Classification—the process of sorting people or things into groups according to shared qualities or characteristics—is increasingly used within healthcare as a means of identifying patients with particular needs and/or risks. This is important because receiving care in hospital can expose some particularly vulnerable groups of patients to increased risk of harm and poor outcomes, for example, the systemic safety inequities experienced by people with learning disabilities.1

Identifying and responding to patients with additional needs

In order to deliver care that meets individual patients’ needs, health services must, first, be able to identify those with additional needs and, second, be able to mobilise an appropriate response to these. Various ways of identifying patients with additional needs and/or risks have been developed. One obvious example is the use of wristbands for those with drug allergies, although the potential for confusion between different schemes at different hospitals has been...

Background

The Safety Case is a regulatory technique that requires organisations to demonstrate to regulators that they have systematically identified hazards in their systems and reduced risks to being as low as reasonably practicable. It is used in several high-risk sectors, but only in a very limited way in healthcare. We examined the first documented attempt to apply the Safety Case methodology to clinical pathways.

Methods

Data are drawn from a mixed-methods evaluation of the Safer Clinical Systems programme. The development of a Safety Case for a defined clinical pathway was a centrepiece of the programme. We base our analysis on 143 interviews covering all aspects of the programme and on analysis of 13 Safety Cases produced by clinical teams.

Results

The principles behind a proactive, systematic approach to identifying and controlling risk that could be curated in a single document were broadly welcomed by participants, but was not straightforward to deliver. Compiling Safety Cases helped teams to identify safety hazards in clinical pathways, some of which had been previously occluded. However, the work of compiling Safety Cases was demanding of scarce skill and resource. Not all problems identified through proactive methods were tractable to the efforts of front-line staff. Some persistent hazards, originating from institutional and organisational vulnerabilities, appeared also to be out of the scope of control of even the board level of organisations. A particular dilemma for organisational senior leadership was whether to prioritise fixing the risks proactively identified in Safety Cases over other pressing issues, including those that had already resulted in harm.

Conclusions

The Safety Case approach was recognised by those involved in the Safer Clinical Systems programme as having potential value. However, it is also fraught with challenge, highlighting the limitations of efforts to transfer safety management practices to healthcare from other sectors.

Background

Inpatient falls are the most common safety incident reported by hospitals worldwide. Traditionally, responses have been guided by categorising patients’ levels of fall risk, but multifactorial approaches are now recommended. These target individual, modifiable fall risk factors, requiring clear communication between multidisciplinary team members. Spoken communication is an important channel, but little is known about its form in this context. We aim to address this by exploring spoken communication between hospital staff about fall prevention and how this supports multifactorial fall prevention practice.

Methods

Data were collected through semistructured qualitative interviews with 50 staff and ethnographic observations of fall prevention practices (251.25 hours) on orthopaedic and older person wards in four English hospitals. Findings were analysed using a framework approach.

Findings

We observed staff engaging in ‘multifactorial talk’ to address patients’ modifiable risk factors, especially during multidisciplinary meetings which were patient focused rather than risk type focused. Such communication coexisted with ‘categorisation talk’, which focused on patients’ levels of fall risk and allocating nursing supervision to ‘high risk’ patients. Staff negotiated tensions between these different approaches through frequent ‘hybrid talk’, where, as well as categorising risks, they also discussed how to modify them.

Conclusion

To support hospitals in implementing multifactorial, multidisciplinary fall prevention, we recommend: (1) focusing on patients’ individual risk factors and actions to address them (a ‘why?’ rather than a ‘who’ approach); (2) where not possible to avoid ‘high risk’ categorisations, employing ‘hybrid’ communication which emphasises actions to modify individual risk factors, as well as risk level; (3) challenging assumptions about generic interventions to identify what individual patients need; and (4) timing meetings to enable staff from different disciplines to participate.

Background

Measures to evaluate high-risk medication safety during transfers of care should span different safety dimensions across all components of these transfers and reflect outcomes and opportunities for proactive safety management.

Objectives

To scope measures currently used to evaluate safety interventions targeting insulin, anticoagulants and other high-risk medications during transfers of care and evaluate their comprehensiveness as a portfolio.

Methods

Embase, Medline, Cochrane and CINAHL databases were searched using scoping methodology for studies evaluating the safety of insulin, anticoagulants and other high-risk medications during transfer of care. Measures identified were extracted into a spreadsheet, collated and mapped against three frameworks: (1) ‘Key Components of an Ideal Transfer of Care’, (2) work systems, processes and outcomes and (3) whether measures captured past harms, events in real time or areas of concern. The potential for digital health systems to support proactive measures was explored.

