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This webinar, as part of Patients Association's Patient Partnership Week, will explore how organisations can partner with patients in the use of health data, placing trust and transparency at the heart of decision making. It will examine how technology currently uses patient data, why involving patient panels is essential, and how this supports better outcomes and public confidence. Register

Last month, Public Policy Projects hosted their annual Patient Safety Forum in partnership with Patient Safety Learning. Held at the Royal College of Surgeons of England in London, it was attended by senior healthcare leaders, patient safety experts, representatives from the HealthTech industry, frontline healthcare professionals and patients.  In this article, Patient Safety Learning reflects on one of the panel discussions—AI for patient safety: Innovation, assurance and strengthening communication. From AI-enabled ambient scribing tools that reduce the burden of administration, to predictive systems capable of detecting early warning signs before harm occurs, AI has significant potential to improve patient care and outcomes. Yet, alongside these benefits come risks—algorithmic errors, data bias, and challenges in maintaining trust, governance and oversight. At the Patient Safety Forum 2026 an expert panel was convened to discuss this topic, with the following members: Clive Flashman, Chief Digital Officer, Patient Safety Learning Dr Alison Cave, Chief Safety Officer, Medicines and Healthcare products Regulatory Agency (MHRA) Anil Mistry, AI Safety Lead, Guy’s and St Thomas’ NHS Foundation Trust Dr Basil Bekdash, Clinical Safety Officer, Sheffield Children’s NHS Foundation Trust Aleksander Alski, Head of Region – USA, Canada and UK, Vasco Electronics Panellists had a lively discussion with each other and the audience about how to balance innovation with assurance, to ensure that the use of AI in healthcare enhances safety rather than undermines it. They spoke about how AI should be understood as a support tool for healthcare professionals—it provides information and removes burden but, ultimately, staff treat patients. In this blog we highlight several key topics that emerged from this debate. Importance of patient safety A key theme running throughout the panel’s discussion was the importance of patient safety being built into AI development at the outset. Clive Flashman from Patient Safety Learning reflected on this point, suggesting that too often this is seen as a compliance ‘tick box’ or treated as an afterthought. Speaking to digital innovators, his message was that “you need to think about this from the very start when you are conceptualising the product”. Panellists also recognised that putting safety at the centre of discussions around AI and healthcare means involving all stakeholders, not just the healthcare professionals using these technologies but suppliers too. Alexander Alski from Vasco Electronics emphasised the importance of this being an area of shared responsibility between suppliers and healthcare providers. Getting regulation right Alison Cave from the MHRA spoke about the ongoing work of the National Commission into the Regulation of AI. This Commission was established by the MHRA to review current regulations and provide recommendations for a new regulatory framework for AI in healthcare. It held a public call for evidence which Patient Safety Learning responded to earlier this year. Discussing how to approach future regulation, she highlighted the importance of ensuring that “the risk is associated with the decision, not the technology itself”. It was noted that in some cases there may be very complex pieces of software in use, but these may be making very low-risk decisions. Panellists underlined the importance of having a risk-proportionate regulatory framework to support safe innovation. Predicting future harm The potential to use AI to identify patient safety issues is understandably an area of significant interest. Last year the Department of Health and Social Care announced that it planned to develop a world-first artificial intelligence (AI) early warning system to automatically identify safety concerns across the NHS. Panellists were asked to consider what examples they had seen of AI moving from reacting to incidents, to predicting and preventing future harm. They spoke about the value of AI as a support tool for clinicians and more broadly how it might be used to identify emerging patient safety issues. Basil Bekdash from Sheffield Children’s NHS Foundation Trust spoke about work that had been trialled in this area, but noted that currently there have not been many examples where these have been proven on a significant scale, stating: “None of them have really quite got to the point where they're proven in widespread deployment and so I'm not going to predict that's going to happen in the next five years.” Tackling bias While an AI tool may be safe when properly implemented and used by a well-trained healthcare professional, it could be potentially dangerous if such training and support is absent. Panellists concurred that having appropriate training and tackling bias were issues of critical importance in ensuring the safety of AI in healthcare. In particularly they discussed risks presented by: Confirmation bias—healthcare professionals favouring AI outputs that align with their pre-existing view and overlooking signals that may challenge this. Automation bias—over-reliance on AI systems and accepting their recommendations without sufficient critical evaluation. Alison Cave from the MHRA said that part of the training should be ensuring that healthcare professionals understand the devices they are using and where there are trade-offs between sensitivity and a specificity. Basil Bekdash from Sheffield Children’s NHS Foundation Trust noted the importance of having in mind the different levels of digital competence of staff, stating that when designing AI systems: “It is best to test by using your least capable people who are the least digitally enabled and that's not a criticism that's just the reality of the normal spread of what people do, and their primary function is to look after patients.” Transparency and patient communication As use of AI grows in healthcare, it is vital that patients understand how this is being applied if they are to have confidence in its safety. Panellists discussed issues around how to inform patients when AI influences their care, particularly when it affects clinical judgments. Anil Mistry from Guy’s and St Thomas’ NHS Foundation Trust suggested that: “If the AI result is going to