Addressing a global gap in safe surgery: revisiting the Surgical Safety Checklist at 20 years

Karolina Kogut, Nobhojit Roy, Mary E. Brindle

The WHO Surgical Safety Checklist (SSC) has been a cornerstone of surgical safety for nearly 20 years—endorsed as a required or recommended standard and adopted by hospitals worldwide.1 However, policy adoption is not the same as meaningful use. Sustained use of the SSC has faltered in many places across the globe over time, and the opportunity to realise the SSC’s full potential is eroding. Despite its proven effectiveness, the SSC is steadily forfeiting its potential as its meaningful use declines across the globe.

In their article ‘Surgical Safety Checklist Awareness and Safety Attitudes in the Eastern Mediterranean Region: A Cross-Sectional Study,’ Gebreal et al expose this critical global gap in checklist awareness and adoption in the Eastern Mediterranean Region (EMR).

The evolution of the...

In 2015, the National Academy of Science, Engineering and Medicine report ‘Improving Diagnosis’ sounded a call to action, urging patient and family engagement (PFE) in diagnostic safety.1 While many clinicians agree in principle with PFE, tangible ways to engage patients and families in diagnostic safety were underexplored.2 Fast forward to 2026 and this issue of BMJ Quality and Safety, where Hill et al3 describe a changed landscape with the results of their scoping review of PFE interventions in diagnosis. In both grey and peer-reviewed literature, the authors identified 260 unique PFE interventions relating to diagnosis, including 213 in the grey literature, originating from healthcare delivery systems, research funders and patient organisations. Patients were the intended primary users for 63% of the interventions, but only 24% were designed with patients. The authors categorised the interventions into 16 different types that could be used across...

Background

The optimal performance of doctors is critical to delivering high-quality, safe healthcare. However, 6–12% of doctors may experience challenges that impact their work performance. In many countries, including the UK, there is variation in the practice and quality of professional support services between different types of organisations. The aims of our study were (1) to identify why, how, in what contexts and for whom professional support works, (2) to develop a guide for healthcare organisations to use to optimise professional support.

Methods

We carried out a realist evaluation consistent with Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) II standards. 45 interviews were conducted with professional support staff and doctors who had undertaken professional support across seven sites in England. Interviews were analysed using a realist logic. To develop the guide, six workshops were held with the same groups plus a patient and public involvement group.

Results

We identified six principles of effective professional support, based on 47 context-mechanism-outcome configurations. (1) Work-place culture influences support-seeking behaviour. (2) Trust and psychological safety are central to enable candid conversations and engagement. (3) Doctors can then develop self-awareness and situational awareness, reframe challenges, accept responsibility where appropriate and recognise structural factors underpinning their difficulties. (4) Doctors are more likely to feel motivated to engage through positive framing. (5) Personal and professional growth occurs when doctors are empowered to make changes to their practice. (6) Cultures that stigmatise help-seeking undermine support, while those that model vulnerability and normalise support enhance the likelihood of positive outcomes.

Conclusions

Our guide provides step-by-step advice to identify key actions for those delivering professional support . Since the realist approach identifies principles and causal explanations, the findings are likely transferable to other settings/countries.

Background

The World Health Organization Surgical Safety Checklist (WHO-SSC) is a global tool designed to enhance teamwork and safety in operating rooms (ORs). Its use remains under-reported in the Eastern Mediterranean Region (EMR). This study aimed to assess WHO-SSC awareness, adoption, satisfaction with its implementation, barriers and its impact on healthcare professionals’ safety attitudes.

Methods

Using a validated questionnaire, a cross-sectional survey was conducted from 11 March to 18 June 2024, in hospitals across 12 countries in the EMR through face-to-face and online distribution. Healthcare professionals’ safety attitude was assessed using the modified operating-room version of the Safety Attitudes Questionnaire. A multiple logistic regression model was used for data analysis, reporting aOR with 95% CIs.

Results

The study involved 3861 respondents, comprising 1348 surgeons, 1277 OR nurses and 1236 anaesthesiologists. Overall, 52.5% of respondents reported awareness of the WHO-SSC. Among those aware, adoption was highest in private and teaching hospitals and lowest in charity hospitals, with 56.2% expressing satisfaction with its implementation. Reported ‘always’ adherence to the checklist was 32% in elective surgeries, compared with 19% in emergency surgeries. The main barriers included a lack of training, time constraints, limited awareness and poor team communication. Overall, 61.4% of participants demonstrated a positive safety attitude. Positive attitudes were associated with older age, being from a low-income and middle-income country, and WHO-SSC awareness.

Conclusion

This study emphasises inadequate awareness and adoption of the WHO-SSC in the EMR. Strengthening training, institutional support and context-specific implementation strategies is essential to enhance surgical safety culture in the region.

Background

Prescribed opioids are potent analgesics associated with high safety risks due to their adverse effects, drug-drug and drug-disease interactions and potential for dependency. To support the implementation of prescribing indicators for further interventions, this study examined the prevalence of different types of potentially hazardous opioid prescribing (PHOP) in general practices across England and investigated underlying factors and variation between practices.

Methods

We conducted a cross-sectional study focusing on adults (aged ≥18 years) at risk of triggering 17 PHOP indicators on 1 April 2021, involving 1358 general practices contributing to the Clinical Practice Research Datalink Aurum. PHOP prevalence was calculated by dividing the number of patients triggering an indicator by the total number at risk. Variation was assessed with intraclass correlation coefficients (ICCs), and multilevel mixed-effects logistic regression models identified associated factors, presented as adjusted ORs (aORs) with 95% CIs.

