Developing and validating a multivariable prediction model for predicting costs of colon surgery

Abstract

AbstractHospitals are burdened with predicting, calculating and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected if a patient suffers from other health issues which hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs which ultimately will lead to operating more cost-efficiently.. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a % MAPE result of 18 – 25.6, our model’s prediction showed decent results to forecast the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary costs for hospitals.