ArticleJournal of multidisciplinary healthcare2023
Assessing the Precision of Surgery Duration Estimation: A Retrospective Study.
Article in Journal of multidisciplinary healthcare, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and Evaluation of an Operative Case Length Prediction Model in Adult Surgical Patients.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Predicting Procedure Time in Pediatric Dental Rehabilitation Under General Anesthesia: The Role of Preoperative Factors and Age-Based Models.Paediatric anaesthesia · 2026Article
- Machine learning-enhanced prediction of operating room occupation time and length of stay: a retrospective cohort study on emergency surgery care pathways.Journal of clinical monitoring and computing · 2025Article
- Enhancing Operating Room Efficiency: The Impact of Computational Algorithms on Surgical Scheduling and Team Dynamics.Healthcare (Basel, Switzerland) · 2024Article
- The Use of Artificial Intelligence for Orthopedic Surgical Backlogs Such as the One Following the COVID-19 Pandemic: A Narrative Review.JB & JS open accessReview
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Authors and funding
5 authors.
Funding
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Abstract
Background and Objectives: The operating room (OR) is considered the highest source of cost and earnings. Therefore, measuring OR efficiency, which means how time and resources are allocated precisely for their intended purposes in the operating room is crucial. Both overestimation and underestimation negatively impact OR efficiency Therefore, hospitals defined metrics to Measuring OR Effeciency. Many studies have discussed OR efficiency and how surgery scheduling accuracy plays a vital role in increasing OR efficiency. This study aims to evaluate OR efficiency using surgery duration accuracy. Methods: This retrospective, quantitative study was conducted at King Abdulaziz Medical City. We extracted data on 97,397 surgeries from 2017 to 2021 from the OR database. The accuracy of surgery duration was identified by calculating the duration of each surgery in minutes by subtracting the time of leaving the OR from the time of entering the OR. Based on the scheduled duration, the calculated durations were categorized as either underestimation or overestimation. Descriptive and bivariate analyses (Chi-square test) were performed using the Statistical Package for the Social Sciences (SPSS) software. Results: Sixty percent out of the 97,397 surgeries performed were overestimated compared to the time scheduled by the surgeons. Patient characteristics, surgical division, and anesthesia type showed statistically significant differences (p <0.05) in their OR estimation. Conclusion: Significant proportion of procedures have overestimated. This finding provides insight into the need for improvement. Recommendations: It is recommended to enhance the surgical scheduling method using machine learning (ML) models to include patient characteristics, department, anesthesia type, and even the performing surgeon increases the accuracy of duration estimation. Then, evaluate the performance of an ML model in future studies.
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