Digital health innovation is moving quickly across the NHS. But developing an innovative technology is only the beginning.

For digital health solutions to become a meaningful part of healthcare delivery, organisations increasingly need to demonstrate that their technology is not only innovative, but also supported by credible evidence.

That is why we are pleased to share that GP Chatbot was selected for the DigitalHealth.London Evidence Generator Bootcamp 2026, joining a cohort of 21 companies selected from around 70 applications.

For GP Chatbot, an AI-powered digital receptionist designed for GP practices, participation in the programme represents an important next step: moving beyond what digital technology can do towards systematically understanding and measuring the difference it makes in real-world primary care.

Why Evidence Matters in Digital Health

Healthcare technology operates in a very different environment from many other technology sectors.

A useful product or promising idea alone is not enough. Digital health technologies need to consider questions around effectiveness, safety, patient experience, accessibility, implementation and value.

For NHS organisations evaluating new technology, these questions matter.

Does the technology improve the patient journey?

How do patients actually engage with it?

Does it integrate effectively into existing workflows?

How do staff experience the technology?

Is it accessible across different patient groups?

And can its operational or economic benefits be demonstrated with credible data?

Building a stronger evidence base around these questions can help healthcare organisations make better-informed decisions about adopting and scaling digital technologies.

This is particularly relevant in primary care, where digital tools are increasingly becoming part of how patients access information, navigate services and communicate with their GP practice.

What Is the DigitalHealth.London Evidence Generator Bootcamp?

The DigitalHealth.London Evidence Generator Bootcamp is a 10-week online programme designed to help digital health companies develop credible approaches to evidence generation.

The programme is particularly relevant to companies that are approaching or have already entered the health and social care market, including the NHS, and want to strengthen the evidence supporting their technology.

Rather than treating evidence generation as something that happens after a product has been widely deployed, the programme encourages digital health innovators to think systematically about evidence as part of their development and adoption journey.

The 2026 programme includes expert-led workshops, peer-to-peer learning, digital resources, self-directed learning and dedicated guidance from an NHS Digital Guide.

For GP Chatbot, this provides an opportunity to develop a more structured approach to understanding and demonstrating its impact within real GP practices.

Moving From Digital Health Innovation to Evidence

One of the most valuable aspects of the programme is the shift in thinking from:

“We have developed an innovative solution.”

to:

“How can we credibly demonstrate the impact of that solution?”

That distinction is important.

Claims about saving time, improving access or supporting patients become considerably more meaningful when they can be examined through appropriate real-world measures.

Through the Evidence Generator Bootcamp, GP Chatbot has been considering several areas relevant to building this evidence base, including the NICE Evidence Standards Framework for Digital Health Technologies, health-economic evidence requirements, patient and user acceptability, accessibility and health inequalities, deployment requirements, scalability and real-world evidence.

The programme has also encouraged consideration of how claimed benefits should actually be measured.

What Does Real-World Evidence Look Like for GP Chatbot?

GP Chatbot is designed to support the digital front door of general practice by helping patients navigate common enquiries and find appropriate information and next steps.

As the technology is used within real practices, there is an opportunity to look beyond simple adoption figures and understand how patients and practice teams actually interact with it.

The next stage of evidence generation will therefore involve working with practices to establish baseline measures and collect real-world data across several areas.

These include:

  • Patient use and engagement: understanding how frequently patients use GP Chatbot and how engagement develops over time.
  • Types and volumes of enquiries: examining the questions and needs patients bring to the digital receptionist.
  • Administrative and reception capacity: exploring whether particular types of digital interaction have the potential to release staff time.
  • Patient navigation and access: understanding how effectively patients are guided towards relevant information and appropriate routes.
  • Patient and staff experience: considering acceptability and how both groups experience the technology.
  • Accessibility and inequalities: examining usage across different population groups and whether digital access works effectively for diverse patient communities.
  • Implementation and adoption: understanding what helps practices successfully introduce and embed the technology.
  • Potential economic and operational benefits: exploring where digital support may create measurable value for primary care.

Together, these areas can provide a much richer picture than any single headline statistic.

Why Real-World Evidence Is Important in Primary Care

General practice is complex.

Two GP surgeries may serve very different patient populations, operate different workflows and experience different patterns of demand. A digital solution that works effectively therefore needs to be understood within the environments where it is actually being used.

This is where real-world evidence in digital health becomes particularly valuable.

Rather than relying solely on assumptions about how technology should perform, real-world evidence can help identify how it performs when patients and staff encounter it during everyday primary care.

It can also reveal where improvements are needed.

For example, usage data may show which types of enquiries patients commonly bring to a digital service. Patient feedback may highlight accessibility challenges. Staff experience may identify workflow improvements or implementation barriers.

Evidence generation therefore isn’t simply about proving that technology works.

It can also help make the technology better.

Accessibility and Health Inequalities Must Be Part of the Conversation

As more patient access becomes digital, accessibility needs to remain central to digital transformation in primary care.

Not every patient interacts with technology in the same way.

Age, language, disability, digital confidence, access to devices and other factors can influence how patients use digital healthcare services.

For that reason, understanding overall usage alone may not provide the complete picture.

Evidence generation should also consider who is using digital health technology, how different groups experience it and where barriers may remain.

This is an important part of developing digital patient access that works for diverse communities rather than simply increasing the number of digital channels available.

Building Evidence Alongside Innovation

Digital health will continue to evolve, and AI is likely to become an increasingly visible part of patient access and healthcare administration.

But innovation and evidence shouldn’t develop separately.

The stronger approach is to build, deploy, measure, learn and improve.

For GP Chatbot, participation in the DigitalHealth.London Evidence Generator Bootcamp is helping establish a framework for doing exactly that.

The next phase will be about translating those learnings into practical evidence generation within primary care: establishing meaningful baselines, working with practices, gathering real-world data and developing a clearer understanding of where the technology creates value and where it can continue to improve.

Because ultimately, innovation may open the door.

Evidence is what builds confidence.