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Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

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  • Miguel Angel Ortíz-Barrios

    (Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia)

  • Dayana Milena Coba-Blanco

    (Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia)

  • Juan-José Alfaro-Saíz

    (Research Centre on Production Management and Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Daniela Stand-González

    (Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia)

Abstract

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8814-:d:618668
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    References listed on IDEAS

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    1. Masoomeh Zeinalnezhad & Abdoulmohammad Gholamzadeh Chofreh & Feybi Ariani Goni & Jiří Jaromír Klemeš & Emelia Sari, 2020. "Simulation and Improvement of Patients’ Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    2. Miguel Ortiz-Barrios & Juan-Jose Alfaro-Saiz, 2020. "A Hybrid Fuzzy Multi-Criteria Decision-Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(06), pages 1485-1548, November.
    3. Ali Azadeh & Fatemeh Rouhollah & Fatemeh Davoudpour & Iraj Mohammadfam, 2013. "Fuzzy modelling and simulation of an emergency department for improvement of nursing schedules with noisy and uncertain inputs," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 15(1), pages 58-77.
    4. 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.
    5. 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.
    6. Silal, Sheetal Prakash, 2021. "Operational research: A multidisciplinary approach for the management of infectious disease in a global context," European Journal of Operational Research, Elsevier, vol. 291(3), pages 929-934.
    7. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    8. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    9. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.
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    1. 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).

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