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Implementation of a model for census prediction and control.

R W Swain, K E Kilpatrick, J J Marsh

    Health Services Research
    |January 1, 1977
    PubMed
    Summary
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    This study presents a hospital census prediction model to optimize elective admissions, minimizing overflow risks. The model aids in daily admissions planning for better hospital resource management.

    Area of Science:

    • Operations Research
    • Healthcare Management
    • Predictive Analytics

    Background:

    • Hospital census management is complex, requiring accurate predictions for resource allocation.
    • Unpredictable patient flow can lead to hospital overflow and inefficient resource utilization.

    Purpose of the Study:

    • To develop and evaluate a predictive model for hospital census.
    • To determine optimal daily elective admissions to maximize hospital census while managing overflow risk.

    Main Methods:

    • A mathematical model was developed to predict daily hospital census.
    • The model calculates elective admissions to maximize short-term census under overflow probability constraints.
    • Implementation involved both computer-based detailed predictions and manual computation for broader recommendations.

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    Main Results:

    • The model successfully predicts hospital census with varying levels of detail.
    • Tested in five hospitals, the model demonstrated feasibility and was integrated into two admissions systems.
    • Implementation highlighted the model's utility in guiding elective admission decisions.

    Conclusions:

    • The developed model offers a systematic approach to hospital census prediction and elective admission optimization.
    • It provides a valuable tool for improving hospital resource management and reducing overflow incidents.
    • The model's successful implementation suggests its practical applicability in real-world healthcare settings.