Results

Thirty-five studies were reviewed with 162 measures in use. Once collated, 29 discrete categories of measures were identified. Most were outcome measures such as adverse events. Process measures included communication and issue identification and resolution. Clinic enrolment was the only work system measure. Twenty-four measures captured past harm (eg, adverse events) and six indicated future risk (eg, patient feedback for organisations). Two real-time measures alerted healthcare professionals to risks using digital systems. No measures were of advance care planning or enlisting support.

Conclusion

The measures identified are insufficient for a comprehensive portfolio to assess safety of key medications during transfer of care. Further measures are required to reflect all components of transfers of care and capture the work system factors contributing to outcomes in order to support proactive intervention to reduce unwanted variation and prevent adverse outcomes. Advances in digital technology and its employment within integrated care provide opportunities for the development of such measures.

Background and objectives

Clinical debriefing (CD) following a clinical event has been found to confer benefits for staff and has potential to improve patient outcomes. Use of a structured tool to facilitate CD may provide a more standardised approach and help overcome barriers to CD; however, we presently know little about the tools available. This systematic review aimed to identify tools for CD in order to explore their attributes and evidence for use.

Methods

A systematic review was conducted in line with PRISMA standards. Five databases were searched. Data were extracted using an electronic form and analysed using critical qualitative synthesis. This was guided by two frameworks: the ‘5 Es’ (defining attributes of CD: educated/experienced facilitator, environment, education, evaluation and emotions) and the modified Kirkpatrick’s levels. Tool utility was determined by a scoring system based on these frameworks.

Results

Twenty-one studies were included in the systematic review. All the tools were designed for use in an acute care setting. Criteria for debriefing were related to major or adverse clinical events or on staff request. Most tools contained guidance on facilitator role, physical environment and made suggestions relating to psychological safety. All tools addressed points for education and evaluation, although few described a process for implementing change. Staff emotions were variably addressed. Many tools reported evidence for use; however, this was generally low-level, with only one tool demonstrating improved patient outcomes.

Conclusion

Recommendations for practice based on the findings are made. Future research should aim to further examine outcomes evidence of these tools in order to optimise the potential of CD tools for individuals, teams, healthcare systems and patients.

There are growing calls for healthcare to confront it’s role in the climate crisis. Estimates suggest that carbon emissions from healthcare constitute 5% of net global emissions. To put this into context, emissions from all air travel are estimated at 3.5% of net global emissions.1 Health systems, organisations and clinicians have been called on to lead efforts to reduce emissions given that the climate crisis presents a major threat to human health.

Ensuring appropriateness of care, and reducing overuse are central planks of strategies suggested in the literature and are increasingly being enacted by large healthcare systems and provider organisations to reduce healthcare’s climate impact.2 However, individual clinicians are often left with little guidance or support in terms of how to do this in practice. To illustrate, the Agency for Health Care Research and Quality (AHRQ) within the United States Department of Health and Human...

I write this commentary as wildfires rage around the world, including in Greece, Italy, Siberia, Algeria and the USA. In my own country, Canada, fires have already consumed over 130 000 km21—the size of Greece—and the wildfire season has not yet ended. Recent months have also seen hundreds of millions of people suffering scorching heatwaves across Europe, China and North America. Residents of Phoenix, Arizona, endured temperatures over 43°C for 31 consecutive days this summer. In Italy, harsh heat in the south occurred at the same time as storms delivered hail the size of tennis balls in the country’s north. This almost ‘end of days’ juxtaposition of extreme weather events within a single country comes as new research indicates that the Gulf Stream may collapse as soon as 2025.1 Loss of these vital ocean currents would constitute a climate tipping point, making extreme storms more frequent...

Over 200 health journals call on the United Nations (UN), political leaders and health professionals to recognise that climate change and biodiversity loss are one indivisible crisis and must be tackled together to preserve health and avoid catastrophe. This overall environmental crisis is now so severe as to be a global health emergency.

The world is currently responding to the climate crisis and the nature crisis as if they were separate challenges. This is a dangerous mistake. The 28th Conference of the Parties (COP) on climate change is about to be held in Dubai while the 16th COP on biodiversity is due to be held in Turkey in 2024. The research communities that provide the evidence for the two COPs are unfortunately largely separate, but they were brought together for a workshop in 2020 when they concluded that: ‘Only by considering climate and biodiversity as parts of the same...

