MACHINE LEARNING & AI FOR HEALTHCARE: A HIMSS EVENT

Boston, MA
Westin Copley Place
June 13-14, 2019

Sujay Kakarmath

Digital Health Scientist
Partners Healthcare Pivot Labs

Dr. Kakarmath is a digital health scientist at Partners Healthcare Pivot Labs and an Instructor at Harvard Medical School. His research is focused on the evaluation of the clinical utility of digital health solutions, including machine learning and artificial intelligence-based products. Dr. Kakarmath's team works closely with technology innovators from academia, startups and industry giants to guide the ideation, design, prototyping, validation, and deployment of digital health solutions. His work has been published in prestigious journals and showcased at major academic conferences such as those of the American Academy of Neurology, the American Medical Informatics Association, the International Society for Pharmacoeconomics and Outcomes Research, the Connected Health Conference, Precision Medicine Summit and HIMSS. 

June 14, 2019
9:35am - 10:15am
America Ballroom

Improving the joy of practice for clinicians is a top priority for all health systems, and machine learning and AI promise to do that, while also personalizing care and improving outcomes. All well and good, but implementing these technologies into clinician workflows is easier said than done.

In this session, speakers identify the considerations and challenges to implementing AI and ML into clinical decision-making. How do we balance the need to provide enough information to point-of-care decision making without overwhelming the physician with too much information to review? How do we measure the validity of algorithms and their impact on decision-making? How do we train physicians to understand how to leverage AI to enable decision-making?

Speakers will address these and other questions and, importantly, they'll make clear that to optimize success, AI and machine learning must add value to clinical workflows and reduce the time physicians spend searching for relevant information to a patient’s care.

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