormous strain to the US healthcare system. In addition, readmission rates for these diseases are adding to the burden.  Healthcare analytics can play a key role.  By following these 4 steps, all of which include data analytics, health systems can begin to reduce readmission rates: 1) Understand your true admission rates. 2) Establish reliable baseline measures. 3) Be aware of balance measures. 4) Establish an EDW.

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Three New Speakers Added to Healthcare Analytics Summit 14 Lineup

We’ve added three more speakers to the 2014 Healthcare Analytics Summit. The presenters, who have a wide-array of experiences and expertise, will be sharing best practices in our breakout sessions. We are grateful and honored to have them share their individual experiences and learnings in their respective sessions. Sign up soon, as we are quickly running out of space.

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Becoming the Change Agent Your Healthcare System Needs

I’ve met many clinical and operational leaders across the U.S. and seen how many have become progressively cynical and disengaged when faced with important healthcare reform issues like cost cutting and tight budgets. These clinicians would agree that equally important are quality and safety issues. However, most don’t have the tools available to actually measure that quality or patient outcomes. When clinicians do have access to the ability to measure, and the work together, I’ve seen enormous energy arise as they ask questions they really care about: What is quality? What do we measure? How do we achieve the best outcome? 

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A Guide to Successful Outcomes Using Population Health Analytics

There seem to be a lot of definitions for population health management and population health analytics. But all these definitions share one thing: outcomes. The goal is to provide quality care outcomes with good patient experience outcomes at a low cost outcome. So, how can organizations systematically improve their outcomes? The answer lies in three key questions: What should be done to provide optimal care? How well are those best practices being followed? And how do those best practices move into everyday care for patients? Using a systematic approach to answering these three questions will lead organizations toward becoming an outcomes improvement machine.

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Healthcare Informatics: Ready to Unleash a New Wave of Advanced Analytics?

Healthcare informatics has come a long way since its founding visionaries saw a way to use technology to extract healthcare data to improve patient care. But a new era has arrived and health systems are now facing the new challenge of maintaining massive amounts of powerful data that’s sitting unused in expensive storage. The next phase of healthcare informatics is for health systems to move from data acquisition to data extraction, so they can use the insights of the data to prioritize which areas will benefit the most by using data to improve quality and reduce costs.

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2014 HFMA Annual National Institute Conference

My three-day summary of the HFMA conference in Las Vegas established this year’s theme of: “This is BIG.” And it’s true. Healthcare is facing a number of BIG issues in healthcare transformation, including: regulatory impact of reform and business intelligence/analytics capabilities. Different keynote addresses and presentations examined each of these issues, and I took away some key messages.

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Baseball Analytics Legend, Billy Beane, Speaking at Healthcare Analytics Summit 2014

Billy Beane, General Manager for the Oakland Athletics, took baseball analytics to a new level when he and his team created a way to find the best players available for the lowest payrolls. Using data to find the best value, he developed a team that was able to compete with teams over twice his payroll. There are many lessons for healthcare in this story, and I’m excited that Billy Beane will be speaking at the 2014 Healthcare Analytics Summit.

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6 Steps for Implementing Successful Performance Improvement Initiatives in Healthcare

A systematic approach to performance improvement initiative includes three components:  analytics, best practice, and adoption. Taking six steps will help an organization to effectively cover all three  components of success. Step 1: Integrate performance improvement into your strategic objectives. Step 2: Use analytics to unlock data and identity areas of opportunity. Step 3: Prioritize programs using a combination of analytics and an adoption system. Step 4: Define the performance improvement program’s permanent teams. Step 5: Use a best practice system to define program outcomes and define interventions. Step 6: Estimate the ROI.

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Clinical Variation in Your Medical Organization?

Clinical variation in a medical practice is a good indicator of care delivery efficiency and effectiveness. Knowing why variation exists can help guide policy and reduce healthcare costs overall. The top four sources of variation in clinical care are: 1. An increasingly complex healthcare environment. 2. Exponentially increasing medical knowledge. 3. Lack of valid clinical knowledge (inadequate evidence for what we do). 4. Overreliance on subjective judgment. Variations in healthcare delivery and utilization can indicate potential opportunities to reduce costs and improve the value of healthcare delivery without compromising patient care.

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5 Reasons Healthcare Data Is Unique and Difficult to Measure

Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique: 1. Much of the data is in multiple places. 2. The data is structured and unstructured. 3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex. 5. Changing regulatory requirements. The answer for this unpredictability and complexity is the agility of a Late-Binding™ Data Warehouse.

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How Analytics Will Help You Achieve the Triple Aim

Many years ago, Dr. Donald Burwick and the IHI developed the concept of the Triple Aim to improve healthcare but little interest ensued. However, with the government’s focus on healthcare reform, there’s now a widespread interest in using the Triple Aim to achieve three main goals: 1) Improve the experience of care. 2) Improve the health of populations. 3) Reduce the per capita costs of healthcare. By using analytics, health systems will be able to achieve all three components of the Triple Aim.

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4 Ways to Reduce Penalties Under the Hospital-Acquired Condition Reduction Program

Hospitals with high rates of hospital-acquired conditions face steep financial penalties if they can’t adhere to CMS’ new rule. But by following these four tips, hospitals will decrease their risk of penalty for HACs: (1) Proactively evaluate, measure, and optimize critical care processes and outcomes.  (2) Put the right coding processes in place to accurately capture your patients’ POA data.  (3) Apply what you’ve learned to create a culture of safety. (4) Consider active surveillance systems to identify HACs and potential patient harm.

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CMS Reporting Requirements – 4 Changes Hospitals Need to Know for 2014

Hospitals need to be proactive to avoid the penalties from CMS’ reporting measures. In specific, there are 4 changes to be aware of: 1) Value based purchasing will include two additional outcome measures: CLABSI and PSI. 2) The Readmissions Reduction Program will be expanded to include COPD and THA/TKA, plus there’s a proposed 3 percent penalty for readmission. 3) Under the new proposed HAC Reduction Program, hospitals with the highest rate of HACs will receive a 1 percent reduction in Medicare inpatient payments. 4) Medicare proposes to align reporting for the Electronic Health Record and Inpatient Quality Reporting.

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Mike Leavitt, Glenn Steele Guide Healthcare Through Storm

Gov. Mike Leavitt and Dr. Glenn Steele both hail from the healthcare industry, and, over the years, their unique backgrounds have given them the opportunity to weather changes and come out on top. I’m looking forward to hearing the observations and advice both leaders will share during the Healthcare Analytics Summit this fall.

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3 Steps to Prioritize Clinical Quality Improvement in Healthcare

With all of the discussion about clinical quality improvement in healthcare, you know you need to find out which areas to focus on for improvement initiatives. But how do you prioritize which areas will provide the greatest results? These three steps will help eliminate the guesswork so you can make decisions based on data: 1) Implement a healthcare enterprise data warehouse foundation. 2) Identify improvement priorities. 3) Gain consensus.

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What Is Data Mining in Healthcare?

This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the best practice system (and systematically applying evidence-based best practices to care delivery), and the adoption system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.

