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Prediction of emergency department patient disposition decision for proactive resource allocation for admission

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Abstract

We investigate the capability of information from electronic health records of an emergency department (ED) to predict patient disposition decisions for reducing “boarding” delays through the proactive initiation of admission processes (e.g., inpatient bed requests, transport, etc.). We model the process of ED disposition decision prediction as a hierarchical multiclass classification while dealing with the progressive accrual of clinical information throughout the ED caregiving process. Multinomial logistic regression as well as machine learning models are built for carrying out the predictions. Utilizing results from just the first set of ED laboratory tests along with other prior information gathered for each patient (2.5 h ahead of the actual disposition decision on average), our model predicts disposition decisions with positive predictive values of 55.4%, 45.1%, 56.9%, and 47.5%, while controlling false positive rates (1.4%, 1.0%, 4.3%, and 1.4%), with AUC values of 0.97, 0.95, 0.89, and 0.84 for the four admission (minor) classes, i.e., intensive care unit (3.6% of the testing samples), telemetry unit (2.2%), general practice unit (11.9%), and observation unit (6.6%) classes, respectively. Moreover, patients destined to intensive care unit present a more drastic increment in prediction quality at triage than others. Disposition decision classification models can provide more actionable information than a binary admission vs. discharge prediction model for the proactive initiation of admission processes for ED patients. Observing the distinct trajectories of information accrual and prediction quality evolvement for ED patients destined to different types of units, proactive coordination strategies should be tailored accordingly for each destination unit.

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Notes

  1. In Figure 1b, we conservatively exclude the case of having only one unoccupied bed to account for any possibility that a bed is temporarily unavailable due to, for example, infection concerns from a fellow roomed patient and so on.

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Author information

Authors and Affiliations

  1. Haskayne School of Business, University of Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada

    Seung-Yup Lee

  2. Department of Industrial & Systems Engineering, Wayne State University, 4815 Fourth St, Detroit, MI, 48202, USA

    Ratna Babu Chinnam & Evrim Dalkiran

  3. Department of Emergency Medicine, Henry Ford Hospital, 2799 W. Grand Blvd, Detroit, MI, 48202, USA

    Seth Krupp & Michael Nauss

Authors
  1. Seung-Yup Lee
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  4. Seth Krupp
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Correspondence to Seung-Yup Lee.

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Appendix

Appendix

Table 11 Model hyperparameters selected and used in classification models
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Table 12 Comparison of prediction results gained by different classification techniques at C3 classification scheme
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Table 13 List of laboratory test, imaging test, and other clinical intervention predictors used to build prediction model at T3-Initial Assessment (including only items) and T4-Initial Lab Results (including available result information of laboratory tests). Note: For the laboratory test results (at T4-Initial Lab Results) that have categories pre-set by the health information technology system in the study hospital, the pre-set categories were entered into the prediction model. While the table lists all the test items included in the prediction modeling, some test items that have almost identical test names were merged as a single item
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Table 14 Comparison of prediction performances from selected admission prediction works done around triage, which is the most commonly selected caregiving epoch in EDs for admission prediction
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Table 15 Disposition decision prediction results comparing prediction model performance at different caregiving epochs for C3 classification scheme
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Table 16 Distribution of important feature values over the C3 classification scheme classes
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Lee, SY., Chinnam, R.B., Dalkiran, E. et al. Prediction of emergency department patient disposition decision for proactive resource allocation for admission. Health Care Manag Sci 23, 339–359 (2020). https://doi.org/10.1007/s10729-019-09496-y

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