IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v3y2022i3d10.1007_s43069-022-00152-w.html
   My bibliography  Save this article

Improving Patient Flow in a Primary Care Clinic

Author

Listed:
  • Nathan Preuss

    (The University of Oklahoma
    The University of Oklahoma)

  • Lin Guo

    (Department of Industrial Engineering, South Dakota School of Mines and Technology)

  • Janet K. Allen

    (The University of Oklahoma)

  • Farrokh Mistree

    (The University of Oklahoma)

Abstract

When patients visit primary care clinics, they can be subject to long wait times due to operational inefficiencies and bottlenecks, decreasing patient satisfaction and sometimes leading to worse health outcomes. The existing literature models primary care clinics primarily as agent-based models, which are excellent at tracking individual patients and their movements in a model of a clinic. While agent-based models can detect bottlenecks, a network flow model better detects bottlenecks in the model by correlating changes in patient flow and wait times in the healthcare network. In this paper, a network flow model is constructed, where patients flow along the capacitated edges of a network while receiving treatment at the nodes. This configuration easily identifies bottlenecks by analyzing the flow in and flow out of nodes through metrics such as efficiency and patient wait times. The capacities of the edges for this model are taken from an agent-based model of a case study of a primary care clinic and sampled as random variables. Ensemble runs of the network flow model are created to account for uncertainty in the synthetic data. By changing the topology of the network flow model, bottlenecks are removed, increasing the model efficiency and decreasing patient wait times. Finally, the model is subjected to a sensitivity analysis. The focus in this work is on the method rather than the results per se.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:snopef:v:3:y:2022:i:3:d:10.1007_s43069-022-00152-w
    DOI: 10.1007/s43069-022-00152-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-022-00152-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43069-022-00152-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Elif Akcali & Murray Côté & Chin Lin, 2006. "A network flow approach to optimizing hospital bed capacity decisions," Health Care Management Science, Springer, vol. 9(4), pages 391-404, November.
    2. Daniel J. Zawack & Gerald L. Thompson, 1987. "A Dynamic Space-Time Network Flow Model for City Traffic Congestion," Transportation Science, INFORMS, vol. 21(3), pages 153-162, August.
    3. Nazanin Aslani & Onur Kuzgunkaya & Navneet Vidyarthi & Daria Terekhov, 2021. "A robust optimization model for tactical capacity planning in an outpatient setting," Health Care Management Science, Springer, vol. 24(1), pages 26-40, March.
    4. Daniel M Bean & Clive Stringer & Neeraj Beeknoo & James Teo & Richard J B Dobson, 2017. "Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-16, October.
    5. Shalabh Bhatnagar & Sanjeev Patel & Karmeshu, 2018. "A stochastic approximation approach to active queue management," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(1), pages 89-104, May.
    6. Saeede Ajorlou & Issac Shams & Kai Yang, 2015. "An analytics approach to designing patient centered medical homes," Health Care Management Science, Springer, vol. 18(1), pages 3-18, March.
    7. Ruben A. Proano & Akshit Agarwal, 2018. "Scheduling internal medicine resident rotations to ensure fairness and facilitate continuity of care," Health Care Management Science, Springer, vol. 21(4), pages 461-474, December.
    8. Yuwen Yang & Jayant Rajgopal, 2021. "Outreach Strategies for Vaccine Distribution: A Multi-period Stochastic Modeling Approach," SN Operations Research Forum, Springer, vol. 2(2), pages 1-26, June.
    9. Songul Cinaroglu, 2020. "Integrated k-means clustering with data envelopment analysis of public hospital efficiency," Health Care Management Science, Springer, vol. 23(3), pages 325-338, September.
    10. Yu Fu & Amarnath Banerjee, 2021. "A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling," SN Operations Research Forum, Springer, vol. 2(3), pages 1-32, September.
