Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation me...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
1/14/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183076903&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183076903 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 1/14/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183076903 183076903 183076903 10.1007/s10916-025-02141-y 183076903 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach. aug: au: Park, Jung-Bin Roh, Gyun-Ho Kim, Kwangsoo Kim, Hee-Soo affil: https://ror.org/04h9pn542 Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea sug: subj: Surgery, Operative Treatment Duration Prediction Models Random Forest Machine Learning Program Development Health Facility Departments Human Funding Source Operating Rooms Algorithms Scales Decision Making, Clinical Retrospective Design Record Review ab: Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2) metrics. Our findings highlighted that Random Forest models excelled in department-specific applications, achieving an MAE of 16.32, an RMSE of 31.19, and an R2 of 0.92, significantly outperforming general models and conventional estimates. This improvement emphasizes the advantage of customizing models to fit the distinct characteristics and data patterns of each department. Additionally, our SHAP-based feature importance analysis identified morning operation timing, ICU ward assignments, operation codes, and surgeon IDs as key factors influencing surgical duration. This suggests that a detailed and nuanced approach to model development can substantially increase prediction accuracy. By providing a more accurate, reliable tool for predicting surgical case durations, our department-specific Random Forest models promise to enhance surgical scheduling, leading to more effective OR management. This approach underscores the importance of leveraging tailored, data-driven models to improve healthcare outcomes and operational efficiency. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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