Sung Lillian, Brudno Michael, Caesar Michael C W, Verma Amol A, Buchsbaum Brad, Retnakaran Ravi, Giannakeas Vasily, Kushki Azadeh, Bader Gary D, Lasthiotakis Helen, Mamdani Muhammad, Strug Lisa
Department of Paediatrics, The Hospital for Sick Children, Institute of Health Policy Management & Evaluation, University of Toronto, Toronto, ON, Canada.
Department of Computer Science, Vector Institute for Artificial Intelligence, University Health Network, University of Toronto, Toronto, ON, Canada.
Front Digit Health. 2025 Mar 14;7:1511943. doi: 10.3389/fdgth.2025.1511943. eCollection 2025.
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.
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.
描述在医疗环境中识别数据科学实施场景的成功与不成功方法，并为未来的场景识别程序提供建议。
来自多伦多七家学术医疗保健机构的代表参加了为期一天的研讨会。要求每家机构介绍其临床数据科学项目，并提供一个在其机构中成功和不成功的场景识别方法示例。采用内容分析法对共同观察结果进行总结。
观察结果归纳为创意生成与价值主张、优先级排序、审批和支持者。成功经验包括推广一系列创意、阐明价值主张、确保与组织优先事项一致、确保审批者能够评判可行性以及识别愿意对项目负责的支持者。
基于学术医疗保健数据科学项目的经验，我们为在医疗环境中识别数据科学实施场景的方法提供了建议。
