“Impactibility Models”: Identifying the Subgroup of High‐Risk Patients Most Amenable to Hospital‐Avoidance Programs
Address correspondence to: Geraint H. Lewis, The Nuffield Trust, 59 New Cavendish Street, London, UK W1G 7LP (email: geraint.lewis@nuffieldtrust.org.uk).
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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.
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- Marcos Estupiñán-Ramírez, Rita Tristancho-Ajamil, María Consuelo Company-Sancho and Hilda Sánchez-Janáriz, Comparación de modelos predictivos para la selección de pacientes de alta complejidad, Gaceta Sanitaria, 10.1016/j.gaceta.2017.06.003, 33, 1, (60-65), (2019).
- Jonathan Stokes, Lisa Riste and Sudeh Cheraghi-Sohi, Targeting the ‘right’ patients for integrated care: stakeholder perspectives from a qualitative study, Journal of Health Services Research & Policy, 10.1177/1355819618788100, 23, 4, (243-251), (2018).
- Sharon Lawn, Sara Zabeen, David Smith, Ellen Wilson, Cathie Miller, Malcolm Battersby and Kevin Masman, Managing chronic conditions care across primary care and hospital systems: lessons from an Australian Hospital Avoidance Risk Program using the Flinders Chronic Condition Management Program, Australian Health Review, 10.1071/AH17099, 42, 5, (542), (2018).
- Zachary M. Grinspan, Anup D. Patel, Baria Hafeez, Erika L. Abramson and Lisa M. Kern, Predicting frequent emergency department use among children with epilepsy: A retrospective cohort study using electronic health data from 2 centers, Epilepsia, 59, 1, (155-169), (2017).
- Stacy Sterling, Felicia Chi, Constance Weisner, Richard Grant, Alix Pruzansky, Sandy Bui, Philip Madvig and Robert Pearl, Association of behavioral health factors and social determinants of health with high and persistently high healthcare costs, Preventive Medicine Reports, 10.1016/j.pmedr.2018.06.017, 11, (154-159), (2018).
- Lala T. Das, Erika L. Abramson, Anne E. Stone, Janienne E. Kondrich, Lisa M. Kern and Zachary M. Grinspan, Predicting frequent emergency department visits among children with asthma using EHR data, Pediatric Pulmonology, 52, 7, (880-890), (2017).
- Carmen Huckel Schneider, James A Gillespie and Andrew Wilson, Implementing system-wide risk stratification approaches: A review of critical success and failure factors, Health Services Management Research, 30, 2, (72), (2017).
- Adam Steventon and John Billings, Preventing hospital readmissions: the importance of considering ‘impactibility,’ not just predicted risk, BMJ Quality & Safety, 26, 10, (782), (2017).
- Jonathan Stokes, Søren Rud Kristensen, Kath Checkland, Sudeh Cheraghi-Sohi and Peter Bower, Does the impact of case management vary in different subgroups of multimorbidity? Secondary analysis of a quasi-experiment, BMC Health Services Research, 17, 1, (2017).
- Andreas D. Meid, Andreas Groll, Ulrich Schieborr, Jochen Walker and Walter E. Haefeli, How can we define and analyse drug exposure more precisely to improve the prediction of hospitalizations in longitudinal (claims) data?, European Journal of Clinical Pharmacology, 73, 3, (373), (2017).
- Joana Mora, Miren David Iturralde, Lucía Prieto, Cristina Domingo, Marie-Pierre Gagnon, Catalina Martínez-Carazo, Anna Giné March, Daniele De Massari, Tino Martí, Marco Nalin, Francesca Avolio, Jean Bousquet and Esteban de Manuel Keenoy, Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study, BMC Health Services Research, 17, 1, (2017).
- David W. Frost, Shankar Vembu, Jiayi Wang, Karen Tu, Quaid Morris and Howard B. Abrams, Using the Electronic Medical Record to Identify Patients at High Risk for Frequent Emergency Department Visits and High System Costs, The American Journal of Medicine, 130, 5, (601.e17), (2017).
- Mark D. Fleming, Janet K. Shim, Irene H. Yen, Ariana Thompson-Lastad, Sara Rubin, Meredith Van Natta and Nancy J. Burke, Patient engagement at the margins: Health care providers' assessments of engagement and the structural determinants of health in the safety-net, Social Science & Medicine, 183, (11), (2017).
- Thomas John Foley and Luke Vale, What role for learning health systems in quality improvement within healthcare providers?, Learning Health Systems, 1, 4, (2017).
- G. Greg Peterson, Jelena Zurovac, Randall S. Brown, Kenneth D. Coburn, Patricia A. Markovich, Sherry A. Marcantonio, William D. Clark, Anne Mutti and Cara Stepanczuk, Testing the Replicability of a Successful Care Management Program: Results from a Randomized Trial and Likely Explanations for Why Impacts Did Not Replicate, Health Services Research, 51, 6, (2115-2139), (2016).
