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Making Families Count have compiled this information for families and friends of people who have been harmed when something has gone wrong in NHS provided or funded healthcare in England. This may mean something unexpected happened in care, or someone has been harmed. This is called a safety event by the NHS.  You will find information about the NHS investigation process and a downloadable template document for a family to use (if you wish) to help to organise your thoughts and feedback, and to provide information to assist the investigation. 

This report from the Men's Health Forum examines the role of community pharmacy in improving men’s health in the UK, the theme of Men’s Health Week 2026. The report sets out a five-point plan that pharmacies should adopt to become a male-friendly pharmacy, which encourages more men to engage. This report’s findings are Based on a survey from a UK-wide poll in 2025 exploring men’s evolving attitudes to health and pharmacy. The report highlighted the following key themes emerging from this: Privacy as a prerequisite: Without genuinely private, professional spaces, men are unlikely to open up. Environment and culture matter: Pharmacies often still feel feminised and transactional, deterring men from engagement. Trust is built through relationships: Men respond to respectful, non-patronising interactions and consistent positive experiences. Meet men where they are: Outreach in community settings and constructive engagement with online and AI-based health information are essential. Services, not sales: The future of community pharmacy might well lie in healthcare services commissioned by the NHS, not retail.

These two reports summarise findings from the National Commission into the Regulation of Artificial Intelligence (AI) in Healthcare’s research and engagement activities and call for evidence. The Commission’s purpose is to advise the Medicines and Healthcare products Regulatory Agency (MHRA) on improving its regulatory framework and to accelerate safe access to AI in healthcare and across the NHS. You can read a summary of Patient Safety Learning’s response to this call for evidence here. The work brought together evidence from patients and the public, healthcare professionals, industry, academics and wider health system stakeholders through public polling, surveys, stakeholder engagement, deliberative research, an open call for evidence, a public Ask Me Anything session and insights from the MHRA’s AI Airlock programme. Thorough analysis of this evidence, 10 key findings have been identified. The report summarises these as follows: 1. There is a clear call for a proportionate, lifecycle-based approach to regulation Stakeholders noted that the current framework, which is designed for more static medical devices, is not well suited to iterative and adaptive AI systems. Across groups, stakeholders called for a proportionate approach that is risk-based, considers patients’ safety and fairness, with clear practical guidance and addresses existing duplication and fragmented oversight. Stakeholders also underlined the importance of strengthening clinical evidence requirements, with strong support for enhancing post-market surveillance and improving coordination. With a more proportionate approach seen as essential for balancing innovation with patient safety. 2. There is strong consensus for significant regulatory reform Across respondent groups of healthcare professionals, healthcare providers and industry, most people said that the existing regulatory framework needed “significant reform” but did not need a “complete overhaul”. Amongst patients and the public, the number of respondents calling for “significant reform” and a “complete overhaul” were similar, with 34% asking for “significant reform”, and 35% for a complete overhaul. 3. There was broad consensus that AI systems will increasingly require continuous post-market surveillance and monitoring Several stakeholders highlighted the need to upgrade current approaches to post market surveillance and monitoring, so they are better suited to AI systems. There was strong consensus that performance and risk cannot be adequately assessed through one-off approvals alone but instead require ongoing, real-world oversight across the lifecycle. Through qualitative evidence, stakeholders called for a more continuous and ongoing approach which helps track performance, monitor safety, and manage compliance across the AI system lifecycle. They also suggested that upgraded approaches need to help manage performance drift, validate performance in real world settings, and track changes in performance over time. 4. Responsibility should be shared across the system, with each individual and institution understanding their essential role and responsibilities There was strong consensus that accountability should not rest with a single person or institution, with respondents favouring a model which better distributes liability across the lifecycle. Patients and members of the public called for a comprehensive approach to accountability that addresses current gaps, healthcare professionals stressed that clinical accountability should be maintained whilst healthcare providers emphasised the need for robust governance structures and clear organisational responsibility. Stakeholders also highlighted uncertainty in how roles, responsibilities, and liability are defined and applied in practice. There were differing views on where liability should sit when an AI system causes or contributes to harm. Some respondents believed that liability should sit with the healthcare professional using the AI system. Another group of respondents argued that liability should sit with the healthcare provider who deploys the AI system. Others suggested that liability should sit with manufacturers, given their role in developing the technology and then maintaining their AI system’s performance. Across responses, there was a consistent emphasis on the need for greater clarity and consistency in how liability is allocated. Many respondents called for structured approaches to distributing liability that reflect the roles of different actors, including manufacturers, healthcare providers, and healthcare professionals. Suggested approaches included shared or distributed liability models that apportion responsibility based on specific circumstances. Stakeholders noted that clearer and more consistent frameworks would help address uncertainty and support the safe use of AI systems in healthcare. 5. Human oversight and responsibility for clinical judgment should be retained There was strong consensus from respondents that AI systems should continue to augment the work of professionals and should not be fully responsible for clinical decision making. Patients and the public emphasised the importance of human involvement in their care, including expectations that clinical decisions involving AI should be checked and validated by a human clinician. Healthcare professionals and professional bodies highlighted the risk of over-reliance on AI outputs at the expense of professional judgement. Industry respondents were supportive of ‘human-in-the-loop' safeguards. 6. Transparency and explainability will be key for the ongoing deployment of AI systems The ability to easily understand how an AI system works and to interpret its outputs will be key for building trust, enabling deployment, and ensuring the safety of an AI system. Patients, public and professionals advised that explanations of AI system outputs need to be clear, and providers called for greater transparency in the procurement process for sourcing AI systems. Industry organisations commented on the need for clearer and more structured regulatory documentation. 7. Data access and use is central to the role of AI in healthcare moving forward Respondents to the Call for Evidence noted that healthcare data is simultaneously an enabler and a barrier to the development and deployment of AI systems in healthcare. Patients and public expressed strong concerns about current approaches to consent for data access and how data is used by commercial entities. Some respondents cited governance and compliance burdens and fragmented data infrastructure as key barriers to development and deployment. Industry respondents called for clear and robust frameworks for accessing data including shared data governance templates and clearer guidance on data standards. 8. There is a need for robust training and improved AI literacy The Call for Evidence found a clear view that robust, ongoing training and clear understanding of AI in healthcare is critical for safe adoption. Healthcare professionals highlighted the risks of a lack of AI-specific training can bring such as increased risk of automation bias. Healthcare providers called for more structured workforce training on AI moving forward. Industry respondents advised that training is also needed for individuals who oversee the governance of AI systems in healthcare. 9. There is a need to improve incident reporting and learning mechanisms There were widespread calls for standardised reporting mechanisms for AI systems. Patients and public called for greater transparency and accountability over where AI is involved in care, including clearer communication when things go wrong. Healthcare professionals raised concerns about underreporting of safety incidents in healthcare more broadly, noting that workload pressures are a significant contributing factor. Responses also suggested limited awareness amongst some healthcare professionals that the existing Yellow Card scheme already applies to medical devices, including AI enabled devices. Healthcare providers highlighted the operational challenges of implementing incident reporting consistently across different settings. Industry respondents called for clearer guidance on how incident reporting should work within AI specific post-market surveillance frameworks. Several respondents also proposed improvements to surveillance and monitoring approaches, including establishing a national reporting system for AI incidents and providing guidance for healthcare professionals on what to report. 10. Patient and public engagement, trust, and communication will continue to be key for the deployment of AI systems. Through the Call for Evidence, trust emerged as a core enabler of AI adoption in healthcare. Patients and the public called for consistent involvement, consent, and clarity over the role of AI systems, whilst professionals highlighted the need to take a proportionate approach to explaining how AI is being used to patients. Providers advised that clear and consistent transparency and communication frameworks are needed whilst industry respondents recognised that trust is key for the uptake of AI systems in healthcare.

