The Importance of Care Management Communication: Keisha’s Story

Ineffective communication between care providers is a major problem. According to the Joint Commission, 80 percent of serious medical errors involve miscommunication between caregivers during the transfer of patients. Care management teams need to place emphasis on good communication to effectively coordinate care and improve health outcomes. This point is illustrated by Keisha’s story, a patient who had a severe heart attack just two days after her catheterization was postponed due to incomplete information and miscommunication between her PCP, cardiologist, and nurse care manager.

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The Best Way to Maximize Healthcare Analytics ROI

When it comes to maximizing analytics ROI in a healthcare organization, the more domains, the merrier. Texas Children’s Hospital started their outcomes improvement journey by using an EDW and analytics to improve a single process of care. It quickly realized the potential for more savings and improvement by applying analytics to additional domains, including:

  • Analytics efficiencies
  • Operations/Finance
  • Organization-wide clinical improvement
The competencies required to launch and sustain such an organizational sea change are all part of a single, defining characteristic: the data-driven culture. This allows fulfillment of the analytics strategy, ensures data quality and governance, encourages data and analytics literacy, standardizes data definitions, and opens access to data from multiple sources. This article highlights the specifics of how Texas Children’s has evolved into an outcomes improvement leader, with stories about its successes in multiple domains.

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Build a Mission-Driven Culture in Healthcare

A mission-driven culture is a must-have in today’s rapidly changing healthcare environment. Culture is a vital component of a successful organization, as it builds an engaged and committed workforce that’s capable of adapting to shifting demands. Four principles form the basis of a mission-driven culture:

  1. Engage life-long learners and great listeners.
  2. Assume positive intent.
  3. Avoid entitlement.
  4. Aim for long-term commitment.

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The Expanding Concept and Role of Care Management: Coordinating Care for Carlos

The healthcare industry is increasingly focusing on care management, and it shows—patients with serious illnesses and injuries are experiencing better outcomes and living longer. But more needs to be done, as demonstrated by Carlos, the patient in this article who was headed toward invasive, expensive care because he had trouble being compliant with his diabetes plan. Care must be coordinated across the continuum, and tailored to the patient. The role of care management is expanding and can become more effective than ever.

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Why the 21st Century Cures Act Is Great News for Healthcare

The 21st Century Cures Act, approved by the U.S. Senate on December 7, 2016, is perhaps the most significant federal legislation as it relates to health information technology (HIT) in years. What the Cures Act means for HIT companies and providers is two key things:

  1. Health information interoperability will be strongly promoted (involves the development of a “trusted exchange framework” which is expected to facilitate the exchange of health information nationally and locally).
  2. Information blocking practices will be strongly prohibited (e.g., Implementing HIT in nonstandard ways likely to increase the complexity or burden of accessing, exchanging, or using electronic health information—with fines as much as $1,000,000).
The Act’s impact on the healthcare industry is monumental, with significantly improved provider access to clinical, financial, and operational data to improve outcomes. The Act’s provisions to promote the free exchange of health information will improve care coordination industrywide.

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Hadoop in Healthcare: Getting More from Analytics

Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight.   Specific use cases where Hadoop adds value data strategy include:

  1. Archiving
  2. Streaming
  3. Machine learning

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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 its 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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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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