Digital health is no longer a fringe topic; it sits at the heart of healthcare transformation. Fueled by rapid advances in artificial intelligence, remote monitoring, and data-driven platforms, the sector promises more personalised, accessible, and proactive care. Yet despite record levels of investment and over 350,000 health apps now on the market, meaningful adoption and measurable outcomes remain elusive.

At the core of this problem is what many are calling the trust and execution gap.

What is the trust and execution gap?

This gap manifests in two critical ways:

  1. Lack of Trust: Patients, caregivers, and the public remain wary of how their personal health data will be used, especially when commercial players are involved. High-profile controversies like Google DeepMind’s access to NHS data have shown how easily trust can be lost when transparency and consent processes are unclear.
  2. Poor Execution: Many digital tools fail to scale because they are not embedded into clinical workflows or aligned with healthcare providers’ incentives. Even the most promising innovation can stall at the pilot phase without co-design and genuine engagement from clinicians, payers, and patients.

From value chains to collaborative innovation ecosystems

To bridge this gap, healthcare systems must reframe how digital health is designed, funded, and delivered. Shifting from traditional value chains to collaborative innovation ecosystems.

A collaborative innovation ecosystem is not just a partnership. It is a dynamic, real-world network of:

  • Patients and citizen groups
  • Clinicians and provider organisations
  • Entrepreneurs and SMEs
  • Academic researchers and data scientists
  • Policymakers, regulators, and payers

These actors co-create, test, and scale digital health solutions that are not only technically sound but trusted, adoptable, and sustainable.

Key characteristics of successful ecosystems:

  • Validation in live healthcare environments.
  • User-centred design grounded in real-world needs and equity.
  • Sustainability through aligned incentives, shared ownership, and business models that endure beyond short-term funding cycles.
A circular diagram showing how collaborative ecosystem activities — including stakeholder engagement, research, Living Labs, and training — create value for patients, providers, policymakers, industry, and researchers. Each activity connects to outcomes like increased trust, adoption, and health system sustainability. Adapted from Carrilho et al., 2023.

The collaborative ecosystem activities and their impact on relevant stakeholders
Source: Carrilho et al., Frontiers in Digital Health, 2023

This graphic visualises how collaborative activities (like co-creation, validation, and training) affect key stakeholders across the ecosystem, improving trust, reducing barriers, and building readiness for real-world adoption.

A real-world example: regional ecosystem for collaborative innovation 

One leading model is the Regional Ecosystem for Collaborative Innovation in Digital Health and Care, spanning Portugal and Spain. This cross-border initiative brings universities, health providers, start-ups, and citizens together to drive local innovation. Its four pillars — network building, applied research, Living Lab experimentation, and workforce training — show how ecosystems can simultaneously build trust and accelerate uptake.

Another example is Canada’s Alberta Netcare system, which used Orion Health’s Amadeus and eReferral platform to:

  • Reduce wait times by as much as 90% in some cases
  • Avoid 42% of unnecessary in-person specialist referrals.
  • Engage over 51,000 users and serve 4.1 million residents with real-time shared records.

These are not isolated use cases. Ontario’s Health 811 Digital Front Door now serves over 15 million people, offering multilingual navigation, symptom checking, and virtual care channels in a cohesive, patient-first experience.

Evolving Use of Digital Technologies in Health

As digital health matures, its applications evolve from documentation and automation to prediction, personalisation, and participation.

A horizontal timeline illustrating the progression of digital health technologies from basic documentation and administration, to automation, remote monitoring, personalised prediction, and participatory care. The chart shows increasing data complexity and value creation as technology matures. Source: National Academy of Medicine, 2021.

Evolving applications of digital technology in health
Source: https://nam.edu/perspectives/the-promise-of-digital-health-then-now-and-the-future/

Collaborative Ecosystems Require Ethical and Transparent Governance

While public-private partnerships (PPPs) and academic-industry collaborations are crucial, they must be guided by ethical governance, transparent consent, and stewardship models that recognise data as a public good.

