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In operating theatres and other high pressure clinical environments, clear identification shouldn’t be a nice ‘extra’, it is a patient safety need. When staff cannot quickly recognise names and roles, communication becomes harder, escalation can be delayed and patients are left unsure who is caring for them. Reviews of patient safety repeatedly show that poor teamwork and unclear roles can contribute to avoidable harm. Danielle Checketts, Managing Director of Eco Ninjas, discusses why being able to identify staff by their names and roles is so important not only for the staff themselves but also patients. She explains how a simple idea, reusable hats with detachable name badges that can be removed before laundering, can support safety and teamwork. In theatre, everyone can look the same. Masks, gowns, visors and lead aprons often cover name badges, while lanyards are easily hidden or turned around. Theatre teams include surgeons, anaesthetists, students, agency staff and industry representatives, yet patients and colleagues are still expected to know who is who. When names, roles and seniority are unclear, questions may go to the wrong person, and valuable seconds can be lost. Even when introductions are made during the WHO surgical safety checklist,[1] names and roles can quickly be forgotten once a procedure is underway. In an emergency, it must be immediately clear who is who. This lack of clarity can lead to: Miscommunication at critical moments. Delays in escalation. Reduced patient confidence and psychological safety. Errors due to misunderstood roles or instructions. This isn’t just theoretical. Liz Fitzhugh, net zero lead and former theatre manager at University Hospitals Coventry & Warwickshire (UHCW), put it simply: “If a patient arrests and someone asks for the crash trolley, either everyone goes or no one goes.” In critical moments, teams need to be immediately identifiable so they can act without hesitation. Liz’s team at UHCW were among the first to introduce name and role theatre caps in 2019. It feels fitting that she was also the person who once asked me to write my name on my disposable cap with a marker pen, quietly sparking the idea that grew into this work. For years, poor identification in theatre has become accepted and been treated as normal. But it shouldn’t be. Patients want to know who is caring for them, and staff work more safely when names and roles are clearly visible. That is why the ‘theatre cap challenge’ gained momentum internationally, highlighting a simple idea: if the hat remains visible when wearing sterile attire, it can help make names and roles visible too. Patient perspectives: what matters most Patients consistently say they want to know who is in the room, who is leading their care and who they can turn to for reassurance. Feedback from surgical and maternity care journeys, including caesarean births, shows that visible names and roles help people feel safer, calmer and better able to engage in what is happening around them. Patients describe feeling more reassured when: Staff introduce themselves clearly. Visible names and roles help patients and colleagues remember who is who after introductions, rather than relying on memory alone. There is consistency in communication throughout their care. When identification is unclear, patients can feel anxious and excluded at the point they are most vulnerable. Visible names and roles do more than support courtesy, they strengthen communication, teamwork and reassurance for patients and families. Infection prevention, hygiene and practical constraints Efforts to improve identification must also align with infection prevention standards. Theatre attire cannot simply be adapted without considering contamination risk, laundering processes and the wider pressure to reduce reliance on single use items. The challenge with current approaches The current embroidered theatre caps improve visibility of names and roles, but they are difficult to manage at scale and fail to support consistent identification for all staff. Students, visitors and temporary staff are often excluded, and new starters can wait months before receiving one. They also create ongoing operational challenges, including time-consuming bespoke ordering, poor fit, loss and replacement costs, outdated roles, and complications with laundering. As Alan Dickens, Theatre Manager at MMUH Birmingham, explains: “Bespoke embroidered caps are hard to manage over time. When staff leave or change roles, the hats issued to them often leave with them or need replacing. This creates ongoing cost for the trust and delays in maintaining accurate identification.” Emerging responses across the NHS Several NHS organisations are now testing a more practical approach: reusable hats with detachable name badges that can be removed before laundering. This keeps identification visible while fitting more easily into real hospital systems. In Somerset, a pilot at Musgrove Park showed how a simple change can support safety and teamwork. Mr Andy Stevenson, orthopaedic consultant at Somerset NHS Foundation Trust, said: “In theatre, there can be a really high turnover of colleagues at times, with new people coming and going all the time. This can make it really difficult to know who is who, let alone what jobs they have. Some days, it will be the first time working with half the people in the room. The badge hats have helped to positively transform communication and safety.” A similar message has come from maternity services. Kathryn Harrison, delivery suite manager at Great Western Hospital, said: “Despite staff introducing themselves in the morning, remembering everyone’s name and role throughout the day is challenging, especially when more than 12 people can be in the room at any one time. The badge hats reinforce this critical stage in safe surgery, improve teamwork and communication, and help break down hierarchical barriers. They can be worn by all staff, students, birthing partners and even the patients wear them on our unit”. Building the evidence base There is growing research interest in identification in healthcare.