Do Hierarchical Condition Category Model Scores Predict Hospitalization Risk in Newly Enrolled Medicare Advantage Participants as Well as Probability of Repeated Admission Scores?

OBJECTIVES: To compare how well hierarchical condition categories (HCC) and probability of repeated admission (P RA ) scores predict hospitalization. DESIGN: Longitudinal cohort study with 12‐month follow‐up. SETTING: A Medicare Advantage (MA) plan. PARTICIPANTS: Four thousand five hundred six newly enrolled beneficiaries. MEASUREMENT: HCC scores were identified from enrollment files. The P RA tool was administered by mail and telephone. Inpatient admissions were based on notifications. The Mann‐Whitney test was used to compare HCC scores of P RA responders and nonresponders. The receiver operating characteristic curve provided the area under the curve (AUC) for each score. Admission risk in the top 5% of scores was evaluated using logistic regression. RESULTS: Within 60 days of enrollment, 45.1% of the 3,954 beneficiaries with HCC scores completed the P RA tool. HCC scores were lower for the 1,783 P RA respondents than the 2,171 nonrespondents (0.71 vs 0.81, P <.001). AUCs predicting hospitalization with regard to HCC and P RA were similar (0.638, 95% confidence interval (CI)=0.603–0.674; 0.654, 95% CI=0.618–0.690). Individuals identified in the top 5% of scores using both tools, using HCC alone, or using P RA alone had higher risk for hospitalization than those below the 95th percentile (odds ratio (OR)=8.5, 95% CI=3.7–19.4, OR=3.8, 95% CI=2.3–6.3, and OR=3.9, 95% CI=2.3–6.4, respectively). CONCLUSION: HCC scores provided to MA plans for risk adjustment of revenue can also be used to identify hospitalization risk. Additional studies are required to evaluate whether a hybrid approach incorporating administrative and self‐reported models would further optimize risk stratification efforts.