Enhancing patient care: machine learning’s role in reducing wait times for medical procedures

Abstract

The healthcare system faces a critical challenge with extended wait times for medical procedures, significantly impacting both patients and healthcare professionals. While increasing funding and hiring more doctors may seem like effective solutions, these approaches are often impractical due to various constraints. This research examines the factors driving medical procedure wait times in Canada, specifically in British Columbia, Nova Scotia, and Quebec, highlighting the urgent need to address delays caused by resource limitations. By leveraging machine learning techniques—including random forest methods, k-means clustering, and linear regression—alongside statistical models such as bar graphs, correlation matrices, and z-score normalization, the study, conducted in both Python and R Studio, identifies key contributors to these delays. Based on the findings, a strategic approach to physician hiring is proposed, emphasizing the optimization of seniority levels. Specifically, the study recommends capping the hiring of entry-level doctors at 18% and senior-level doctors at 5%, while increasing the absolute population of entry-level physicians by 27% and reducing the physician-to-100,000 population ratio by 2%, which could lead to a 15% reduction in wait times. By addressing the complexities of medical procedure delays, this research aims to enhance the efficiency and fairness of surgical care delivery.