Improving Physician Decision-Making and Patient Outcomes Using Analytics: ACase Study with the World’s Leading Knee Replacement Surgeon

Abstract

Every year in the United States more than 300,000 knee replacements are performed. According to Time magazine, this number is expected to increase by 525% by the year 2030. Although knee surgeries are a highly effective treatment, patients are still prone to post-surgery complications which patients, physicians, and insurance companies all hope to minimize. In collaboration with one of the world’s leading knee replacement surgeons, we address this problem using their domain expertise with our analytics capabilities. We show how analysis of unstructured data with patient demographics, patient health data, and insurance codes can help better support the physicians in the diagnosis phase by assessing patient risk of developing complications or the risk of total knee replacement surgery failure. We identified the factors that led to successful knee surgeries (minimal complications and visits) by utilizing various classification algorithms such as random forest and logistic regression. We use these predictive models to provide a recommender system to support the interest of the patient, the hospital, and the insurance company, which helps find the right balance of post-operative patient success and total post-operative treatment costs to try and minimize the rate of relapse and additional physician visits. In the recent past, various studies have been carried out to predict outcomes of total knee replacement surgeries, but most if not all the studies have used similar parameters like pain score or functional score of knees to characterize surgeries as a failure or success. In our study, we have created a new parameter, based on three different conditions (number of post-op visits, direct complications from ICD codes for total knee replacement surgery complications, and whether a revision surgery has been carried out). We show that factors such as BMI, smoking, blood pressure, and age were statistically significant parameters for a surgery outcome. The surgeon performing the surgery was also a significant factor determining the outcome. This could be due to the different techniques used by different surgeons. Our model could save millions of dollars per year by detecting two-thirds of actual complications that would occur. We believe healthcare providers and consulting firms who are developing analytics-driven solutions for their clients in the healthcare industry will find our study novel and inspiring.