Objectives: To compare predictive modeling (PM), selection by primary care physician (PCP), and a combination of both as approaches to prospective patient identification for care management programs.
Study design: Observational study.
Methods: A total of 6026 beneficiaries of a statutory health insurance program in Germany served as a sample for patient identification by PM and selection by PCP. The resulting mutually exclusive subpopulations were compared for care needs (eg, morbidity burden), healthcare utilization (previous all-cause hospitalizations and predicted costs), and prior participation in intensified care programs (as a proxy for amenability). Data sources were insurance claims data and a patient survey.
Results: Patients were selected for eligibility in a care management program by PM (n = 301), selection by PCP (n = 203), or a combination of both (n = 32). Compared with 5490 nonselected patients, all eligible patients had significantly higher morbidity burden and more previous hospitalizations. Compared with selection by PCP, PM identified patients at significantly higher risk for future healthcare utilization, with predicted annual healthcare costs of 8760 euro (95% confidence interval [CI], 8314-9205 euro) vs 4541 euro (95% CI, 4094-4989 euro) (P <.01). Compared with patients selected by PM, patients selected by PCP had significantly higher rates of prior participation in intensified care programs (80.8% vs 56.4%, P <.01). Patients selected independently by both approaches seemed to be at high risk for future healthcare utilization, with predicted annual healthcare costs of 8279 euro (95% CI, 7465-9092 euro), and 84.6% had prior participation in intensified care programs.
Conclusions: Identification of high-risk patients most likely to benefit from and participate in care management programs may be facilitated by a combination of PM and selection by PCP.