Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions - Lillian Sung; Michael Brudno; Michael C. W. Caesar; Amol A. Verma; Brad Buchsbaum; Ravi Retnakaran; Vasily Giannakeas; Azadeh Kushki; Gary D. Bader; Helen Lasthiotakis; Muhammad Mamdani; Lisa Strug (Frontiers in Digital Health)
ObjectivesTo describe successful and unsuccessful approaches to identify scenarios for data science implementations within healthcare settings and to provide recommendations for future scenario identification procedures.Materials and methodsRepresentatives 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.ResultsObservations 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.ConclusionBased on academic healthcare data science program experiences, we provided recommendations for approaches to identify scenarios for data science implementations within healthcare settings.