Medical device makers have been rushing to add AI to their products. While proponents say the new technology will revolutionize medicine, regulators are receiving a rising number of claims of patient injuries. This Reuters Special Report investigates some of the hazards associated with AI-enabled medical devices, including errors in a navigation system integrated into a medical device used in ENT surgery, AI software used for prenatal ultrasound scans that misidentified fetal body parts and AI assisted heart monitors that failed to recognise abnormal rhythms.  Issues with the capacity of the U.S. Food and Drug Administration (FDA) to review the flood of new AI-enabled medical devices are also raised, as well as concerns that the FDA's traditional approach to regulating medical devices may no longer be fit for purpose.

The Contemporary Ergonomics & Human Factors 2026 contains the proceedings of the Chartered Institute of Ergonomics & Human Factors Annual Conference, which was held 27-29 April 2026 at the East Midlands Conference Centre, Nottingham, UK. The conference represents a diverse range of application areas across healthcare, rail, aviation, nuclear, construction, medical devices and more. Themes range across human factors methods and tools, human-AI teaming, wellness and inclusion, safe systems and human factors integration.

The law has always struggled to keep up with technological change. With AI, the pace of change is so rapid that this gap feels less like a step and more like a widening gulf.   A recent White Paper, produced through a collaboration between the MPS Foundation, York University’s Centre for Assuring Autonomy, and the Improvement Academy at the Bradford Institute for Health Research, highlights how clinicians could find themselves exposed when their decisions are influenced by AI recommender systems. Such systems analyse patient data and suggest personalised treatment plans, diagnoses or medications.  There are also concerns about who might be held liable in the event of a claim relating to AI scribes, automated documentation assistants, triage algorithms, and other forms of clinical decision support. These all share a common feature: they shape clinical reasoning, records, and workflows without taking autonomous responsibility for the outcomes. Under the current legislative framework, there is a risk that doctors could be held wholly liable if an AI suggestion turns out to be wrong and they have followed it. That’s because the existing product liability regime was never designed with AI in mind.   This paper from Medical Protection aims to set out the challenges we expect clinicians will face, and the action policymakers can take now to make sure AI delivers benefits without leaving doctors unfairly exposed. 

