Geraint H Lewis1. 1. The Nuffield Trust, London, UK. geraint.lewis@nuffieldtrust.org.uk
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.
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.
Authors: Andreas D Meid; Andreas Groll; Ulrich Schieborr; Jochen Walker; Walter E Haefeli Journal: Eur J Clin Pharmacol Date: 2016-12-24 Impact factor: 2.953
Authors: G Greg Peterson; Jelena Zurovac; Randall S Brown; Kenneth D Coburn; Patricia A Markovich; Sherry A Marcantonio; William D Clark; Anne Mutti; Cara Stepanczuk Journal: Health Serv Res Date: 2016-10-24 Impact factor: 3.402
Authors: Mark D Fleming; Janet K Shim; Irene H Yen; Ariana Thompson-Lastad; Sara Rubin; Meredith Van Natta; Nancy J Burke Journal: Soc Sci Med Date: 2017-04-20 Impact factor: 4.634
Authors: Ben J Marafino; Alejandro Schuler; Vincent X Liu; Gabriel J Escobar; Mike Baiocchi Journal: Health Serv Res Date: 2020-10-30 Impact factor: 3.402
Authors: Tobias Freund; Michel Wensing; Stefan Geissler; Frank Peters-Klimm; Cornelia Mahler; Cynthia M Boyd; Joachim Szecsenyi Journal: Am J Manag Care Date: 2012-04-01 Impact factor: 2.229
Authors: Victoria Woodhams; Simon de Lusignan; Shakeel Mughal; Graham Head; Safia Debar; Terry Desombre; Sean Hilton; Houda Al Sharifi Journal: BMC Health Serv Res Date: 2012-06-10 Impact factor: 2.655
Authors: Nuria Toro Polanco; Iñaki Berraondo Zabalegui; Itziar Pérez Irazusta; Roberto Nuño Solinís; Mario Del Río Cámara Journal: Int J Integr Care Date: 2015-06-24 Impact factor: 5.120
Authors: Joan Gené Badia; Alícia Borràs Santos; Joan Carles Contel Segura; Carlos Ascaso Terén; Laura Corredoira González; Ester Limón Ramírez; Pedro Gallo de Puelles Journal: BMC Health Serv Res Date: 2013-08-15 Impact factor: 2.655