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In June 2023 the Secretary of State for Health and Social Care announced that HSSIB would undertake a series of investigations focused on mental health inpatient settings. This overarching report brings together and explores cross-cutting patient safety risks across five individual investigations. The aim of this report is to examine patient safety risks identified across the following HSSIB investigations: Creating conditions for learning from deaths and near misses in inpatient and community mental health services: Assessment of suicide risk and safety planning (12 September 2024) Creating conditions for the delivery of safe and therapeutic care to adults in mental health inpatient settings (24 October 2024) Mental health inpatient settings: out of area placements (21 November 2024) Mental health inpatient settings: Supporting safe care during transition from inpatient children and young people’s mental health services to adult mental health services (12 December 2024) Mental health inpatient settings: Creating conditions for learning from deaths in mental health inpatient services and when patients die within 30 days of discharge (30 January 2025) Findings Safety, investigation, and learning culture There remains a fear of blame in mental health settings when safety events happen. This contributes to a more defensive culture despite staff actively wanting to learn. Many recommendations to support learning for improvements in mental health care do not lead to implemented actions. Reasons for this include a lack of impact assessment resulting in unintended consequences, no clear recipient involved in the development of recommendations, and duplicated recommendations across organisations. System integration and accountability The integration of health and social care within an integrated care system currently relies on relationships, with an expectation and hope that they will work well. However, where this is not the case, a lack of clear accountability can result in poor outcomes for people with mental illness and severe mental illness. The delivery of care for people with mental illness and severe mental illness is challenging because health and social care services are not always integrated and their goals are not always aligned. Physical health of patients in mental health inpatient settings There are gaps in the provision of physical health care for people with severe mental illness, including inconsistent health checks, poor emergency responses, and misattribution of physical symptoms to mental illness. The misattribution of physical symptoms to patients’ mental health was observed and had the potential to contribute to worsened patient outcomes. National reports, strategies and research have made recommendations to improve the physical health of people with severe mental illness. However, there is evidence that recommendations are delayed in implementation and people continue to die prematurely. Integrated care boards lack the required data and the necessary analytical capability to assess disparities in access, experience and outcomes related to the physical health needs of people with severe mental illness. There is variation in how the physical health checks are carried out on mental health inpatient wards, with limitations in processes for following up on patients’ physical health needs. There is variation in the knowledge, skills and experience of staff who undertake physical health checks and in the environments in which these checks take place. Patients may not always be supported in terms of health education about their physical health risks and modifiable risk factors, for example smoking, dietary advice and physical activity. Caring for people in the community Integrated care boards cannot consistently draw reliable insights from data at national, system or local level, to optimise and improve services, patient care, and outcomes across mental health pathways of care. This results in variability in service provision which does not always meet the needs of individual patients or local populations. Inpatient ‘bed days’ are taken up by people who no longer need them, because people who are clinically fit for discharge are delayed in being transferred to their home or a suitable residence (appropriate placement). Reasons for delayed discharges include issues with housing support and establishing suitable accommodation. This means patients are not always in the right place of care. Barriers to discharge affect patient flow and may result in delays in admission for people with severe mental illness. This means they have to be cared for in a community setting while waiting for an inpatient bed. There is variation across the country in how drug and alcohol services are provided. The variation does not allow for fair and equitable treatment for all patients. Community services are vital to support people to stay as well as possible and to prevent hospital admissions. However, there is variation in community service provision across the country. Staffing and resourcing Staffing and resource constraints in inpatient and community mental health settings impact their ability to provide safe and therapeutic care. In inpatient settings, constraints contribute to mental health wards aiming to staff for ‘safety’ but not always for ‘therapy’. Challenges for staff include the emotionally demanding nature of their work; this can lead to staff burnout and sickness, and further strain on services. There are gaps in mental health workforce planning, particularly in community services where there is no evidence based workforce planning tool to support a standardised staffing establishment setting model. Digital support for safe and therapeutic care A lack of interoperability or integration between digital systems affects the provision of care across mental health, acute and community providers. Challenges in securing appropriate funding impacts on the ability of hospitals to integrate and update their digital services and infrastructure. Electronic patient record functionality is often not available or does not meet staff needs, and so it is not used. Examples include absent functions for food and fluid balance monitoring and risk assessment of venous thromboembolism (blood clots). Challenges in providing and maintaining patient-facing technology, for example televisions and payphones, impacts on the therapeutic environment and the ability of patients to maintain contact with families and loved ones. Where technology for monitoring patients had been introduced, implementation has required considerations to ensure it is used appropriately, is patient-centred, maintains therapeutic engagement, and supports patients to feel safe. Suicide risk and safety assessment ‘Doing’ tasks, like ‘ticking’ checklists, overshadow meaningful, empathetic ‘being’ interactions with patients. Open, compassionate conversations that build trust and therapeutic relationships, enabling patients to own their risk while feeling supported, can help mitigate this. Investigation processes can contribute to a fear of blame, and subsequently contribute to defensive practices such as checklists and a ‘tick box’ culture. This inhibits open and honest conversations and the ability to put the patient, as their authentic self, at the heart of them. Safety recommendations HSSIB recommends that the Department of Health and Social Care continues to work with the ‘recommendations but no action working group’ and other relevant organisations, to ensure that recommendations made by national organisations specific to mental health inpatient settings are reviewed. This work should consider the mechanisms that supported or hindered the implementation of actions from these recommendations. This may help the Department of Health and Social Care understand what has worked when implementing actions from recommendations and enable learning about why some recommendations have not achieved their intention. HSSIB recommends that the Secretary of State for Health and Social Care directs and oversees the identification and development of a patient safety responsibilities and accountabilities strategy related to health and social care integration. This is to support the management of patient safety risks and issues that span integrated care systems. Safety observation National bodies can improve patient safety in mental health inpatient settings in England by supporting provider investment in equipment, digital systems and physical environments to enable conditions within which staff are able to provide, and patients can receive, safe and therapeutic care.

