BMJ Quality & Safety最新文献

Objective: Diagnostic delay is a pervasive patient safety problem that disproportionately affects historically underserved populations. We aim to systematically examine and synthesise published qualitative studies on patient experiences with diagnostic delay among historically underserved racial and ethnic populations.

Data sources: PubMed.

Eligibility criteria: Primary qualitative studies detailing patient or caregiver-reported accounts of delay in the diagnosis of a disease among underserved racial and ethnic populations; conducted in the USA; published in English in a peer-reviewed journal (years 2012-2022); study cohort composed of >50% non-white racial and ethnic populations.

Data analysis: Primary outcomes were barriers to timely diagnosis of a disease. Screening and thematic abstraction were performed independently by two investigators, and data were synthesised using the 'Model of Pathways to Treatment' conceptual framework.

Results: Sixteen studies from multiple clinical domains were included. Barriers to timely diagnosis emerged at the socioeconomic and sociocultural level (low health literacy, distrust in healthcare systems, healthcare avoidance, cultural and linguistic barriers), provider level (cognitive biases, breakdown in patient-provider communication, lack of disease knowledge) and health systems level (inequity in organisational health literacy, administrative barriers, fragmented care environment and a lack of organisational cultural competence). None of the existing studies explored diagnostic disparities among Asian Americans/Pacific Islanders, and few examined chronic conditions known to disproportionately affect historically underserved populations.

Discussion: Historically underserved racial and ethnic patients encountered many challenges throughout their diagnostic journey. Systemic strategies are needed to address and prevent diagnostic disparities.

Objective: To assess the association of county-level bias about black and white people with patient experience, influenza immunisation, and quality of clinical care for black and white older US adults (age 65+ years).

Design: Linear multivariable regression measured the cross-sectional association of county-level estimates of implicit and explicit bias about black and white people with patient experiences, influenza immunisation, and clinical quality-of-care for black and white older US adults.

Participants: We used data from 1.9 million white adults who completed implicit and explicit bias measures during 2003-2018, patient experience and influenza immunisation data from respondents to the 2009-2017 Medicare Consumer Assessment of Healthcare Providers and Systems (MCAHPS) Surveys, and clinical quality-of-care data from patients whose records were included in 2009-2017 Healthcare Effectiveness Data and Information Set (HEDIS) submissions (n=0.8-2.9 million per measure).

Main outcome measures: Three patient experience measures and patient-reported influenza immunisation from the MCAHPS Survey; five HEDIS measures.

Results: In county-level models, higher pro-white implicit bias was associated with lower immunisation rates and worse scores for some patient experience measures for black and white adults as well as larger-magnitude black-white disparities. Higher pro-white implicit bias was associated with worse scores for some HEDIS measures for black and white adults but not with black-white disparities in clinical quality of care. Most significant associations were small in magnitude (effect sizes of 0.2-0.3 or less).

Conclusions: To the extent that county-level pro-white implicit bias is indicative of bias among healthcare providers, there may be a need for interventions designed to prevent such bias from adversely affecting the experiences and preventive care of black patients and the clinical quality of care for all patients.

Introduction: Use of investigations can help support the diagnostic process of patients with cancer in primary care, but the size of variation between patient group and between practices is unclear.

Methods: We analysed data on 53 252 patients from 1868 general practices included in the National Cancer Diagnosis Audit 2018 using a sequence of logistic regression models to quantify and explain practice-level variation in investigation use, accounting for patient-level case-mix and practice characteristics. Four types of investigations were considered: any investigation, blood tests, imaging and endoscopy.

Results: Large variation in practice use was observed (OR for 97.5th to 2.5th centile being 4.02, 4.33 and 3.12, respectively for any investigation, blood test and imaging). After accounting for patient case-mix, the spread of practice variation increased further to 5.61, 6.30 and 3.60 denoting that patients with characteristics associated with higher use (ie, certain cancer sites) are over-represented among practices with lower than the national average use of such investigation. Practice characteristics explained very little of observed variation, except for rurality (rural practices having lower use of any investigation) and concentration of older age patients (practices with older patients being more likely to use all types of investigations).

Conclusion: There is very large variation between practices in use of investigation in patients with cancer as part of the diagnostic process. It is conceivable that the diagnostic process can be improved if investigation use was to be increased in lower use practices, although it is also possible that there is overtesting in practices with very high use of investigations, and in fact both undertesting and overtesting may co-exist.

Identifying high and poorly performing organisations is common practice in healthcare. Often this is done within a frequentist inferential framework where statistical techniques are used that acknowledge that observed performance is an imperfect measure of underlying quality. Various methods are employed for this purpose, but the influence of chance on the degree of misclassification is often underappreciated. Using simulations, we show that the distribution of underlying performance of organisations flagged as the worst performers, using current best practices, was highly dependent on the reliability of the performance measure. When reliability was low, flagged organisations were likely to have an underlying performance that was near the population average. Reliability needs to reach at least 0.7 for 50% of flagged organisations to be correctly flagged and 0.9 to nearly eliminate incorrectly flagging organisations close to the overall mean. We conclude that despite their widespread use, techniques for identifying the best and worst performing organisations do not necessarily identify truly good and bad performers and even with the best techniques, reliable data are required.

Objective: The objective of this review was to develop a taxonomy of pressures experienced by health services and an accompanying taxonomy of strategies for adapting in response to these pressures. The taxonomies were developed from a review of observational studies directly assessing care delivered in a variety of clinical environments.

Design: In the first phase, a scoping review of the relevant literature was conducted. In the second phase, pressures and strategies were systematically coded from the included papers, and categorised.

Data sources: Electronic databases (MEDLINE, Embase, CINAHL, PsycInfo and Scopus) and reference lists from recent reviews of the resilient healthcare literature.

Eligibility criteria: Studies were included from the resilient healthcare literature, which used descriptive methodologies to directly assess a clinical environment. The studies were required to contain strategies for managing under pressure.

Results: 5402 potential articles were identified with 17 papers meeting the inclusion criteria. The principal source of pressure described in the studies was the demand for care exceeding capacity (ie, the resources available), which in turn led to difficult working conditions and problems with system functioning. Strategies for responding to pressures were categorised into anticipatory and on-the-day adaptations. Anticipatory strategies included strategies for increasing resources, controlling demand and plans for managing the workload (efficiency strategies, forward planning, monitoring and co-ordination strategies and staff support initiatives). On-the-day adaptations were categorised into: flexing the use of existing resources, prioritising demand and adapting ways of working (leadership, teamwork and communication strategies).

Conclusions: The review has culminated in an empirically based taxonomy of pressures and an accompanying taxonomy of strategies for adapting in response to these pressures. The taxonomies could help clinicians and managers to optimise how they respond to pressures and may be used as the basis for training programmes and future research evaluating the impact of different strategies.