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

Milbank Q. 2010 Jun;88(2):240-55. doi: 10.1111/j.1468-0009.2010.00597.x.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Forecasting / methods
  • Hospitalization / statistics & numerical data
  • Humans
  • Interviews as Topic
  • Models, Theoretical
  • Preventive Health Services / methods*
  • Preventive Health Services / statistics & numerical data
  • Risk Assessment
  • Risk Factors
  • United States