Patient transfers accounted for nearly half of falls events reported to ECRI in a new analysis, underscoring how routine patient movement activities can create major safety vulnerabilities.  The latest data analysis from the ECRI and the Institute for Safe Medication Practices Patient Safety Organization (PSO) shows that patient transfers, toileting and ambulation-related falls are the most common event types. About 30% of falls reported involved patients under 65 years old, challenging the assumption that fall prevention is solely or exclusively an issue for older adults. Data findings When falls happen: Patient transfers, toileting, and ambulation—all routine, necessary care activities—collectively account for more than 85% of reported falls.Transfer is by far the highest risk moment, accounting for nearly half of all falls (45.3%). Toileting is the second most common trigger at 30.7%, while falls occurring during ambulation accounted for 9.4%. Transfer-related falls were defined as those that involve patient movement from one surface or location to another, such as between a bed, chair, stretcher, or wheelchair. Ambulation-related falls occurred while patients were walking or moving through care environments, with or without assistance, including in their hospital room or hallway. Which patients are at risk: Falls are not limited to older patients. Working-age adults (18–64) represented the largest single age group in this analysis, accounting for 29.3% of falls events. This is a reminder that fall risk assessment and prevention protocols, especially in acute care settings, should not overlook younger adults. Where do most falls occur: In this data snapshot, falls are overwhelmingly concentrated in acute care facilities (68.1%) such as hospitals. Falls were also reported across post-acute care facilities like nursing homes, rehabilitation centres, home health, ambulatory care behavioural health, and cancer centres. This is somewhat a reflection of the membership base of the ECRI and ISMP PSO, which includes more acute care hospitals and health systems than nursing homes and post-acute care facilities. Power of reporting: The analysis demonstrates the importance of detailed event reporting. More than 9,000 of the reports were noted as ‘near-misses’ or unsafe conditions (rather than serious events or incidents of harm), which reflects ongoing efforts to encourage reporting. Organizations that collect and analyse near miss events are given insight into conditions, workflows, and processes that could lead to harm and more importantly an opportunity to prevent harm. Large “unknown” categories within fall location and patient age suggest an opportunity to better capture this information to strengthen organisations’ ability to fully understand risk patterns and identify opportunities for improvement.

This update presents statistics from the Learn from Patient Safety Events (LFPSE) service, a national NHS system for the recording and analysis of patient safety events that occur in healthcare. The LFPSE definition of a patient safety incident is something unexpected or unintended has happened, or failed to happen, that could have or did lead to patient harm for one or more person(s) receiving healthcare. This report shares the patient safety incident data from January to March 2026. Count of Event Types in LFPSE – based on patient safety event records from January to March 2026 LFPSE brings the feature to record patient safety event types beyond incidents. Recorders can also upload patient safety risks, outcomes and instances of good care. This is to ensure the database contains more instances of care that the healthcare system can learn from instead of only detailing errors involving patients. In this period, 829,300 events were recorded to LFPSE, the majority of which were recorded as patient safety incidents (96.96%). Count of patient safety incidents by maximum physical harm – based on patient safety incident records from January to March 2026 Sometimes a problem in care can affect more than one patient, or none at all. To capture this, as a new feature of LFPSE, recorders can submit information for multiple patients per incident, meaning there can be multiple degrees of harm per incident. For the following figure and table NHS England have taken the highest harm level per incident. NHS England identified and removed 64,223 incidents where the number of patients affected was unknown. Preliminary analysis suggests that these records likely represent incidents with no patients involved. NHS England will continue further data quality checks to validate these figures. During this quarter, 739,846 incidents had recorded a degree of harm. The majority of these incidents (94.2%) recorded low or no physical harm to patients. LFPSE has a new variable for grading of the psychological harm associated with the recorded patient safety incidents. This is an experimental field which seeks to explore if responses to safety incidents need to be different if psychological harm is considered separately from physical harm, rather than rolling them together into one measure, as was done in the National Reporting Learning Service (NRLS). Currently, there is low confidence in the grading of psychological harm, as users familiarise themselves with its use, and as such, it is excluded from this report. Related reading – previous quarterly data publications NHS England: Patient Safety Event Data Quarterly Publication – Quarter 3 2025/26 (October to December 2025) NHS England: Patient Safety Event Data Quarterly Publication – Quarter 2 2025/26 (July to September 2025) NHS England: Patient Safety Event Data Quarterly Publication – Quarter 1 2025/26 (April to June 2025) NHS England: Patient Safety Event Data Quarterly Publication – Quarter 4 2024/25 (January to March 2025) NHS England: Patient Safety Event Data Quarterly Publication – Quarter 3 2024/25 (October to December 2024)