Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists

Louise Russell, Laurie A. Norton, David Pagnotti, Christianne Sevinc, Sophia Anderson, Darra Finnerty Bigelow, Lauren G. Iannotte, Michael Josephs, Ryan McGilloway, Iwan Barankay, Mary E. Putt, Peter P. Reese, David A. Asch, Lee R. Goldberg, Shivan J. Mehta, Monique S. Tanna, Andrea B. Troxel and Kevin G. Volpp
Additional contact information
Laurie A. Norton: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
David Pagnotti: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Christianne Sevinc: Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA
Sophia Anderson: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Darra Finnerty Bigelow: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Lauren G. Iannotte: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Michael Josephs: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Ryan McGilloway: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Iwan Barankay: Department of Management and Department of Business Economics and Public Policy, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
Mary E. Putt: Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Peter P. Reese: The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA
David A. Asch: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Lee R. Goldberg: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Shivan J. Mehta: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
Monique S. Tanna: Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
Andrea B. Troxel: Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, USA
Kevin G. Volpp: Department of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA

Medical Decision Making, 2021, vol. 41, issue 1, 9-20

Abstract: Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are potentially the best and often only source of cost information but rarely incorporate costing studies. This article provides a guide for researchers to help them collect and analyze, during the trial and with little additional effort, the information needed to inform potential adopters of the costs of adopting a behavioral intervention. A key challenge in using trial data is the separation of implementation costs, the costs an adopter would incur, from research costs. Based on experience with 3 randomized clinical trials of behavioral interventions, this article explains how to frame the costing problem, including how to think about costs associated with the control group, and describes methods for collecting data on individual costs: specifications for costing a technology platform that supports the specialized functions required, how to set up a time log to collect data on the time staff spend on implementation, and issues in getting data on device, overhead, and financial incentive costs.

Keywords: behavioral intervention; clinical trial; costing; medication adherence; outcomes research; pragmatic trial; preventive services; translating research into practice (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:1:p:9-20

DOI: 10.1177/0272989X20973160

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