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Simulation studies provide a unique opportunity to develop a deeper understanding of how healthcare workers manage risk in everyday care. In this issue of the journal, Mumma and colleagues1 use a simulation design to analyse how nurses think during infection prevention and control practices and identify the cognitive skills that are associated with high performance.
Most nurse educators are familiar with the low-fidelity glow germ simulation intended to make nursing students and other health care providers aware of cross-contamination and the ubiquitous nature of microorganisms. Mumma et al have taken this exercise to another level by using actual microorganisms, thereby increasing the stakes of the simulated experience. The study has considerable methodological rigour and is an exciting way to highlight a unique use of simulation. Several important ideas and insights stem from reading this article.
The study looked at how 42 nurses provided care for two simulated...
In healthcare, as in life, the adage ‘variety is the spice of life’ often holds true. Variation can represent individual patient preferences, but when it comes to the quality of healthcare, variation can also be unwanted and harmful. Analysis of variation in a quality-of-care indicator assumes that finding only limited variation is a good thing, suggesting consistently high compliance with evidence-based guidelines and providing evidence of equity. In this editorial, we consider how variation is and should be quantified, comment on the findings of a review1 in this issue of BMJ Quality and Safety, and explore whether measurement at the hospital level is best for learning. We conclude by reflecting on the assumption that only limited variation is good.
How is variation analysed? Take CT scanning for suspected stroke as an example. This should be done soon after the patient arrives in the emergency department. The scan...
When patients and clinicians do not speak the same language, the quality and safety concerns that can arise seem evident. However, the literature on the association between language and a host of health outcomes is vast and varied. In this issue of BMJQS, Chu et al share the results of their well-conducted systematic review and meta-analysis of the relationship between a patient’s spoken language and hospital readmissions and emergency department (ED) revisits.1 They report that adult inpatients who prefer a non-dominant language are more likely to experience an unplanned hospital readmission or ED revisit after discharge. Moreover, they found that children whose parents spoke a non-dominant language had more ED revisits. The authors’ work is a thoughtful synthesis of a somewhat disparate literature and offers a starting point to consider key challenges in the broader area of research on linguistic inequities in healthcare.
Language as a...Relatively little is known about the cognitive processes of healthcare workers that mediate between performance-shaping factors (eg, workload, time pressure) and adherence to infection prevention and control (IPC) practices. We taxonomised the cognitive work involved in IPC practices and assessed its role in how pathogens spread.
MethodsForty-two registered nurses performed patient care tasks in a standardised high-fidelity simulation. Afterwards, participants watched a video of their simulation and described what they were thinking, which we analysed to obtain frequencies of macrocognitive functions (MCFs) in the context of different IPC practices. Performance in the simulation was the frequency at which participants spread harmless surrogates for pathogens (bacteriophages). Using a tertiary split, participants were categorised into a performance group: high, medium or low. To identify associations between the three variables—performance groups, MCFs and IPC practices—we used multiblock discriminant correspondence analysis (MUDICA).
ResultsMUDICA extracted two factors discriminating between performance groups. Factor 1 captured differences between high and medium performers. High performers monitored the situation for contamination events and mitigated risks by applying formal and informal rules or managing their uncertainty, particularly for sterile technique and cleaning. Medium performers engaged more in future-oriented cognition, anticipating contamination events and planning their workflow, across many IPC practices. Factor 2 distinguished the low performers from the medium and high performers who mitigated risks with informal rules and sacrificed IPC practices when managing tradeoffs, all in the context of minimising cross-contamination from physical touch.
ConclusionsTo reduce pathogen transmission, new approaches to training IPC (eg, cognitive skills training) and system design are needed. Interventions should help nurses apply their knowledge of IPC fluidly during patient care, prioritising and monitoring situations for risks and deciding how to mitigate risks. Planning IPC into one’s workflow is beneficial but may not account for the unpredictability of patient care.
There is a critical need to identify specific causes of and tailored solutions to diagnostic error in sexual and gender minority (SGM) populations.
PurposeTo identify challenges to diagnosis in SGM adults, understand the impacts of patient-reported diagnostic errors on patients’ lives and elicit solutions.
MethodsQualitative study using in-depth semistructured interviews. Participants were recruited using convenience and snowball sampling. Recruitment efforts targeted 22 SGM-focused organisations, academic centres and clinics across the USA. Participants were encouraged to share study details with personal contacts. Interviews were analysed using codebook thematic analysis.
ResultsInterviewees (n=20) ranged from 20 to 60 years of age with diverse mental and physical health symptoms. All participants identified as sexual minorities, gender minorities or both. Thematic analysis revealed challenges to diagnosis. Provider-level challenges included pathologisation of SGM identity; dismissal of symptoms due to anti-SGM bias; communication failures due to providers being distracted by SGM identity and enforcement of cis-heteronormative assumptions. Patient-level challenges included internalised shame and stigma. Intersectional challenges included biases around factors like race and age. Patient-reported diagnostic error led to worsening relationships with providers, worsened mental and physical health and increased self-advocacy and community-activism. Solutions to reduce diagnostic disparities included SGM-specific medical education and provider training, using inclusive language, asking questions, avoiding assumptions, encouraging diagnostic coproduction, upholding high care standards and ethics, involving SGM individuals in healthcare improvement and increasing research on SGM health.
ConclusionsAnti-SGM bias, queerphobia, lack of provider training and heteronormative attitudes hinder diagnostic decision-making and communication. As a result, SGM patients report significant harms. Solutions to mitigate diagnostic disparities require an intersectional approach that considers patients’ gender identity, sexual orientation, race, age, economic status and system-level changes.
Efforts to mitigate unwarranted variation in the quality of care require insight into the ‘level’ (eg, patient, physician, ward, hospital) at which observed variation exists. This systematic literature review aims to synthesise the results of studies that quantify the extent to which hospitals contribute to variation in quality indicator scores.
MethodsEmbase, Medline, Web of Science, Cochrane and Google Scholar were systematically searched from 2010 to November 2023. We included studies that reported a measure of between-hospital variation in quality indicator scores relative to total variation, typically expressed as a variance partition coefficient (VPC). The results were analysed by disease category and quality indicator type.
ResultsIn total, 8373 studies were reviewed, of which 44 met the inclusion criteria. Casemix adjusted variation was studied for multiple disease categories using 144 indicators, divided over 5 types: intermediate clinical outcomes (n=81), final clinical outcomes (n=35), processes (n=10), patient-reported experiences (n=15) and patient-reported outcomes (n=3). In addition to an analysis of between-hospital variation, eight studies also reported physician-level variation (n=54 estimates). In general, variation that could be attributed to hospitals was limited (median VPC=3%, IQR=1%–9%). Between-hospital variation was highest for process indicators (17.4%, 10.8%–33.5%) and lowest for final clinical outcomes (1.4%, 0.6%–4.2%) and patient-reported outcomes (1.0%, 0.9%–1.5%). No clear pattern could be identified in the degree of between-hospital variation by disease category. Furthermore, the studies exhibited limited attention to the reliability of observed differences in indicator scores.
ConclusionHospital-level variation in quality indicator scores is generally small relative to residual variation. However, meaningful variation between hospitals does exist for multiple indicators, especially for care processes which can be directly influenced by hospital policy. Quality improvement strategies are likely to generate more impact if preceded by level-specific and indicator-specific analyses of variation, and when absolute variation is also considered.
PROSPERO registration numberCRD42022315850.
