Impactibility Models: Identifying the Subgroup of High-Risk Patients Most Amenable to Hospital-Avoidance Programs

Methods: This research used semistructured interviews with representatives of thirty American organizations that build, use, or appraise predictive models for health care. Findings: Impactibility models may refine the output of predictive models by (1) giving priority to patients with diseases that are particularly amenable to preventive care; (2) excluding patients who are least likely to respond to preventive care; or (3) identifying the form of preventive care best matched to each patient's characteristics. Conclusions: Impactibility models could improve the efficiency of hospital-avoidance programs, but they have important implications for equity and access.