This letter from the London Fire Brigade draws attention to two key issues that may have implications for fire safety within your hospital, and to request that you review your Fire Risk Assessments accordingly. 1. Corridor use for additional bed capacity Recent news reports and material circulating on social media indicate that some hospitals are increasingly using corridors for additional bed capacity. The use of corridors in this way can present significant challenges to fire safety, including: obstruction of fire escape routes increased fire load in circulation spaces. delayed evacuation times in the event of an emergency. 2. Fire Door Recall – Office for Product Safety and Standards You will be aware that there has been a Product Recall concerning certain hospital fire doors installed across England and Wales, affecting approximately 70 sites, with a significant concentration in London and the Southeast. The manufacturer has identified the affected units and has been in contact with project managers at impacted locations. Where correct fire doors are not used, properly fitted and maintained, and corridors are used to house patients, this can exacerbate the spread of smoke and fire, compromise escape routes, and significantly increasing the risk to life. Given the above risks, we ask that you review your fire risk assessment to ensure that you are compliant with the Regulatory Reform (Fire Safety) Safety and appropriate guidance, to safeguard your patients, staff and visitors in the event of a fire.

In this paper from The Strategy Unit, authors make no attempt whatsoever to dispute the upsides of digital. Time, experience and evaluation will show what gains digital technology has to offer. Instead, they focus exclusively on digital downsides, primarily from the perspective of ‘person-centred care’: They used a wide lens. Rather than focusing down on specific digital technologies, they took a broad definition and sought to examine more general risks and challenges.  Cited downsides included: Making care more transactional: ‘With triage through an algorithm you're only allowed to have one [problem]...It forces consultations to be very transactional’. Compounding disadvantage: ‘Having multiple interacting disadvantages makes it harder to keep with the pace that digital access to care is going at’. Creating disadvantage: ‘We are creating the inverse data quality law: the availability of high-quality data varies inversely with the need for healthcare’. ‘Blaming’ individuals: ‘We use digital products to say to people ‘you should lose weight’ or ‘you have a gambling problem’ - and this puts systemic issues back onto the individual’. Making Evidence Based Medicine harder: ‘When I tried to get data about how many people were using it [an app they were evaluating], and at what times of day, and then how much it costs to provide, how many staff were doing what - I was told I couldn't have this data because it was commercially sensitive’. Attraction to the ‘cutting edge’ rather than the basics: ‘You've got finite resources. Do you spend on bytes versus bricks, for example? So, where you invest in cutting edge technology, that might be expensive, and that means you've got less to spend on physical infrastructure to deliver care in’. Fuelling mechanical thinking: ‘I'm not so much worried about machines becoming more like us…what I worry about is people becoming more and more like machines…Our work [as clinicians] has become less fulfilling as it has been taken over by mechanistic thinking’.

