Yong-Fang Kuo1,2, Yu-Li Lin2, Daniel Jupiter2,3. 1. Departments of Internal Medicine and Sealy Center on Aging. 2. Preventive Medicine and Population Health. 3. Orthopaedic Surgery and Rehabilitation, University of Texas Medical Branch, Galveston, TX.
Abstract
BACKGROUND: Studying team-based primary care using 100% national outpatient Medicare data is not feasible, due to limitations in the availability of this dataset to researchers. METHODS: We assessed whether analyses using different sets of Medicare data can produce results similar to those from analyses using 100% data from an entire state, in identifying primary care teams through social network analysis. First, we used data from 100% Medicare beneficiaries, restricted to those within a primary care services area (PCSA), to identify primary care teams. Second, we used data from a 20% sample of Medicare beneficiaries and defined shared care by 2 providers using 2 different cutoffs for the minimum required number of shared patients, to identify primary care teams. RESULTS: The team practices identified with social network analysis using the 20% sample and a cutoff of 6 patients shared between 2 primary care providers had good agreement with team practices identified using statewide data (F measure: 90.9%). Use of 100% data within a small area geographic boundary, such as PCSAs, had an F measure of 83.4%. The percent of practices identified from these datasets that coincided with practices identified from statewide data were 86% versus 100%, respectively. CONCLUSIONS: Depending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States.
BACKGROUND: Studying team-based primary care using 100% national outpatient Medicare data is not feasible, due to limitations in the availability of this dataset to researchers. METHODS: We assessed whether analyses using different sets of Medicare data can produce results similar to those from analyses using 100% data from an entire state, in identifying primary care teams through social network analysis. First, we used data from 100% Medicare beneficiaries, restricted to those within a primary care services area (PCSA), to identify primary care teams. Second, we used data from a 20% sample of Medicare beneficiaries and defined shared care by 2 providers using 2 different cutoffs for the minimum required number of shared patients, to identify primary care teams. RESULTS: The team practices identified with social network analysis using the 20% sample and a cutoff of 6 patients shared between 2 primary care providers had good agreement with team practices identified using statewide data (F measure: 90.9%). Use of 100% data within a small area geographic boundary, such as PCSAs, had an F measure of 83.4%. The percent of practices identified from these datasets that coincided with practices identified from statewide data were 86% versus 100%, respectively. CONCLUSIONS: Depending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States.
Authors: Yong-Fang Kuo; Mukaila A Raji; Yu-Li Lin; Margaret E Ottenbacher; Daniel Jupiter; James S Goodwin Journal: Med Care Date: 2019-11 Impact factor: 2.983
Authors: David C Goodman; Stephen S Mick; David Bott; Therese Stukel; Chiang-hua Chang; Nancy Marth; Jim Poage; Henry J Carretta Journal: Health Serv Res Date: 2003-02 Impact factor: 3.402
Authors: Katherine E Mues; Alexander Liede; Jiannong Liu; James B Wetmore; Rebecca Zaha; Brian D Bradbury; Allan J Collins; David T Gilbertson Journal: Clin Epidemiol Date: 2017-05-09 Impact factor: 4.790