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Bobbi Brown Leslie Falk

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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Russ Staheli

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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Eric Just

Powering Medical Research With Data: The Research Analytics Adoption Model (Webinar)

Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.

Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.

Content presented and discussed:

  • A summary of some of the challenges in using data and analytics for research
  • A research analytics adoption framework for all organizations interested in using clinical data for research
  • What is needed from a workflow and organizational perspective to power research with data

We hope you enjoy.

 

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Russ Staheli

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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Brian Eliason David Crockett

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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Dale Sanders

4 Options for Choosing the Best Healthcare Analytics Solutions

Analytics is a buzzword in healthcare today. You hear it often: “What does an organization need to succeed in a value-based care environment? Robust analytics.” But what exactly does that mean? Anyone who has looked into implementing “analytics” for their organization knows that a multitude of options for healthcare analytics are available—and each vendor touts its approach to analytics as the best. I’d like to take a moment here to summarize four primary analytics options available to healthcare organizations today: 1) hosted analytics service providers, 2) “best of breed” point solutions, 3) EMR vendors, 4) healthcare data warehouse platform providers.

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Russ Staheli

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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Dale Sanders

Healthcare Analytics Adoption Model: A Framework and Roadmap (white paper)

Outlined for practical use in the healthcare industry, this eight-level, industry-specific roadmap acts as a guide for organizations striving to be truly data driven. Developed by an independent, cross-industry group, this white paper explains the Analytics Adoption Model, its history and purpose. Organizations can then use the model to determine direction, highlight goals and gauge progress, as well as see the next steps needed to achieve goals associated using data to drive and assess clinical improvements.

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Dale Sanders

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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Tom Burton

The Best Approach to Healthcare Analytics

Healthcare has remained entrenched in its cottage industry-style of operation, even within huge medical centers and significant medical innovation. The result, as documented by Dr. John Wennberg’s Dartmouth Atlas of Health Care project , is unwarranted variation in the practice of medicine and in the use of medical resources including underuse of effective care, misuse of care, and overuse of care provided to specific patient populations. The root of the problem, Wennberg concludes, is that there is no healthcare “system.” At Health Catalyst, we agree. Healthcare needs to be systematized and standardized in three key areas:1) healthcare analytics or measurement, 2) adoption or how teams and work are organized, and 3) best practice or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.

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Analytics - Additional Content

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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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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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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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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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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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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 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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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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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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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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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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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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4 Options for Choosing the Best Healthcare Analytics Solutions

Analytics is a buzzword in healthcare today. You hear it often: “What does an organization need to succeed in a value-based care environment? Robust analytics.” But what exactly does that mean? Anyone who has looked into implementing “analytics” for their organization knows that a multitude of options for healthcare analytics are available—and each vendor touts its approach to analytics as the best. I’d like to take a moment here to summarize four primary analytics options available to healthcare organizations today: 1) hosted analytics service providers, 2) "best of breed" point solutions, 3) EMR vendors, 4) healthcare data warehouse platform providers.

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Problems With Relying on EHR Analytics For Your ACO: You Need a True Data Warehouse

Many CIOs, along with their other C-suite colleagues, are anticipating a catharsis on completing massive EHR deployment projects. Before long, however, they come to the unwelcome realization that the EHR is just one component needed to provide the actionable intelligence health systems need to survive in a value-based purchasing environment.

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The Best Approach to Healthcare Analytics

Healthcare has remained entrenched in its cottage industry-style of operation, even within huge medical centers and significant medical innovation. The result, as documented by Dr. John Wennberg’s Dartmouth Atlas of Health Care project , is unwarranted variation in the practice of medicine and in the use of medical resources including underuse of effective care, misuse of care, and overuse of care provided to specific patient populations. The root of the problem, Wennberg concludes, is that there is no healthcare “system.” At Health Catalyst, we agree. Healthcare needs to be systematized and standardized in three key areas:1) healthcare analytics or measurement, 2) adoption or how teams and work are organized, and 3) best practice or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.

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How to Decrease Hospital Acquired Infections with the Power of Healthcare Data

How much time do you routinely spend on hospital-acquired infection (HAI) surveillance activities and reporting for Central-Line Associated Bloodstream Infections (CLABSIs) and Catheter-Associated Urinary Tract Infections (CAUTIs)? CLABSIs and CAUTIs are largely preventable infections that typically result in longer patient stays, increased mortality, as well as increased care costs- estimated at over $20,000 per CLABSI. Do you wish you could decrease surveillance waste and spend more time preventing hospital acquired infections? A client of ours just reduced their time for CLABSI and CAUTI surveillance activities by 90%.

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