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Forward-looking Genius Ray Kurzweil Will Show Us the Future

Ray Kurzweil has been described as a “thinking machine” and “Edison’s rightful heir.” I saw a taste of that when I watched his 2005 TED talk. He described how humans will merge with technology and disease will be cured with nano-devices. He even foresaw Google Glass and Siri. This makes me very excited to see him at the 2014 Healthcare Analytics Summit. It will be an amazing opportunity to hear from one of the most innovative minds of our time.

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Improving Clinical Workflow: An Example from the Emergency Department

ED workflow, when it is efficient, saves time, money, and lives. In the shift to value-based reimbursements, it can also significantly affect payment. A few ways CMS measures the care quality in an ED includes median time of arrival to department for admitted patients, for discharged patients, and median time from admit decision to departure for admitted patients. This means that the workflow of EDs will be publicly evaluated. To improve workflow and throughput, it’s helpful to look at two things: a value-stream map and throughput analytics. Using the Health Catalyst application Patient Flow Explorer, you can see all this and more.

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Why My Excellent Cleveland Clinic Experience Made Me Excited to Invite Dr. Jim Merlino as a Keynote Speaker to HAS 2014

My recent trip to the Cleveland Clinic went smoother than I could have ever expected. I had almost no waiting time and everything was seamless. The staff always seemed in control and efficient. I suspected data played a role. I was right—the person behind my experience, Dr. James Merlino their Chief Experience Officer, will be speaking at HAS 2014 about Using Data to Transform the Patient Experience.

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For the New World of Healthcare, A Declaration of Independence Is Only the Beginning

In the Wall Street Journal, Dr. Craviotto makes the case that it’s time for healthcare providers to defy mandates dictated by government officials not even working in healthcare. Although, I share in this frustration, I also see a way out that will improve patient care and lower costs. The healthcare profession has some of the most highly educated and committed members of any industry, but the system in which they work has not changed much in over 100 years. By using clinician skills and implementing data analytics, organizations can develop care processes that are efficient, reliable, and waste free. It’s been successfully implemented at a few organizations already. It’s time the rest of the industry catches up.

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Hospital Revenue Cycle Management: 5 Ways to Improve

Besides improving your information systems and educating your staff on the ins and outs of managing revenue, there are many more opportunities for improvement. Here are five suggestions to help health systems improve their revenue cycle management: 1. trend and benchmark your healthcare data; 2. use an enterprise data warehouse to mine your healthcare data; 3. constantly ask frontline staff for suggestions; 4. monitor all payer contracts; and 5. maintain convenient and caring touch points with patients.

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EDW Cloud Hosting: Is It Right for Your Health System?

Hosting data in the cloud is a hot topic in the IT world, and now health systems are starting to embrace the technology, since it’s secure and HIPAA-compliant. While it’s not suitable for all organizations, many will benefit by using cloud hosting for their EDW. This article will explain the pros and help you understand if your organization is a good candidate.

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Will Healthcare Transformation Affect the Rule of Rescue?

The Rule of Rescue has held particular significance in the United States where the importance of the individual has long been a part of our cultural fabric. In the U.S., we tend to count ourselves as not fully human unless we pull out all the stops. Increasingly, however, healthcare ethicists and policymakers are asking whether this same moral instinct to rescue, regardless of cost, should be applied in the emergency room, the hospital or the community clinic.

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Three Approaches to Predictive Analytics in Healthcare

Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three  common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden).  The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.

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The Best Organizational Structure for Healthcare Analytics

After working with many healthcare organizations to help them implement the appropriate EDW for their needs, we’ve learned how important it is to create cross-functional teams from across the organization. Why? These cross-functional teams will simultaneously improve clinical and financial outcomes and demonstrate ROI. By following this approach, you’ll experience the following advantages:

  • Removal of organizational barriers between team members
  • Prioritization of BI and analytic efforts according to institutional readiness and need
  • Engagement and prioritization from appropriate leadership
  • Buy in from each level of the organization to improvement goals

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How to Determine the Best Interventions for Clinical Quality Improvement Projects

Health systems use clinical quality improvement projects as a means to improve patient care, but the real improvement in care can only result from deliberate action by your teams. This action is called an intervention and becomes the solution that addresses your clinical quality improvement goal. The secret to selecting the right intervention is this: choose something that offers “gain” or improvement and then provides a means to “sustain that gain.” Finding efficient, effective interventions can be a challenge without knowing where to start, but by using an Aim statement, your teams will have a working template for each of their clinical quality improvement projects.

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Introducing the Healthcare Analytics Summit 2014

We are excited to announce the Healthcare Analytics Summit, on September 24-25, in Salt Lake City.  This summit will be different from any conference or analytics forum you may have attended.  You will hear from the best thinkers from across the industry. You will experience analytics-rich breakout sessions.  Our goal is to make this the best, most educational and practical healthcare analytics conference on the planet. Unlike any other summit, this experience will be driven by data using live session polling, real-time analysis, and advanced event applications at your fingertips so you can touch, taste, and breathe analytics. We hope that HAS ‘14 will accelerate your application of analytics best practices in your current role and avoid the time-consuming and costly pitfalls of others who learned by trial and error.

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Simplifying the Meaningful Use Stages: The Case for Eliminating Stages 2 and 3

Meaningful use is a federal program with good intentions, but it fails to produce meaningful results for clinicians and patients. A recent study found no correlation between quality of care and observance of meaningful use requirements. Stage 1 was a much-needed prod for the industry, but it’s time to let organizations drive themselves to fully use their EMRs. The core principles of EMR utilization include guidelines around: Encounters, Medications, Problem Lists, Allergies, Orders, Progress Notes, and In Basket. Stages 2 and 3 of meaningful use should be eliminated.

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What is an Accountable Care Organization (ACO)?

The ACO concept can be generically defined as a group of health care providers, potentially including doctors, hospitals, health plans and other health care constituents, who voluntarily come together to provide coordinated high-quality care to populations of patients  This article, written by two physician executives with years of accountable care experience, gives a robust overview of the ACO concept including:  the history, range of payment models, the new accountability and payment structures, a comparison between traditional insurance vs ACO models, key barriers and challenges, and most importantly, the key criteria needed for ACO success.

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Anatomy of Healthcare Delivery Model: How a Systematic Approach Can Transform Care Delivery

Read about this breakthrough model and framework, developed and refined by Dr. David Burton during his 25 years of executive healthcare experience. This model creates a framework that maps major healthcare processes into common patterns and process flows that can then be used to systematically examine and improve healthcare delivery. By using a systematized framework to reduce variations in clinical and operational processes, health systems can experience sustainable cost and quality gains. This framework won’t eliminate critical thinking, but it will provide a standardized, evidence-based approach to care delivery, which will bring all care up to the same, high standard.

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Healthcare Data Warehouse Models Explained

Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.

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How We Really Feel about Working at Health Catalyst

As Health Catalyst, we love working here. Besides the great benefits, work environment, and opportunities for growth, we’re most appreciative of the way our leaders care about us. Watch this fun video to see what it’s like to work here.