affect their patient’s care, and it's going to limit their access to finite resources like a waiting list or appointments or ICU beds, then absolutely have that sort of communication.” However, he also spoke about some of the challenges this raises; for example, if a patient asked about whether AI has been used in their care. In practice this could cover a very broad range of areas, from the use of ambient scribes to take notes to tools that analyse images from scans. Panellists indicated that transparency needed to be balanced and proportionate to both the risk and impact on individual care. Governance requirements AI healthcare technologies have significant scope to evolve and change over time. When they iterate rapidly (with new versions being released at regular intervals) it can be difficult for existing governance frameworks, designed for other types of medical devices, to keep up. Panellists discussed the importance of having flexibility to governance arrangements. There was the suggestion that lower risks tools (such as those in Class 1 for Medical Devices under the MHRA framework in the UK) should have greater flexibility, with higher levels of scrutiny reserved for decision-influencing tools. It was also made clear that any new regulation will need to carefully consider the level of ongoing evaluation that will be required to account for these systems evolving and changing over time. This may be much longer than for other medical devices and change at significant pace. One audience member commented that with these tools becoming increasingly complex, in the future “realistically there is going to be a need for an AI tool that assesses AI tools”. Panellists also considered how procurement processes could act as potential leverage mechanisms for AI technologies in healthcare. It was noted they offer the potential opportunity to embed the open standards we want to see being used by AI technologies in the earliest stages of their design, putting safety concerns at the centre of the product before it ever reaches patients. Improving the quality of data Data accuracy, completeness and representativeness is key to ensuring AI technologies work safely in health and care environments. Panellists noted that poor foundational data standards undermine AI model training and lead to unreliable outputs. Their discussion reflected that a significant proportion of time is often spent on data cleaning before even applying AI. Improving this would have wider benefits for research, operational efficiency and public healthcare. As we increase the use of AI health technologies, it is vital that we do not embed existing health inequalities. Following on from comments in an earlier session from Professor Bola Owolabi from the Care Quality Commission, Alison Cave from the MHRA noted a “perennial challenge in all of our areas is to ensure that the training data is representative”. Training data for AI systems must be representative of diverse populations and care settings. Sharing insights from the frontline If healthcare organisations, professionals and suppliers are to share responsibility for the safe implementation of AI technologies in healthcare, this must go hand in hand with shared learning. Panellists discussed the need for sustained and transparent feedback loops between suppliers, regulators and healthcare organisations. On this point an audience member asked: “How do we ensure our learning keeps pace so that existing insight from frontline teams that really know the business can optimally inform the evolution of products, but without stifling the pace?” Panellists highlighted the absence of standardised mechanisms for frontline staff to provide real-time, structured feedback to AI suppliers on safety issues. One proposed suggestion to this was the potential to mandate native feedback functionality within AI health technologies. This would mean that feedback mechanisms are built directly into the AI tool’s user interface and workflow, allowing those using them to provide input about the AI’s output without leaving the system. Find out more about the Patient Safety Forum 2026 You can read more about different discussions and panel sessions at this year’s event in the below: Patient voice, safety and the NHS 10 Year Plan: Reflections from the Patient Safety Forum 2026 Safe systems, safe cultures: reflections from the Patient Safety Forum 2026

Working across frontline emergency care, patient safety and digital patient safety over the course of my 22-year career in the NHS has given me a unique perspective on how digital systems shape real clinical practice. As a paramedic now working as a Clinical Safety Officer within NHS Wales, I’ve seen first‑hand how digital tools can support safer care—but also how they can contribute to patient harm when things don’t work as intended. In this blog, I reflect on the challenges of identifying issues and, more importantly, assessing patient harm in a digital context. These thoughts aren’t theoretical, they come from day‑to‑day reality: the calls, the investigations, the conversations and the moments where something in the digital healthcare system doesn’t work the way it should—and a patient feels the impact. I’m sharing these thoughts to stimulate conversation, hopefully build shared understanding and help strengthen our collective approach to digital patient safety across the UK. The growing complexity of digital healthcare Digital healthcare has evolved rapidly, and with that evolution comes complexity. Electronic health records, diagnostic platforms, telehealth solutions, national and local systems—all interacting with each other in ways that aren’t always obvious. When something goes wrong, pinpointing where the issue originated can be incredibly challenging. Was it a configuration setting? A workflow design flaw? A user misunderstanding? A vendor update? A mismatch between national and local versions of the same system? Add to that, the fact that some third‑party suppliers are unable or unwilling to share detailed technical information (I assume due to concerns that competitors may gain access to it) makes it even harder to determine how the incident occurred or how to prevent it from happening again. Interconnected systems, shared responsibilities Because digital care rarely sits within a single organisation, the responsibilities for harm often cross boundaries too. Different organisations use systems differently. Local configurations vary. Some teams rely on national services; others are still using legacy versions. All of this makes investigation slower, more complicated and highly dependent on strong cross‑organisational collaboration. No single organisation