Surgical control time estimation variability: Implications for medical systems and the future integration of AI and ML models

Background

Accurate estimation of surgical procedure times, crucial for optimizing healthcare access, patient outcomes, and cost-effectiveness, is essential for operating room efficiency. Surgical control time (SCT) is a preoperative estimate by surgeons representing their predicted time to complete the surgery, spanning from completion of anesthesia induction to surgical site closure.

Methods

In this within-subjects, longitudinal study, we examined the differences between predicted surgical control times versus actual SCTs and determined variability by surgical specialty. We included cases regardless of classification (i.e., outpatient or inpatient), type of surgery (i.e., elective, urgent, or emergent), or level of complexity (i.e., major or minor). We ran Shapiro–Wilk tests to assess the normality of the difference in actual versus predicted surgical control times (dSCT) by surgical specialty. We used a generalized linear model (GLM) with robust clustered variance and pairwise comparisons of surgical specialties (with Bonferroni adjustment for family-wise error rate) to assess differences in the prediction accuracy of SCTs by specialty.

Results

We analyzed 14,438 surgical cases performed by 168 surgeons across 13 specialties from January 2019 to January 2023. 11 of 13 specialties had higher actual than predicted times, suggesting an overall pattern of underestimating SCTs. On average, surgeries took 12.3 % longer than predicted, with surgeons underestimating SCTs by an average of 10.4 min. SCTs comprised 78 % of the total operative time. The four specialties with the largest underestimations of SCTs were neurosurgery (27.04 min), orthopedics (22.75 min), urology (19.4 min, and plastic surgery (18.67 min), while two specialties exhibited overestimations, namely ear nose and throat (11.14 min) and pediatrics (–3.21 min). GLM results and pairwise comparisons showed that surgeons significantly differed in their SCT prediction by surgical specialty.

Conclusions

Our findings showed significant differences across surgical specialties in the accuracy of predicting surgical control times. These results have implications for integrating evolving technologies such as artificial intelligence and machine learning models to assist surgical administrators in accurately predicting surgical case durations and optimizing resource allocation.