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A Markov chain model for analysis of physician workflow in primary care clinics

Author

Listed:
  • Sujee Lee

    (University of Wisconsin)

  • Philip A. Bain

    (SSM Health Dean Medical Group)

  • Albert J. Musa

    (SSM Health Dean Medical Group)

  • Jingshan Li

    (University of Wisconsin)

Abstract

This paper studies physician workflow management in primary care clinics using terminating Markov chain models. The physician workload is characterized by face-to-face encounters with patients and documentation of electronic health record (EHR) data. Three workflow management policies are considered: preemptive priority (stop ongoing documentation tasks if a new patient arrives); non-preemptive priority (finish ongoing documentation even if a new patient arrives); and batch documentation (start and finish documentation when the desired number of tasks is reached). Analytical formulas are derived to quantify the performance measures of three management policies, such as physician’s daily working time, patient’s waiting time, and documentation waiting time. A comparison of the results under three policies is carried out. Finally, a case study in a primary care clinic is carried out to illustrate model applicability. Such a work provides a quantitative tool for primary care physicians to design and manage their workflow to improve care quality.

Suggested Citation

  • Sujee Lee & Philip A. Bain & Albert J. Musa & Jingshan Li, 2021. "A Markov chain model for analysis of physician workflow in primary care clinics," Health Care Management Science, Springer, vol. 24(1), pages 72-91, March.
  • Handle: RePEc:kap:hcarem:v:24:y:2021:i:1:d:10.1007_s10729-020-09517-1
    DOI: 10.1007/s10729-020-09517-1
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    References listed on IDEAS

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    1. Linda V. Green & Sergei Savin, 2008. "Reducing Delays for Medical Appointments: A Queueing Approach," Operations Research, INFORMS, vol. 56(6), pages 1526-1538, December.
    2. H. Xie & T. J. Chaussalet & P. H. Millard, 2005. "A continuous time Markov model for the length of stay of elderly people in institutional long‐term care," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 51-61, January.
    3. Lixiang Jiang & Ronald Giachetti, 2008. "A queueing network model to analyze the impact of parallelization of care on patient cycle time," Health Care Management Science, Springer, vol. 11(3), pages 248-261, September.
    4. Junwen Wang & Shichuan Quan & Jingshan Li & Amy Hollis, 2012. "Modeling and analysis of work flow and staffing level in a computed tomography division of University of Wisconsin Medical Foundation," Health Care Management Science, Springer, vol. 15(2), pages 108-120, June.
    5. Xiang Zhong & Hyo Kyung Lee & Molly Williams & Sally Kraft & Jeffery Sleeth & Richard Welnick & Lori Hauschild & Jingshan Li, 2018. "Workload balancing: staffing ratio analysis for primary care redesign," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 6-29, June.
    6. Sheldon H. Jacobson & Shane N. Hall & James R. Swisher, 2006. "Discrete-Event Simulation of Health Care Systems," International Series in Operations Research & Management Science, in: Randolph W. Hall (ed.), Patient Flow: Reducing Delay in Healthcare Delivery, chapter 0, pages 211-252, Springer.
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    Cited by:

    1. Nathan Preuss & Lin Guo & Janet K. Allen & Farrokh Mistree, 2022. "Improving Patient Flow in a Primary Care Clinic," SN Operations Research Forum, Springer, vol. 3(3), pages 1-22, September.

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