Enhancing Accuracy in Pediatric Inpatient Hospital Cost Estimation: A Machine Learning-Based Approach

Abstract

Abstract Purpose: Accurately estimating inpatient billing costs during admission is important for financial planning in healthcare. Traditional methods have limitations in capturing true cost; hence, data-driven approaches are needed to improve hospital cost estimation in complex and dynamic environments. The main objective of this study is to predict a deviation between the initial hospital bill estimate and the actual bill charged at the time of discharge. This study is also focused on identifying the major factors contributing towards the cost of hospital stay. Methods This study utilized dataset of approximately 22,000 pediatric patients (under 18 years of age). The main features of the dataset included medical conditions, hospital administration details, and socio-demographic information. The methodology utilizes named entity recognition techniques to extract structured data from unstructured textual data. Subsequently, a variety of machine learning classification models are trained and tested to predict deviations in hospital bill estimates. Results: The boosting ensemble and artificial neural network classifier models performed best in predicting the deviations in the billing cost, with best accuracy, AUC and F1-scores of 80%, 77% and 77% respectively. The analysis of the important features revealed that age, length of stay, financial status of patients as main features to predict deviation in hospital bill estimates. Conclusions: The results obtained from our study demonstrate that leveraging machine learning techniques provides a reliable and efficient means of improving the performance of hospital billing estimations. These findings have significant implications for healthcare practitioners, enabling them to make more informed decisions and allocate resources effectively.