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Using Machine Learning to facilitate Collaborative Clinical Decision Making in Chronic Wound Care: A Design Science Approach
Abstract: Many multi-disciplinary group decision-making scenarios are most effectively addressed with Collaborative Decision Making (CDM). Although CDM is critical for decision making quality, its feasibility diminishes when domain experts are unavailable, and decisions are time critical, as is the case in the chronic wound decisions. Clinical Decision Support systems (CDSS) that support CDM have been developed, but they require all collaborators to be available to contribute at the time of decision. This presentation will focus on how we utilizing the design science research approach to develop and evaluat a Machine Learning (ML) artifact that represents domain knowledge of expert collaborators in their absence and ensures accurate prediction of CDM. The resulting CDSS model for chronic wound care decisions takes (as inputs) wound features extracted from wound images and recommends (as the output) one of three standard of care pathways, i.e., to (1) continue current treatment, (2) request a change in treatment or (3) refer patient to a wound specialist clinic.
Bio: Dr. Bengisu Tulu is a Professor of Information Systems in the WPI Business School at Worcester Polytechnic Institute (WPI), Worcester, MA and one of the founding members of the Healthcare Delivery Institute at WPI. She received her Ph.D. in Management of Information Systems and Technology from Claremont Graduate University, Claremont, CA. Dr. Tulu’s research interests include development and implementation of Health Information Technologies (HIT) and digital health interventions. Dr. Tulu has been designing patient and provider facing digital health applications to support patients and clinicians managing chronic conditions. Her research has been supported by more than $30 million in federal grants from the National Science Foundation, National Institutes of Health, Agency for Healthcare Research & Quality and the Veterans Affairs. Her publications have appeared in leading journals such as Journal of the American Medical Informatics Association, Journal of Medical Internet Research, Journal of the AIS, IEEE Transactions on Engineering Management, and IEEE Transactions on Biomedical Engineering.