Measuring population health risks using inpatient diagnoses and outpatient pharmacy data
OBJECTIVE: To examine and evaluate models that use inpatient encounter data and outpatient pharmacy claims data to predict future health care expenditures.
DATA SOURCES/STUDY DESIGN: The study group was the privately insured under-65 population in the 1997 and 1998 MEDSTAT Market Scan (R) Research Database. Pharmacy and disease profiles, created from pharmacy claims and inpatient encounter data, respectively, were used separately and in combination to predict each individual's subsequent-year health care expenditures.
PRINCIPAL FINDINGS: The inpatient-diagnosis model predicts well for the low-hospitalization under-65 populations, explaining 8.4 percent of future individual total cost variation. The pharmacy-based and in patient-diagnosis models perform comparably overall, with pharmacy data better able to split off a group of truly low-cost people and inpatient diagnoses better able to find a small group with extremely high future costs. The model th at uses both kinds of data performed significantly better than either model alone, with an R2 value of 11.8 percent.
CONCLUSIONS: Comprehensive pharmacy and inpatient diagnosis classification systems are each helpful for discriminating among people according to their expected costs. Properly organized and in combination these data are promising predictors of future costs.
Yang Zhao, Randall P. Ellis, Arlene S. Ash, David Calabrese, John Z. Ayanian, James P. Slaughter, Lori Weyuker, and Bruce Bowen. "Measuring population health risks using inpatient diagnoses and outpatient pharmacy data" Health services research 36.6 Pt 2 (2005).
Available at: http://works.bepress.com/arlene_ash/47