Healthcare is a dynamic and complex industry, where even minor errors can have far-reaching consequences for patients, providers, and organisations. “Beyond the Bedside” takes you on a transformative journey through the intricacies of patient safety, equipping healthcare professionals, leaders, and policymakers with the knowledge and tools needed to navigate risks, investigate incidents, and foster a safety culture. These two books goes beyond surface-level understanding to explore the hidden hazards within healthcare systems. They illuminates the interplay between human factors, system design, and environmental risks, highlighting how these elements combine to create vulnerabilities. Through real-life examples, the text sheds light on the human stories behind the statistics, creating a compelling case for why patient safety must remain at the forefront of healthcare priorities. The books delves into the foundational concepts of identifying hazards in healthcare. Readers will gain insights into cutting-edge tools like Bowtie analysis, Safety-II approaches, and STAMP (Systems-Theoretic Accident Model and Processes) that go beyond traditional methods. Adopting a proactive stance, the book empowers healthcare professionals to spot risks before they escalate into incidents. Beyond the Bedside: Unveiling Hazards, Mitigating Risks, and Mastering Patient Safety Investigations: 1 Beyond the Bedside: Unveiling Hazards, Mitigating Risks, and Mastering Patient Safety Investigations: 2

Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. The countries that best maintained improvements in life expectancy after 2011 (Norway, Iceland, Belgium, Denmark, and Sweden) did so through better maintenance of reductions in mortality from cardiovascular diseases and neoplasms, underpinned by decreased exposures to major risks, possibly mitigated by government policies. The continued improvements in life expectancy in these five countries during 2019–21 indicate that these countries were better prepared to withstand the COVID-19 pandemic. By contrast, countries with the greatest slowdown in life expectancy improvements after 2011 went on to have some of the largest decreases in life expectancy in 2019–21. These findings suggest that government policies that improve population health also build resilience to future shocks. Such policies include reducing population exposure to major upstream risks for cardiovascular diseases and neoplasms, such as harmful diets and low physical activity, tackling the commercial determinants of poor health, and ensuring access to affordable health services.

In healthcare environments, staff members can become exposed to substances hazardous to health as part of their day-to-day work that can lead to adverse outcomes to health. By sharing our claims data as a catalyst for learning, we aim to encourage improvements in reducing harm and improving staff safety. This resource outlines risks associated with these exposures, and illustrates learning from claims through illustrative case stories and an analysis of recurring themes in settled claims. NHS Resolution received 371 claims for harm caused by exposure to substances hazardous to health from incidents occurring between 1 April 2013 and 31 March 2023. The total cost for closed claims was £5,989,451. Of these 371 claims, there were 165 that were settled with damages paid. The total cost of damages paid was £2,471,880, excluding defence and claimant costs. 58 of the 371 claims are still open, they have been excluded from this analysis. These claims could go on to settle with or without damages.

This page describes how a Functional Resonance Analysis Method (FRAM) model is built, and how it can be used for either event analysis or risk assessment. A brief “How To” outline A FRAM primer How to use FRAM The FRAM model visualiser - The FMV allows a user to build and edit a FRAM model and to visualise it. The FMV runs as a web application in any modern web browser on any operating system. It has been most extensively tested (and therefore has the most predictable behaviour) using Microsoft Edge and Google Chrome.

