APHSA Symposium 2019

Panel 3: Analytics to Answer Policy Questions. Fred Wulczyn (Chapin Hall) and Matt Stearmer (Chief Data Officer, Ohio Department of Medicaid) presented examples of how data can be used to answer policy-relevant questions that demonstrate the value of building analytics capacity. Tracy Wareing Evans (APHSA) moderated the session. FredWulczyn’s presentation emphasized the importance of starting with a policy question and asking more granular questions to understand variation so that effective, targeted solutions can be developed. As a use case, he started with this state-level policy question “To reduce over- representation of black children in foster care, what resources are needed and how should they be allocated?” To answer this question, decision-makers need to dive more deeply into the data to answer questions about variation, including: (1) where are the racial disparities occurring? (2) what are the characteristics of those places compared to other places? (3) are the disparities attributable to how long a child stays in care? and (4) what are the disparities in guardianship vs. adoption? His slides (Appendix C-5) , included graphs showing quantitative variations that could be used by decision-makers to improve targeting of resources to achieve their policy goal. Matt Stearmer described lessons from Ohio’s experiences using data to improve healthcare delivery. For example: • It is critical to anticipate and model unintended consequences of policy decisions. Ohio’s well-intended policy of closing down “pill mills” to stem the opioid epidemic drove people with addiction into the illicit market, unexpectedly driving death rates even higher. • An effective way to improve data quality is to engage people in developing solutions. Five years ago, 30 percent of Ohio’s Medicaid race and ethnicity data was incomplete, stemming in part from people’s distrust of the medical system. After Ohio invited people to participate on a working group to develop solutions, they achieved a 95 percent completion rate. • Real-time data analytics projects that generate useful information for decision-makers demonstrate the value of analytics. When Ohio was redesigning its behavioral health system, the data analytics team joined data to answer policy questions in real time, making it a vital resource.

• A strong data governance framework is critical to ensuring data can be linked to answer all key policy questions.

Panel 4: Thought Leader Panel: This panel of industry experts described strategies for accelerating the use of analytics. Presenters were Erika Robbins (Vice President, The Lewin Group), Jeanne McNeil (Director, Delivery Operations, Optum), and Don Johnson (Chief Technology Officer, Optum). The panel was moderated by AndrewCone (Senior VP, Optum). Erika Robbins used LongTerm Services and Supports (LTSS) as a use case for how to improve data literacy across the workforce and accelerate the use of analytics for improvement. In many organizations, a gap exists between data and analytics leaders and organizational units responsible for program policy and operations. Closing the gap requires understanding the knowledge base and learning styles of people in the organization, which she classified into four groups: Data Aristocrat, Data Knight, Data Dreamer, and Data Doubter. 4 She offered strategies for engaging each type of individual to strengthen their data literacy, and for using an analytical advisory group to identify priorities and develop a knowledge growth plan. She described several projects The Lewin Group is conducting with the U.S. Administration on Community Living and in Georgia to improve data dissemination by tailoring information and visualizations to the end-user. (Please see slides in Appendix C-6 .) Jeanne McNeil provided examples of Michigan projects that use analytics to drive program improvement . These included: • matching data fromMedicaid and the RyanWhite HIV program to identify individuals enrolled in both whose services could be covered by Medicaid, thus freeing up more slots for RyanWhite coverage; • matching beneficiary data for Medicaid, SNAP, andWIC to identify individuals who could potentially be enrolled in additional programs to help them achieve better outcomes; • evaluating Drug and Mental Health Court

recidivism programs by comparing the outcomes of participants to an equivalent population that did not participate; and

• identifying high risk individuals for the Medicaid Beneficiary Monitoring Program who appear to be overutilizing or misusing their Medicaid benefits, and identifying whether beneficiaries should be candidates for the Opioid Health Home.

4 https://www.qlik.com/us/bi/-/media/635E0FBF5EC54CC49793DC3FDB5F5DA2.ashx


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