- Alison Porter, Mark Rhys Kingston, Bridie Angela Evans, Hayley Hutchings, Shirley Whitman and Helen Snooks, It could be a ‘Golden Goose’: a qualitative study of views in primary care on an emergency admission risk prediction tool prior to implementation, BMC Family Practice, 17, 1, (2016).
- Ivan Dueñas-Espín, Emili Vela, Steffen Pauws, Cristina Bescos, Isaac Cano, Montserrat Cleries, Joan Carles Contel, Esteban de Manuel Keenoy, Judith Garcia-Aymerich, David Gomez-Cabrero, Rachelle Kaye, Maarten M H Lahr, Magí Lluch-Ariet, Montserrat Moharra, David Monterde, Joana Mora, Marco Nalin, Andrea Pavlickova, Jordi Piera, Sara Ponce, Sebastià Santaeugenia, Helen Schonenberg, Stefan Störk, Jesper Tegner, Filip Velickovski, Christoph Westerteicher and Josep Roca, Proposals for enhanced health risk assessment and stratification in an integrated care scenario, BMJ Open, 6, 4, (e010301), (2016).
- Chandra J. Cohen, Natalie Flaks-Manov, Marcelo Low, Ran D. Balicer and Efrat Shadmi, High-Risk Case Identification for Use in Comprehensive Complex Care Management, Population Health Management, 18, 1, (15), (2015).
- Yang Cui, Colleen Metge, Xibiao Ye, Michael Moffatt, Luis Oppenheimer and Evelyn L Forget, Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada, Journal of Health Services Research & Policy, 20, 2, (83), (2015).
- Sarah R Deeny and Adam Steventon, Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data, BMJ Quality & Safety, 10.1136/bmjqs-2015-004278, 24, 8, (505-515), (2015).
- Nick Goodwin and Albert Alonso, The Role of eHealth in Supporting Care Coordination, Managing eHealth, 10.1057/9781137379443_13, (161-173), (2014).
- Emma Wallace, Ellen Stuart, Niall Vaughan, Kathleen Bennett, Tom Fahey and Susan M. Smith, Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults, Medical Care, 52, 8, (751), (2014).
- Juan F. Orueta, Arturo García-Álvarez, Manuel García-Goñi, Francesco Paolucci, Roberto Nuño-Solinís and C. Mary Schooling, Prevalence and Costs of Multimorbidity by Deprivation Levels in the Basque Country: A Population Based Study Using Health Administrative Databases, PLoS ONE, 9, 2, (e89787), (2014).
- A. L. Huntley, R. Thomas, M. Mann, D. Huws, G. Elwyn, S. Paranjothy and S. Purdy, Is case management effective in reducing the risk of unplanned hospital admissions for older people? A systematic review and meta-analysis, Family Practice, 30, 3, (266), (2013).
- Efrat Shadmi and Tobias Freund, Targeting patients for multimorbid care management interventions: the case for equity in high-risk patient identification, International Journal for Equity in Health, 12, 1, (70), (2013).
- Joan Gené Badia, Alícia Borràs Santos, Joan Carles Contel Segura, Carlos Ascaso Terén, Laura Corredoira González, Ester Limón Ramírez and Pedro Gallo de Puelles, Predictors of mortality among elderly dependent home care patients, BMC Health Services Research, 10.1186/1472-6963-13-316, 13, 1, (2013).
- Geraint Lewis, Lorraine Wright and Rhema Vaithianathan, Multidisciplinary Case Management for Patients at High Risk of Hospitalization: Comparison of Virtual Ward Models in the United Kingdom, United States, and Canada, Population Health Management, 15, 5, (315), (2012).
- Victoria Woodhams, Simon de Lusignan, Shakeel Mughal, Graham Head, Safia Debar, Terry Desombre, Sean Hilton and Houda Al Sharifi, Triumph of hope over experience: learning from interventions to reduce avoidable hospital admissions identified through an Academic Health and Social Care Network, BMC Health Services Research, 12, 1, (2012).
- Mark D. Fleming, Janet K. Shim, Irene Yen, Meredith Natta, Christoph Hanssmann and Nancy J. Burke, Caring for “Super‐utilizers”: Neoliberal Social Assistance in the Safety‐net, Medical Anthropology Quarterly, , (2018).
- Alessandra Buja, Michele Rivera, Elisa De Battisti, Maria Chiara Corti, Francesco Avossa, Elena Schievano, Stefano Rigon, Vincenzo Baldo, Giovanna Boccuzzo and Mark H. Ebell, Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients, Journal of Aging and Health, 10.1177/0898264318817091, (089826431881709), (2018).
