Machine Learning for Dynamic Pricing in Healthcare: A Predictive Approach

Hospitals may modify service charges based on availability, demand, location, time of day, and resource usage by employing a method called "dynamic pricing." This effort proposes the introduction of a dynamic pricing model to address the significant capital expenses associated with providing medical services, including those related to infrastructure and expensive medical equipment. It implements machine learning techniques, in the forms of Decision Tree Regressor with changes made according to dynamics in the markets and to choices of customers. Dynamic pricing increases accessibility, hence results in customer satisfaction by maximizing operational efficiency by the transfer of loads between fixed peak hours and off-peak hours along with planning prices according to distinct needs and conditions. Such policies enhance the brand value of the hospital and market visibility because resource allocation becomes efficient through customized and competitive pricing strategy.