Use of Medicare Data to Identify Team-based Primary Care: Is It Possible?

Yong-Fang Kuo 1 2 3 4, Mukaila A Raji 1 2, Yu-Li Lin 3, Margaret E Ottenbacher 3, Daniel Jupiter 3, James S Goodwin 1 2 3 4

Affiliations

PMID: 31568165
PMCID: PMC6791761 (available on 2020-11-01)
DOI: 10.1097/MLR.0000000000001201

Abstract

Background: It is unclear whether Medicare data can be used to identify type and degree of collaboration between primary care providers (PCPs) [medical doctors (MDs), nurse practitioners, and physician assistants] in a team care model.

Methods: We surveyed 63 primary care practices in Texas and linked the survey results to 2015 100% Medicare data. We identified PCP dyads of 2 providers in Medicare data and compared the results to those from our survey. Sensitivity, specificity, and positive predictive value (PPV) of dyads in Medicare data at different threshold numbers of shared patients were reported. We also identified PCPs who work in the same practice by Social Network Analysis (SNA) of Medicare data and compared the results to the surveys.

Results: With a cutoff of sharing at least 30 patients, the sensitivity of identifying dyads was 27.8%, specificity was 91.7%, and PPV 72.2%. The PPV was higher for MD-nurse practitioner/physician assistant pairs (84.4%) than for MD-MD pairs (61.5%). At the same cutoff, 90% of PCPs identified in a practice from the survey were also identified by SNA in the corresponding practice. In 5 of 8 surveyed practices with at least 3 PCPs, about ≤20% PCPs identified in the practices by SNA of Medicare data were not identified in the survey.

Conclusions: Medicare data can be used to identify shared care with low sensitivity and high PPV. Community discovery from Medicare data provided good agreement in identifying members of practices. Adapting network analyses in different contexts needs more validation studies.