{"id":11890,"date":"2026-07-27T14:37:51","date_gmt":"2026-07-27T13:37:51","guid":{"rendered":"https:\/\/www.rldatix.com\/en-uki\/?p=11890"},"modified":"2026-07-27T14:37:52","modified_gmt":"2026-07-27T13:37:52","slug":"ai-and-patient-safety-the-risks-the-opportunities-and-what-needs-to-change","status":"publish","type":"post","link":"https:\/\/www.rldatix.com\/en-uki\/resources\/ai-and-patient-safety-the-risks-the-opportunities-and-what-needs-to-change\/","title":{"rendered":"AI and Patient Safety: The Risks, the Opportunities and What Needs to Change"},"content":{"rendered":"\n<p>AI is already reshaping healthcare, from ambient voice scribes in GP practices to patients self-diagnosing through large language models before they reach a clinician. But the question of whether it is making care safer or introducing new risk is not being answered clearly enough. In this session from the Connected Health &amp; Care Summit 2026, Mark Linggood of RLDatix and Clive Flashman, Chief Digital Officer at Patient Safety Learning, hold a frank discussion on the evidence, the regulatory gaps and the role AI could play in improving incident reporting itself.\u00a0<\/p>\n\n\n\n<p>Watch a candid discussion between RLDatix and Patient Safety Learning on how AI is affecting patient safety, what the research shows, where the regulatory gaps exist and how AI could transform incident reporting.\u00a0<\/p>\n\n\n\n<div style=\"padding:56.25% 0 0 0;position:relative;\"><iframe src=\"https:\/\/player.vimeo.com\/video\/1210455375?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"What Does the Rise in AI Mean for Patient Safety?\"><\/iframe><\/div><script src=\"https:\/\/player.vimeo.com\/api\/player.js\"><\/script>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">What the session covers and the key findings discussed<\/h2>\n\n\n\n<p>This fireside chat covers both the risks and opportunities of AI in healthcare, grounded in recent research and practical experience. The key points discussed were:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A Canadian Medical Association study found patients who followed AI advice were five times more likely to experience harm, and 97% of physicians had personally intervened to prevent harm from AI or online advice\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>An Oxford University study found that the models themselves were generally accurate, but the human-model interaction was the primary source of risk, with a difference in one word in a symptom description producing life-or-death differences in advice\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AI models are now capable of hallucinating citations, generating fake references that appear to come from journals like the BMJ but do not correspond with genuine, published sources\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The UK&#8217;s current reporting systems and regulatory frameworks are not designed for an AI-enabled world, with LFPSE categorising AI-related incidents only as generic &#8220;IT issues&#8221;\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The Oxford study reported that none of the tested models is ready for deployment in direct patient care\u00a0<\/li>\n<\/ul>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">The challenges discussed and where AI creates opportunity<\/h2>\n\n\n\n<p>The discussion identified a fundamental gap in how AI-related patient safety incidents are reported and regulated. The MHRA, which has a role in regulating AI-enabled medical devices in the UK, has received fewer reports than its international counterparts, according to the discussion. Clive Flashman argues that people do not know they can report to the MHRA, and they do not always recognise AI as the direct cause of harm. The speakers also discussed an increase in complaints to professional regulators (GMC, NMC) relating to clinicians use of AI without patient consent.&nbsp;<\/p>\n\n\n\n<p>On the opportunity side, both speakers see significant potential for AI in incident reporting itself. Clive Flashman proposes that AI could create new, data-driven taxonomies from PSII reports, identify leading indicators by correlating historical incident patterns with conditions like staffing levels, weather and agency usage, and reduce reporter burden by pulling contextual patient data from EPR systems automatically. Mark Linggood highlights RLDatix research with NHS Wales showing that when agency usage rises by one standard deviation, harm incidents increase by approximately 25%.&nbsp;<\/p>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">What this means in practice and who it is relevant to\u00a0<\/h2>\n\n\n\n<p>The session paints a picture of a healthcare system where AI is already being used widely but where the safety infrastructure has not kept pace. Ambient voice scribes are being used in many GP practices already. Patients are consulting AI models before seeing clinicians. Yet the reporting systems, regulatory frameworks and taxonomies needed to track the safety impact of these tools are either absent or inadequate.&nbsp;<\/p>\n\n\n\n<p>Flashman is direct about what needs to happen. Reporting systems need to capture AI-related incidents as a distinct category. The MHRA needs clearer pathways for reporting. The national AI commission, due to report back in summer 2026, needs people who understand both AI and patient safety, not just one or the other. And any AI used in direct patient care needs the &#8220;human in the loop,&#8221; defined broadly to include not just clinicians but patient safety experts and patients themselves.&nbsp;<\/p>\n\n\n\n<p>Linggood highlights the vendor perspective: interoperability between EPR systems and incident reporting tools remains a major barrier to the kind of data triangulation that would make AI most useful. The data exists, but the systems do not share it.&nbsp;<\/p>\n\n\n\n<p>This session is relevant to patient safety leads, governance teams, chief digital officers, medical directors, nursing leaders, clinical informatics teams and anyone responsible for AI governance, incident reporting or regulatory compliance across health and social care.