Quantile regression forests for individualized surgery scheduling

Dean, Arlen; Meisami, Amirhossein; Lam, Henry; Oyen, Mark P. Van; Strömblad, Christopher T; Kastango, Nick

doi:10.1007/s10729-022-09609-0

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…QRF, on the other hand, allows researchers to generate prediction intervals that are non-parametric, flexible, and adaptive to different data distributions. This capability is invaluable for quantifying uncertainties in a wide range of research areas, including finance (Córdoba et al, 2021), environmental sciences (Fang et al, 2018;Francke et al, 2008;Zhang et al, 2018), healthcare (Dean et al, 2022;Molinder et al, 2020), and more. A crucial difference between QRF and many other quantile regression approaches is that after training a QRF once, one has access to all the quantiles at inference time, whereas most approaches require retraining separately for each quantile.…”

Section: Statement Of Needmentioning

confidence: 99%

quantile-forest: A Python Package for Quantile Regression Forests

Johnson

2024

JOSS

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Section: Statement Of Needmentioning

confidence: 99%

quantile-forest: A Python Package for Quantile Regression Forests

Johnson

2024

JOSS

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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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Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem

Tsang,

Shehadeh,

Curtis

et al. 2024

Operations Research

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Improving Operating Room, Surgery, and Anesthesiologist Scheduling Using Stochastic and Robust Optimization Efficient planning and scheduling of operating room (OR) activities is crucial for managing costs and delivering high-quality surgical care. However, this task is extremely complex for several reasons. First, it requires coordinating multiple resources, such as ORs and anesthesiologists. Second, in addition to limited OR capacity and time, there is a significant shortage of anesthesiologists required to perform surgeries. Third, surgery durations are uncertain and difficult to predict. Ignoring such uncertainty may lead to substantial overtime, idling, and surgery delays, among other schedule deficiencies. Thus, hospital managers could benefit greatly from advanced methodologies to improve OR utilization, surgical care, and quality as well as to minimize OR operational costs. In “Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem,” M. Y. Tsang, K. S. Shehadeh, F. E. Curtis, B.R. Hochman, and T. E. Brentjens propose computationally tractable stochastic programming and distributionally robust optimization methodologies for an integrated allocation, assignment, sequencing, and scheduling problem under uncertainty involving multiple ORs, anesthesiologists, and surgery types. Using real-world surgery data and a case study from a health system in New York, they conduct extensive experiments demonstrating the computational efficiency of the proposed methodologies, allowing for their implementation in practice. Moreover, they show the negative consequences of adopting the existing non-integrated approaches and provide valuable practical insights.

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Quantile regression forests for individualized surgery scheduling

Cited by 4 publications

References 44 publications

quantile-forest: A Python Package for Quantile Regression Forests

quantile-forest: A Python Package for Quantile Regression Forests

Improving preoperative prediction of surgery duration

Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem

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