Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty.

INTRODUCTION

In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational efficiency improvements. This includes an increased use of machine learning (ML) to predict duration of surgery (DOS) and length of stay (LOS) for total knee and total hip arthroplasty, which can be utilized for optimizing resource allocation to satisfy medical and operational limitations. This paper explores the development and performance of ML models in predicting DOS and LOS.

METHODS

A systematic search of publications between 2010-2023 was conducted following PRISMA guidelines. Considering the inclusion and exclusion criteria, 28 out of 722 gathered papers from PubMed, Web of Science, and manual search were included in the study. Descriptive statistics was used to analyze the extracted data regarding data preprocessing, model development, and model performance assessment.

RESULTS

Most of the papers work on LOS as a binary variable. Patient's age was identified as the most frequently used and reported as important variable for predicting DOS and LOS. Investigations also illustrated that within the resulting 28 papers, more than 71% of models reached good to perfect performance based on the area under the receiver operating characteristic curve (AUC), where artificial neural networks and ensemble learning models had the biggest share among the best-performing models.

CONCLUSION

The utilization of ML models is increasing in the literature. The current performance level indicates that ML can potentially turn to powerful tools in predicting DOS and LOS for different purposes. Meanwhile, the literature is not matured yet in reporting real-life application. Future studies can focus on model specification and validation by considering empirical application.