Abstract
Since the emergence of COVID-19, many hospitals have encountered challenges in performing efficient scheduling and good resource management to ensure the quality of healthcare provided to patients is not compromised. Operating room (OR) scheduling is one of the issues that has gained our attention because it is related to workflow efficiency and critical care of hospitals. Automatic scheduling and high predictive accuracy of surgical case duration have a critical role in improving OR utilization. To estimate surgical case duration, many hospitals rely on historic averages based on a specific surgeon or a specific procedure type obtained from electronic medical record (EMR) scheduling systems. However, the low predictive accuracy with EMR data leads to negative impacts on patients and hospitals, such as rescheduling of surgeries and cancellation. In this study, we aim to improve the prediction of surgical case duration with advanced machine learning (ML) algorithms. We obtained a large data set containing 170,748 surgical cases (from Jan 2017 to Dec 2019) from a hospital. The data covered a broad variety of details on patients, surgeries, specialties and surgical teams. In addition, a more recent data set with 8,672 cases (from Mar to Apr 2020) was available to be used for external evaluation. We computed historic averages from the EMR data for surgeon- or procedure-specific cases, and they were used as baseline models for comparison. Subsequently, we developed our models using linear regression, random forest and extreme gradient boosting (XGB) algorithms. All models were evaluated with R-square (R2), mean absolute error (MAE), and percentage overage (actual duration longer than prediction), underage (shorter than prediction) and within (within prediction). The XGB model was superior to the other models, achieving a higher R2 (85 %) and percentage within (48 %) as well as a lower MAE (30.2 min). The total prediction errors computed for all models showed that the XGB model had the lowest inaccurate percentage (23.7 %). Overall, this study applied ML techniques in the field of OR scheduling to reduce the medical and financial burden for healthcare management. The results revealed the importance of surgery and surgeon factors in surgical case duration prediction. This study also demonstrated the importance of performing an external evaluation to better validate the performance of ML models.
A Machine Learning Study to Improve Surgical Case Duration Prediction