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Value-based Purchasing: Start with These Patients

Almost 90 percent of healthcare spending is associated with traditional pay-for-service contracts. But value-based purchasing encourages effective and proactive healthcare delivery. The Cayman Islands Health Services Authority started their value-based purchasing program, even agreeing to a 3 percent reduction in payments for selected diagnoses and procedures. The list of patients the system start with includes: Inpatient procedures (knee replacements, hip replaces, hip and knee replacement revisions, and hip and knee arthroscopy), Outpatient procedures (cataract removal, perinatal care, hysterectomy, and maternity), Chronic conditions (asthma, diabetes, end stage renal), and Acute conditions (upper respiratory infection). The US healthcare system should set a goal to have 80 percent of healthcare dollars going to value-based contracts by 2019.

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The Great Equation in Healthcare is Wrong

The “Great Equation” in healthcare—medical care equals health—is wrong. Approximately 95 percent of our healthcare spending goes to direct medical care rather than population-wide preventative services. Yet, 40 percent of deaths are caused by preventable behavioral patterns. Spending on health is not necessarily bad, but the cost of healthcare is rising faster than household incomes. We need to slow the growth rate of healthcare spending, and we have to spend more effectively.

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How To Consolidate Healthcare Terminology for Better Quality and CMS Reporting

There are many different healthcare terminology standards. Quality reports try to take advantage of these terminology standards, but there is not a single, widely accepted standard to use. Clinical codes are needed for CMS reports, but ACOs commonly use claims data instead. There are many solutions to the multi-terminology problem, including mapping or paying a vendor to reconcile the standards. These solutions are not flexible or adaptable enough and rarely succeed. A healthcare data warehouse creates a single solution that allows for all terminology standards to exist in one place to be pulled out for reporting purposes when needed.

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Shifting from EMRs to Clinical Data Warehousing and Analytics

Recently Gartner came out with a seminal healthcare IT report with a striking recommendation. They acknowledged the all-consuming effort that has surrounded the wave of EMR adoption and rollouts. However, they suggested a new wave was coming. They recommended that once an EMR was rolled out, the top IT priority was to develop an enterprise-class data warehouse. They also listed 5 classic mistakes that could be avoided based on 25 years of data warehousing experience. Read further to get the highlights and a free copy of the actual report.

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Health Information Technology: Why Point Solutions Strike Out

Most organizations purchase a point solution because they’re feeling a particular pain, and they want it to stop. They may have other pains as well, but they don’t notice them at the time. Once they fix the first pain another may crop up, so they purchase a point solution for that. And so it continues until they have all these individual solutions. It’s like a physician treating individual symptoms instead of looking at the entire body to see if there is something bigger going on.

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What’s Good About U.S. Healthcare

Recently, there has been a lot of negative press about what is happening in the US healthcare system.  However, despite our challenges, it is important to recognize that our healthcare is still the best the world has ever seen.  From significant increases in average life expectancy, to astounding mortality decreases from key diseases such as heart disease and stroke, we have made significant strides.  While the first part of the century focused on improvements in public health, the second half of the century showed improvements based on advances in hospital and clinic treatments.  In this article, Dr. John Haughom shares the postive perspective of our US healthcare system.  

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Three Essential Systems for Effective Population Health Management

An effective population health management program must include three systems: Healthcare Analytics, Best Practice, and Adoption. Organizations with only one or two of these systems often display symptoms of weak and ineffective capability for population health management.  But when you have a analytics foundation based upon a data warehouse, combined with evidence-based practices contained in a best practice system, and the ability to deploy and implement systematic changes to healthcare processes, health systems are truly prepared to manage population of patients.

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ACO Success Requires Precise Patient Population Definitions

An ACO will fail without precise patient population definitions. ACOs need to define populations for many reasons, including identifying their members and attributing those patients to the correct physician and performing population health analytics. The challenges to a good population definition are: multiple providers per member, multiple data sources, and multiple identifiers for each member. Using a clinical integration hierarchy to refine population and subpopulations will solve a lot of these issues. A data warehouse is the foundation that makes it possible.

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The Health Data Analytics Hype Cycle

Health data analytics is the third wave of health IT we’re undertaking, right after data capture and data sharing. Having excellent analytics capability will provide the return on investment for the massive amounts of spending happening in health IT in the past few years. Buzzwords, like “big data” and “analytics” are becoming commonplace., however this also takes away from their effectiveness. According to the Gartner Hype Cycle technology has five key phases in its lifecycle: Technology Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slop of Enlightenment, and Plateau of Productivity. Now that we have widespread adoption of EHRs, the superior use of analytics will be a dominant factor for success over the next four to five years.

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Healthcare Business Intelligence: What Your Strategy Needs

Business intelligence may hold tremendous promise but it  can’t answer healthcare’s challenges unless it’s built on the solid foundation of a clinical data warehouse. Learn the definition of business intelligence, why a clinical data warehouse is needed for any healthcare BI strategy, the various options in data warehousing, which one is most effective for hospitals and the industry and why.

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Geisinger CEO Talks About Why Healthcare Business Intelligence Is Critical to Their Success

The CEO of Geisinger, Glen Steele Jr. MD, recently published a terrific article on how business intelligence was key to their population health efforts. His comments encompassed so many of our experiences in this area that we wanted to share his insights as well. He had four keys areas of insight including 1) “there’s almost no outcome that can’t be improved, 2) their success in population health was due to using “insightful use of data to drive behavior change”, 3)  health systems were “probably about to enter the 19th century” in analytics use, held back in some ways by the “legitimate regulatory concerns have always taken precedence over true innovation in data analytics,” and 4) their most important strategic aim being “innovation and quality” with the most desired outcome being to “change behavior.”

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The Best Data Architecture: Know When to Bind Your Healthcare Data

The most common types of data architectures for EDWs are: the enterprise data model, the summary data mart (also called the star schema), and the adaptive or Late-Binding™ data model. These three models differ in when they bind (early to late). So what does it mean to bind data late? It doesn’t mean to bind everything late, but the model recognizes that rules and vocabularies that are volatile should be bound later. Once a clear analytics use case calls for the data, then bind it. The binding option for a late-binding architecture are: Bind in the source system. Bind during ETL to the source mart. Bind in the source mart. Bind during ETL to the customized data mart (SAM). Bind in the SAM. Bind in the visualization layer.

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The Key to ACO and Value-based Purchasing Success: Lowering Cost Structure

Health systems and large physician groups will need to focus on lowering their cost structures to survive in a value-based future. To succeed, systems must understand their cost structures on a granular level. Only at this detailed view can they identify variation, find the causes for it, and fix it. An enterprise data warehouse provides the platform for aggregating data from clinical and financial system into usable analytics.

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Meaningful Use and ACO Reporting: Why an EMR Is Only a Partial Solution

Meaningful Use and ACO reports are just two of a plethora of ever-increasing external healthcare reporting requirements. An EMR is only a partial solution due to limitations in data turnaround time, data and logic multi-purposing, and being relegated to single-vendor, homogenous environments. Learn about a solution that helps you streamline your Meaningful Use and reporting requirements and can be leveraged for clinical quality improvement, population health and predictive analytics.

