Machine learning predictive models reduced the root mean square error for elective and acute cardio-thoracic surgery durations by 19% and 52%, respectively, compared to the current clinical model.
Observational (n=2,098)
No
Do machine learning ensemble models improve the prediction of cardio-thoracic surgery duration compared to the current clinical practice model?
Machine learning ensemble models incorporating patient and surgery complexity features significantly improve the prediction of cardio-thoracic surgery duration compared to historical surgeon averages, offering a tool to optimize operating room utilization.
Absolute Event Rate: 0.8% vs 0.99%
p-value: p=0.002
Effective capacity management of operation rooms is key to avoid surgery cancellations and prevent long waiting lists that negatively affect clinical and financial outcomes as well as patient and staff satisfaction. This requires optimal surgery scheduling, leveraging essential parameters like surgery duration, post-operative bed type and hospital length-of-stay. Common clinical practice is to use the surgeon's average procedure time of the last N patients as a planned surgery duration for the next patient. A discrepancy between the actual and planned surgery duration may lead to suboptimal surgery schedule. We used deidentified data from 2294 cardio-thoracic surgeries to first calculate the discrepancy of the current model and second to develop new predictive models based on linear regression, random forest, and extreme gradient boosting. The new ensamble models reduced the RMSE for elective and acute surgeries by 19% (0.99 vs 0.80, p = 0.002) and 52% (1.87 vs 0.89, p < 0.001), respectively. Also, the elective and acute surgeries "behind schedule" were reduced by 28% (60% vs. 32%, p < 0.001) and 9% (37% vs. 28%, p = 0.003), respectively. These improvements were fueled by the patient and surgery features added to the models. Surgery planners can benefit from these predictive models as a patient flow AI decision support tool to optimize OR utilization.
Nikolova-Simons et al. (Tue,) conducted a observational in Cardio-thoracic surgery (n=2,098). Machine learning predictive models vs. Current clinical practice model (surgeon's average procedure time of the last 10 patients) was evaluated on Root mean square error (RMSE) for elective surgery duration (95% CI 0.76-0.85, p=0.002). Machine learning predictive models reduced the root mean square error for elective and acute cardio-thoracic surgery durations by 19% and 52%, respectively, compared to the current clinical model.
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