<\/p>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently asked questions<\/h2>\n\n\n\n<p><strong>Who takes part in this session?<\/strong><br>The session features Mark Linggood, Product Operations Director from RLDatix and Clive Flashman, Chief Digital Officer at Patient Safety Learning. Flashman was previously instrumental in setting up the National Reporting and Learning System at the National Patient Safety Agency, the first central repository for patient safety incidents and national learning. He also advises health tech startups and entrepreneurs. The session is structured as a fireside chat with audience participation.<\/p>\n\n\n\n<p><strong>What does the Canadian Medical Association study show about AI and patient harm?<\/strong><br>The study surveyed patients and approximately 650 doctors across Canada. It found that patients who followed AI advice were five times more likely to experience harm. Additionally, 97% of physicians reported that they had personally intervened to prevent patients from suffering harm as a result of information they had found online or through AI tools. Flashman describes this as a staggering number, noting that it means virtually every doctor surveyed had experienced this.\u00a0<\/p>\n\n\n\n<p><strong>What did the Oxford University study find about AI models in healthcare?<\/strong>\u00a0<br>The Oxford study tested major AI models including OpenAI, Claude and Mistral. It found that the models themselves were generally accurate, but the problem lay in the human-model interaction. In one example, two identical symptom descriptions for a subarachnoid haemorrhage produced completely different advice based on a single word difference: whether the headache came on &#8220;suddenly.&#8221; One scenario advised lying down in a darkened room, while the other advised calling 999 immediately. The study concluded with a direct statement: &#8220;None of the tested models is ready for deployment in direct patient care.&#8221;\u00a0<\/p>\n\n\n\n<p><strong>Why are AI-related patient safety incidents not being reported properly?<\/strong><br>Flashman identifies three problems. First, the LFPSE taxonomy categorises AI-related incidents only as generic &#8220;IT issues,&#8221; alongside problems like laptops not working or servers going down, so there is no way to distinguish AI-specific harm. Second, people do not know they can report AI incidents to the MHRA. Third, people do not always recognise that AI was the direct cause of harm. The MHRA has received far fewer reports than its international peers, not because incidents are not happening but because the reporting pathways are unclear.<\/p>\n\n\n\n<p><strong>How could AI improve incident reporting?<\/strong>\u00a0<br>Both speakers see significant potential. Flashman proposes that AI could create new taxonomies from PSII data by analysing what organisations are actually reporting and building categorisation structures from the ground up. He also suggests linking EPR systems with incident reporting tools so that AI can pull relevant patient data automatically, reducing the burden on reporters. Linggood highlights the potential for AI to identify leading indicators by correlating historical incident patterns with contextual factors such as staffing levels, agency usage and seasonal conditions. Both agree that human experts remain essential to validate AI-generated outputs.\u00a0<\/p>\n\n\n\n<p><strong>What is the regulatory position on AI in UK healthcare?<\/strong>\u00a0<br>The UK has taken an innovation-led approach to AI regulation, in contrast to the EU&#8217;s centrally regulated model. Regulation has been largely delegated to the MHRA, which Linggood describes as not entirely clear what to do with it on all levels. Flashman calls it &#8220;a bit of the Wild West.&#8221; A national AI commission is due to report back in summer 2026, with Henrietta Hughes as the patient safety lead. Flashman notes that the commission includes many AI experts and a few patient safety experts, but arguably nobody who understands both fields deeply.<\/p>\n\n\n\n<p><strong>What does the session say about ambient voice technology?<\/strong>\u00a0<br>Flashman explains that ambient voice scribes are already in many GP practices and will soon be in thousands of hospital settings. Transcription is a Class 1 medical device with low risk, but summarisation is potentially Class 2A because it change the meaning of information. NHS England has indicated support for integrated AVTs, which Flashman suggests may favour tools built into EPR systems like Epic and Oracle Health over standalone providers. He also notes that not all settings want recordings, particularly mental health.\u00a0<\/p>\n\n\n\n<p><strong>What correlation has RLDatix found between workforce data and patient safety?<\/strong>\u00a0<br>Working with NHS Wales, RLDatix has found that when agency usage on a ward rises by one standard deviation from the norm, the rate of harm incidents increases by approximately 25%. Mark Linggood is careful to distinguish correlation from causality, a point Flashman reinforces, but both agree that triangulating workforce and safety data provides important contextual intelligence that can help organisations understand and act on emerging risks.\u00a0<\/p>\n\n\n\n<p><a href=\"https:\/\/www.rldatix.com\/en-uki\/connected-health-care-summit-2026-sessions-on-demand\/\">&lt;&lt; Back to Sessions on Demand<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is already reshaping healthcare, from ambient voice scribes in GP practices to patients self-diagnosing&#8230;<\/p>\n","protected":false},"author":10,"featured_media":11891,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[213],"tags":[352,390,400],"class_list":["post-11890","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-videos","tag-connected-health-care-summit","tag-connected-health-care-summit-2026-on-demand","tag-patient-safety-learning","primary-category-videos"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI and Patient Safety: Risks, Opportunities &amp; 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