Key governance principles include:

  • Clear and proportionate consent processes
  • Intellectual property agreements that preserve public benefit
  • Oversight mechanisms to ensure long-term accountability and trustworthiness

Successful partnerships are underpinned by shared expectations, iterative design processes, and robust evaluation metrics that measure health outcomes and system value.

A layered diagram mapping relationships among healthcare stakeholders: patients, providers, policymakers, researchers, and industry. Arrows illustrate feedback loops and interactions required for successful digital health implementation, including trust-building, shared governance, and aligned incentives. Source: JACC, 2018.

Framework for stakeholder relationships for the use of digital technology
Source: https://www.sciencedirect.com/science/article/pii/S0735109718344139

This framework outlines how alignment between policymakers, solution developers, and end-users is essential for trustworthy, scalable digital health. It reinforces the need for shared accountability and bidirectional value flows.

Designing for Execution: Embedding into Clinical and Operational Workflows

Execution isn’t just a matter of technology readiness; it’s about workflow integration.

Digital tools must:

  • Be interoperable by design, leveraging standards like FHIR to plug into existing infrastructure
  • Support low-friction user experiences for both patients and clinicians
  • Be backed by reimbursement models that reward value, not just activity

Reimagining Digital Health Innovation for Real-World Impact

Digital health innovation is accelerating, but its true impact depends on more than breakthrough technologies. To reach scale and drive meaningful change, we must build collaborative ecosystems where:

  • Data is stewarded ethically
  • Solutions are co-created with users
  • Outcomes are shared, not extracted

As healthcare systems confront demographic pressures, rising chronic disease, and constrained resources, collaborative innovation ecosystems offer a path forward.

They align trust, execution, and shared value, move us from pilots to impact, and, most importantly, put people at the centre of digital care.

Authored by Tom Varghese, Global Product Marketing & Growth Manager at Orion Health.

References:

Bak, Marieke A. R., Daan Horbach, Alena Buyx, and Stuart McLennan. 2025. “A Scoping Review of Ethical Aspects of Public–Private Partnerships in Digital Health.” npj Digital Medicine 8 (129). https://doi.org/10.1038/s41746-025-01515-3.

Carrilho, Joana, Diogo Videira, Cláudia Campos, Luis Midão, and Elísio Costa. 2023. “Changing the Paradigm in Health and Care Services: Modern Value Chains Using Open Innovation for the Creation of New Digital Health Solutions.” Frontiers in Digital Health 5: 1216357. https://doi.org/10.3389/fdgth.2023.1216357.

Dzau, V. J., McClellan, M. B., McGinnis, J. M., Burke, S. P., Coye, M. J., Diaz, A., Daschle, T., Frist, W. H., Gawande, A. A., Henney, J. E., Leavitt, M. O., Parker, R. M., Slavitt, A. M., Steele, G. D., Thompson, T. G., Winkenwerder, W., & Zerhouni, E. A. (2022, February 28). The promise of digital health: Then, now, and the future. NAM Perspectives. National Academy of Medicine. https://doi.org/10.31478/202202e

Ford, Kelsey L., Jennifer D. Portz, Shuo Zhou, Starlynne Gornail, Susan L. Moore, Xuhong Zhang, and Sheana Bull. 2021. “Benefits, Facilitators, and Recommendations for Digital Health Academic–Industry Collaboration: A Mini Review.” Frontiers in Digital Health 3: 616278. https://doi.org/10.3389/fdgth.2021.616278.

Sharma, A., Harrington, R. A., McClellan, M. B., Turakhia, M. P., Eapen, Z. J., Steinhubl, S., Mault, J. R., Majmudar, M. D., Roessig, L., Chandross, K. J., Green, E. M., Patel, B., Hamer, A., Olgin, J., Rumsfeld, J. S., Roe, M. T., & Peterson, E. D. (2018). Using digital health technology to better generate evidence and deliver evidence-based care. Journal of the American College of Cardiology, 71(23), 2680–2690. https://doi.org/10.1016/j.jacc.2018.03.523