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7 Tips for Increasing Physician Engagement

Starting a clinical quality improvement initiative requires shifting from a personality-centric culture to one that is data-driven, with near real-time data to help providers make better decisions and improve the quality of outcomes. But turning plentiful data into meaningful information represents a significant change. From Dr. J. Kevin Croston, MD, CMO at North Memorial Health Care, here are seven tips to gaining physician buy-in: 1. Get the Physicians Engaged Early; 2. Find Champions Among the Medical Leadership; 3. If Your Project Is Large, Choose One Area of Focus; 4. Build a Broad but Specific Guidance Team; 5. One You Have  Plan, Follow It; 6. Make the Results Data-driven; 7. Be an Agent for Change

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A New Way to Look at Healthcare Data Models

Describing healthcare data models can quickly get very technical. We prefer to use an analogy: making and sticking to a grocery list. With this analogy, audiences can quickly see the differences between dimensional, enterprise and adaptive data models and determine which one will work best for their organization’s needs.

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Why Predictive Modeling in Healthcare Requires a Data Warehouse

Interest in predictive modeling is part of a larger trend to employ business and clinical intelligence applications in healthcare. Until recently, organizations that had the ability to mine and analyze data were mostly conducting retrospective analyses. Using tools available today, organizations with the right technical infrastructure, including a data warehouse, can link predictions to specific clinical priorities, set up new workflows, apply analytics to emergency departments and to slowly changing clinical situations and more.

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The Medicare Sustainable Growth Rate Repeal: Implications for Your Health System

The Sustainable Growth Rate (SGR) Repeal and Medicare Provider Payment Modernization Act of 2014, introduced to Congress on February 6, 2014, is a bipartisan attempt to address perceived failings of the current Medicare physician payment system, the so-called “Doc Fix.” Below, two industry experts provide commentary on the bill’s proposed changes and the implications for health systems and providers.

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Clinical Data Warehouse: Why You Really Need One

From time to time, people question whether they really need a clinical data warehouse. The first wave of data warehousing projects didn’t deliver much value and healthcare CIOs had plenty of tasks to accomplish in the meantime. But today’s technology, data, and regulations scream for an analytics solution and a clinical data warehouse can offer this. A hospital should consider reporting requirements and technical requirements. Finally, when an organization has access to its data, leaders are empowered to make informed decisions.

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Ten Great Behaviors of the Best Healthcare IT Vendors

I’ve been a CIO in various forms throughout my decades-long healthcare IT career and have always help healthcare IT vendors to high expectations. Now that I’m on the vendor side of healthcare IT, I hold our company and myself to those same high levels of expectations. Here are the ten great behaviors I expect of my vendors:

  • Help me compete, hire, measure, save, listen, expand, plan and innovate, migrate, prove, and evolve.

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Quality Improvement in Healthcare: Where Is the Best Place to Start?

One of the biggest challenges providers face in their quality improvement efforts is knowing where to get started. In my experience, one of the best ways to overcome that “where do we begin?” factor is by using data from an enterprise data warehouse to look for high-cost areas where there are large variations in how health care is delivered. Variation found through the KPA is an indicator of opportunity. The more avoidable variation that is reflected in a particular care process, the more opportunity there is to reduce that variation and standardize the process. Suppose after performing a KPA you discover three areas of opportunity. How do you determine which one to pursue, especially if it’s your first journey into process improvement? The most obvious answer would seem to be the one with the largest potential ROI. That may not always be the best course to pursue, however. You will also want to take into consideration the readiness/openness to change in each of those areas.

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Healthcare Careers: From Nursing to Health Information Technology

I always knew I wanted a healthcare career. I didn’t know I end up going from nursing to health information technology (IT). I loved caring for patients directly and making a difference in the life of an individual. For years, I thrived on the challenge of being a nurse and when I started to look for a new challenge, I found a way to impact patients millions of a time instead of just one-at-a-time in health IT. As I researched the opportunity of working for Health Catalyst, I was impressed by the fact that the company was founded and run by healthcare veterans—people who have actually worked in the halls of hospitals. I feel like I’ve come full circle. Working in health IT, we are making a difference in healthcare—for nurses, for physicians, and for their patients. I look forward to transforming healthcare with Health Catalyst for a long time to come.

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Practice Management Solutions: The Key to Efficient Primary Care Delivery

Primary care practices are being asked to treat more and more patients—while reimbursement levels shrink. One of the best ways to tackle this challenge is to make sure that every member of a clinic’s staff is empowered to operate at the maximum level of his or her qualifications. For this to succeed, a system for coordinating care across the patient panel is necessary. And a solution built on the foundation of a healthcare EDW can do that, including: i. Generate near real-time lists of patients who aren’t compliant with recommended, evidence-based care guidelines. ii. Show clinicians and staff a patient’s health status at a glance. iii. Increase efficiency and accuracy of working with healthcare data. iv. View clinical, operational, and regulatory information about your patients all in one place. v. Track and manage patient volume. vi. Analyze performance of your patient panel as a whole—and your performance on various regulatory measures. vii. Drill down into data for further insights into practice performance

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BI Tools: 5 Reasons Why They Can’t Replace Your Healthcare Data Warehouse

An EDW is the only viable solution for driving healthcare analytics. This fact has resulted in many BI tools and visualization solutions being marketed as cloud data warehouses, promising quick, user-friendly answers. While they do a great job of visualizing data and exposing it to end users, these tools cannot replace an EDW for 5 reasons in particular: i. BI tools don’t optimize healthcare data- optimizing data and exposing data-quality issues represents a significant chunk of the initial stages of an EDW project. BI tools just can’t offer this functionality. ii. BI tools can’t handle large amounts of healthcare data- one patient encounter can general hundreds of rows of data, meaning that reports from BI tools will be slow to generate and inefficient. iii. BI tools don’t work well with healthcare data at different levels of granularity- Some tools have difficulty displaying the one-to-many and many-to-many data relationships required in healthcare. iv. BI tools can’t optimize healthcare data for multiple user types- Applying logic against the data so it is understandable at multiple levels for different audiences is something BI tools simply cannot do. v. BI tools don’t provide for modularity, understandability, and code reuse

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Rising Healthcare Costs: Why We Have to Change

With rising healthcare costs, we hear so often about rate pressures on hospitals and the risk these pressures pose for their future. With healthcare reform, the burden of rising healthcare costs is shifting from payers to providers. Hospitals need to move toward value-based reimbursement models or they will face a -15.8 operating margin by 2021.Over the last 15 years premiums and employee contributions for an average family with health insurance sponsored by an employer have risen 167%. Along with these facts, government payers are reimbursing at lower levels becoming a negative margin for hospitals. These changes are not necessarily easy and can seem overwhelming. The question is whether your hospital will be a pioneer on the trail or will delay until it’s too late. The best way to get started is to understand exactly where you are today—your current cost structure and how each area of your organization is performing in terms of quality and cost, using an EDW.

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The Key to Overcoming the Challenges of Transparency in Healthcare

Transparency in healthcare will drive cost and quality improvements for payers, providers, and patients alike. Consumers want to know true out-of-pocket costs and quality information for their providers. Physicians want to know about their potential referrals partners’ level of quality. In addition, transparency between payers and providers needs to improve to drive down costs. This trend presents challenges such as limited/siloed data access and cultural change. An EDW and analytics system can help transform a health system, improving value and patient/staff satisfaction.

