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Exploring Systemic Influences on Data-Informed Learning: Document Review of Policies, Procedures, and Legislation
David Wiljer1, Spencer Williams, Rebecca Charow
1Dr. Wiljer: Executive Director, Education Technology and Innovation, University Health Network, and Professor, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, and Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Mr. Williams: Research Analyst, Education, Technology and Innovation, University Health Network, Toronto, Ontario, Canada. Ms. Charow: Research Associate, Education, Technology and Innovation, University Health Network, and PhD Student, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Dr. Campbell: Director, Curriculum, UGME, and Associate Professor, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada. Dr. Davis: Professor, Emeritus, Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada, and Adjunct Professor, Medical Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates. Ms. Younus: Research Associate, Education, Technology and Innovation, University Health Network, Toronto, Ontario, Canada. Dr. Mylopoulos: Scientist and Associate Director of Training Programs, Wilson Centre, University Health Network, and Program Director, Health Professions Education Research, Institute of Health Policy, Management and Evaluation and Dalla Lana School of Public Health, and Associate Professor, Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada Dr. Okrainec: Head, Division of General Survey, Peter A. Crossgrove Chair in General Surgery and Director, Temerty/Chang Telesimulation Centre, University Health Network, and Associate Professor, Department of Surgery, University of Toronto, Toronto, Ontario, Canada. Dr. Silver: Staff Psychiatrist, Centre for Addiction and Mental Health, and Professor, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.Dr. Tavares: Scientist, Wilson Centre, University Health Network, and Assistant Professor, Temerty Faculty of Medicine and Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Dr. Sockalingam: Vice President of Education and Clinician Scientist, Centre for Addiction and Mental Health, and Professor, Department of Psychiatry, University of Toronto; Wilson Centre Researcher, University Health Network, Toronto, Ontario, Canada.
Introduction:
Despite the support for and benefits of data-driven learning, physician engagement is variable. This study explores systemic influences of physician use of data for performance improvement in continuing professional development (CPD) by analyzing and interpreting data sources from organizational and institutional documents.
Methods:
The document analysis is the third phase of a mixed-methods explanatory sequential study examining cultural factors that influence data-informed learning. A gray literature search was conducted for organizations both in Canada and the United States. The analysis contains nonparticipant observations from professional learning bodies and medical specialty organizations with established roles within the CPD community known to lead and influence change in CPD.
Results:
Sixty-two documents were collected from 20 Canadian and American organizations. The content analysis identified the following: (1) a need to advocate for data-informed self-assessment and team-based learning strategies; (2) privacy and confidentiality concerns intersect at the point of patient data collection and physician-generated outcomes and need to be acknowledged; (3) a nuanced data strategy approach for each medical specialty is needed.
Discussion:
This analysis broadens our understanding of system-level factors that influence the extent to which health information custodians and physicians are motivated to engage with data for learning.
