Surgical procedure prediction using medical ontological information

Predicting slot lengths of MRI exams to decrease observed discrepancies between planning and execution

This retrospective study aimed to reveal discrepancies between planned (Tplan) and actual (Tact) slot lengths of abdomen MRI exams, and to improve Tplan by predicting slot lengths via a machine learning algorithm. Tplan and Tact were retrieved from RIS and modality logfiles, respectively, covering 3038 MRI exams of 17 protocols performed at an abdomen department. Comparisons showed that 30% of exams exceeded planned slot lengths. On the other hand, exams completed within planning failed to manifest good adherence to schedule, as many of them were assigned with an unnecessarily long slot. While adjusting the planned exam duration by a fixed amount of time for each protocol could move Tplan closer to the mean or median Tact, the large spread of Tact would still be unaffected. This is why this study goes one step further, introducing a method to predict the required slot length not only per protocol, but for each individual exam. A Random Forest Regression model was trained on historic data to predict individual slot lengths (Tpred) based on patient and exam context. The correlation between Tpred and Tact was found to be better than that of Tplan and Tact, with Pearson correlation factors of 0.66 and 0.50, respectively. The overall adherence to schedule was also improved by the prediction, as seen by a reduction of both the root mean squared error (-28%) and the standard deviation (-16%) of the differences between planned/predicted slot times and Tact. To provide further insights into the discrepancies between planning and execution of MRI exams, nineteen exams from the Liver protocol with verified clinical information were selected. This case study showed that patient conditions, diagnostic purposes and the selection of sequences during exams could explain some variations of exam durations, but the potential for improving the exam time prediction by including this additional context is limited.

Artificial Intelligence in Operating Room Management

This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.

Improving preoperative prediction of surgery duration

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.