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Database vs Data Warehouse: A Comparative Review

What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.

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Healthcare Payers and Providers: The Best System for Process Improvement

Healthcare payers and providers are using new types of financial arrangements to motivate process improvement. i. Pay for Performance-a straightforward inventive that rewards improvement based on established metrics. ii. Bundled Payments-a payer gives a single payment for all provider services, holding providers accountable for reducing waste and eliminating duplication iii. Accountable care organization-a total-cost-of-care system that rewards or penalizes based on the total cost of a patient population All three arrangement mean that payers and providers must work together to figure out how to improve processes. A three-system approach is the best way to drive improvement and healthcare transformation. i. Analytics System-the technology and expertise to gather data, make sense of it, and standardize measurements ii. Best Practice System-standardizing best practices iii. Adoption System-standardizing organizational work and team structures

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Star Schema vs. Late-Binding™: Best Approach for a Healthcare Data Warehouse

The star schema approach to data warehouses is simple and straightforward. Its design is considered best practice for a wide variety of industries. But it lacks the flexibility and adaptability necessary for the healthcare industry. A Late-Binding™ approach, on the other hand, is designed specifically for the analytics needs of healthcare providers. It offers the flexibility to mine the vast number of variables and relationships in healthcare data effectively and leave room for the inevitable future changes.

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Prescriptive Analytics Beats Simple Prediction for Improving Healthcare

Predictive analytics alone cannot offer meaning without context, especially in health care. In order to be successful, prediction tools should be content-drive and clinical-driven. Prescriptive analytics can improve health care better than simple predictions can. Analytics should be used with clinical leaders that have the willingness to act on appropriate intervention measure. This “in context” prediction should include not only the evidence, but also the interpretation and recommended actions for each predicted category or outcome. An underlying data warehouse platform is key to gathering rich data sets necessary for training and implementing predictors.

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Big Data in Healthcare: Separating The Hype From The Reality

The term Big Data seems to be everywhere. It can be defined by three characteristics: volume, velocity, and variety. Traditional data management techniques include the ever-popular approach of using SQL interactions with a relational database. Here’s how health care fits into big data as defined above: i. Volume- A typical healthcare firm stores less than 500 terabytes of data, as opposed to an investment firm that stores almost 4,000 terabytes. A traditional database engine can handle far more data than most health organizations produce. This means healthcare systems should carefully consider the tradeoff before switching to big data tools. ii. Velocity- The speed at which some applications generate new data can overwhelm a system’s ability to store that data. However, most healthcare data is entered by employees unlikely to generate data fast enough to overwhelm a typical SQL database. iii. Variety- There are three different forms of data in most large healthcare institutions. Discretely codified billing and clinical transactions are well suited for relational data models. The third form of data in healthcare consists of blobs of text. While stored electronically, there is very little analysis done on this data today, because SQL is not able to effectively query or process these large strings. There is much opportunity for progress in this area.

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The Year of Healthcare Data Analytics

In this article, Brian Ahier – a Health Catalyst guest contributor and industry expert – predicts that 2014 will prove to be the year of healthcare data analytics. There will be a marked shift away from volume and toward value for both healthcare delivery and payments. ACOs and Patient-centered Medical Homes will flourish. 2014 will be a perfect storm of regulatory change, business drivers, and technology solutions. The ACA established the value-based purchasing program and it will be essential to leverage healthcare data effectively to drive value-based decision-making. Predictive analytics solutions can generate and evaluate hypotheses, and determine a confidence level for the hypotheses. But comparative analytics, predictive analytics, and NLP will not solve all of health care's problems. A successful organization must have tools with the ability to score predicted outcomes to better guide the care team on the need to intervene, when and how to intervene, and a feedback loop to create a learning healthcare system.

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Keys to a Successful Health Catalyst Data Warehouse Platform and Analytics Implementation

We have found that many customers have similar questions about how the implementation process works when rolling out a Health Catalyst Late-Binding ™ data warehouse platform and analytics solutions.  So, we thought it would be useful to produce a document that we hope will answer the majority of these and other common questions. The keys for a successful Health Catalyst implementation are outlined step-by-step format. Pre-step (most important): Identify key personnel resources needed on the health system side, 1) Implementation Planning, 2) Deploy Hardware, 3) Technical Kickoff Meeting with the Client and Health Catalyst Deployment Teams, 4) Access Source Data, 5) Install Platform, 6) Load Data, 7) Install Foundational Applications, 8) Install Discovery Applications, and 9) Install Advanced Applications At the beginning of the project, Health Catalyst will begin a collaborative implementation planning process resulting in a timeline tailored to each project. Some projects can be accelerated, with the initial phase completed in 90 days.  Your health system will have questions specific to your organization and your circumstances. We are happy to answer those in person.

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The Survey Results For Our Three Advertising Concepts

Thanks to all who gave input to our quick advertising concept survey. We received more feedback and participation in that email than any other email ever in the history of Health Catalyst! So we would like to share with you the results of your input. As a reminder, we sent out a survey asking for your feedback on three potential advertising concepts. We asked for your feedback in determining which of the three concepts did the best job of introducing or communicating a short, high-level message regarding Health Catalyst. The concepts were just initial ideas but using very different approaches. Here were the results of your feedback: We had an overwhelmingly favored winner – concept #1. This concept was preferred by 59.1% of you, with only 16.8% preferring ad concept #2, and 24.1% preferring ad concept #3. You also rated this ad concept a 7.01 on a scale of 1-10, compared to 4.53 for ad concept #2 and 5.27 for ad concept #3.

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4 Bold Predictions for Healthcare Analytics in 2014 and Beyond

Looking ahead, 2014 feels like the turning point for analytics: those who have invested smartly will find themselves at a competitive advantage; those who haven’t will find themselves playing catchup.
Prediction #1: Health systems that invested in data warehousing as the foundation of their analytics strategy will emerge as industry and market-share leaders.
Prediction #2: Health systems that have not yet made a data warehouse investment will look for quick answers in their EHRs.
Prediction #3: Health systems will soon realize that EHR providers can’t provide the help they need for healthcare analytics.
Prediction #4: When it comes to analytics, no organization will be able to afford to sit on the fence.

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The Best Background for a Career in Healthcare IT

The perfect background for a career in healthcare IT may not be what you expect. Mike Doyle shares his story, starting in music and ending up at Health Catalyst. He explains that the most effective people can come from just about any background.

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The Difficulty of Change

Effecting change can be a difficult or intimating endeavor. Dr. Burton shares three quotes from some of the greatest minds in history that can inspire leaders to persevere.

