Enhancing financial stability in healthcare through data-driven risk assessments with machine learning

Machine learning algorithms can significantly enhance financial stability and operational efficiency in healthcare financial management systems. Achieving sustainable financial growth and improved healthcare quality requires not only data-driven decision-making but also a strong strategic focus. This study applies advanced machine learning models—beyond the commonly used approaches to conduct financial risk analysis and estimate life expectancy, addressing the substantial financial instability and variations in life expectancy observed across countries worldwide. The analysis uses a comprehensive dataset that includes current healthcare expenditure, out-of-pocket spending, and healthcare expenditure as a percentage of GDP for all countries with available data. The models employed include linear regression, long short-term memory (LSTM) networks, bi-directional LSTM (BiLSTM) networks, and deep neural networks (DNN). Among these, the BiLSTM model demonstrates strong performance, with R-squared values of 90.6% for training data and 85.3% for testing data. The findings indicate that per capita healthcare expenditure is the most significant predictor of life expectancy, followed by out-of-pocket expenditure and healthcare spending relative to GDP. Although more sophisticated algorithms hold promise for addressing financial challenges in healthcare systems, they often lack generalizability and interpretability, limiting their usefulness in real-world financial stability assessments. Therefore, effective financial planning and careful allocation of healthcare resources remain essential for improving health outcomes and strengthening global healthcare financial management systems.