[2][3][4] We have started to work with medical schools on exploring the impact on training environments, role visibility and communication. This is helping to strengthen the evidence base for scalable, system-wide approaches. Students can be included simply using a badge with their name and role alongside a standard fitted hat. Towards integrated, system-based solutions The challenges across current approaches show the need for solutions that fit existing NHS processes, including laundering and distribution, while also identifying temporary staff, visitors and students. The most effective solutions will improve safety without creating new inefficiencies. A call to action Clear identification in healthcare is not optional. It is a practical safety intervention. When people can immediately see names and roles, communication improves, hierarchy softens, patients feel more reassured and teams are better able to act quickly when it matters most. If the NHS is serious about reducing avoidable harm, improving teamwork and strengthening patient experience, visible identification should be part of the solution. Wearing a detachable badge on a reusable theatre cap sounds very simple but this is a small change that can make a very big difference to the safety of patients. References World Health Organization. WHO Surgical Safety Checklist. Kouba LP, Fabi A, Bayer S, et al. Labeled surgical caps improve perioperative patient safety and interprofessional communication in the operating room: a scoping reviewe. Patient Saf Surg, 2026; 20:(9). Liverpool University Hospitals NHS Foundation Trust (LUHFT) and Warwick Med. Case study – Switching to Reusable Theatre Caps. NHS England. Douglas N, Demeduik S, Conlan K. Surgical caps displaying team members' names and roles improve effective communication in the operating room: a pilot study. Patient Saf Surg 2021;15:27. doi: 10.1186/s13037-021-00301-w.

Protocols, targets and pathways save lives. They give us essential structure to deliver safe, high‑volume care with finite resources, and they have transformed the NHS for the better. But as the healthcare experience becomes increasingly streamlined, Hannah Little, Assistant Chief Nursing Officer at North Bristol NHS Trust, asks: who are we leaving behind? One size rarely fits all We often hear about what healthcare can learn from efficiency‑led industries such as automotive manufacturing, where success is defined by pace, scale and uniform outcomes. And indeed, cross‑industry learning has benefited the NHS enormously. But context matters. People are not cars rolling off a production line. We are complex, diverse human beings with individual social, psychological and clinical needs. And I wonder how far we can push a target‑driven model before we start hearing louder public concern about the fact that, in healthcare, one size rarely fits all. Finding the sweet spot As a nurse, I see individuals deliver personalised care brilliantly. I see colleagues who instinctively adapt, interpret and flex protocols to truly meet the needs of their patients and families. What worries me is not the people—it’s systems that increasingly constrains them. There is a 'sweet spot' between regulation, targets and national mandate on one side, and freedom to innovate on the other. That tension is necessary: too much control and we lose space for creativity; too little and we invite unsafe variation. When the balance is right, systems evolve safely, testing change within a clear structure while allowing for the flexibility that person‑centred care requires. The weight of national targets Standards and strong governance are essential to quality. But how do we ensure they don’t swallow the space needed for anything else? Over recent decades, the weight of national targets has grown heavier. The NHS Oversight Framework was intended to bring much‑needed clarity—a more focused set of national priorities that would reduce noise and strengthen local autonomy. At the 2026 Patient Safety Forum, national leaders spoke about a welcome cultural shift away from over‑mandating and toward local devolution. But this shift appears to be landing alongside a net reduction in resource and ever higher stakes to deliver. So instead of fewer mandates and more autonomy, we may be facing fewer mandates and less capacity for innovation. This raises a critical question: after the targets are met, is there enough resource left for the other things that matter? The things that support sustained performance? Targets tend to serve the 80% who fit neatly onto the healthcare conveyor belt. Without additional support for those who don’t, we risk widening health inequalities. Equity requires adaptability to be hard-wired into pathways—and adaptability requires headroom. The trade-offs Are we comfortable with where we are now? Has the pendulum swung into the place we need for 2026? Everyone recognises that resources are limited. But when limited resources necessitate laser focus on a small number of priorities, are the trade‑offs services have to make the right ones for population health? What will we think, looking back in five to ten years? Will we feel confident that a model which rewards optimising delivery for the majority was worth potentially widening the gap for those who didn’t fit standard pathways? Unlike other industries (e.g. Apple, which famously