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Classification of hospital admissions into emergency and elective care: a machine learning approach

Author

Listed:
  • Jonas Krämer

    (Universität Hamburg)

  • Jonas Schreyögg

    (Universität Hamburg)

  • Reinhard Busse

    (Technische Universität Berlin)

Abstract

Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model for classifying inpatient admissions based on a patient’s primary diagnosis as either emergency care or elective care and predicting urgency as a numerical value. We use supervised machine learning techniques and train the model with physician-expert judgments. Our model is accurate (96%) and has a high area under the ROC curve (>.99). We provide the first comprehensive classification and urgency categorization for inpatient emergency and elective care. This model assigns urgency values to every relevant diagnosis in the ICD catalog, and these values are easily applicable to existing hospital data. Our findings may provide a basis for policy makers to create incentives for hospitals to reduce the number of inappropriate ED admissions.

Suggested Citation

  • Jonas Krämer & Jonas Schreyögg & Reinhard Busse, 2019. "Classification of hospital admissions into emergency and elective care: a machine learning approach," Health Care Management Science, Springer, vol. 22(1), pages 85-105, March.
    • Handle: RePEc:kap:hcarem:v:22:y:2019:i:1:d:10.1007_s10729-017-9423-5
    DOI: 10.1007/s10729-017-9423-5
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    References listed on IDEAS

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    Cited by:

    1. Mensen, Anne, 2022. "Concentration of hospital capacities and patients' access to care," Ruhr Economic Papers 952, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
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    4. Vanessa Ress & Eva‐Maria Wild, 2024. "The impact of integrated care on health care utilization and costs in a socially deprived urban area in Germany: A difference‐in‐differences approach within an event‐study framework," Health Economics, John Wiley & Sons, Ltd., vol. 33(2), pages 229-247, February.
    5. Ricardo Ocaña-Riola & Carmen Pérez-Romero & Mª Isabel Ortega-Díaz & José Jesús Martín-Martín, 2021. "Multilevel Zero-One Inflated Beta Regression Model for the Analysis of the Relationship between Exogenous Health Variables and Technical Efficiency in the Spanish National Health System Hospitals," IJERPH, MDPI, vol. 18(19), pages 1-18, September.
    6. Michael R. Johnson & Hiten Naik & Wei Siang Chan & Jesse Greiner & Matt Michaleski & Dong Liu & Bruno Silvestre & Ian P. McCarthy, 2023. "Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions," Health Care Management Science, Springer, vol. 26(3), pages 477-500, September.
    7. Timo Schulte & Tillmann Wurz & Oliver Groene & Sabine Bohnet-Joschko, 2023. "Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions," IJERPH, MDPI, vol. 20(6), pages 1-16, March.

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