Equity is one of the six core healthcare quality domains in ‘Crossing the Quality Chasm’, published by the Institute of Medicine in 2001.1 While substantial quality measurement and improvement work has focused on improving safety, patient-centredness, timeliness, efficiency and efficacy (the other five domains), far less has focused on health equity measurement and improvement. This is in part due to limited adoption of standardised definitions of racial and ethnicities and therefore limited availability of high-quality data on race and ethnicity.2 Having accurate data is a key first step in addressing health inequities, since what is measured influences what is done.3 There are substantial efforts to improve these data availability and quality by healthcare systems, nationally and internationally.2 Currently, adequate efforts require several steps: the decision to collect data, ensuring the quality of data being collected, and reconciliation of race and ethnicity...

Within healthcare services worldwide, there is a continual emphasis on innovation, including the development, evaluation and improvement of new and existing healthcare interventions and services to improve patient outcomes. In addition to evaluating efficacy, it is also important to evaluate how innovations are used in ‘real-world’ settings. A key part of this is process evaluation: understanding how interventions and services are implemented and engaged with. For example, recent Medical Research Council guidance on researching the effectiveness of complex interventions highlights the importance of measuring implementation and context, including the measurement of ‘fidelity’.1

‘Fidelity’ has been proposed to have five related domains, including fidelity of design, training, delivery (whether intervention components, as outlined in the intervention protocol, are delivered as planned), receipt (whether participants understand and are able to perform required skills) and enactment (whether participants use skills in daily life).2 Both receipt and enactment have...

The seminal report To Err is Human focused on a wide range of serious patient safety concerns; diagnostic error was mentioned only in passing.1 Very little data were available on the magnitude of harm related to diagnostic errors at that time, except for a back-of-the-napkin estimate that diagnostic error could be responsible for 40 000–80 000 in-hospital deaths annually.2 The problem finally received its due 15 years later, when the National Academy of Medicine asserted that "... most of us will experience at least one diagnostic error in our lifetime, sometimes with devastating consequences".3

In this context, the paper by Newman-Toker et al in this issue of BMJ Quality & Safety is a welcome contribution, presenting an extensively researched set of estimates that proposes that harm may be an order-of-magnitude larger.4 The paper is the third part of a larger study,...

Background

Emerging evidence has shown racial and ethnic disparities in rates of harm for hospitalised children. Previous work has also demonstrated how highly heterogeneous approaches to collection of race and ethnicity data pose challenges to population-level analyses. This work aims to both create an approach to aggregating safety data from multiple hospitals by race and ethnicity and apply the approach to the examination of potential disparities in high-frequency harm conditions.

Methods

In this cross-sectional, multicentre study, a cohort of hospitals from the Solutions for Patient Safety network with varying race and ethnicity data collection systems submitted validated central line-associated bloodstream infection (CLABSI) and unplanned extubation (UE) data stratified by patient race and ethnicity categories. Data were submitted using a crosswalk created by the study team that reconciled varying approaches to race and ethnicity data collection by participating hospitals. Harm rates for race and ethnicity categories were compared with reference values reflective of the cohort and broader children’s hospital population.

Results

Racial and ethnic disparities were identified in both harm types. Multiracial Hispanic, Combined Hispanic and Native Hawaiian or other Pacific Islander patients had CLABSI rates of 2.6–3.6 SD above reference values. For Black or African American patients, UE rates were 3.2–4.4 SD higher. Rates of both events in White patients were significantly lower than reference values.

Conclusions

The combination of harm data across hospitals with varying race and ethnicity collection systems was accomplished through iterative development of a race and ethnicity category framework. We identified racial and ethnic disparities in CLABSI and UE that can be addressed in future improvement work by identifying and modifying care delivery factors that contribute to safety disparities.

Background

In many quality improvement (QI) and other complex interventions, assessing the fidelity with which participants ‘enact’ intervention activities (ie, implement them as intended) is underexplored. Adapting the evaluative approach used in objective structured clinical examinations, we aimed to develop and validate a practical approach to assessing fidelity enactment—the Overall Fidelity Enactment Scale for Complex Interventions (OFES-CI).

Methods

We developed the OFES-CI to evaluate enactment of the SCOPE QI intervention, which teaches nursing home teams to use plan-do-study-act (PDSA) cycles. The OFES-CI was piloted and revised early in SCOPE with good inter-rater reliability, so we proceeded with a single rater. An intraclass correlation coefficient (ICC) was used to assess inter-rater reliability. For 27 SCOPE teams, we used ICC to compare two methods for assessing fidelity enactment: (1) OFES-CI ratings provided by one of five trained experts who observed structured 6 min PDSA progress presentations made at the end of SCOPE, (2) average rating of two coders’ deductive content analysis of qualitative process evaluation data collected during the final 3 months of SCOPE (our gold standard).