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Why Bigger is Not Better with Healthcare Solutions Companies

Small healthcare technology companies are better at providing healthcare-specific solutions than large, non-industry-specific technology companies. The reasons for this are multifaceted. i. Patience- Healthcare is a cautious industry and healthcare providers like to start small. ii. Fragmentation and Skepticism- There are many market segments in the healthcare industry, and newly evolving segments are crowded and confusing. iii. Brand defocus- Larger companies need to consider the entire company, including the non-healthcare-focused business units iv. Best-of-Breed vs. Corporate Branding- Business units within large, multi-brand technology companies are compelled to sell their corporate products rather than the true “right tool for the job.” v. Deal Structure- Large companies have very structured sales and contracting processes that are not well-suited to the flexibility and adaptability needed for the ever-evolving healthcare industry.

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How an EDW Enables the Best Healthcare Visualizations

Healthcare Business Intelligence tools provide value by running powerful visualizations. But the three best types of visualizations can only be run when using a healthcare enterprise data warehouse. These include: i. Statistical Process Control Charts, ii. Scenario Analysis Visualizations, and iii. Visualizations placed into the hands of clinicians and frontline staff.

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The Best Way to Make Payer and Provider Healthcare Data Accessible

Payers and providers agree that focusing on quality is important. CMS, the HEDIS, and the Joint Commission all have quality measures hospitals need to consider. Payers have their own set of quality initiatives. Healthcare data is an important part of determining the performance of those quality measures. But complications abound when trying to combine payer and provider healthcare data and measurements. The result is a time-consuming process resulting in reduced ability to analyze the data to drive improvements. Having payer and provider data available would be a huge advantage and is possible through a healthcare enterprise data warehouse. A Late-Binding™ approach provides the flexibility and adaptability needed for quality initiative teams to effect real change within their organization.

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How the Change to ICD-10 Codes Will Improve Healthcare Analytics

ICD-10 will increase the number of diagnosis codes from 13,000 (in ICD-9) to roughly 68,000. Only 24% of these codes will map one-to-one. The switch from ICD-9 to ICD-10 is mandated for October 2014, which is soon considering all the change happening in healthcare right now. However disruptive the change to ICD-10 may seem, the new codes will increase specificity, allowing faster billing and better healthcare analytics. Embracing the switch now will allow organizations to realize the benefits sooner.

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Build vs. Buy a Healthcare Enterprise Data Warehouse: Which is Best for You?

One of the most common questions we hear is some flavor of  "should we build or buy a data warehouse?”   This is a very understandable question.  And there are multiple reasons  for either building your own data warehouse or buying it.  We acknowledge that.  And even though we know that we naturally advocate the advantages of buying a data warehouse, we also know that buying a data warehouse is not for everyone, and we understand the circumstances in which health systems will choose to build their own data warehouse.  In this article, we do our best to collect the pros and cons of each scenario, and even suggest a hybrid solution, so that you can better assess your situation, understand your options and make the best decision for you.  

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Are EHRs Truly Improving the Quality of Healthcare? A Closer Look.

We know that healthcare costs are increasing and that value will become more important than ever. But the concept of value can be subjective. When it comes to healthcare, we can’t afford to be subjective in our assessment of value. I like referring to the Porter equation where value is equal to quality over cost. What this equation makes clear is that we must markedly improve the quality of healthcare in order to improve value. The adoption of the EHRs in clinical systems should help drive the quality agenda. But it's important to recognize that EHRs alone may not be sufficient to deliver data intelligence, to really deliver data to clinicians in a meaningful way that will help them improve value.

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Key Things to Look for in an ACO Analytics Solution

The best solution for leveraging data to drive clinical and financial improvement in an ACO environment is a healthcare enterprise data warehouse (EDW) with a flexible, Late-Binding™ architecture. Why? Because a successful analytics solution for an ACO must be one that: i. 1. Gives rapid time-to-value. ii. 2. Adapts easily to the changing needs of an organization.

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Understanding Population Health Management: A Diabetes Example

Measuring is where successful population health management starts. You can’t do much to manage your diabetes population if you can’t accurately identify that population or see how your population is trending. Identifying diabetes patients and measuring compliance has been difficult in the past—but today, with a healthcare enterprise data warehouse (EDW) and analytics tools, it doesn’t have to be. (Your EMR won’t be enough.) By establishing an EDW, you create a data foundation that enables you to manage your diabetes population in sophisticated ways.

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From Meaningful Use to Meaningful Analytics

Guest contributor, Brian Ahier, describes the transition from “meaningful use” to meaningful analytics and achieving high-quality care. Since meaningful use is requiring greater interoperability and data sharing, there is now much greater opportunity to aggregate data at a community level and have an even broader data set than just the EHR to mine for clinical intelligence. One benefit from HIE, besides improved care coordination, is the ability to perform queries and apply analytical tools to those data that were not previously available. The five health outcomes policy priorities included in meaningful use are: 1. Improve quality, safety, efficiency and reduce health disparities 2. Engage patients and families 3. Improve care coordination 4. Improve population and public health 5. Ensure adequate privacy and security protections for personal health information

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How to Improve Patient Outcomes for Chronic Diseases and Comorbidities

Managing and treating patients with chronic conditions and comorbidities is difficult without coordination between the various treating physicians. To improve patient outcomes for such complex situations, an enterprise data warehouse can deliver the necessary quality improvements and coordinated care these types of patient populations require.

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Data Warehousing in Healthcare: Is It Necessary?

Wondering if you really need a healthcare data warehouse or if the technology you already possess if enough? Data warehousing enables healthcare analytics. It will help fulfill reporting requirements so your analysts can concentrate on analyzing data. It can offer near real-time answers to many questions, whether financial, clinical, or technical. Eventually, you’ll wonder “How did I ge tby so long without it?”

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Selecting a Business Intelligence Healthcare Platform: 3 Criteria to Consider

Healthcare organizations have many choices when selecting a business intelligence healthcare platform. As the Vice President of Technology, I’m often asked what should be considered in that choice. I recommend looking at the Healthcare Analytics Adoption Model. It starts with a foundation of a data warehouse infrastructure and includes other criteria around implementation that can make or break success. The Analytics Adoption Model gives organizations a roadmap for understanding and leveraging the capabilities of healthcare analytics.

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Overcoming Clinical Data Problems in Quality Improvement Projects

Starting your clinical quality improvement projects with access to data you’ve never seen before is exciting! But as analysis starts, you notice missing and incomplete data. Data quality problems are one of the most common but unexpected initial challenges of any substantive clinical quality improvement. project. Anny and Kathy both share keys to success learned from years of experience to overcome that trough of despair.

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How Healthcare Business Intelligence Drives Smarter Decisions

Business Intelligence is a loosely defined, but commonly used, term that means various things to different people. It seems to have become a catch-all phrase for three classes of technology: 1. Enterprise data warehouse (EDW) systems used to aggregate and standardize data across an organization 2. Reporting tools that visualize data (visualization tools), typically representing a snapshot of information captured at a particular point in time 3. Discovery tools that allow users to proactively drill down and through data sets, asking questions and uncovering information in real time about the performance of their organization

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Success Stories in Healthcare: How Texas Children’s Hospital Won the 2013 Transformational Leadership Award

After realizing that the EHR was not the silver bullet that had been expected, the Texas Children’s Hospital team began formulating the foundational framework for an organizational transformation. They took a bold, integrated approach to healthcare analytics, data management and quality improvement by adopting a healthcare data warehouse. They understood that technology alone wouldn’t enable them to improve the overall value of care; doing that would also require a fundamental culture change. Beginning in September 2011, the hospital worked with Health Catalyst to implement a clinical, analytic, and process-based framework for value-based transformation. The framework is based on a three-system approach

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In Healthcare Predictive Analytics, Big Data Is Sometimes a Big Mess

Those in Big Data and Healthcare Analytics circles will seldom hear the phrase “less is more.” In a clinical setting however, there is an important lesson to learn in regards to the effective execution of predictive analytics. We should not confuse more data with more insight. More data is simply more—as in more tables, more lists, more replicates, more clinics, more controls, more rows, tables of tables and lists of lists, etc. You get the idea. In short, for predictive analytics to be effective in a clinical venue, a specific focus will always trump global utility.

