Objectives: With the advent of healthcare payment reform, identifying high-risk populations has become more important to providers. Existing risk-prediction models often focus on chronic conditions. This study sought to better understand other factors to improve identification of the highest risk population. Study Design: A retrospective cohort study of a paneled primary care population utilizing 2010 data to calibrate a risk prediction model of hospital and emergency department (ED) use in 2011. Methods: Data were randomly split into development and validation data sets. We compared the enhanced model containing the additional risk predictors with the Minnesota medical tiering model. The study was conducted in the primary care practice of an integrated delivery system at an academic medical center in Rochester, Minnesota. The study focus was primary care medical home patients in 2010 and 2011 (n = 84,752), with the primary outcome of subsequent hospitalization or ED visit. A total of 42,384 individuals derived the enhanced risk-prediction model and 42,368 individuals validated the model. Predictors included Adjusted Clinical Groups-based Minnesota medical tiering, patient demographics, insurance status, and prior year healthcare utilization. Additional variables included specific mental and medical conditions, use of high-risk medications, and body mass index. Results: The area under the curve in the enhanced model was 0.705 (95% CI, 0.698-0.712) compared with 0.662 (95% CI, 0.656-0.669) in the Minnesota medical tiering-only model. New high-risk patients in the enhanced model were more likely to have lack of health insurance, presence of Medicaid, diagnosed depression, and prior ED utilization. Conclusions: An enhanced model including additional healthcarerelated factors improved the prediction of risk of hospitalization or ED visit.
|Original language||English (US)|
|Number of pages||9|
|Journal||American Journal of Managed Care|
|State||Published - Jul 1 2016|
ASJC Scopus subject areas
- Health Policy