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An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector

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  • Miguel Ortiz-Barrios
  • Juan-José Alfaro-Saiz

Abstract

Emergency Care Networks (ECNs) were created as a response to the increased demand for emergency services and the ever-increasing waiting times experienced by patients in emergency rooms. In this sense, ECNs are called to provide a rapid diagnosis and early intervention so that poor patient outcomes, patient dissatisfaction, and cost overruns can be avoided. Nevertheless, ECNs, as nodal systems, are often inefficient due to the lack of coordination between emergency departments (EDs) and the presence of non-value added activities within each ED. This situation is even more complex in the public healthcare sector of low-income countries where emergency care is provided under constraint resources and limited innovation. Notwithstanding the tremendous efforts made by healthcare clusters and government agencies to tackle this problem, most of ECNs do not yet provide nimble and efficient care to patients. Additionally, little progress has been evidenced regarding the creation of methodological approaches that assist policymakers in solving this problem. In an attempt to address these shortcomings, this paper presents a three-phase methodology based on Discrete-event simulation, payment collateral models, and lean six sigma to support the design of in-time and economically sustainable ECNs. The proposed approach is validated in a public ECN consisting of 2 hospitals and 8 POCs (Point of Care). The results of this study evidenced that the average waiting time in an ECN can be substantially diminished by optimizing the cooperation flows between EDs.

Suggested Citation

  • Miguel Ortiz-Barrios & Juan-José Alfaro-Saiz, 2020. "An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
  • Handle: RePEc:plo:pone00:0234984
    DOI: 10.1371/journal.pone.0234984
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    References listed on IDEAS

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    1. Kaushal, Arjun & Zhao, Yuancheng & Peng, Qingjin & Strome, Trevor & Weldon, Erin & Zhang, Michael & Chochinov, Alecs, 2015. "Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 18-31.
    2. Chantal Baril & Viviane Gascon & Dominic Vadeboncoeur, 2019. "Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(12), pages 2019-2038, December.
    3. Baier, Natalie & Geissler, Alexander & Bech, Mickael & Bernstein, David & Cowling, Thomas E. & Jackson, Terri & van Manen, Johan & Rudkjøbing, Andreas & Quentin, Wilm, 2019. "Emergency and urgent care systems in Australia, Denmark, England, France, Germany and the Netherlands – Analyzing organization, payment and reforms," Health Policy, Elsevier, vol. 123(1), pages 1-10.
    4. Jonathan Karnon & James Stahl & Alan Brennan & J. Jaime Caro & Javier Mar & Jörgen Möller, 2012. "Modeling Using Discrete Event Simulation," Medical Decision Making, , vol. 32(5), pages 701-711, September.
    5. Jennifer Gillespie & Sally McClean & Lalit Garg & Maria Barton & Bryan Scotney & Ken Fullerton, 2016. "A multi-phase DES modelling framework for patient-centred care," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(10), pages 1239-1249, October.
    6. Yong-Hong Kuo & Omar Rado & Benedetta Lupia & Janny M. Y. Leung & Colin A. Graham, 2016. "Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 120-147, June.
    7. Sheard, Sally, 2018. "Space, place and (waiting) time: reflections on health policy and politics," Health Economics, Policy and Law, Cambridge University Press, vol. 13(3-4), pages 226-250, July.
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    Cited by:

    1. Diego Tlapa & Ignacio Franco-Alucano & Jorge Limon-Romero & Yolanda Baez-Lopez & Guilherme Tortorella, 2022. "Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    2. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    3. Miguel Angel Ortíz-Barrios & Dayana Milena Coba-Blanco & Juan-José Alfaro-Saíz & Daniela Stand-González, 2021. "Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review," IJERPH, MDPI, vol. 18(16), pages 1-31, August.
    4. Diego Tlapa & Guilherme Tortorella & Flavio Fogliatto & Maneesh Kumar & Alejandro Mac Cawley & Roberto Vassolo & Luis Enberg & Yolanda Baez-Lopez, 2022. "Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review," IJERPH, MDPI, vol. 19(15), pages 1-23, July.

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