2026 ASEE Annual Conference & Exposition

Student Engagement and Training in Healthcare Engineering with Advanced Data Analytics Solutions

Presented at NSF Grantees Poster Session II

This paper presents the design, implementation, and evaluation of a multi-course curricular transformation and outreach initiative to align undergraduate and graduate engineering education with evolving healthcare workforce demands. Three restructured courses, Decision Support Systems in Healthcare Systems, Computer Simulation Applications for Healthcare Systems, and Introduction to Data Mining for Healthcare, were complemented by community outreach activities, including high school workshops and a Family Café. The project integrates data analytics, discrete-event simulation, and digital decision-making tools using Excel/VBA, SQL, FlexSim, and Python. A structured pedagogical framework grounded in Bloom’s taxonomy and experiential learning guided learning outcome development. Mixed-method pre- and post-assessments demonstrated statistically meaningful gains in student confidence, technical proficiency, problem-solving ability, and healthcare application competence. The initiative aligns closely with NSF IUSE goals by improving student learning, broadening participation, strengthening experiential learning, and promoting scalable curricular innovation. Results demonstrate a sustainable and transferable model for healthcare-focused engineering education.

Authors

Dr. Md Fashiar Rahman http://orcid.org/0000-0002-0437-2587 University of Texas at El Paso [biography]

Dr. Md Fashiar Rahman is an Assistant Professor of the Industrial, Manufacturing and Systems Engineering (IMSE) Department at The University of Texas at El Paso. He holds a Ph.D. degree in Computational Science Program. He has years of research experience in different projects in the field of image data mining, machine learning, deep learning, and computer simulation for industrial and healthcare applications. In addition, Dr. Rahman has taught various engineering courses in industrial and manufacturing engineering. His research area covers advanced quality technology, AI application in smart manufacturing, health care applications, computational intelligence/data analytics, and decision support systems.
Prof. Tzu-liang Bill Tseng http://orcid.org/0000-0002-3903-529X University of Texas at El Paso [biography]

Dr. Bill Tseng is a Professor and Chair of Department of Industrial, Manufacturing and Systems Engineering at the UTEP. He is also a Director of Research Institute for Manufacturing & Engineering Systems, the host institute of Texas Manufacturing Assistan
Dr. Sreenath Chalil Madathil http://orcid.org/0000-0002-7503-9984 State University of New York at Binghamton [biography]

Dr. Sreenath Chalil Madathil joins Systems Science and Industrial Engineering at Binghamton University after working as an Assistant Professor at the University of Texas at El Paso (UTEP) for the last three years. Before UTEP, he worked as a research scientist for the Watson Institute of Systems Excellence (WISE) at the Research Foundation for the State University of New York at Binghamton. He completed his Ph.D. and MS in Industrial Engineering from Clemson University and received his undergraduate degree from Mahatma Gandhi University, India.

His research expertise lies in applying operations research, simulation modeling, and data analysis techniques to healthcare problems, focusing on patient-centered care and healthcare disparities. He has several publications in high-impact journals such as Computers and Industrial Engineering, Healthcare Management Science, Expert Systems with Applications, and Applied Soft Computing. He has also received funding from the National Science Foundation and the U.S. Department of Commerce. Furthermore, he has industrial and national lab work experience with organizations such as Los Alamos National Lab, BMW Manufacturing, and Cognizant Technology Solutions.

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