Results

Among 3 121 852 patients observed, 361 505 (11.58%, 95% CI 11.54, 11.62) triggered at least one PHOP indicator, yielding an ICC of 0.07 (95% CI 0.06, 0.07). The prevalence of the 17 PHOP indicators ranged from 1.97% to 32.02%. Significant variability was noted across the 17 indicators, especially for persistent opioid prescriptions in patients with alcohol use issues (ICC 0.08, 95% CI 0.07, 0.09), chronic obstructive pulmonary disease or asthma (ICC 0.08, 95% CI 0.07, 0.09) and hypothyroidism (ICC 0.07, 95% CI 0.06, 0.07). Patients from the most deprived regions (aOR 1.28, 95% CI 1.22, 1.34) and the Northwest of England (aOR 1.73, 95% CI 1.66, 1.81) had a higher risk of PHOP.

Conclusions and relevance

The high prevalence of PHOP, particularly among the most socioeconomically disadvantaged populations, emphasises existing prescribing risks and the need for their appropriate consideration within primary care. The high variation between practices indicates potential for improvement through targeted practice-level intervention.

Introduction

Actively engaging patients is essential for diagnostic excellence and patient safety.

Objectives

To (1) identify and synthesise interventions facilitating patient and family engagement (PFE) across the diagnostic process, and (2a) assess patient involvement and (2b) equity considerations in their design or implementation.

Design

This scoping review followed Arksey and O’Malley’s framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) guidelines. An advisory panel guided the review. We searched Medline, Embase, CINAHL, PsycInfo and Northern Light for peer-reviewed literature and conducted grey literature searches using DuckDuckGo and targeted websites. Search terms focused on PFE and diagnostic error. Eligible interventions were published in English between January 1999 and July 2024 and supported PFE in at least one step of the National Academies of Sciences, Engineering, and Medicine (NASEM) diagnostic process. Narrative reviews, case studies and editorials were excluded. Interventions were mapped to the NASEM steps; data were extracted on patient involvement and equity.

Results

Of the 11 630 studies screened, 250 were included, representing 260 interventions. Most (n=213; 85.2%) were from the grey literature, and patients were primary users (n=166; 63.8%). Interventions spanned all diagnostic process steps but were most common in treatment (n=122; 46.9%) and history taking (n=100; 38.5%), with few in referrals (n=10, 3.8%) and physical examinations (n=6, 2.3%). The evidence base was weak: grey literature interventions lacked high-quality studies, and among the 37 peer-reviewed studies, three were randomised controlled trials, each limited by small samples or high attrition. Only 63 interventions (24.2%) were designed with patients, and 48 (18.5%) incorporated equity.

Conclusion

PFE interventions exist across the diagnostic process, but few target referrals and physical examinations. The evidence remains weak, and current interventions cannot be considered effective. Future research should prioritise equity, patient involvement and rigorous evaluation.

The rapid growth in quality indicators (QIs) has increased complexity in selecting those that are effective for monitoring provider quality and patient outcomes. Existing selection methodologies are often insufficiently transparent or standardised and influenced by subjective opinions. To develop an instrument for evidence-based collection and evaluation of QIs that are suitable for quality monitoring, and which can be applied to various healthcare areas (HCAs; defined by care setting). The instrument was developed with HCA-specific experts, who provided feedback on its components and piloted its use in the Swiss context. We conducted a literature search with snowballing to identify prioritisation criteria and weighted these using the Analytic Hierarchy Process (AHP). We developed a template to facilitate data collection and the QI evaluation. The final QUALICATOR instrument consists of five steps: (1) definition of search scope; (2) utilisation of 12 prioritisation criteria across four dimensions (relevance, scientific soundness, usability, feasibility); (3) application of a data collection template objectifying the prioritisation criteria; (4) preselection via knockout criteria and (5) final prioritisation via weighting multicriteria decision analysis. Final criteria weights derived via the AHP varied substantially across HCAs (eg, relevance: 19.16%–57.10%; scientific soundness: 5.50%–39.50%; usability: 13.17%–33.75%; feasibility: 11.78%–45.57%), reflecting HCA-specific priorities. A web-based prototype is available to support a user-friendly application. This QUALICATOR instrument provides a transparent, scalable approach to navigate through a growing body of QIs with further validation needed. It provides a methodological framework and proof of concept, rather than a ready-to-use solution. It shows policymakers, providers and payers a path to make informed decisions about which QIs to prioritise, monitor, invest in and act on.

In 2026, artificial intelligence (AI) systems are deployed at scale to support clinical decision-making. Algorithms detect cardiac arrhythmias from ECGs, classify skin lesions from photographs and predict deterioration in critically ill patients. These tools are valuable. However, they share a critical vulnerability: they are trained on labelled datasets where the labels (the diagnoses) derive from clinical assessments recorded in electronic health records (EHRs). The fundamental assumption is that these clinical diagnoses represent the ground truth. This assumption merits examination.

In my practice as a forensic pathologist conducting forensic pathology reviews in malpractice litigation, I regularly encounter cases where the clinical diagnosis recorded in the patient’s medical record diverges from the findings at autopsy. These discrepancies raise an uncomfortable question for the emerging field of algorithmic medicine: If AI systems are trained on clinical labels without pathological verification, are they being taught to recognise disease or to replicate diagnostic error?

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