Net Profit Margin Forecasting with Machine Learning Methods in Hospital Finance Management

Abstract

Hospital information management systems (HIMS) were managed using paper-based systems with individual efforts in the pre-computer period. Today, in parallel with technological developments, it is carried out digitally in an electronic environment. HIMS software usually includes modules such as patient follow-up-registration-appointment, clinical, medical records, radiology, laboratory, drug management, billing, reporting, and hospital management. Accounting records are processed in the finance management submodule within the hospital management module. Artificial intelligence models used in many sectors for financial estimation in hospital finance management have been found worthy of research considering the benefits of the hospital's financial management. Financial data of private hospitals traded on the stock exchange between 2009-2023 were used in the study. A total of 97 financial reports from 5 different private hospitals and 776 raw data obtained from the reports constitute the study's data set. "Net Profit Margin" has been estimated over the data set. The most reliable and closest-to-reality algorithm was determined by making five different algorithm trials in the PHYTON programming language. The most successful result was obtained with the Random Forest algorithm. It has been seen that hospitals can make this estimation using Random Forest when they want to predict financial data for future periods.

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