Case study of the prediction of elective surgery durations in a New Zealand teaching hospital

Soh, Kian Wee; Walker, Cameron; O'Sullivan, Michael J.; Wallace, Jonathan; Grayson, David E.

doi:10.1002/hpm.3046

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…However, general and plastic surgeons underestimated the time required for procedures, while orthopedic surgeons overestimated them by a small margin. These findings are consistent with previous studies that have identified the challenges associated with accurate time predictions in the OR [ 23 , 24 , 25 ]. The implications of these findings are significant for healthcare providers.…”

Section: Discussionsupporting

confidence: 93%

A Comparison of Surgeon Estimated Times and Actual Operative Times in Pediatric Dental Rehabilitation under General Anesthesia. A Retrospective Study

Alotaibi

Aljuaid

2023

JCM

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This retrospective study aimed to compare the accuracy of the pediatric dental surgeon’s estimated operative times for dental rehabilitation under general anesthesia (DRGA) in pediatric patients. This study population included 674 pediatric patients who underwent DRGA at the study facility between January 2022 and December 2022, using convenience sampling to select patients who met our inclusion criteria. Data were collected from electronic medical and anesthesia records based on several factors, including patient-related factors such as age and gender, surgeon-related factors such as rank and experience, and anesthesia-related factors such as induction and recovery time (in minutes). This study highlights a significant difference between the surgeon’s estimated time (SET) and actual operative time (AOT) for pediatric DRGA procedures, with a mean difference of 19.28 min (SD = 43.17, p < 0.0001), indicating a tendency for surgeons to overestimate surgery time. Surgical procedure time was the strongest predictor of this discrepancy, with an R square value of 0.427 and a significant p-value of 0.000. Experience with surgeons, anesthesia induction, and recovery time were also significant predictors. Meanwhile, age, gender, and rank of surgeons did not significantly predict the difference between SET and AOT. Therefore, the study suggests that surgeons should adjust their estimates for pediatric DRGA procedures, specifically emphasizing a more accurate estimation of surgery time, to ensure adequate resource allocation and patient outcomes.

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Section: Discussionsupporting

confidence: 93%

A Comparison of Surgeon Estimated Times and Actual Operative Times in Pediatric Dental Rehabilitation under General Anesthesia. A Retrospective Study

Alotaibi

Aljuaid

2023

JCM

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“…To evaluate the predictive models we split the data into training and testing data based on the year of operations with the intention of simulating real-world settings where past records are used to predict upcoming events in the future [ 7 , 14 , 24 – 27 ]. The training dataset included elective operations from November 2014 to June 2019, a total of 41,794 operations, and the testing dataset contained operations from July 2019 to June 2020, a total of 10,377 operations.…”

Section: Methodsmentioning

confidence: 99%

“…One aspect that helps to optimise OR usage is having a more precise booking of OR schedules [ 7 , 8 ]. It has been shown that accurate estimation of operation durations may result in a higher efficiency of ORs [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Improving preoperative prediction of surgery duration

Riahi,

Hassanzadeh,

Khanna

et al. 2023

BMC Health Serv Res

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Background Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon’s estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement. Methods We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon’s estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration. Results The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods. Conclusion The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.

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“…We consider a surgical dataset studied by Soh et al [20] as part of a research collaboration between the University of Auckland and the Waitemata District Health Board (DHB). The Waitemata DHB is the largest in New Zealand by catchment population, and provides both acute and elective statefunded surgical services at operating theatres across three sites: North Shore Hospital (within the main building or the Elective Surgery Centre) and Waitakere Hospital.…”

Section: Jma To Predict Surgery Durationsmentioning

confidence: 99%

Comparison of Jackknife and Hybrid-Boost Model Averaging to Predict Surgery Durations: A Case Study

et al. 2020

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We implement jackknife model averaging (JMA) and a new prediction technique-hybrid-boost model averaging (HbMA)to a surgical dataset that includes categorical explanatory variables. The model requirements for HbMA are different to that for JMA. HbMA generally does not require decent models to be included in the model average. However, the utility of HbMA is limited by the possibility of multiple solutions for the HbMA weights. Both model averaging approaches are comparable under the appropriate conditions. Among all the model averages considered, the best jackknife model average gives slightly better predictions of the surgery durations than the best hybrid-boost model average when evaluated on our surgical dataset. Finally, we discuss several methods that may further improve the performance of HbMA.

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Case study of the prediction of elective surgery durations in a New Zealand teaching hospital

Cited by 6 publications

References 15 publications

A Comparison of Surgeon Estimated Times and Actual Operative Times in Pediatric Dental Rehabilitation under General Anesthesia. A Retrospective Study

A Comparison of Surgeon Estimated Times and Actual Operative Times in Pediatric Dental Rehabilitation under General Anesthesia. A Retrospective Study

Improving preoperative prediction of surgery duration

Comparison of Jackknife and Hybrid-Boost Model Averaging to Predict Surgery Durations: A Case Study

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