Healthcare Cost-Benefit Analysis Using Machine Learning: A Panel Data Modelling Approach

Cost-benefit analysis (CBA) is a financial assessment technique used to assess the advantages of an intervention and the cost related to that decision of healthcare investment. In mixed health systems like India's, it is essential to understand the costs of care for various categories of healthcare businesses. This research analyzes the relationship between non-current assets and expenses incurred using the financial statements of select private-sector healthcare providers in India. The CBA is based on a machine learning approach to panel regression using R programming that evaluates healthcare financial viability and desirability, effectiveness, profitability, and sustainability of the investment and helps them make informed decisions about resource allocation and prioritization, with a focus on examining the impact of variables such as cost of deliverables, net working capital, and net profit over time. Measured factors, including the cost of materials used, employee benefit costs, finance costs, other expenses, non-current assets, cash flows from operating activities, networking capital, profit, and depreciation, were used to understand the healthcare sector's cost dynamics. The findings indicate that non-current assets significantly lower the cost of providing healthcare in India. In particular, analyses show that net profit and cash flow from operational operations positively influence healthcare delivery costs, whereas net working capital has a negative effect.