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Activity Number: 119 - SPEED: Government and Health Policy
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Government Statistics Section
Abstract #329450 Presentation
Title: Statistically Supporting Health Policy Decision-Making
Author(s): Frank Yoon*
Companies: IBM Watson Health
Keywords: observational study; blocked design; Bayesian; permutation test; health policy; analytics

Healthcare reform has been spurred by recent innovations in service and payment delivery, such as accountable care organizations or behavioral health integration, often tested in pilots or other limited settings. To make decisions about scaling these pilots to the national level, policymakers must know their impacts. With large administrative claims databases, the analyst can estimate those impacts using observational methods grounded in (1) good study design and (2) flexible analytics. By good study design, we will demonstrate how blocking or stratification removes bias due to confounders, and by flexible analytics, we will illustrate computationally quick approaches to estimate impacts with fewer statistical assumptions. In a commercial claims database, we will describe an analytic approach to estimate impacts of a behavioral health program that could be scaled nationwide.

Authors who are presenting talks have a * after their name.

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