Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions

Lillian Sung ^1,2* Michael Brudno ^3,4 Michael C.W Caesar ^2,5 Amol A Verma ^2,6,7 Bradley Russell Buchsbaum ^8,9 Ravi Retnakaran ¹⁰ Vasily Giannakeas ¹¹ Azadeh Kushki ^12,13 Gary Bader ¹⁴ Helen Lasthiotakis ¹⁵ Muhammad Mamdani ^16,7 Lisa Strug ¹⁵

• ¹ Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
• ² Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
• ³ Department of Computer Science, Faculty of Arts and Science, University of Toronto, Toronto, Ontario, Canada
• ⁴ Vector Institute for Artificial Intelligence, University Health Network, Toronto, Canada
• ⁵ University Health Network (UHN), Toronto, Ontario, Canada
• ⁶ Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
• ⁷ Unity Health Toronto, Toronto, Ontario, Canada
• ⁸ Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario, Canada
• ⁹ Baycrest Centre, Rotman Research Institute (RRI), Toronto, Ontario, Canada
• ¹⁰ Division of Endocrinology, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
• ¹¹ Women’s College Research Institute, Women’s College Hospital, Toronto, Canada
• ¹² Institute of Biomedical Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, Ontario, Canada
• ¹³ Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
• ¹⁴ Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
• ¹⁵ Data Sciences Institute, University of Toronto, Toronto, Canada
• ¹⁶ University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Education and Research in Medicine, University of Toronto, Toronto, Canada

Objectives: To describe successful and unsuccessful approaches to identify scenarios for data science implementations within healthcare settings and to provide recommendations for future scenario identification procedures.Representatives from seven Toronto academic healthcare institutions participated in a one-day workshop. Each institution was asked to provide an introduction to their clinical data science program and to provide an example of a successful and unsuccessful approach to scenario identification at their institution. Using content analysis, common observations were summarized.Results: Observations were coalesced to idea generation and value proposition, prioritization, approval and champions. Successful experiences included promoting a portfolio of ideas, articulating value proposition, ensuring alignment with organization priorities, ensuring approvers can adjudicate feasibility and identifying champions willing to take ownership over the projects.Based on academic healthcare data science program experiences, we provided recommendations for approaches to identify scenarios for data science implementations within healthcare settings.