narrowed its product line to recover focus), healthcare cannot simply do fewer things well. Complex populations do not disappear because they fall outside a national priority. When centrally governed targets narrow without a corresponding rise in local capacity, the burden of adapting care falls to already stretched individuals. And when that happens, quality, equity and outcomes inevitably feel the strain. So what is the solution? If we care about equity and the safety and health of whole populations, resource to adapt and personalise care needs to be preserved. We need open, honest analysis of the trade-offs being made at policy level. Do we have the right set of priorities? Are we incentivising organisations to only pick low‑hanging fruit? And crucially: are we preserving the resource required to deliver personalised, equitable care? Passionate individuals cannot carry this burden alone. Flexibility must be designed into the system, not left to chance. And perhaps the answer is not fewer targets—but targets that incentivise equity as much as efficiency. Call to action Policymakers and senior leaders must prioritise embedding flexibility within national frameworks for all sectors by protecting resource for personalised care, incentivising equity alongside efficiency and enabling local systems to adapt. Without deliberate action, we risk incentivising services that work well for many, but fail those most in need.

Stefan Peil summarises a pilot study he has done to see whether a structured systems model can support the preparation of a morbidity and mortality (M&M) conference discussion. The example used is a coronary angiography risk scenario to explore whether a model-based representation of patient safety knowledge could serve as a reliable basis for an artificial intelligence (AI)-assisted decision template. The work was produced to address a practical problem in patient safety: relevant information for M&M preparation is often spread across diagrams, reports and team knowledge, which can slow and make shared understanding less consistent. The pilot study, therefore, examined whether systems modelling could help organise, make transparent and reuse safety relevant information in a more structured way. The full study is attached at the end of this page. The challenge The identified challenge was the lack of a structured, reusable approach to preparing patient safety discussions for M&M conferences. The aim was not to automate clinical judgement, but to test whether a model-based risk analysis derived from team knowledge could serve as a structured input for drafting an M&M decision template. M&M preparation often relies on fragmented information and informal interpretation. In complex clinical environments, such as coronary angiography, risks do not arise from a single isolated factor. They emerge from the interaction between tasks, people, technology, information flow and organisational conditions. In this specific pilot example, the safety concern was a risk scenario in coronary angiography in which cognitive overload during real-time decision-making and escalation could contribute to complications not being detected in time. This formed the basis for testing whether a structured model could provide a clearer and more traceable starting point for discussion. Method and measures To explore this, a systems model based on Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 was created in Systems Modeling Language (SysML) using SPARX Enterprise Architect. The objective was to represent the work system, the contributory task factor, the resulting risk and the proposed measures in a traceable form. The model focused on one coronary angiography scenario. The critical task factor was described as cognitive density in real-time decision-making and potential escalation. In the model, this contributed to the risk that complications would not be detected in time. The text states an impact on quality of care, an occurrence rating described as relevant and an overall risk class of moderate. The proposed measures were: pre-procedure briefing risk-adapted staffing standardised laboratory layout regular simulation drills. The intended achievement was a more structured, transparent and reusable basis for M&M preparation and discussion. Outcomes and lessons learned The pilot showed that a structured model can be a useful way to organise safety-relevant knowledge. Because the model linked work system elements, risks and measures in a traceable way, it provided a clearer starting point for discussion than unstructured text alone. The practical process tested in this pilot was: defining a relevant patient safety scenario in coronary angiography modelling the work system and the contributory task factor linking this to a patient safety risk documenting possible mitigating measures using the model as the basis for an AI-assisted one-page decision template. One important observation was that the AI-generated output reflected the underlying model's content. This suggests that a structured model can support more consistent synthesis than relying only on memory or informal interpretation. The text does not describe multiple alternative technical approaches in detail, so it cannot be stated from the source whether other options were formally compared or ruled out. It also does not state direct patient involvement. Staff involvement is referenced indirectly by using team knowledge as an input to the model. The text does not report formal measurement tools, outcome metrics, time savings, patient safety indicators or model costs. Therefore, no validated impact measurement can be claimed from the source. A key lesson learnt was that AI can assist with drafting and synthesis, but cannot replace clinical