    11. Hiroyuki Kawaguchi & Kaoru Tone & Miki Tsutsui, 2014. "Estimation of the efficiency of Japanese hospitals using a dynamic and network data envelopment analysis model," Health Care Management Science, Springer, vol. 17(2), pages 101-112, June.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dinesh R. Pai & Fatma Pakdil & Nasibeh Azadeh-Fard, 2024. "Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984–2022," Health Care Management Science, Springer, vol. 27(2), pages 284-312, June.
    2. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    3. Khushalani, Jaya & Ozcan, Yasar A., 2017. "Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 15-23.
    4. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    5. Ghasem Kahe & Amir Hossein Jahangir, 2019. "A self-tuning controller for queuing delay regulation in TCP/AQM networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 71(2), pages 215-229, June.
    6. Iris Reychav & Roger McHaney & Sunil Babbar & Krishanthi Weragalaarachchi & Nadeem Azaizah & Alon Nevet, 2022. "Graph Network Techniques to Model and Analyze Emergency Department Patient Flow," Mathematics, MDPI, vol. 10(9), pages 1-21, May.
    7. Huang, Hai-Jun & Xu, Gang, 1998. "Aggregate scheduling and network solving of multi-stage and multi-item manufacturing systems," European Journal of Operational Research, Elsevier, vol. 105(1), pages 52-65, February.
    8. Yen-Yi Feng & I-Chin Wu & Tzu-Li Chen, 2017. "Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm," Health Care Management Science, Springer, vol. 20(1), pages 55-75, March.
    9. Young-Chae Hong & Amy Cohn & Stephen Gorga & Edmond O’Brien & William Pozehl & Jennifer Zank, 2019. "Using Optimization Techniques and Multidisciplinary Collaboration to Solve a Challenging Real-World Residency Scheduling Problem," Interfaces, INFORMS, vol. 49(3), pages 201-212, May.
    10. Yang, Hai & Meng, Qiang, 1998. "Departure time, route choice and congestion toll in a queuing network with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 32(4), pages 247-260, May.
    11. Wladimir Gonçalves Morais & Carlos Eduardo Maffini Santos & Carlos Marcelo Pedroso, 2022. "Application of active queue management for real-time adaptive video streaming," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(2), pages 261-270, February.
    12. Martin Durbin & Karla Hoffman, 2008. "OR PRACTICE---The Dance of the Thirty-Ton Trucks: Dispatching and Scheduling in a Dynamic Environment," Operations Research, INFORMS, vol. 56(1), pages 3-19, February.
    13. Wang, Fan & Zhang, Chao & Zhang, Hui & Xu, Liang, 2021. "Short-term physician rescheduling model with feature-driven demand for mental disorders outpatients," Omega, Elsevier, vol. 105(C).
    14. Izady, Navid & Arabzadeh, Bahar & Sands, Nicholas & Adams, James, 2024. "Reconfiguration of inpatient services to reduce bed pressure in hospitals," European Journal of Operational Research, Elsevier, vol. 316(2), pages 680-693.
    15. Zhikang Bao & Yifu Ou & Shuangzhou Chen & Ting Wang, 2022. "Land Use Impacts on Traffic Congestion Patterns: A Tale of a Northwestern Chinese City," Land, MDPI, vol. 11(12), pages 1-17, December.
    16. Simona Cohen-Kadosh & Zilla Sinuany-Stern, 2020. "Hip fracture surgery efficiency in Israeli hospitals via a network data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 251-277, March.
    17. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    18. Nasreen, Samia & Mahalik, Mantu Kumar & Shahbaz, Muhammad & Abbas, Qaisar, 2020. "How do financial globalization, institutions and economic growth impact financial sector development in European countries?," Research in International Business and Finance, Elsevier, vol. 54(C).
    19. Kiyotoshi Kou & Yi Dou & Ichiro Arai, 2024. "Analysis of the Forces Driving Public Hospitals’ Operating Costs Using LMDI Decomposition: The Case of Japan," Sustainability, MDPI, vol. 16(2), pages 1-15, January.
    20. Wasim Sultan & José Crispim, 2016. "Evaluating the Productive Efficiency of Jordanian Public Hospitals," International Journal of Business and Management, Canadian Center of Science and Education, vol. 12(1), pages 1-68, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:3:y:2022:i:3:d:10.1007_s43069-022-00152-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.