The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses

2025-04-10

AI Summary

Escalating healthcare costs and disproportionately high inpatient expenditures necessitate targeted interventions to reduce hospitalization risk. Method: This study proposes a machine learning (ML)-driven, synergistic intervention strategy integrating medication adherence and preventive care. Leveraging heterogeneous electronic health record data, we developed and comparatively evaluated logistic regression, random forest, gradient boosting machine (GBM), and artificial neural network models to predict five-year high-hospitalization-risk patients. Contribution/Results: GBM achieved the highest predictive accuracy (81.2%). For the first time, we quantitatively demonstrated that high medication adherence and regular preventive care independently reduce hospitalization risk by 38.3% and 37.7%, respectively. Furthermore, ML-guided personalized interventions yielded a positive return on investment (ROI > 1). These findings provide an empirically validated, scalable framework for precision resource allocation and value-based healthcare delivery.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.

Problem

• Predicting hospitalization risk using machine learning models
• Reducing healthcare costs through medication adherence
• Optimizing preventive care with personalized ML interventions

Innovation

• Used Gradient Boosting for hospitalization prediction
• Analyzed medication adherence impact on costs
• Applied ML for personalized preventive care