Results

Using Cicchetti’s classification, inter-rater reliability between two coders who derived the gold standard enactment score was ‘excellent’ (ICC=0.93, 95% CI=0.85 to 0.97). Inter-rater reliability between the OFES-CI and the gold standard was good (ICC=0.71, 95% CI=0.46 to 0.86), after removing one team where open-text comments were discrepant with the rating. Rater feedback suggests the OFES-CI has strong face validity and positive implementation qualities (acceptability, easy to use, low training requirements).

Conclusions

The OFES-CI provides a promising novel approach for assessing fidelity enactment in QI and other complex interventions. It demonstrates good reliability against our gold standard assessment approach and addresses the practicality problem in fidelity assessment by virtue of its suitable implementation qualities. Steps for adapting the OFES-CI to other complex interventions are offered.

Background

Diagnostic errors cause substantial preventable harms worldwide, but rigorous estimates for total burden are lacking. We previously estimated diagnostic error and serious harm rates for key dangerous diseases in major disease categories and validated plausible ranges using clinical experts.

Objective

We sought to estimate the annual US burden of serious misdiagnosis-related harms (permanent morbidity, mortality) by combining prior results with rigorous estimates of disease incidence.

Methods

Cross-sectional analysis of US-based nationally representative observational data. We estimated annual incident vascular events and infections from 21.5 million (M) sampled US hospital discharges (2012–2014). Annual new cancers were taken from US-based registries (2014). Years were selected for coding consistency with prior literature. Disease-specific incidences for 15 major vascular events, infections and cancers (‘Big Three’ categories) were multiplied by literature-based rates to derive diagnostic errors and serious harms. We calculated uncertainty estimates using Monte Carlo simulations. Validity checks included sensitivity analyses and comparison with prior published estimates.

Results

Annual US incidence was 6.0 M vascular events, 6.2 M infections and 1.5 M cancers. Per ‘Big Three’ dangerous disease case, weighted mean error and serious harm rates were 11.1% and 4.4%, respectively. Extrapolating to all diseases (including non-‘Big Three’ dangerous disease categories), we estimated total serious harms annually in the USA to be 795 000 (plausible range 598 000–1 023 000). Sensitivity analyses using more conservative assumptions estimated 549 000 serious harms. Results were compatible with setting-specific serious harm estimates from inpatient, emergency department and ambulatory care. The 15 dangerous diseases accounted for 50.7% of total serious harms and the top 5 (stroke, sepsis, pneumonia, venous thromboembolism and lung cancer) accounted for 38.7%.

Conclusion

An estimated 795 000 Americans become permanently disabled or die annually across care settings because dangerous diseases are misdiagnosed. Just 15 diseases account for about half of all serious harms, so the problem may be more tractable than previously imagined.

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.

Studying near-miss errors is essential to preventing errors from reaching patients. When an error is committed, it may be intercepted (near-miss) or it will reach the patient; estimates of the proportion that reach the patient vary widely. To better understand this relationship, we conducted a retrospective cohort study using two objective measures to identify wrong-patient imaging order errors involving radiation, estimating the proportion of errors that are intercepted and those that reach the patient. This study was conducted at a large integrated healthcare system using data from 1 January to 31 December 2019. The study used two outcome measures of wrong-patient orders: (1) wrong-patient orders that led to misadministration of radiation reported to the New York Patient Occurrence Reporting and Tracking System (NYPORTS) (misadministration events); and (2) wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder (RAR) measure, a measure identifying orders placed for a patient, retracted and rapidly reordered by the same clinician on a different patient (near-miss events). All imaging orders that involved radiation were extracted retrospectively from the healthcare system data warehouse. Among 293 039 total eligible orders, 151 were wrong-patient orders (3 misadministration events, 148 near-miss events), for an overall rate of 51.5 per 100 000 imaging orders involving radiation placed on the wrong patient. Of all wrong-patient imaging order errors, 2% reached the patient, translating to 50 near-miss events for every 1 error that reached the patient. This proportion provides a more accurate and reliable estimate and reinforces the utility of systematic measure of near-miss errors as an outcome for preventative interventions.

Healthcare quality and safety span multiple topics across the spectrum of academic and clinical disciplines. Keeping abreast of the rapidly growing body of work can be challenging. In this series, we provide succinct summaries of selected relevant studies published in the last several months. Some articles will focus on a particular theme, whereas others will highlight unique publications from high-impact medical journals.

Key points

A randomised controlled trial showed that a communication coach improved cardiologists’ ability to respond to patients with empathy, elicit questions and facilitate enhanced conversational flow. Cardiologists reported that a communication coach helped their clinical practice. JAMA Intern Med; 1 June 2023

In a randomised controlled trial conducted across multiple hospital sites, a written communication tool provided to clinicians significantly improved documentation of goals-of-care discussions in the electronic medical record, with a more substantial impact on patients in racial or...