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Managing capacity for virtual and office appointments in chronic care

Author

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  • Xiao Yu

    (University of Michigan-Dearborn)

  • Armagan Bayram

    (University of Michigan-Dearborn)

Abstract

Patients living with a chronic disease often require regular appointments and treatments. Due to the constraints on the availability of office appointments and the capacity of physicians, access to chronic care can be limited; consequently, patients may fail to receive the recommended care suggested by clinical guidelines. Virtual appointments can provide a cost-effective alternative to traditional office appointments for managing chronic conditions. Advances in information technology infrastructure, communication, and connected medical devices are enabling providers to evaluate, diagnose, and treat patients remotely. In this study, we build a capacity allocation model to study the use of virtual appointments in a chronic care setting. We consider a cohort of patients receiving chronic care and model the flow of the patients between office and virtual appointments using an open migration network. We formulate the planning of capacity needed for office and virtual appointments with a newsvendor model to maximize long-run average earnings. We consider differences in treatment and diagnosis effectiveness for office and virtual appointments. We derive optimal capacity allocation policies and implement numerical experiments. With the model developed, capacity decisions for office and virtual appointments can be made more systematically with the consideration of patient disease progressions.

Suggested Citation

  • Xiao Yu & Armagan Bayram, 2021. "Managing capacity for virtual and office appointments in chronic care," Health Care Management Science, Springer, vol. 24(4), pages 742-767, December.
  • Handle: RePEc:kap:hcarem:v:24:y:2021:i:4:d:10.1007_s10729-021-09546-4
    DOI: 10.1007/s10729-021-09546-4
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    References listed on IDEAS

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    Cited by:

    1. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).

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