Demonstrating the benefits of digital health: Evidence generation

How can we generate evidence to demonstrate the effectiveness and benefits of digital health solutions reliably?

The rise of digital health solutions is transforming the landscape of healthcare, supporting personal health and assisting healthcare providers with diagnosis and treatment. Over the past decade, significant investments have bolstered digital health, and an increasing number of new companies are emerging in this field1,2. This holds immense promise in enhancing healthcare access and outcomes, promoting equity, and boosting healthcare organisations' operational efficiency and cost-effectiveness.

Yet, key challenges remain:

  • How can healthcare providers or users assess the benefits of digital health solutions? 

  • What type of evidence should companies produce to substantiate these benefit claims?

Digital health solutions vary widely, so there's no one-size-fits-all standard for the needed evidence. The type of evidence depends on the solution’s purpose and the regulatory requirements of the respective market. Solutions with a medical purpose for example, like Software as a Medical Device () in the US, Medical Device Software () in the EU, or digital therapeutics, need regulatory approval and clinical evidence of safety and efficacy3. The required types of evidence are clinical association, analytical, and clinical performance in the intended environment.

Also for products without a medical purpose, like electronic health records or clinical workflow tools, and potentially less stringent regulatory requirements, it is important to demonstrate their clinical, financial, operational, and experiential benefits to showcase their value3. This might include peer-reviewed evidence of clinical impact, health economic research, operational efficiency data, and qualitative research on usability and adoption.

The traditional methods of evidence generation, such as(RCTs), are not the best fit for digital health solutions4. Digital health solutions evolve quickly, and drug trials demand long-term stability and conformity. Moreover, these trials often have strict criteria for who can participate, which might not reflect the diversity of real-world populations that will actually use the technology in everyday life. Additionally, these trials can be very costly and time-consuming4.

  • Simulation studies ​​evaluate new digital health solutions safely, efficiently, and cost-effectively before introducing them to the real world.

  • Real-world evidence (RWE) generated by digital health solutions adds value in the early stages of evaluation and during monitoring after introduction to the market. For example,for digital health solutions highlights the growing importance of RWE in supporting the diagnosis and treatment of diseases as well as patient-initiated lifestyle changes.

  • Platform trials (PTs) are a novel study type that may be useful for evaluating quickly evolving digital health solutions as these trials are designed to be adaptive, allowing for interventions to be modified or changed completely over time5.

Robust evidence generation is key to effective digital transformation in healthcare. Taking the required steps to generate evidence will benefit all stakeholders in digital health, and above all will benefit the patients served (directly or indirectly) by digital health solutions. Looking ahead, the benefits of new and pragmatic approaches to evidence generation must be promoted to support the delivery of impactful digital health solutions to clinicians and patients.

The development of effective, evidence-based solutions alone is not enough. Engaging effectively with clinicians will be necessary to demonstrate the benefits of digital health solutions for their patients and clinical workflows.

Download our white paper on evidence generation for digital health solutions


  1. Cohen, Dorsey, Mathews, Bates and Safavi. (2020). NPJ digital medicine 3, 1-10

  2. Krasniansky, Evans, and Zweig. (2022). Report available at[Accessed Jan 2024]

  3. Conroy, Fontana, Prime, Ghafur. (2023) White paper avaiable at[Accessed Jan 2024]

  4. Guo, Chaohui et al. “Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches.” NPJ digital medicine vol. 3 110. 27 Aug. 2020, doi:10.1038/s41746-020-00314-2

  5. Aqib A, Lebouché B, Engler K, Schuster T. Feasibility of a Platform Trial Design for the Development of Mobile Health Applications: A Review. Telemedicine and e-Health. 2023 Apr 1;29(4):501-9.

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