Data Storyteller

As of CDC's 2017 National Diabetes Statistics Report, 30.3 million people, or 9.4% of the total U.S. population, have diabetes. Of these 30.3 million, only 23.1 million are diagnosed—while the other estimated 7.2 million are undiagnosed. Additionally, more than 1 in 3 adults or 84.1 million people in the U.S. have prediabetes, including nearly half of people age 65 and older. According to 2018 UDS data, an estimated 15.1% of Federally Qualified Health Center patients nationwide have diabetes, an increase over recent years. Of these approx. 2.4 million plus patients living with diabetes, approximately 33% have uncontrolled diabetes, with HbA1c equal to or above 9% or have had no test in the year. This has remained relatively stable since 2016. These statistics bring forth the need for improvement in the care of diabetes; several resources and research outcomes are profiled here with specific takeaways for health centers.

Panel management is an essential function of a health center. When done well, it smooths the scheduling and operations of the health center; when done poorly it creates challenges with productivity, patient continuity, Quality Improvement reporting, and more. This resource offers guidance on improving panel management activities, including real-life examples from two health centers of the challenges and successes in managing panels.

This Data Dictionary provides a single point of reference for data mapping and interpretation for all of the indicators in your quality reports. Organization of the data definitions in this tool provides a reference for the team of all such definitions that impact reports and alerts in the analytics application.

Health Catalyst published the inaugural version of the Healthcare Analytics Adoption Model in 2012, a proposed framework to measure the adoption and meaningful use of data warehouses and analytics in healthcare in ways similar to the well-known HIMSS Analytics EMRAM model. A second version of the Health Analytics Adoption Model was released in 2013.

This can be a helpful framework for understanding what is infrastructure and processes are needed to use data successfully to manage the health of your population and engage in alternative payment arrangements that continue to gain traction. 

This paper is designed to help you with your PHM analytics buying decision.  It outlines general criteria to help you assess a clinical analytics vendor; discusses the technology and change management an effective analytics solution should support; and introduces a Healthcare Analytics Adoption Model that will help you analyze vendors and evaluate your own plans for analytics adoption.