Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume.
Saved in:
| Title: | Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume. |
|---|---|
| Authors: | Singh, Arjun1, Farmer, Patrick E.1, Tully, Jeffrey L.1, Waterman, Ruth S.1, Gabriel, Rodney A.1,2 ragabriel@health.ucsd.edu |
| Source: | Journal of Medical Systems. 5/21/2025, Vol. 49 Issue 1, p1-10. 10p. |
| Subjects: | Hospital utilization, Human services programs, Patients, Surgery, Prediction models, Hospital admission & discharge, Retrospective studies, Descriptive statistics, Electronic health records, Machine learning, Length of stay in hospitals, Regression analysis |
| Abstract: | The objective of this study was to develop a machine learning model utilizing data from the electronic health record (EHR) to model length of stay and daily surgical volume, in order to subsequently predict daily surgical inpatient bed utilization. Machine learning is increasingly used to aid healthcare decision-making and resource allocation. Surgical inpatient bed utilization is a key metric of hospital efficiency and an ideal target for optimization. EHR data from all surgical cases over one year at a single institution was obtained. Data from the first 32 weeks of the year were used to train the model with the remaining data used to validate and test the models. Various machine learning approaches were explored to predict hospital length of stay and surgical volume. Seasonal Autoregressive Integrated Moving Average (SARIMA) was used to forecast daily surgical bed requirements. The root mean squared error (RMSE) was reported. For predicting bed utilization > 2 weeks in the future, our optimized models improved prediction from an RMSE of 43.1 to 24.4 beds. For predicting bed utilization in 2 weeks, our optimized models improved prediction from an RMSE of 42.6 to 24.8 beds. Finally, predicting bed utilization same day demonstrated an RMSE of 22.7 beds. We described the architecture of a machine learning approach to forecast surgical bed utilization. Forecasting use of surgical resources may decrease stress on a hospital system through more accurate predicting of the ebbs and flows of hospital needs. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | The objective of this study was to develop a machine learning model utilizing data from the electronic health record (EHR) to model length of stay and daily surgical volume, in order to subsequently predict daily surgical inpatient bed utilization. Machine learning is increasingly used to aid healthcare decision-making and resource allocation. Surgical inpatient bed utilization is a key metric of hospital efficiency and an ideal target for optimization. EHR data from all surgical cases over one year at a single institution was obtained. Data from the first 32 weeks of the year were used to train the model with the remaining data used to validate and test the models. Various machine learning approaches were explored to predict hospital length of stay and surgical volume. Seasonal Autoregressive Integrated Moving Average (SARIMA) was used to forecast daily surgical bed requirements. The root mean squared error (RMSE) was reported. For predicting bed utilization > 2 weeks in the future, our optimized models improved prediction from an RMSE of 43.1 to 24.4 beds. For predicting bed utilization in 2 weeks, our optimized models improved prediction from an RMSE of 42.6 to 24.8 beds. Finally, predicting bed utilization same day demonstrated an RMSE of 22.7 beds. We described the architecture of a machine learning approach to forecast surgical bed utilization. Forecasting use of surgical resources may decrease stress on a hospital system through more accurate predicting of the ebbs and flows of hospital needs. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 01485598 |
| DOI: | 10.1007/s10916-025-02201-3 |
