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Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

Author

Listed:
  • Christina C. Bartenschlager

    (University of Augsburg
    Ohm University of Applied Sciences Nuremberg
    University of Augsburg)

  • Milena Grieger

    (University of Augsburg)

  • Johanna Erber

    (Technical University of Munich, School of Medicine, University Hospital Rechts Der Isar)

  • Tobias Neidel

    (University of Augsburg)

  • Stefan Borgmann

    (Klinikum Ingolstadt)

  • Jörg J. Vehreschild

    (Goethe University Frankfurt
    University of Cologne, University Hospital of Cologne
    Partner Site Bonn-Cologne)

  • Markus Steinbrecher

    (University Hospital Augsburg)

  • Siegbert Rieg

    (University Hospital Freiburg)

  • Melanie Stecher

    (University of Cologne, University Hospital of Cologne
    Partner Site Bonn-Cologne)

  • Christine Dhillon

    (University Hospital Augsburg)

  • Maria M. Ruethrich

    (University Hospital Jena)

  • Carolin E. M. Jakob

    (University of Cologne, University Hospital of Cologne
    Partner Site Bonn-Cologne)

  • Martin Hower

    (Pneumology, Infectiology and Internal Intensive Care Medicine)

  • Axel R. Heller

    (University of Augsburg)

  • Maria Vehreschild

    (University Hospital Frankfurt, Goethe University Frankfurt)

  • Christoph Wyen

    (Praxis am Ebertplatz
    University Hospital of Cologne)

  • Helmut Messmann

    (University Hospital Augsburg)

  • Christiane Piepel

    (Klinikum Bremen-Mitte)

  • Jens O. Brunner

    (University of Augsburg
    Technical University of Denmark
    Data and Development Support)

  • Frank Hanses

    (University Hospital Regensburg)

  • Christoph Römmele

    (University Hospital Augsburg
    University Hospital Augsburg)

Abstract

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.

Suggested Citation

  • Christina C. Bartenschlager & Milena Grieger & Johanna Erber & Tobias Neidel & Stefan Borgmann & Jörg J. Vehreschild & Markus Steinbrecher & Siegbert Rieg & Melanie Stecher & Christine Dhillon & Maria, 2023. "Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways," Health Care Management Science, Springer, vol. 26(3), pages 412-429, September.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:3:d:10.1007_s10729-023-09647-2
    DOI: 10.1007/s10729-023-09647-2
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    References listed on IDEAS

    as
    1. Lepenioti, Katerina & Bousdekis, Alexandros & Apostolou, Dimitris & Mentzas, Gregoris, 2020. "Prescriptive analytics: Literature review and research challenges," International Journal of Information Management, Elsevier, vol. 50(C), pages 57-70.
    2. Richard M. Wood & Adrian C. Pratt & Charlie Kenward & Christopher J. McWilliams & Ross D. Booton & Matthew J. Thomas & Christopher P. Bourdeaux & Christos Vasilakis, 2021. "The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study," Medical Decision Making, , vol. 41(4), pages 393-407, May.
    Full references (including those not matched with items on IDEAS)

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