BACKGROUND: There remains considerable uncertainty about whether prospective or concurrent risk adjustment (RA) is preferable. Although concurrent models have better predictive power than prospective models, the large payments associated with concurrent RA create incentives for fraudulent coding. A hybrid strategy--in which prospective payments were used for patients with low expected costs and concurrent payments were available upon the diagnosis of a small number of common, expensive conditions--might improve predictive performance while requiring less auditing than fully concurrent RA. In addition, within-condition RA (using clinical data) for the selected conditions could further improve predictive power. OBJECTIVES: To assess how such a hybrid strategy might perform, focusing on a small number of chronic, expensive conditions that are verifiable (hence auditable). SUBJECTS AND MEASURES: All patients from seven health plans who had two complete years of utilization data were considered. RA models were estimated among patients younger than 65 (n = 319,209) using the Hierarchical Coexisting Conditions (HCC) model with and without stratification of the sample based on the presence of one or more of 100 verifiable, expensive, predictive conditions (VEP100). R2 and predictive ratios were calculated for each model studied. RESULTS: Patients with a VEP100 condition (9.3% of the population) accounted for 84.3% of the variation in cost. R2 was 0.08 using a prospective HCC model on the entire population, but increased to 0.26 for a hybrid using prospective HCCs on the 90.7% of the sample without a VEP100 condition and a simple concurrent model consisting of dummy variables for each of the VEP100 conditions. CONCLUSION: Combined with targeted auditing, a hybrid approach to RA could improve our ability to match payments to costs. However, because this would require additional, costly data collection, more research is needed to determine whether this benefit justifies the data collection and auditing burden.
BACKGROUND: There remains considerable uncertainty about whether prospective or concurrent risk adjustment (RA) is preferable. Although concurrent models have better predictive power than prospective models, the large payments associated with concurrent RA create incentives for fraudulent coding. A hybrid strategy--in which prospective payments were used for patients with low expected costs and concurrent payments were available upon the diagnosis of a small number of common, expensive conditions--might improve predictive performance while requiring less auditing than fully concurrent RA. In addition, within-condition RA (using clinical data) for the selected conditions could further improve predictive power. OBJECTIVES: To assess how such a hybrid strategy might perform, focusing on a small number of chronic, expensive conditions that are verifiable (hence auditable). SUBJECTS AND MEASURES: All patients from seven health plans who had two complete years of utilization data were considered. RA models were estimated among patients younger than 65 (n = 319,209) using the Hierarchical Coexisting Conditions (HCC) model with and without stratification of the sample based on the presence of one or more of 100 verifiable, expensive, predictive conditions (VEP100). R2 and predictive ratios were calculated for each model studied. RESULTS:Patients with a VEP100 condition (9.3% of the population) accounted for 84.3% of the variation in cost. R2 was 0.08 using a prospective HCC model on the entire population, but increased to 0.26 for a hybrid using prospective HCCs on the 90.7% of the sample without a VEP100 condition and a simple concurrent model consisting of dummy variables for each of the VEP100 conditions. CONCLUSION: Combined with targeted auditing, a hybrid approach to RA could improve our ability to match payments to costs. However, because this would require additional, costly data collection, more research is needed to determine whether this benefit justifies the data collection and auditing burden.
Authors: Theodore A Omachi; Steven E Gregorich; Mark D Eisner; Renee A Penaloza; Irina V Tolstykh; Edward H Yelin; Carlos Iribarren; R Adams Dudley; Paul D Blanc Journal: Med Care Date: 2013-08 Impact factor: 2.983
Authors: Jeremy A Irvin; Andrew A Kondrich; Michael Ko; Pranav Rajpurkar; Behzad Haghgoo; Bruce E Landon; Robert L Phillips; Stephen Petterson; Andrew Y Ng; Sanjay Basu Journal: BMC Public Health Date: 2020-05-01 Impact factor: 3.295