Transitional Care Management: Five Steps to Fewer Readmissions, Improved Quality, and Lower Cost

Reducing readmissions is an important metric for health systems, representing both quality of care across the continuum and cost management. Under the Affordable Care Act, organizations can be penalized for unreasonably high readmission rates, making initiatives to avoid re-hospitalization a quality and cost imperative. A transitional care management plan can help organizations avoid preventable readmissions by improving care through all levels in five steps:

  1. Start discharge at the time of admission.
  2. Ensure medication education, access, reconciliation, and adherence.
  3. Arrange follow-up appointments.
  4. Arrange home healthcare.
  5. Have patients teach back the transitional care plan.

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A Behind-the-Scenes Look at Healthcare IT Analyst Rankings and Reports: What You Should Know

Healthcare leaders often turn to healthcare IT analyst rankings and reports for information that drives vendor-related decision making. Knowing the key differences between several notable healthcare and cross-industry IT analysts—what methodologies they employ to gather data, their missions and goals (ranking vs. consulting), and how much of their own opinions they interject (unbiased vs. opinionated)—will help healthcare leaders be more educated consumers of the reports and rankings that saturate healthcare. This article provides a high-level overview of the key differences between several healthcare IT analysts:

  • KLAS Research (ranking focus)
  • Black Book Rankings (ranking focus)
  • Chilmark Research (ranking and consulting focus)
  • Advisory Board (consulting focus)
It also looks at the most notable cross-industry IT analysts that apply a healthcare-specific lens to their findings:
  • Gartner
  • International Data Corporation
  • Frost & Sullivan
Healthcare leaders with the ability to interpret these rankings and reports to extract the information they need, will make them more effective decision makers.

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Machine Learning 101: 5 Easy Steps for Using it in Healthcare

Machine learning, used in the context of healthcare, is not about computers replacing doctors or rolling robots dispensing bedside care to patients. Perhaps a better term would be data-driven healthcare because it is the process of using historical patient data in a predictive model to determine the likelihood of a healthcare-related outcome. The black box that is the machine is no more than an algorithm trained by data. Most importantly, the predictions can be used by doctors to optimize decision making in real time, thus reducing readmissions, infection rates, and other complications that drive up costs and lower the quality of care. This article explains some machine learning basics, dispels some misconceptions, and outlines five steps to its implementation:

  1. Define the use case.
  2. Prepare the data.
  3. Train the model.
  4. Make predictions on new data.
  5. Deliver the risk score for use in clinical decision support.
Machine learning is destined to be a digital partner for physicians, executives, and health systems focused on improving clinical, financial, and operational performance. To get there, it must first be understood.

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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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HAS 17 Metrics—From Attendee Experience to Superheroes

Using survey results from the 2017 Healthcare Analytics Summit™ (HAS) conference, the HAS team has compiled an infographic of key insights. Featured metrics include:

  1. Overall satisfaction: 99.4 percent
  2. Likelihood of recommending to a friend: 98.4 percent.
  3. Attendee metrics, such as healthcare experience (27.6 percent with 11 to 20 years) and type of organizations represented (healthcare, 45.7 percent).
  4. Organizational population health status (successful initiatives, 63.2 percent).
  5. Organizational level of analytics adoption (intermediate, 49.7 percept).
Combined, the HAS 17 metrics revealed high overall satisfaction with what attendees viewed as a true educational experience. Participants also reported deep healthcare expertise, a positive outlook on population health and value-based care.

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Population Health Documentary Highlights Three Success Stories Transforming Healthcare

The documentary, “A Coalition of the Willing: Data-Driven Population Health and Complex Care Innovation in Low-Income Communities” shows how precision medicine and care management can be effective tools for successful population health. The film highlights three programs that use data to hotspot populations of high-risk, high-need patients, and then deploy unique, targeted care management inventions. The documentary, which initially aired during the 2017 Healthcare Analytics Summit, presents hopeful solutions, scalable across diverse patient populations, that are leading to exceptional results and the future of healthcare transformation.

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What Is a Healthcare Data Lake and Why Do You Need One? Imagine a Supermarket

Using a supermarket analogy, this article helps healthcare leaders understand what data lakes are (open reservoirs for vast amounts of data), why they’re essential (they reduce the time and resources required to map data), and how they integrate with three common analytic architectures:

  1. Early-Binding Data Warehouse
  2. Late-Binding Data Warehouse
  3. Map-Reduce Hadoop System
Data lakes are useful parts of all three platforms, but deciding which platform to integrate a data lake with depends heavily on a health system’s resources and infrastructure. Once understood and appropriately integrated with the optimal analytics platform, data lakes save health systems time, money, and resources by adding structure to data only as use cases arise.

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On the Population Health and Cost Transformation Stage, Care Management Is the Star

Joe’s story isn’t a unique one in the U.S. Having been admitted to the hospital five times in one month, Joe isn’t taking his medications and doesn’t exercise. In short, he struggles to follow his care plan. The Care Management Show, an entertaining, interactive theatrical performance, demonstrates why health systems need to adopt innovative, data-driven approaches to care management that prevent patients from falling through the cracks by integrating all aspects of patient care:

  • Data integration.
  • Patient stratification and intake.
  • Care coordination.
  • Patient engagement.
  • Performance measurement.
Throughout the show, the audience witnesses how care management done right transforms not only Joe’s life, but also Millrock Hospital’s profitability. We see how health systems can leverage technology to engage their “Joes” to increase care plan adherence and, ultimately, improve patient outcomes.

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Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes

The Adverse Childhood Experience (ACE) study conducted by the CDC and Kaiser Permanent showed a strong correlation between ACEs and negative health outcomes later in life (e.g., risky health behaviors, chronic health conditions, and early death). ACE scores help paint a more complete picture of a person’s health history—a more comprehensive data snapshot of the entire patient. Given that ACE scores build better data sets and machine learning relies on high-quality data, health systems should incorporate these nutrient-rich data sources into their machine learning models to better predict negative health outcomes, allow for earlier interventions, and improve outcomes. Healthcare machine learning is evolving to use ACE scores and lifestyle data (e.g., eating habits) to improve population health management.

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Healthcare Analytics Summit 2017 Final Day: Thursday Recap

On Thursday, the last day of the Healthcare Analytics Summit, attendees learned about a “flipped” health system from Maureen Bisognano; found out from Robert DeMichiei that we “have a cost problem” (but it’s not what you think); discovered the four ways healthcare got into this predicament—and what to do about it—according to David Nash, MD; and saw what a “Coalition of the Willing” can do in low-income communities with the HAS Documentary.

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