Healthcare Models and Applications

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The cluster of healthcare models & applications focuses on the application of engineering and design methodologies and problem-solving skills to tackle the important problems that arise in the healthcare industry. It aims to improve efficiency, productivity, patient experience and patient access in healthcare systems using mathematical and scientific tools. Working in close collaboration with healthcare professionals, researchers in this cluster develop tools, methodologies and protocols that allow for the safe, efficient and cost-effective delivery of healthcare with improved outcomes. Such research involves the creation of knowledge and hypothesis generation, predictive modeling, decision sciences, robust device design, statistics and optimization. In addition, work in this cluster involves the development of computational algorithms to implement the tools and calibrate them to healthcare data. This multidisciplinary field integrates knowledge from a variety of different areas, such as computer science, communication, public policy, management and all aspects of industrial engineering.

Current ISE Focus

Currently, nearly all ISE faculty have shown interest or conducted research in the cluster of healthcare models & applications with specific focus on the applications of mathematical modeling, stochastic processes, dynamic programming, and simulations to healthcare problems. Additionally, some ISE faculty have devoted time to studying the relationship between technology and aging, human factors, the workplace and aging, the development of an integrated framework through wearable sensors, edge computing and cloud platform for continuous healthcare monitoring. Other research interests include data-driven analytics and optimization, nonlinear system dynamics, transfer learning, explainable modeling and complex network theory for modeling, monitoring and control of large-scale complex systems with the applications in healthcare. Other areas of related and growing interest and recognition include the use of predictive analytics in projecting post-discharge readmissions, emergency department visits, population health and mortality.

Opportunities for Interdisciplinary Collaboration:

There is tremendous opportunity to leverage the tools of predictive analytics, optimization, modeling, and simulation to the CoE’s thrust area of Healthcare Engineering through collaborations with ECE, BME, Miller School of Medicine, and the Business School. Currently, some of our faculty members hold secondary appointments with the Business School.

CoE Thrust Supported: Healthcare Engineering

Graduate Courses Offered: IEN 613 Quality Management in the Services Sector

Number of Ph.D. Students and Research Personnel: 18       

Faculty Participants

Coordinator: Vincent Omachonu

ISE Members: Joseph Sharit

Other Department & School Participants: TBA