L2M – Healthcare Systems Performance Optimization using Reinforcement Learning-Enhanced Functional Resonance Analysis Method

Healthcare systems operate as complex socio-technical environments, involving unpredictable interactions among patients, clinicians, technologies, administrators, and institutional policies. These systems are often under significant pressure to improve performance, reduce costs, and enhance patient safety—yet they frequently lack the tools to simulate the consequences of operational decisions before implementation. Traditional approaches like retrospective analysis or static models fail to capture dynamic system behavior and interdependencies, especially when working with limited or sensitive data.

This project addresses that gap by developing a simulation-based decision-support tool that integrates the Functional Resonance Analysis Method (FRAM) with Reinforcement Learning (RL). FRAM helps map functional relationships within healthcare processes, capturing variability in everyday work. RL, in turn, enables the system to learn from simulated experiences by rewarding effective decisions and avoiding poor ones—thereby identifying optimal strategies even in complex and low-data environments.

The aim is to provide hospital administrators, system planners, and policymakers with a user-friendly platform for exploring “what-if” scenarios without disrupting real-world operations. By simulating changes in patient flow, staffing, or resource allocation, the tool can uncover unintended consequences and support more resilient planning.

Through the Lab2Market Validate program, we will conduct structured customer discovery interviews, engage with healthcare stakeholders, and incorporate their feedback to refine the tool’s usability, interface design, and integration requirements. The ultimate goal is to bridge the gap between academic research and real-world application by building a product that is technically robust, practically usable, and commercially viable.

This innovation not only supports more informed healthcare decision-making but also contributes to Canada’s broader priorities around improving public service delivery, fostering applied AI innovation, and enhancing productivity in a resource-constrained health system.

Faculty Supervisor:

Doug Smith

Student:

Partner:

DMZ Ventures Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program: