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Partnered innovation to implement timely and personalized care: A case study
Enola K Proctor1, Virginia R McKay1, Emre Toker2
1Brown School, Washington University in St. Louis, Saint Louis, MO, USA.
Background:
Understanding how to translate research discoveries into solutions for healthcare improvement is a priority of NIH-funded Clinical and Translational Science Awards (CTSA). This study, supported by one CTSA, aims to capture one process of shaping and implementing innovations to advance the timeliness and patient-centeredness of cardiovascular care. Specifically, we sought to understand a partnership between a private digital health startup company, a university innovation lab, and an academic health system's cardiology program pursuing this goal.
Findings:
The collaboration proceeded through clear phases to address the questions and challenges: problem definition, exploration and formalization of the partnership, innovation co-creation and pilot test, and scale-up planning. Phases were punctuated by key decisions, such as forming the partnership, negotiating terms of the partnership, iterating form and features of the innovation, and exploring sufficiency of its value-add for scale-up and sustainment. Key implementation concepts were apparent, including implementation strategies (e.g., champions and iterative trialing) and the implementation outcomes of acceptability, sustainment, and scale-up. Participants identified potential risks of collaboration, reflected on their co-creation process, and the value of engaging stakeholders in innovation design. Findings may inform subsequent collaborations between innovators and translational researchers.
Methods:
We conducted a case study to understand the partnership; characterize the questions they pursued, their decision points, information and data sources; and identify the challenges and risks. Data were collected through a series of four focus groups with members of each partnering organization. A transdisciplinary research team iteratively worked to condense and synthesize data from audio recorded transcripts into a case narrative.
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