title: "Impactibility models": identifying the subgroup of high-risk patients most amenable to hospital-avoidance programs.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
authors.affiliation: The Nuffield Trust, London, UK. geraint.lewis@nuffieldtrust.org.uk
authors.forename: Geraint H
authors.initials: GH
authors.lastname: Lewis
mesh_terms: D005544:Forecasting / Q000379:methods; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007407:Interviews as Topic; D008962:Models, Theoretical; D011314:Preventive Health Services / Q000379:methods* / Q000706:statistics & numerical data; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States