NDLI: Improving preoperative prediction of surgery duration

Content Provider	Springer Nature : BioMed Central
Author	Riahi, Vahid Hassanzadeh, Hamed Khanna, Sankalp Boyle, Justin Syed, Faraz Biki, Barbara Borkwood, Ellen Sweeney, Lianne
Abstract	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.
Related Links	https://bmchealthservres.biomedcentral.com/counter/pdf/10.1186/s12913-023-10264-6.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14726963
DOI	10.1186/s12913-023-10264-6
Journal	BMC Health Services Research
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-12-02
Access Restriction	Open
Subject Keyword	Public Health Health Administration Health Informatics Nursing Research Operating room Operation duration Prediction models Individual patient characteristics Extreme gradient boosting
Content Type	Text
Resource Type	Article
Subject	Health Policy

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