How to Design a Clinical Trial for Your Digital Health Product?

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Proving clinical efficacy is one of the biggest challenges for digital health companies. However, it is key to getting buy-in from stakeholders. How can we design trials for digital health tools so they are rigorously validated and regulated? Why shouldn’t we design clinical trials for digital health products using the same approach we have for drug trials?

Read the key takeaways from the Masterclass on ‘How to Design a Clinical Trial for Your Digital Health Product?’ by Suzanne Clough, Chief Medical Officer at AmalgamRx.

• Positive results of digital health studies, while potentially critical to commercial success, do not always lead to commercial success. Evidence generation and commercial strategies need to go hand in hand. They can be parallel tracks; you can do a soft commercial launch to improve the product and, in the meantime, continue to generate evidence. 

• There is a big difference between pharma products and digital health. For digital health you never have a final product, the intervention matures over time, hence the evidence generation strategy for digital health should be different from the traditional approach.

• The study design and the outcomes you need to prove will depend on how mature the digital health product is. There are six stages of intervention maturity in the life cycle of a digital product. There is a big lift between the prototype stages and an efficacy study, pilot products often are not true efficacy-ready products. The digital health industry usually fails when going from proving efficacy to proving effectiveness. 

• Checking usability before going to efficacy studies is critical. The digital health industry often jumps to efficacy studies too early, before ensuring the product is not a burden for the end-users. 

• There are a variety of study designs that may be suited for digital health tools. Which one to use depends on: 1) the lifecycle stage of the product; 2) what you want/need the study to do for you as a business; 3) end-user/customer threshold for acceptance; 4) what the regulators suggest if anything, the study should demonstrate; 5) will end users/customers see its utility outside of the clinical trial. 

• RCT design without subgroups doesn’t allow for enough insight into what part of the intervention had an impact on outcomes, which is something payers ask. Attribution to intervention is critical. Payers may also ask about the outcomes of their specific populations, however, attributing what works in each population is hard. Incorporating statistical analysis in the trial could help determine what works in different patient populations, however, doing this well is still a huge challenge for the industry.

(This content was originally published on 4th May, 2022.)