can fully assess digital‑related harm in isolation, but still we try! The challenge for non-patient‑facing Health Bodies For organisations like mine, there is an added complexity: we don’t have direct clinical access to patients. This means our ability to assess harm depends on the engagement of colleagues across health boards and trusts—many of whom are experiencing significant operational pressures. Data security and privacy Sharing information about harm while protecting patient data is essential, but not always simple. We must balance transparency with strict confidentiality requirements. Digital errors, diagnostic risks and human interpretation Not all harm is caused directly by digital systems. Sometimes the system works correctly, but the presentation of the data creates an issue, or the clinician/user interaction or interpretation of the data is the issue. Other times, issues stem from algorithmic limitations, technical malfunctions or messaging fabric (infrastructure that connects the system components and allows them to communicate) problems. Determining whether harm originated with the tool, the user or the interaction between them is rarely straightforward, and tools like Systems Engineering Initiative for Patient Safety (SEIPS) are vital in breaking this complexity down. Training, local workarounds and the gaps no one talks about Training remains a significant challenge. National bodies like mine are not responsible for delivering frontline training, and local approaches vary widely. This leads to several risks: Depth and quality of training varies. Important system features may be misunderstood or overlooked. Safety considerations are not always emphasised during training. Local 'shortcuts'—never designed, tested or approved—become normal practice. Once these shortcuts become embedded in everyday workflows, they can be incredibly difficult to unwind. Yet they often play a significant role in digital‑related incidents. The existing DCB0129 and DCB0160 standards provide a useful foundation, but they offer limited guidance on how to investigate and learn from digital incidents. They were designed at a time when digital healthcare was far less complex than it is today. Suppliers don’t like to highlight their products weaknesses or errors made; therefore, there is vast variation in the quality of investigation reports shared post incident. Rather than worrying about reputational damage, I wish the focus was on candour and opportunities for learning and development. The timeliness problem: when harm takes time to surface Digital harm isn’t always immediate. It may be a misfiled result, a confusing display or a workflow that gradually introduces delay. Additional challenges include: Variation in national policy timescales (in Wales six differing policies provide timescale guidance). The need for clinical review to confirm harm. Limited capacity among clinicians supporting digital investigations. This can make it difficult to meet regulatory expectations for timely disclosure—even when everyone involved is committed to doing the right thing. Freedom to Speak Up: a critical enabler of early detection Speaking up plays a vital role in identifying digital‑related safety issues early. Many concerns emerge informally at first—“this doesn’t look right” or “this field always causes confusion.” If staff feel unsure about raising these concerns, they can remain hidden until harm occurs. Strengthening a Freedom to Speak Up culture is essential. It provides all staff a protected route to escalate concerns, even when they feel uncertain or worry that a system issue might be dismissed as user error or a training gap. I firmly believe that a strong speaking up culture means digital risks are more likely to be surfaced early, before they become incidents. A rapidly changing safety landscape Wales has seen significant changes in digital governance and health policy in recent years, from the transition from NHS Wales Informatics Service (NWIS) to Digital Health & Care Wales (DHCW) to updates in national structures (NHS Executive now NHS Performance & Improvement) and regulatory expectations. As I type, the National Health Service (Concerns, Complaints and Redress Arrangements) (Wales) Regulations 2011 (often referred to as 'Putting Things Right') are undergoing review and update. These shifts can create uncertainty about roles, responsibilities and reporting pathways. When something goes wrong, it’s not always clear who is responsible for what—and this ambiguity can complicate harm assessment. Where digital meets traditional healthcare Digital systems are embedded into clinical workflows, communication pathways and multi‑team processes. Every interface, integration point and manual interaction/data entry represents a potential source of risk. Reviewing these interconnected pathways is rarely quick or straightforward, but it is essential for understanding how digital harm occurs and how it can be prevented. Conclusion and call to action: building a safer digital future together The reflections in this paper highlight the complexity of digital patient safety work. Digital systems bring enormous potential for improving care, but they also introduce new risks that we are still learning how to manage. To address these challenges, we need a coordinated national approach that brings together healthcare organisations, digital suppliers, clinical safety experts, policymakers and frontline staff. This means: Updating and strengthening digital safety standards. Improving consistency in both incident investigation and harm assessment. Enhancing training and digital literacy. Supporting timely, transparent reporting. Facilitating availability of clinicians to undertaken harm reviews. Encouraging openness and speaking up. Improved incident data triangulation. Thematic analysis of incidents and nationally shared learning. Building stronger cross‑organisational collaboration. Most importantly, we need a culture where digital concerns are raised early and acted upon quickly. The opportunity ahead is significant, as are some of the challenges… But I truly believe that by working together, we can shape a safer digital health landscape—one that protects patients, supports professionals and ensures that innovation enhances care rather than complicating it. Further reading on the hub: How do we harness technology responsibly to safeguard and improve patient care? NHS England warns electronic patient record could pose ‘serious risks to patient safety’: what can we learn? The foundations for a safe digital service delivery in health—A blog by Rob Ludman Applying a robust approach to digital clinical safety in diagnosis b