Compression stockings might be unnecessary for patients at moderate or high risk of blood clots who are undergoing planned surgery. A study called GAPS suggests that anti-clotting medicine alone is just as effective as using it in combination with compression stockings.  Researchers involved in the large randomised controlled study recommend that guidelines for preventing blood clots such as deep vein thrombosis (DVT) and pulmonary embolism (PE) should be changed. They suggest that compression stockings should no longer be standard care for most patients having planned surgery and taking anti-clotting medicine while in hospital. This could save the NHS in England around £63 million per year.

Society faces a growing set of risks from advanced emerging technologies. While there has been discussion on some of these risks, a comprehensive overview does not exist, and it is not clear what methods are suited to identify future risks. This scoping review aimed to synthesise current knowledge regarding the risks associated with emerging technologies. The findings show that a diverse set of technologies and risks have been considered, with ten risk themes identified: risks to human health and wellbeing, sub-standard technology risks, legal and ethical risks, privacy and security risks, socioeconomic impacts, ecological and environmental risks, malicious use risks, geopolitical risks, technological unemployment risks, and existential threats. It is concluded that there is a need to expand the focus of prospective risk assessments to consider the organisational, sociotechnical and societal systems in which emerging technologies will be deployed. The development of a future technology risks classification scheme is also recommended.

Project proposal to improve equality and reduce health inequalities. This NHS guidance is to assist organisations to develop a Standard Operating Process (SOP) for managing Covid-19 risk assessments.

This cohort study examined how hospital six early warning scores compare with one another, based on 362,926 patient encounters. The authors compared three proprietary artificial intelligence (AI) early warning scores: Simultaneous Epic Deterioration Index (EDI) Rothman Index (RI) eCARTv5 (eCART) against three publicly available simple aggregated weighted scores: Modified Early Warning Score (MEWS) National Early Warning Score (NEWS) NEWS2 scores. In the study, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. The authors concluded that, given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.

Healthcare professionals are reminded to inform patients about the common and serious side effects associated with glucagon-like peptide-1 receptor agonists (GLP-1RAs). Advice for healthcare professionals: Inform patients upon initial prescription and when increasing the dose about the common risk of gastrointestinal side effects which may affect more than 1 in 10 patients. These are usually non-serious, however can sometimes lead to more serious complications such as severe dehydration, resulting in hospitalisation. Be aware that hypoglycaemia can occur in non-diabetic patients using some GLP-1RAs for weight management; ensure patients are aware of the symptoms and signs of hypoglycaemia and know to urgently seek medical advice should they occur. Patients should also be warned of the risk of falsified GLP-1RA medicines for weight loss if not prescribed by a registered healthcare professional, and be aware that some falsified medicines have been found to contain insulin. Be aware there have been reports of potential misuse of GLP-1RAs for unauthorised indications such as aesthetic weight loss report suspected adverse drug reactions to the Yellow Card scheme.

Delirium is a common but underdiagnosed state of disturbed attention and cognition that afflicts one in four older hospital inpatients. It is independently associated with a longer length of hospital stay, mortality, accelerated cognitive decline and new-onset dementia. Risk stratification models enable clinicians to identify patients at high risk of an adverse event and intervene where appropriate. The advent of wearables, genomics, and dynamic datasets within electronic health records (EHRs) provides big data to which machine learning (ML) can be applied to individualise clinical risk prediction. ML is a subset of artificial intelligence that uses advanced computer programmes to learn patterns and associations within large datasets and develop models (or algorithms), which can then be applied to new data in rapidly producing predictions or classifications, including diagnoses. The objectives of this review from Strating et al. were to: (1) provide a more contemporary overview of research on all ML delirium prediction models designed for use in the inpatient setting; (2) characterise them according to their stage of development, validation and deployment; and (3) assess the extent to which their performance and utility in clinical practice have been evaluated.