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Tele-medicine question response service: Analysis of benefits and costs

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  • Cho, David D.
  • Stauffer, Jon M.

Abstract

Medicare and other healthcare payers are increasing reimbursement for tele-monitoring with in-home devices, but have not expanded reimbursement to include tele-medicine question response services. We model the costs and benefits of providing these services in a multi-period setting for healthcare payers and providers compared to a central planner baseline model. We also consider how patients’ risk level and potentially shorter skilled nursing facility stays can increase the benefit of these services. While many payers worry that reimbursing for question response services will cause excessive payouts, we find that payers would benefit from providing a per-call subsidy to providers to incentivize optimal question response service staffing levels. We also find that payers could provide a per-patient subsidy for patients discharged early from skilled nursing facilities that would more than cover the costs of providing the question response service for all patients. These models are grounded in data and multiple discussions with regional home health providers that provide these question response services.

Suggested Citation

  • Cho, David D. & Stauffer, Jon M., 2022. "Tele-medicine question response service: Analysis of benefits and costs," Omega, Elsevier, vol. 111(C).
  • Handle: RePEc:eee:jomega:v:111:y:2022:i:c:s0305048322000718
    DOI: 10.1016/j.omega.2022.102664
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    1. 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.
    2. David D. Cho & Kurt M. Bretthauer & Kyle D. Cattani & Alex F. Mills, 2019. "Behavior Aware Service Staffing," Production and Operations Management, Production and Operations Management Society, vol. 28(5), pages 1285-1304, May.
    3. Bürgy, Reinhard & Michon-Lacaze, Hélène & Desaulniers, Guy, 2019. "Employee scheduling with short demand perturbations and extensible shifts," Omega, Elsevier, vol. 89(C), pages 177-192.
    4. Vincent W. Slaugh & Alan A. Scheller‐Wolf & Sridhar R. Tayur, 2018. "Consistent Staffing for Long‐Term Care through On‐Call Pools," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2144-2161, December.
    5. Cory S. Capps & Dennis W. Carlton & Guy David, 2020. "Antitrust Treatment Of Nonprofits: Should Hospitals Receive Special Care?," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1183-1199, July.
    6. Restrepo, María I. & Rousseau, Louis-Martin & Vallée, Jonathan, 2020. "Home healthcare integrated staffing and scheduling," Omega, Elsevier, vol. 95(C).
    7. Kibaek Kim & Sanjay Mehrotra, 2015. "A Two-Stage Stochastic Integer Programming Approach to Integrated Staffing and Scheduling with Application to Nurse Management," Operations Research, INFORMS, vol. 63(6), pages 1431-1451, December.
    8. Jonathan E. Helm & Adel Alaeddini & Jon M. Stauffer & Kurt M. Bretthauer & Ted A. Skolarus, 2016. "Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization," Production and Operations Management, Production and Operations Management Society, vol. 25(2), pages 233-257, February.
    9. Cappanera, Paola & Scutellà, Maria Grazia & Nervi, Federico & Galli, Laura, 2018. "Demand uncertainty in robust Home Care optimization," Omega, Elsevier, vol. 80(C), pages 95-110.
    10. Noah Gans & Ger Koole & Avishai Mandelbaum, 2003. "Telephone Call Centers: Tutorial, Review, and Research Prospects," Manufacturing & Service Operations Management, INFORMS, vol. 5(2), pages 79-141, September.
    11. Itai Gurvich & James Luedtke & Tolga Tezcan, 2010. "Staffing Call Centers with Uncertain Demand Forecasts: A Chance-Constrained Optimization Approach," Management Science, INFORMS, vol. 56(7), pages 1093-1115, July.
    12. Jeong‐ha (Cath) Oh & Zhiqiang (Eric) Zheng & Indranil R. Bardhan, 2018. "Sooner or Later? Health Information Technology, Length of Stay, and Readmission Risk," Production and Operations Management, Production and Operations Management Society, vol. 27(11), pages 2038-2053, November.
    13. Xiang Liu & Michael Hu & Jonathan E. Helm & Mariel S. Lavieri & Ted A. Skolarus, 2018. "Missed Opportunities in Preventing Hospital Readmissions: Redesigning Post‐Discharge Checkup Policies," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2226-2250, December.
    14. Mattia, Sara & Rossi, Fabrizio & Servilio, Mara & Smriglio, Stefano, 2017. "Staffing and scheduling flexible call centers by two-stage robust optimization," Omega, Elsevier, vol. 72(C), pages 25-37.
    15. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    16. J. Michael Harrison & Assaf Zeevi, 2005. "A Method for Staffing Large Call Centers Based on Stochastic Fluid Models," Manufacturing & Service Operations Management, INFORMS, vol. 7(1), pages 20-36, September.
    17. Dennis J. Zhang & Itai Gurvich & Jan A. Van Mieghem & Eric Park & Robert S. Young & Mark V. Williams, 2016. "Hospital Readmissions Reduction Program: An Economic and Operational Analysis," Management Science, INFORMS, vol. 62(11), pages 3351-3371, November.
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