judgement, governance or safety review. Any output generated from the model still needs to be checked against the source material and reviewed by responsible clinical and patient safety leads. Impact This work is only a prototype, not as a formal effectiveness study. As a result, the impact that can be claimed is limited. The main result was that the structured model appeared to support: clearer organisation of safety-relevant knowledge better traceability between work system factors, risks and proposed measures a more consistent starting point for multidisciplinary discussion reuse of modelled information for drafting a one-page M&M decision template. At the same time, the the study is explicit about what was not demonstrated. The pilot did not test whether the approach: improved patient outcomes reduced harm shortened preparation time in routine practice improved care delivery in a measurable way. A further limitation was that only a single, limited example was used, and some information was withheld for data protection reasons. This means the results were narrower than would be needed for broader implementation decisions. What worked was the structured linkage between the work system, contributory factors, risks and measures. What remains uncertain is whether this translates into measurable operational benefit in routine clinical governance. A likely barrier to improvement is the need for continued expert review, because AI-generated output cannot be used without clinical validation and governance oversight. If repeated, the next stage would need a clearer evaluation design, including defined measures of clarity, consistency, usability and possibly preparation time. Next steps The next step is a practical pilot in real clinical governance settings. A suitable next-stage comparison would be conventional M&M preparation versus model-supported preparation in a small, clearly defined pilot. The proposed questions for the next phase are: Does the approach improve clarity and shared understanding? Does it help teams identify contributory factors more systematically? Does it support consistency and traceability of measures related to patient safety? The study does not provide evidence of long-term organisational change, staff reaction, patient impact statistics or system-wide implementation results. Therefore, those elements cannot yet be stated as outcomes. However, based on insights from the pilot study, the anticipated longer-term value would be to make patient safety knowledge: more structured more reusable easier to discuss across professional groups more clearly linked to the wider work system rather than to isolated errors. A sensible next step would, therefore, be a controlled local test with defined governance, clinical review and evaluation criteria before any broader adoption.

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

HLTH Europe 2026 is the continent’s largest healthcare innovation conference, bringing together over 5,000 attendees from more than 50 countries. The event focuses on digital health, health tech, life sciences, and healthcare system transformation, providing a platform for decision-makers, innovators, and specialists to explore trends, solutions, and collaborations in European healthcare. The conference features a comprehensive programme, including: Keynotes and panels on healthcare IT, patient data exchange, AI in healthcare, interoperability, mental health, healthy ageing, and system change. Workshops and presentations led by industry leaders and experts. Networking opportunities with healthcare providers, policymakers, investors, pharmaceutical companies, tech companies, hospitals, and start-ups. Exhibition zones such as the Start-up Village, Investor Lounge, Policy Pavilion, and the NL Health~Holland Pavilion showcasing Dutch health tech innovations. Agenda Clive Flashman, Patient Safety Learning's Chief Digital Officer, will be leading a panel at the conference on Thursday 18 June on 'Blind trust: What happens to medical misinformation when we can no longer trust our own eyes?' Find out more. Register for the event here

Thriving through compassion and community: Sharing stories for the future of health systems Join 1,400+ professionals from 80 countries at the world’s most energising healthcare conference on quality, safety, and patient-centred innovation. Register

Across the healthcare sector, evidence of patient involvement leading to positive outcomes is ever-growing; however, little movement seems to have been made. Oxleas NHS Foundation Trust have been working to increase involvement across all aspects of their Trust, not only in patient facing services but also within Corporate services. Find out more from the presentation slides and poster presentation attached. Oxleas were finalists in the 2025 Picker Experience Network Awards (PEN Awards). In 2021, Oxleas’ Quality Management Team (QMT) implemented the 'Improving Lives' internal assurance programme, which included training staff to use frameworks and tools to support the assessment of services and prepare clinical teams for CQC inspections. In 2023, the Quality team identified that feedback from patients and families was not being prioritised for collection during reviews. Following discussion with the Involvement Team and reviewing current involvement opportunities, Quality Team recognised how lived experience can improve outcomes and user satisfaction across services. They decided to develop and introduce the concept of 'Lived Experience Reviewers' into quality systems. This method was not based on principles or research but has been based on feedback and population need. This is an innovative approach to enabling those with lived experience to have a say in how their services are running.

