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Digital health evidence generation bootcamp: a self-reported mixed-method evaluation exploring programme outcomes

bmjinnov · 2025-12-30 · canonical JSON source

6 visible annotations · policy: published · automated confidence ≥ 75.00%

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Objective There is a growing consensus that digital health technologies (DHTs) need to demonstrate clear benefits, including contributing to care that is safe and clinically effective, if they are to provide the expected return on investment. However, DHT product owners and developers can fail to pinpoint the exact evidence they need to generate for this validation. This study assessed an Evidence Generation Bootcamp (EG bootcamp) designed to enhance DHT product owners’ and developers’ knowledge, skills, motivation, attitudes and behaviours regarding evidence generation activities, thereby encouraging the integration of these practices into company strategy.Methods This self-reported, mixed methods study adapted Kirkpatrick’s evaluation model to assess the EG bootcamp. Rigour occurred through the triangulation of multiple sources of data, including a Net Promoter Score, a pre-post survey, postbootcamp interviews, a longitudinal survey at 6 months and a break-even analysis. Outcomes were used to produce a programme logic model.Results 14 companies participated in the delivery of the EG bootcamp, October–December 2022. 13 companies completed the pre-post survey, 11 participated in online interviews and six provided responses to the follow-up survey after 6 months. The EG bootcamp was influential in changing participants’ knowledge and skills. Additionally, it has impacted participating companies’ decision-making, their strategic direction and their choice of partnerships, simplifying the process of evidence generation.Conclusion The EG bootcamp made the complex situation of choosing an evidence generation path ‘simple’ and supported companies to pinpoint the exact information they needed to deliver robust digital health evidence.