Context: Predictive models can be used to identify people at high risk of unplanned hospitalization, although some of the high-risk patients they identify may not be amenable to preventive care. This study describes the development of "impactibility models," which aim to identify the subset of at-risk patients for whom preventive care is expected to be successful.
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.