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Evaluating the Efficacy of Connected Healthcare: An Empirical Examination of Patient Engagement Approaches and Their Impact on Readmission

Author

Listed:
  • Suparerk Lekwijit

    (Department of Information Systems, W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85281)

  • Christian Terwiesch

    (Operations, Information and Decisions Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • David A. Asch

    (Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Kevin G. Volpp

    (Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Connected healthcare is a form of health delivery that connects patients and providers through connected health devices, allowing providers to monitor patient behavior and proactively intervene before an adverse event occurs. Unlike the costs, the benefits of connected healthcare in improving patient behavior and health outcomes are usually difficult to determine. In this study, we examine the efficacy of a connected health system that aimed to reduce readmissions through improved medication adherence. Specifically, we study 975 patients with heart disease who received electronic pill bottles that tracked medication adherence. Patients who were nonadherent received active social support that involved different types of feedback, such as text messages and calls. By integrating data on adherence, intervention, and readmission, we aim to (1) investigate the efficacy of connected healthcare in promoting medication adherence, (2) examine the relationship between medication adherence and readmission, and (3) develop a dynamic readmission risk-scoring model that considers medication adherence and use the model to better target nonadherent patients. Our findings suggest that patients are more likely to become adherent when they or their partners receive high levels of intervention that involve personalized feedback and when the intervention is escalated quickly and consistently. We also find that long-term adherence to three common heart medications is strongly associated with reduced readmission risk. Lastly, using counterfactual simulation, we apply the dynamic readmission risk-scoring model to our setting and find that, when using an intervention strategy that prioritizes high-risk patients, we obtain 10% fewer readmissions while using the same effort level from the patient support team.

Suggested Citation

  • Suparerk Lekwijit & Christian Terwiesch & David A. Asch & Kevin G. Volpp, 2024. "Evaluating the Efficacy of Connected Healthcare: An Empirical Examination of Patient Engagement Approaches and Their Impact on Readmission," Management Science, INFORMS, vol. 70(6), pages 3417-3446, June.
  • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:6:p:3417-3446
    DOI: 10.1287/mnsc.2023.4865
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    References listed on IDEAS

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    2. Li, Yijun & Guo, Mengzhuo & Kadziński, Miłosz & Zhang, Qingpeng & Xu, Chenxi, 2025. "Data-driven preference learning methods for sorting problems with multiple temporal criteria," European Journal of Operational Research, Elsevier, vol. 323(3), pages 918-937.
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