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The Best Way to Track and Improve Cancer Patient Outcomes

The cancer care delivery system is in crisis- amplified by the complexity of cancer care and historical limitations in quality-improvement tools. Learn how this hospital used data warehousing and business intelligence tools to increase their staging data capture by 450% and identified variation in care.

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How to Evaluate a Clinical Analytics Vendor: A Checklist

Based on 25 years of healthcare IT experience, Dale outlines a detailed set of criteria for evaluating clinical analytic vendors. These criteria include 1) completeness of vision, 2) culture and values of senior leadership, 3) ability to execute, 4) technology adaptability and supportability, 5) total cost of ownership, 6) company viability, and 7) nine elements of technical specificity including data modeling, master data management, metadata, white space data, visualization, security, ETL, performance and utilization metrics, hardware and software infrastructure.

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How to Drive ROI in Your Healthcare Improvement Projects

Hospitals and healthcare systems need a systematic approach and tools to demonstrate ROI from their healthcare improvement projects. Bobbi Brown, VP of Financial Engagement, shares a four-step process for demonstrating ROI: 1) define the project and business need, 2) begin to quantify ROI, 3) recruit, train and plan, and 4) evaluate costs, revenue and direct benefits. Download the Health Catalyst Clinical Improvement Financial and Executive Communications tools for estimating, calculating and communicating your ROI results.

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Hospital Data Warehouse: The Best Reporting Tool for Efficient, Consistent Hospital Reports

Hospital reporting can be a stressful and time-consuming process with finance, quality, human resources and clinical departments scrambling to complete data for reports - often times with maddeningly inconsistent data. Too familiar with this hospital report anxiety? Bobbi Brown explains how a data warehouse can help by enabling efficient and scalable reporting, and enabling consistent reporting that everyone can trust.

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Texas Children’s Hospital Reduces Clinical Quality Improvement Project Time by 85%

Clinical Quality Improvement projects follow a standard process: articulate the problem statement; define the population; identify data gaps; collect the data; and, implement evidence based practice interventions to deliver measurable and sustainable improvement. If you’ve led or been a part of these projects, you know the kick-off process of articulating the problem and defining the population can take six or more months.Texas Children’s Hospital found the secret to rapid-cycle clinical quality process improvement and reduced their project time by 85% using a healthcare enterprise data warehouse (EDW), population registries and healthcare analytics.

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Reducing Waste in Asthma Healthcare: The Impact of Data

Texas Children's Hospital is moving to a form of value-based payment under which hospitals that improve efficiency can keep the savings. To succeed under the new payment system, Texas Children's leaders needed the ability to analyze and better manage specific populations of patients, especially those with costly, chronic conditions like asthma.Texas Children's found 5 ways to reduce waste and improve the quality of care for their asthma patients across the care continuum. Learn how they reduced length of stay by 11 hours and achieved and sustained a 49 percent decrease in unnecessary chest X-ray orders.

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Accountable Care Means Sharing Data Between Payers and Providers: You’ll Need More than an EHR

Accountable care is changing the way Payers and Providers look at their healthcare data. Many healthcare enterprises believed that their Electronic Health Record (EHR) would be the silver bullet to this data problem, but they are beginning to discover the limitations of the EHR for managing at the enterprise-level all of the information necessary for effective risk-sharing. Health information exchanges (HIEs) help eliminate data silos but are not designed to store or analyze the data with the level of sophistication required for supporting a risk-sharing model. The reality is, until now, providers and payers have lacked consistent incentives to share data.

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The Glaring Omission in Healthcare: Patient Satisfaction and Outcome Data

As a business person and a CIO, the only two metrics that really matter to me are employee satisfaction and customer satisfaction. As fellow CIOs can attest, we are inundated with metrics. Managing a complex IT environment in a healthcare setting is like surfing in a hurricane of metrics, at every layer of technology that we manage, from the data center to the software application. But... the only two metrics that really matter are employee satisfaction and customer satisfaction. Every other metric is a means to those two ends.

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Advanced Analytics Holds the Key to Achieve the Triple Aim and Survive Value-based Purchasing

Every hospital and health system has to juggle significant IT needs with a limited budget. In the middle of these demands and possibilities, hospital executives have to prioritize and decide which technology solutions are the most critical to the health of their organization. I call these most critical IT solutions “survival software.” Advanced clinical analytics solutions are the survival software of the near future, as they really hold the key to achieving the triple aim and survive value-based purchasing.

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4 Essential Lessons for Adopting Predictive Analytics in Healthcare

Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include 1) confusing data with insight, 2) confusing insight with value, 3) overestimating the ability to interpret the data, and 4) underestimating the challenge of implementation.

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Texas Children’s Hospital Uses Healthcare Data Warehouse as an Alternative to EHR Reports

One of my clients, Texas Children’s Hospital, recently made tremendous strides in this data-driven journey. Getting data from their EHR in a timely fashion was difficult, time consuming and resource intensive. Now, with the proper tools in place, namely a healthcare enterprise data warehouse, a suite of healthcare analytics applications and a process for information deployment, they have shifted the cost curve to drastically increase the availability and usability of information.T CH used their healthcare enterprise data warehouse (EDW) to meet demands for EHR data and reports, and slashed their reporting costs by 67%.

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Healthcare Analytics: Realizing the Value of Health IT

Healthcare has been slowly moving through three waves of digitization and health data management: data collection, data sharing, and data analytics. Data collection and sharing waves have been having some success, spurred on by the HITECH Act and implementation of electronic health records and health information exchanges. They have not yet significantly impacted costs or quality in healthcare. The third wave of analytics is ready to crash on our shores and I believe we will actually begin to see an IT infrastructure that support the new payment and care delivery models which are emerging. Guest blogger Brian Ahier explains how healthcare can work to realize the value of their IT systems and the healthcare analytics adoption model.

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Why the CIO Has the Opportunity to Play a Transformative Role in the New Wave of Analytics

All of us quietly yearn to be heroes. CIOs are no exception. We want to harness the power of healthcare analytics, using information technology to dramatically improve healthcare quality and costs. Despite their privileged position atop the IT food chain, though, only a handful of healthcare CIOs ever get to realize this dream. Why? Simply put, CIOs never own both the data content and application layers of any meaningful technology, at the business transformation level. Which is why the Enterprise Data Warehouse (EDW) represents a CIO’s last chance to be a transformational hero in healthcare.

