Ask the Doctor to Prescribe a YouTube Video? An Augmented Intelligence Video Analytics Approach for Patient Education and Health Literacy
Video sharing social media platforms, such as YouTube, offer an effective way to deliver medical information that may be more understandable for the public, with the potential to improve health literacy, patient-physician interactions, self-care and outcomes. Few studies have identified scalable, replicable and efficient technology-enabled interventions, delivered as evidence-backed digital therapeutics, to improve the ease with which patients and health professionals can retrieve understandable medical information to manage chronic and infectious conditions. We propose an augmented intelligence approach that synthesizes annotations from domain experts, deep learning and co-training methods from machine learning and a systematic approach to extract patient education constructs on understandability and encoded medical information to develop an automated, generalizable, video classification solution. We further examine the simultaneous impact of understandability and validated medical information in a video on several dimensions of collective engagement by conducting a multiple-treatment propensity score based matching approach that allows us to implement a quasi-randomization research design. While confirming common assessments of the relationship between user engagement and patient education materials, our analysis quantifies the nuanced effects using collective viewing data in the specific context of understandability of complex medical information encoded in patient education videos found on YouTube, with implications for research and practice.
Rema Padman is Trustees Professor of Management Science and Healthcare Informatics in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. She is also Thrust Leader of Healthcare Informatics Research at iLab and Research Area Director for Operations and Informatics at the Center for Health Analytics at the Heinz College, and Adjunct Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine. She received B.Tech. in Chemical Engineering from the Indian Institute of Technology, Kanpur, India, PhD in Operations Research from the University of Texas at Austin and National Library of Medicine Senior Fellowship in Applied Informatics from The University of Pittsburgh School of Medicine. She is an elected Fellow of the American Medical Informatics Association (FAMIA).
Dr. Padman’s current research investigates healthcare informatics, analytics and operations, data-driven decision support, and process modeling and risk analysis, in the context of clinical and consumer-facing information technology interventions in healthcare delivery and management, such as e-health, m-health, chronic and infectious disease management, and workflow analysis. She has developed, applied, and evaluated models and methods drawn from optimization, machine learning, statistics and behavioral science for designing and investigating these IT interventions in the emergency, inpatient, ambulatory, and consumer self-health management settings.
She has published extensively, served on editorial boards of major academic journals in Operations Research and Information Systems and advised healthcare informatics projects for provider, payer, pharmaceutical, consulting, and nonprofit organizations. She has also served on proposal review panels of US and international funding agencies and received funding for her research from federal agencies, healthcare organizations and foundations. She has received several Best Paper awards, the IBM Faculty Award, and Teaching Excellence awards. She has been a visiting professor at universities in the US and abroad, and keynote speaker at multiple conferences. Becker’s Hospital Review recognized her as one of the top 110 women in MedTech in 2017 and she was nominated for the 2018 HIMSS Most Influential Women in Health IT Award.
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