By Devesh Sinha, Chief Clinical Information Officer, Barking, Havering and Redbridge University Hospitals NHS Trust – Devesh is a guest speaker at this year’s Digital Health Rewired Conference on 18-19 March 2025
Ahead of his forthcoming presentation at Digital Health Rewired 2025, Devesh Sinha, Chief Clinical Information Officer at Barking, Havering and Redbridge University Hospitals NHS Trust, reflects on the trust’s experiences implementing AI in clinical practice and the delicate balance required between innovation and cultural evolution.
As healthcare continues to evolve at an unprecedented pace, the integration of Artificial Intelligence (AI) in clinical settings presents both extraordinary opportunities and significant challenges. Drawing from our experiences at one of London’s largest acute NHS trusts I’ve witnessed firsthand how AI can transform healthcare delivery – but only when we carefully consider the human elements alongside the technological capabilities.
When we first began our AI journey, like many healthcare leaders, we were captivated by the potential of these technologies to revolutionise patient care. However, our experience has taught us that successful AI implementation requires much more than just sophisticated technology. It demands a delicate balance between innovation and cultural adaptation.
Take, for instance, our implementation of speech AI for clinical letter dictation. The traditional process involved clinicians dictating letters after patient consultations, which then required medical secretaries to transcribe, format, and process them before sending to GPs – a workflow that could take over a month to complete. The initial pilot showed remarkable promise – reducing these letter delivery times to GPs from 37 days to just four days. However, the real-world implementation told a different story. As we scaled the solution, we encountered significant cultural resistance and varying adoption rates among clinicians. Some staff expressed concerns about job security, particularly for medical secretaries, whose roles were deeply embedded in this transcription workflow, while others were hesitant to change their established documentation practices.
The adoption curve: a journey of persistence
What we’ve learned is that AI adoption follows a natural curve – one that often includes initial resistance, gradual acceptance and eventually, meaningful integration. In our case, while the initial implementation saw average letter delivery times increase due to cultural barriers, persistent engagement and support eventually led to a stabilisation at 19-23 days – a significant improvement from our starting point, though not as dramatic as the pilot suggested.
This experience highlights a crucial lesson: the success of AI in healthcare depends not just on the technology’s capabilities, but on our ability to manage the human aspects of change. As I often say, you can have the most sophisticated solution for a highly intense area, but if you don’t give enough attention to how people will adopt it, it’s bound to fail.
Ethics and accountability in AI implementation
One of our most important initiatives has been the establishment of an AI and analytics ethics group. This wasn’t just a bureaucratic exercise – it was a fundamental step in ensuring that our AI implementations serve our entire patient population fairly and effectively. The group helps us navigate complex ethical challenges, particularly in population health-based AI, where biases can have serious consequences for patient care.
The ethics group serves as a crucial checkpoint, ensuring that our AI solutions don’t just solve technical problems but also align with our values as healthcare providers. This is particularly important when dealing with diverse patient populations, where AI systems must be carefully evaluated for potential biases or limitations.
Cultural change and professional impact
The impact of AI on healthcare professionals is a sensitive yet crucial aspect of implementation. When we launched our stroke imaging AI solution, the response was so strong that I jokingly say I needed to wear a helmet and shield when walking past the radiology department. This highlights the very real concerns healthcare professionals have about AI replacing their roles.
However, our experience has shown that successful AI implementation isn’t about replacement – it’s about enhancement. By actively engaging with clinicians, addressing their concerns, and demonstrating how AI can support rather than supplant their expertise, we’ve been able to build greater acceptance and trust in these technologies.
Looking ahead: a five-year vision
As we look to the future, our vision for AI in healthcare is both ambitious and grounded in practical reality. We’re focusing on developing clear problem statements before implementing AI solutions – ensuring that we’re using technology to address specific, well-defined clinical needs rather than implementing AI for its own sake.
In the next five years, I envisage a healthcare system where AI is seamlessly integrated into clinical workflows, supporting decision-making while maintaining the crucial human elements of healthcare delivery. This vision includes robust ethical frameworks guiding AI implementation, enhanced clinical decision support systems that complement professional expertise, streamlined administrative processes that free up clinicians for patient care, and improved access to specialist expertise through AI-enabled tools.
The journey of implementing clinical AI is not a sprint but a marathon. Success requires patience, persistence, and a deep understanding of both the technical and human elements involved. As we continue to advance in this field, we must remain focused on our ultimate goal: improving patient care and outcomes.
The lessons we’ve learned – about the importance of cultural adoption, the need for ethical oversight, and the value of clear problem statements – will be crucial as we continue to develop and implement AI solutions in healthcare. By sharing these experiences and insights, we hope to contribute to a broader dialogue about how to effectively and responsibly advance healthcare through AI implementation.
Our experience at Barking, Havering and Redbridge shows that while AI holds tremendous promise for healthcare transformation, its successful implementation depends on our ability to navigate the complex interplay of technology, human factors, and organisational culture. As we continue this journey, maintaining this balanced perspective will be crucial for realising the full potential of AI in healthcare. I look forward to sharing more of these insights and experiences at the upcoming Digital Health Rewired Conference on 18-19 March 2025.