The 10 Year Health Plan for England sets an ambition of the NHS becoming the most AI-enabled care system in the world. Subsequent discussions around this have often centred on how new technologies powered by AI have the potential to improve patient care and outcomes. However, there has been significantly less attention on the implications of this for patient safety. This report, authored by Grayson Katzenbach, an attendee at the roundtable, and Patient Safety Learning’s Chief Digital Officer Clive Flashman, considers how AI can be deployed safely across the health and care system. It draws on a discussion at the Healthcare Excellence Through Technology (HETT) 2025 roundtable on this topic, which convened leaders from healthcare, academia, law and industry. Their discussion explored how innovation, governance and culture must evolve together to ensure that AI maintains and strengthens, rather than compromises, patient safety.  The report (attached below) summarises key themes that emerged from the HETT roundtable discussion on Tuesday 7 October 2025 and highlights five cross-cutting priority actions and recommendations emerging from this. 1. Strengthen safety culture and leadership alignment Actions: National health and care leaders should embed AI safety governance within existing patient safety frameworks, ensuring consistent oversight across sectors. Provider boards and senior leadership teams should assign clear roles for AI oversight, learning and accountability. Professional regulators and safety bodies should align expectations for digital safety leadership across care settings. Recommendations: Embed AI safety within broader organisational safety culture and learning systems. Link leadership accountability for digital safety to existing performance and governance metrics. 2. Build workforce capability and human factors competence Actions: Education and professional bodies (e.g., GMC, NMC, Royal Colleges) should define core AI literacy and human factors competencies. Health and care organisations should incorporate digital safety and automation-bias training into staff development. Industry partners should co-design training materials and user-centred design standards for safe implementation. Recommendations: Make AI and data science literacy a standard element of clinical and care curricula. Embed human factors principles into technology design, procurement and deployment throughout the health and care system. 3. Improve data quality and representativeness Actions: National data and standards bodies should align data quality requirements across health, private and social care providers. Provider organisations should implement continuous feedback loops to identify and correct bias in AI outputs. Developers and suppliers should publish evidence on data representativeness and model performance. Recommendations: Establish a sector-wide approach to improving data completeness, coding accuracy and population representativeness. Promote cross-organisational data sharing and transparency to support collective learning on bias and model drift. 4. Embed continuous governance and lifecycle assurance Actions: Regulators and system leaders should develop post-deployment monitoring requirements for AI systems in all care settings. Developers and provider organisations should share responsibility for ongoing surveillance, incident reporting and performance assurance of AI-based systems to ensure transparent, fair and sustained accountability. National policy bodies should clarify where accountability lies for ongoing surveillance of deployed systems. Recommendations: Adopt adaptive, proportionate regulation that supports both innovation and accountability. Require regular post-deployment reviews and transparent reporting to sustain public and professional confidence. Extend Patient Safety Incident Response Framework (PSIRF) principles of learning from harm and near miss events to digital and AI systems to embed shared responsibility between developers and providers. 5. Align innovation pace with system readiness Actions: Policy and funding bodies should link innovation incentives to demonstrated safety, benefit and operational readiness. Developers and providers should conduct staged roll-outs and real-world evaluations before large-scale deployment. Recommendations: Ensure that patient and service safety remains the rate-limiting factor in AI adoption. Reward measured, evidence-led implementation rather than rapid scale-up driven by market or policy pressure. Promote adaptive evaluation models that enable learning while reducing risk.

Join the Developing NHS Innovation event — a key event driving the adoption of impactful healthcare technologies. This is a unique opportunity to connect patient groups, government, industry, and NHS bodies to share insights, streamline innovation, and build meaningful partnerships. The UK aims to be world-leading in improving lives, transforming care, and boosting the economy through innovation. This event supports the Government’s life sciences plan, including the development of an NHS innovation and adoption strategy—designed to simplify procurement, reform incentives, and fast-track regulatory approval for new technologies and medicines. Register