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Patient Heal Thyself: Reducing Hospital Readmissions with Technology‐Enabled Continuity of Care and Patient Activation

Author

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  • Carrie Queenan
  • Kellas Cameron
  • Alan Snell
  • Julia Smalley
  • Nitin Joglekar

Abstract

Patients’ skills, knowledge, and motivation to actively engage in their health care are assessed with the patient activation measure (PAM). The literature on the role of PAM, when patient counseling is coupled with a technology enabled continuity of care intervention, is scant. We model the patient–health care provider feedback loop and learning through error corrections to explore the relations between continuity of care, PAM and patient readmissions. We test this model using data from a randomized, controlled field experiment. Our data show a direct effect of technology‐enabled continuity of care, together with its interaction with PAM, reduces readmissions over the base case without technology enabled continuity of care. Using exploratory analysis, we further show how a machine learning algorithm can be used to predict PAM, that can potentially furnish health care providers with useful information during the process of supporting their patients.

Suggested Citation

  • Carrie Queenan & Kellas Cameron & Alan Snell & Julia Smalley & Nitin Joglekar, 2019. "Patient Heal Thyself: Reducing Hospital Readmissions with Technology‐Enabled Continuity of Care and Patient Activation," Production and Operations Management, Production and Operations Management Society, vol. 28(11), pages 2841-2853, November.
    • Handle: RePEc:bla:popmgt:v:28:y:2019:i:11:p:2841-2853
    DOI: 10.1111/poms.13080
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    Cited by:

    1. Han, Shuihua & Chen, Linlin & Su, Zhaopei & Gupta, Shivam & Sivarajah, Uthayasankar, 2024. "Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining," Journal of Business Research, Elsevier, vol. 179(C).
    2. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    3. Onur Demiray & Evrim D. Gunes & Ercan Kulak & Emrah Dogan & Seyma Gorcin Karaketir & Serap Cifcili & Mehmet Akman & Sibel Sakarya, 2023. "Classification of patients with chronic disease by activation level using machine learning methods," Health Care Management Science, Springer, vol. 26(4), pages 626-650, December.
    4. Cho, David D. & Stauffer, Jon M., 2022. "Tele-medicine question response service: Analysis of benefits and costs," Omega, Elsevier, vol. 111(C).
    5. Asunur Cezar & Srinivasan Raghunathan & Sumit Sarkar, 2020. "Adversarial Classification: Impact of Agents’ Faking Cost on Firms and Agents," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2789-2807, December.
    6. Sriram Somanchi & Idris Adjerid & Ralph Gross, 2022. "To Predict or Not to Predict: The Case of the Emergency Department," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 799-818, February.
    7. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

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