You just finished presenting the results of your research project at the annual conference for your field. Your novel digital health intervention demonstrated a meaningful and robust impact on outcomes. This is a big moment; you’ve worked hard to get here. It’s time for Q&A. The questions are all good ones, and you’re armed with all the answers. Except one: “How will this great work be translated into practice? When will it be available for me to use?”

Surely you’d like to have a better answer than “never,” following the fate of 9 out of 10 research interventions that are never translated into clinical practice, or “17 years,” the average time it takes for those that do make it to clinical environments (Morris, 2011). Surely you’d rather not see this work end up on the shelf when it could instead improve people’s lives. And yet, researchers like you may struggle to find the right technologies that support both research and clinical contexts.

The challenges of translating digital health programs from research to the real world are well established. Simon Mathews et al. (2019) and Madalina Sucala et al. (2019) recently published comprehensive overviews of the topic, complete with translation frameworks for evidence-based digital health programs. Technology, they note, is a fundamental piece of the puzzle for translation. And given the complex and rapidly-evolving health technology landscape, it is one that many find to be especially challenging to get right. It doesn’t need to be.

Your technology strategy could be this simple

A digital health program’s suitability for research and real-world contexts depends heavily on its underlying technology. Most health technology solutions are designed for either research or the real world, but not both. It’s not unusual for a health technology solution designed to support a research study with a few hundred participants to fail to support thousands of users, or miss essential real-world capabilities and considerations. When the technologies used for research are not suited for the real world, digital health programs must undergo major work to be translated and may even need to be recreated with new technologies, costing time and money — barriers that can result in failure to implement potentially impactful programs in the real world.

Pattern Health simplifies the technology strategy for research translation by providing a solution that supports both research and the real world, with a smooth transition between the two.

Technology for research and the real world

What does a technology solution capable of supporting both research and the real world look like?

Shared needs — Digital health programs for research share much in common with those for the real world. Both need:

  • To collect and interpret biometric and self-reported data as they guide users along their health journey

  • The highest standards of data privacy and security

  • To be capable of serving a large number of users with a diverse range of needs: different languages and abilities, and a proliferating assortment of devices

Research needs — Digital health programs used for research have unique needs for:

  • Informed consent

  • Experimentation

  • Advanced data collection and analysis capabilities

  • Intellectual property (IP) rights and protections that ensure an organization can publish its research results

Real-world needs — In the real world, digital health programs need:

  • Electronic Health Records (EHR) systems integration

  • Support for a variety of enrollment pathways (self-enroll, provider-prescription) and workflows

  • Intellectual property (IP) rights and protections that ensure an organization can commercialize its programs in the real world

  • A marketplace that allows organizations to make programs available to use by outside organizations and people through a range of pricing and payment models

  • A sustainable model for maintaining the technology solution

Pattern Health supports digital programs, from research to the real world

The benefits of a technology strategy that supports both research and the real world extend beyond translation. It gives researchers the opportunity to work with Real-World Data (RWD) to generate Real-World Evidence (RWE), allowing them to better understand intervention effects in the real world, beyond premarket trials. RWE research has the potential to answer questions that were previously infeasible — understanding interactions with other interventions, off label use, rare side effects — and is only possible with technology that supports both research and real-world requirements.

At Pattern Health we enable and accelerate digital health research, including Real World Evidence, and provide a seamless path for translation to the real world — including, if desired, external distribution through the Pattern Health Exchange. We do this to allow researchers to focus on what they do best: innovate. We believe it’s the only effective approach to evidence-based innovation in health, and the best way to improve people’s lives.

Our goal at Pattern Health is to provide technology that gives researchers more opportunities to make impactful discoveries, and allows them to easily translate their work to the real world. So that when you’re on that presentation stage and you get the question, “When will it be available?”, you can smile and answer confidently, “Soon! We have a plan to implement this broadly, make it available for you to use, and to continue improving it over time.”



  1. Morris, Z. S., Wooding, S., & Grant, J. (2011). The answer is 17 years, what is the question: understanding time lags in translational research. Journal of the Royal Society of Medicine, 104(12), 510-520.

  2. Mathews, S. C., McShea, M. J., Hanley, C. L., Ravitz, A., Labrique, A. B., & Cohen, A. B. (2019). Digital health: a path to validation. NPJ digital medicine, 2(1), 38.

  3. Sucala, M., Ezeanochie, N. P., Cole-Lewis, H., & Turgiss, J. (2019). An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions. Translational behavioral medicine.

Written by: Ed Holzwarth