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3 ways the Delphi Method and other consensus techniques support MedTech

Posted: 28th June 2024
Categories: Uncategorised

The adoption of a MedTech innovation relies not only on demonstrating positive results from clinical trials, but also heavily on the strategy for positioning within a specific context to enable better patient outcomes.

Evidence development is therefore a priority, from the design phase and throughout the life cycle of the product.1 This ensures decisions that are being made are informed, and then implemented in healthcare systems. For this, discussions with key stakeholders are necessary.

Consensus-led techniques such as the Delphi method allow understanding of agreement regarding a specific topic (or, in this case, procedure). Formal consensus techniques can and have been used in healthcare since the 1950s to capture insights about the opinion of experts, particularly useful in the absence of conclusive empirical data.2

Having conducted studies for the past decade to support MedTech innovations, here are 3 ways we have found the Delphi method support innovations:


The Delphi method can help define the unmet need


Consensus-led evidence can be used to understand the views and experiences of the audience it serves, notably patients, healthcare professionals, and payers, to solidify the need the innovation responds to. The Delphi method, for example, involves a series of questionnaires sent over multiple rounds to a panel of experts to reach consensus, and is a credible technique for defining unmet need.

Since MedTech innovations often have numerous potential applications, having a clear focus for the positioning of the product on the market is essential. Additionally, a MedTech innovation must integrate within existing pathways and workflows or demonstrate the need for those to change to be adopted. 3

Consensus-led evidence that demonstrates how an innovation addresses a specific gap in the healthcare system will influence its acceptance and adoption once it is launched. For instance, we employed the Delphi method to understand and emphasize the need for standardising the use of leadless pacemakers (LPs) for certain patient groups, in a context where LPs remained underutilised compared to transvenous pacemakers but have the potential to reduce risks for specific patients (e.g. patients at high risk of infection, on hemodialysis, with previous cardiac device infections, immunocompromised etc). The expert recommendations offered focus areas to improve education, training, registry development, and appropriate patient selection for LPs.


The Delphi method can help establish best practice


Generating consensus-led evidence can help establish clarity and inform the relevant stakeholders around the best methods, procedures, or processes of managing a specific condition or intervention.

When there is a lack of uniformity regarding disease or intervention management, the Delphi method can help by generating evidence within and for a specific context, an area where clinical trial data is limited.

For example, we used the Delphi method to help define best practice for optimal robotic colorectal surgery. Surveying surgeons, the results allowed the definition of recommendations to standardise robotic colorectal surgery, including frequent patient repositioning for maintaining visibility and access during colorectal robotic-assisted surgeries. This supported the seamless table position changes possible to achieve through the use of Integrated Table Motion (ITM) systems.

Credible evidence around best practice can then support change and make decisions actioned when integrated into a campaign. In this case it started with the publication of findings in a peer-reviewed journal, then dissemination of knowledge through the development of various audience-specific materials aimed at educating and mobilising peers within the medical community.


The Delphi method can help update guidelines


Consensus-led generation methods are instrumental in creating and updating clinical guidelines. Beyond the use of techniques such as the Delphi method by guideline developers themselves, insights from consensus-led evidence can help engage with policymakers and ensure that the voices of key stakeholders such as experts and patients are heard to support the adoption of an innovation.

For example, in 2023 the FDA released a guidance document to provide its recommendations on the development of medical devices that are supported by machine learning algorithms. Triducive used an alternative to the Delphi method to generate expert opinion evidence: a Nominal group technique (NGT). As a structured decision-making method, an NGT encourages equal and individual participation from group members to generate, clarify, and prioritise ideas or solutions.  In that case, it made it possible to identify consensus between US radiologists on the standardisation of AI in radiology to support policy engagement with the White House’s actions for safe and trustworthy AI.


About us


At Triducive we deliver consensus-led evidence that gets published and supports change for MedTech teams around the world.

Our team has been delivering Delphi Consensus for over ten years around the world, boasting over 50+ peer-reviewed publications, and fostering positive change.

Get in touch with us to learn about the way we design and facilitate Delphi consensus and also how we help turn this new evidence into powerful advocacy-based campaigns.


  1. AdvaMed. 2017. Understanding Evidence & Value in Medical Technologies. https://www.advamed.org/wp-content/uploads/2021/11/Understanding-Evidence-Value-Medical-Technologies-May-2017.pdf.
  2. Murphy, MK, CFB Sanderson, NA Black, J Askham, DL Lamping, T Marteau, and CM McKee. “Consensus Development Methods, and Their Use in Clinical Guideline Development.” Health Technology Assessment 2, no. 3 (1998) https://www.journalslibrary.nihr.ac.uk/hta/hta2030/#/full-report
  3. Government of the United Kingdom. Medical Technology Strategy. 2023. https://www.gov.uk/government/publications/medical-technology-strategy/medical-technology-strategy.