Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Management

TL;DR

This paper introduces three data-driven AI methods to improve healthcare resource management during crises, addressing uncertainties and aiding decision-making in hospitals.

Contribution

It presents novel AI-based tools using reinforcement learning, genetic algorithms, and clustering for healthcare resource planning under uncertainty.

Findings

Effective resource allocation strategies identified

AI methods outperform traditional planning approaches

Tools applicable to various healthcare crisis scenarios

Abstract

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e. Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial), lead to shifts in planning and budgeting, but most importantly, reduces confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This manuscript presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.