design clinical decision support

Machine-learning-powered decision-support tools promise improved healthcare quality through complementary insights on patient diagnosis, treatment options and prognosis. However most of them failed when moving from labs to clinical practice. My research examines and reframes the role and form they take, addressing real-world adoption challenges.

CHI'16 Presentation
A field study of clinical decision making in 3 major implant centers across US, to probe on how intelligent tools can help;
Q. Yang, J. Zimmerman, A. Steinfeld, L. Carey, J. Antaki. 2016. Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help. in Proceedings of CHI'16, San Jose, the United States, Apr 2016. [CHI Best Paper Nomination, top 4%]   >> pdf
An integrative reflection on the gaps between machine learning and real world complexities;
Q. Yang. 2016. Bridging Machine Learning and Real-world Complexities with Interaction Design. in CHI'16 Human-centered Machine Learning Workshop, San Jose, the United States, Apr 2016. >> pdf
Messages for the healthcare audience
Q. Yang, M. Kanwar, J. Zimmerman, J. Antaki, L. Carey, “VAD Patient Selection: WhatHappens In Real-World Practice?” in Abstracts of The International Society for Heart and Lung Transplantation (ISHLT) Conference '16, Washington D.C, the United States, May 2016.
Q. Yang, “VAD Patient Selection: What Happens In Real-World Practice?” Presentation in American Heart Association (AHA) Research Fellow Day '16, Pittsburgh, the United States, Mar 2016.  >> Research Fellow day
An integrative review of related works in data science, medicine and social/organizational science;
Q. Yang, J. Zimmerman, A. Steinfeld. 2015. Review of Medical Decision Support Tools : Emerging Opportunity for Interaction Design. In Proceedings of IASDR'15, Brisbane, Australia, Nov 2015.  >> pdf

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