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Abstract

Measuring primary care (PC) performance and designing payment systems that reward value rather than volume have been a great challenge due in large part to lack of reliable risk adjustment mechanisms pertinent to primary care. Using risk scores designed for total resource needs to assess PC performance or set PC payment rates is inadequate because high-cost patients may not have high needs in PC and vice versa. The greatest challenge in developing a risk algorithm for PC is that significant components of PC providers’ workload are unobservable but needed in the modeling. In this study, we sought to overcome this challenge by analyzing 5,172,773 patients in the U.S. Veterans Affairs (VA) healthcare system to identify potential proxies of the unobservable PC workload. By combining the number of PC visits and prescription drug classes, we formed a proxy for the expected PC workload, which enabled us to develop a case-mix algorithm pertaining to primary care. The resultant algorithm with high explanatory power (R² = 0.702) is based on a publicly available patient classification system to account for patient comorbidities and thus can be used by other health systems to compare PC performance, workload, staffing levels, and to set more equitable payment rates.

Keywords

performance management primary care risk adjustment staffing

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Case-mix for assessing primary care value (CPCV)

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