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My Thanks to Healthcare IT Professionals … Along with a Look Back and Forward

Healthcare automation isn't a new concept ... and for that author Larry Grandia, who's been in the business for more than four decades, is grateful. "The industry has been on a breathtakingly expensive and time consuming sprint for the past decade or so automating essential operational information systems. Capping this technology effort with a foundation-based data strategy — an enterprise data warehouse, robust analytics and a sound data governance plan — holds the promise of a rich harvest from decades of significant IT investment and untold professional effort. My personal thanks to the many IT professionals and healthcare vendors and suppliers that have gotten the industry to this pivotal base camp. I firmly believe the peak is in sight!

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A Guide to Knowing Which Healthcare Terminology Standard to Use

"SNOMED, HL7, ICD-9 , ICD-10, CPT codes: With so many healthcare terminology standards available, which one should you be using? Well, it really depends on what you’re trying to accomplish. Different healthcare terminology standards were developed to fulfill distinct purposes. Each one generally does a good job meeting its purpose. Match your purpose to your standard, and you probably have a winner. There are billing, clinical, laboratory, and pharmacy terminologies. Health Catalyst’s expert shows you the trick to picking the one that’s right.

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How Texas Children’s Hospital Reduced Their Healthcare Labor Costs

Texas Children’s Hospital, a client of mine, found they needed an immediate solution to address their labor and productivity challenges....The Health Catalyst Labor Productivity Advanced Application delivered a view of staffing levels, volume and productivity across Texas Children's various cost centers. The application enables the business and unit managers to track and manage their resources. It delivers information into the hands of decision makers to help them manage their business. Managers can track performance as often as daily to see exactly how well labor is being allocated and make rapid modifications to counter scheduling problems. In the event that labor utilization outpaces volume, managers can drill further into labor data to understand utilization at the job code level.

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Predictive Analytics: Healthcare Hype or Reality?

In healthcare, popular buzzwords and hot topics always come and go. Technically sexy topics such as big data, bioinformatics, predictive analytics or genomic medicine are tossed in and about sales calls, funding proposals, journal articles and blogs for a few years and then folks move on to the next big thing. The buzzword fever around predictive analytics will likely continue to rise and fall. Unfortunately, lacking the proper infrastructure, staffing and resource to act when something is predicted with high certainty to happen, we fall short of the full potential of harnessing historic trends and patterns in patient data. In other words, without the willpower for clinical intervention, any predictor – no matter how good – is not fully utilized.

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3 Best Practices for Payer-Provider Collaboration to Improve Patient Care

Payers and providers have the same goals—both want to improve patient care and lower the cost trend. Obviously, collaboration is key. Bobbi identifies 3 collaborative best practices to pave the road for a productive, beneficial relationship: 1) clinical measurements and metrics, 2) payer-provider transparency, and 3) joint clinical improvement.

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How Would An Accountable Care Approach Change How A Patient is Treated?

How would an Accountable Care approach change how a patient is treated? It’s important to recognize that accountable care isn’t just a piece of legislation or a new organizational or payment structure. Nor is it just applying technological advances to make healthcare more efficient. It is a fundamental shift in making people accountable for how care is delivered and experienced. And it is founded in the shared responsibility we all have—patients, providers and payers—to make sure our healthcare dollars are used wisely and well. In this Insight, Luke shares an example of a healthcare episode of resource misallocation that could have been avoided by accountable care.

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5 Myths You Won’t Need to Worry About When Adopting a Clinical Data Warehouse

  If you’ve been thinking about implementing a clinical enterprise data warehouse (EDW), chances are you have a few questions about the possible problems you’ll encounter. In this Insight, Mike addresses some fears that are actually common myths including: 5) I can't provide broad access to my EDW, 4) users don't need or want the ability to write SQL queries, 3) I don't need an EDW -- my BI tool does everything I need, 2) EDWs are too expensive, and 1) EDWs take too long to complete.

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Late-Binding™ vs. EMR-based Models: A Comparison of Healthcare Data Warehouse Methodologies

  Baffled by the options for healthcare data warehouses? Here, Eric compares two models: Late-Binding™ and EMR-based. Many organizations are taking a wait-and-see approach with analytics solutions provided by EMR vendors and other out-of-the-box solutions. In this post, Eric compares two models of a data warehouse: Late-Binding™ and EMR-based. He also outline important factors to consider when planning for long-term success in data warehousing and analytics.

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7 Essential Practices for Data Governance in Healthcare

While information and data security is a long-standing body of practice and knowledge in corporations, data governance is less mature, especially in healthcare. As a result of this lower maturity, there is a tendency to operate in extremes, either too much governance or too little. Over time, as data and analytic maturity increases, the healthcare industry will find a natural equilibrium. In this post, Dale identifies simple practices of data governance in 7 areas: 1) balanced, lean governance, 2) data quality, 3) data access, 4) data literacy, 5) data content, 6) analytic prioritization, and 7) master data management.

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Defining Patient Populations Using Analytical Tools

Defining patient populations is an important first step when identifying opportunities for clinical improvement, but it can be a daunting one. How can a clinician easily find a specific patient population? Then, once found, how does that list turn into actionable steps that improve outcomes? In this Insight, Kathy describes how Health Catalyst helps clients define patient populations by using the Cohort Builder application and then using risk stratification, an exciting methodology that assists in identifying outcomes for specific patient populations.

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How Analytics Will Lower Waste and Reduce Costs for the Healthcare Industry

One of the major contributing factors to escalating hospital costs is patient variation and waste associated with the delivery of care. Hospitals have begun to address waste through a variety of methods such as Six Sigma, LEAN and other healthcare quality process improvement techniques. While these methods are effective at dealing with administrative costs, a much greater return can be gained by concentrating on the clinical or patient care costs. Clinical work teams coupled with data and healthcare analytics reduce costs by helping your organization reduce variation, leading to lowering cost trends as the revenue trend flattens. To fully understand your costs and identify areas of waste, you need good data.

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3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve Healthcare’s Problems

I had a recent opportunity to engage in an online discussion with a well-known healthcare analytics vendor about the value of comparative analytics, predictive analytics, and natural language processing (NLP) in healthcare. This vendor was describing a beautiful new world of the future, in which comparative data, in particular, would be the cornerstone of our industry’s turnaround. The executive summary of my response: "Beware the smoke and mirrors" because 1) comparative data doesn't drive improvement, 2) predictive analytics fails to include outcomes, 3) gaps in industry healthcare data limits the effectiveness of NLP.

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6 Surprising Benefits of Healthcare Data Warehouses: Getting More Than You Expected

Recently, I invited a group of my colleagues to share some examples of unexpected benefits they had witnessed at healthcare organizations that feature powerful, thriving EDW initiatives. The number of responses I received was overwhelming; more than I could possibly hope to include in one blog post. With a goal of hopefully sharing all of them within a continuing series, here are some excerpts, reprinted with permission and in the words of the “EDW Elders” within our company. These include 1) negotiating with insurance companies, 2) Stage 1 Meaningful Use self-certification, 3) data quality issues, 4) financial data comparisons, 5) EMR user log data, and 6) employee satisfaction data.

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