6.4 Open science
Goal: Accelerate innovation through shared learning
Advance the field by strategically sharing trial learnings and validation evidence, leveraging sDHTs to accelerate medical product development. Contributions to open-access consortia expedite regulatory acceptance for digital measures, actively reducing the future validation burden for all stakeholders.
Overview
Accelerate the entire ecosystem
Sharing your non-competitive learnings is a powerful act of leadership that benefits everyone. This step guides you on how to contribute de-identified data and validation protocols to open-access consortia in alignment with FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
By strategically sharing your validation work and lessons learned, you contribute to a shared body of evidence that helps establish industry standards for digital measures. This approach directly reduces the high cost and time burden associated with repeating validation studies in every single trial, accelerating the entire ecosystem.
For adopters: You reduce future validation costs by leveraging community data and accelerating the movement toward commonly accepted digital measures.
For developers: You contribute to a growing body of evidence that supports the scientific credibility of your product’s core measure, helping partners adopt it with greater confidence.
For regulators: Encourage a collective evidence base for faster approvals and standardized practices across stakeholders.
in practice
Step-by-step actions: Contributing to the ecosystem
Disseminate findings and data
Prepare summaries, manuscripts, or abstracts on your validation journey; target conferences, peer-reviewed journals, or collaborative consortia efforts.
Value: This builds the public evidence base for the credibility of a digital measure in a specific trial context.
Compliance: Apply FAIR principles (Findable, Accessible, Interoperable, Reusable) responsibly to foster collaboration that accelerates innovation without compromising intellectual property (IP).
Contribution: Engage consortia (e.g., DATAcc by DiMe) by contributing de-identified datasets, validation insights, and subject matter expertise to working groups.
Precompetitive data sharing and IP management
Transforming novel digital measures from proprietary assets into industry standards requires precompetitive collaboration to establish clear evidentiary criteria.
Success depends on finding a neutral forum that aligns stakeholders with regulators—such as DATAcc by DiMe. As an FDA-aligned collaborative community, DATAcc co-develops conceptual models and shared evidence for digital measures. Contributing to this work grants your team early visibility into emerging best practices, helping to de-risk your product evolution and validation plan.
Precompetitive examples
PRO TIP
Worried about managing IP concerns?
Precompetitive sharing focuses exclusively on de-identified data, methods, and validation protocols. The goal is to advance the understanding and acceptance of the measure itself (e.g., that gait speed detects meaningful change), leaving the underlying, unique algorithm (the intellectual property) proprietary to the developer.
Your call to action
Take the next step in driving medical product development forward by identifying and engaging with open science initiatives in sDHT adoption. Start by exploring opportunities like DATAcc by DiMe’s working groups, where you can contribute validation expertise to collaborative projects and even interact directly with regulators.
By participating, you’ll help improve research efficiency, enable better real-world evidence generation, build critical relationships, and ultimately drive fit for purpose adoption of sDHTs for medical product development, leading to better outcomes for the patients we’re all working to serve.
key takeaways
Closing the loop
Individual trial learnings can be translated into durable value for both organizations adopting sDHTs and teams developing them. By validating performance in real-world use, refining the business case, and contributing evidence back to the ecosystem, you complete a full adoption cycle that strengthens future deployments, technology iteration, and regulatory engagement.
Make it count
You have successfully traversed the sDHT adoption roadmap. But innovation is a circle, not a straight line. The end of this validation journey is simply the starting point for your next, more efficient deployment.
Think of this roadmap as your navigator. You may have used it linearly for your first pilot, but as you mature, you will use it dynamically:
Revisit Section 3 when considering a new regulatory question.
As you continue this work, remember that specific, high-value tools are embedded throughout every stage of this roadmap. We encourage you to revisit the Regulatory and Industry Spotlights curated for each section to stay aligned with regulatory guidances and peer best practices. Additionally, explore our sDHT Adoption Library, which houses over 250 resources to support every step of your journey.
Apply the evidence generated and the processes refined through this roadmap to reduce barriers in future studies, accelerate learning across programs, and limit redundant effort across the ecosystem.
We invite you to bookmark this roadmap and return as the landscape evolves. These resources will continue to expand in response to new evidence, updated regulatory guidance, and emerging best practices that support fit-for-purpose use of sDHTs in medical product development.
6.4 Open science
Library resources to guide you
The sDHT roadmap library gathers 200+ external resources to support the adoption of sensor-based digital health technologies. To help you apply the concepts in this section, we’ve curated specific spotlights that provide direct access to critical guidance and real-world examples, helping you move from strategy to implementation.
Features essential guidance, publications, and communications from regulatory bodies relevant to this section. Use these resources to inform your regulatory strategy and ensure compliance.
Gathers real-world examples, case studies, best practices, and lessons learned from peers and leaders in the field relevant to this section. Use these insights to accelerate your work and avoid common pitfalls.