Observational studyJournal of medical Internet research2025
Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study.
Observational study in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
backgroundEmergency department (ED) admissions are one of the most critical decisions made in health care, with 40% of ED visits resulting in inpatient hospitalization for Medicare patients. A main challenge with the ED admissions process is the inability to move patients from the ED to an inpatient unit quickly. Identifying hospital discharge volume in advance may be valuable in helping hospitals determine capacity management mechanisms to reduce ED boarding, such as transferring low-complexity patients to neighboring hospitals. Although previous research has studied the prediction of discharges in the context of inpatient care, most of the work is on long-term predictions (ie, discharges within the next 24 to 48 hours) in single-site health care systems. In this study, we approach the problem of inpatient discharge prediction from a system-wide lens and evaluate the potential interactions between the two facilities in our partner multisite system to predict short-term discharge volume.
objectiveThe objective of this paper was to predict discharges from the general care units within a large tertiary teaching hospital network in the Midwest and evaluate the impact of external information from other hospitals on model performance.
methodsWe conducted 2 experiments with 174,799 discharge records from 2 hospitals. In Experiment 1, we predicted the number of discharges across 2 time points (within the hour and the next 4 hours) using random forest (RF) and linear regression (LR) models. Models with access to internal hospital data (ie, system-agnostic) were compared with models with access to additional data from the other hospitals in the network (ie, system-aware). In Experiment 2, we evaluated the performance of an RF model to predict afternoon discharges (ie, 12 PM to 4 PM) 1 to 4 hours in advance.
resultsIn Experiment 1 and Hospital 1, RF and LR models performed equivalently, with R
conclusionsShort-term discharges in multisite hospital systems can be locally predicted with high accuracy, even when predicting hours in advance.
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