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Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based
Lillian Sung1, Michael Brudno2, Michael C W Caesar3
1Department of Paediatrics, The Hospital for Sick Children, Institute of Health Policy Management & Evaluation, University of Toronto, Toronto, ON, 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.
Materials And Methods:
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.
Conclusion:
Based on academic healthcare data science program experiences, we provided recommendations for approaches to identify scenarios for data science implementations within healthcare settings.
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