To stratify traditional risk‐adjustment models by health severity classes in a way that is empirically based, is accessible to policy makers, and improves predictions of inpatient costs.
Stratifying health care populations based on measures of health severity is a powerful method to achieve more accurate cost predictions. Insurers who ignore the predictive advances of sample stratification in setting risk‐adjusted premiums may create strong financial incentives for adverse selection. Finite mixture models provide an empirically based, replicable methodology for stratification that should be accessible to most health care financial managers.
