Continuous real-time prediction of surgical case duration using a modular artificial neural network

Abstract

Background: Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration.

Methods: Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs.

Results: The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3-6.5]; P<0.001) than the Bayesian approach. The modular artificial neural network also had the highest accuracy in identifying operating theatres that would overrun 15:00 (accuracy at 1 h prior=89%) compared with the Bayesian approach (80%) and a naïve approach using the scheduled duration (78%).

Conclusions: A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.

Keywords: artificial neural network; economics; healthcare costs; machine learning; operating theatre efficiency; procedure duration; statistical model; surgery.

Fig 1

Architecture of modular artificial neural network. LSTM, long-short term memory.

Fig 2

Sample model outputs. Procedure name: ‘INSERTION BILATERAL TISSUE EXPANDER AND PLACEMENT OF BILATERAL ALLODERM (Bilateral); BILATERAL MASTECTOMY SIMPLE SKIN NIPPLE SPARING W/ RECON (PROPHYLACTIC) (Bilateral Breast)’. MANN, modular artificial neural network.

Fig 3

Time error of different approaches to predicting surgical duration. CRPS, continuous ranked probability score; MANN, modular artificial neural network.

Fig 4

Performance of predictive models binned by time error (continuous ranked probability score) and percentage of total surgical duration elapsed. Number of cases in each bin is expressed as a percentage of the total 9092 cases in the test data set. MANN, modular artificial neural network.