BMJ Quality & Safety最新文献

Introduction: Duplicate medical records occur when a single patient is assigned multiple medical record numbers within an instance of an electronic health record, potentially associated with fragmented care and adverse outcomes. Despite these concerns, limited research has evaluated the correlation between duplicate charts and patient outcomes.

Objective: To examine the association between duplicate charts and patient outcomes, including hospital length of stay, 30-day readmission, rapid response events, intensive care unit (ICU) level of care, and in-hospital mortality.

Methods: This retrospective cohort study analysed hospitalised patients aged 18-89 across 12 hospitals within a large multi-region health system from July 1, 2022 to June 30, 2023. Propensity score matching balanced covariates between patients with and without duplicate charts. Primary outcomes included in-hospital mortality, rapid response events, ICU level of care, hospital length of stay, 30-day readmission and 30-day emergency department visits. Standardised mean differences assessed group balance, and multivariable logistic or linear regression models, adjusted for discharge service and disposition, examined the relationship between patients with and without duplicate records and the selected outcomes.

Results: After matching, 1698 patients with duplicate charts were compared with 4388 without. Patients with duplicate records had significantly higher odds of adverse outcomes, including 30-day readmission (OR=1.3, 95% CI 1.1 to 1.5, p=0.0122), ICU level of care (OR=3.5, 95% CI 3.1 to 4.0, p<0.0001), and in-hospital mortality (OR=4.7, 95% CI 3.7 to 6.0, p<0.0001). Additionally, hospital length of stay was 32% longer (p<0.0001) for patients with duplicate charts.

Conclusion: Patients with duplicate medical records demonstrated higher odds of adverse patient outcomes compared with those without, including increased mortality, ICU level of care, and prolonged hospitalisation. These findings highlight the need for research to understand the impacts of duplicate charts.

Background: Diagnostic errors remain a significant challenge in healthcare, with cognitive biases, particularly availability bias, playing a critical role. While previous studies focused on controlled experimental settings, this study investigates the real-world impact of clinical practice on susceptibility to the availability bias.

Methods: Using a cross-sectional survey-based design, we assessed 1015 physicians' responses to clinical cases in the form of vignettes after they attended (vs did not attend) a patient with the disease of interest in the last 7 days. Physicians' specialisations and years of experience were also measured, allowing us to assess the presence of the availability bias and the interacting roles of clinical experience and expertise.

Results: Recent clinical encounters with a disease increased the likelihood of erroneously diagnosing it (availability bias, b=0.165, SE=0.070, p=0.018). Among specialists, having 10 or more years of experience decreased the likelihood of an erroneous diagnostic (b=-0.361, SE=0.180, p=0.046). The results indicated distinct effects of experience and specialisation on susceptibility to the availability bias.

Conclusions: Availability bias can be triggered by real clinical encounters and is modulated by the interplay between experience and domain-specific expertise. Structured, feedback-rich exposure-rather than mere experience-appears crucial to mitigating cognitive biases. These findings reinforce the role of knowledge-both formal and experiential-in mitigating the bias, and the importance of developing true expertise.

Introduction: Patient safety event reporting systems are widely used, yet organisations face challenges analysing the high volume of incident reports. While low-harm events represent the majority of submissions, they are rarely examined systematically due to the time and resources required to manually review the complex, lengthy, narrative data. Emerging technology like large language models (LLMs) offers new opportunities to address this gap. This study aimed to develop and evaluate an artificial intelligence-driven approach to identify patient safety issues, uncover system-level trends and assess readiness for implementation within a US healthcare system.

Methods: We quantitatively evaluated OpenAI's GPT-4o model accuracy in analysing patient safety event reports and qualitatively assessed pre-implementation outcomes. The LLM extracted safety problems from 9357 free-text narratives and then generated a taxonomy of 'parent' and 'child' subcategories. The model labelled every report's problem list using the taxonomy, and dashboards were developed to visualise trends. Patient safety experts reviewed two separate subsets of reports (n=100 and n=219) to validate the LLM's accuracy. We conducted 10 stakeholder interviews to assess the dashboards' acceptability, appropriateness and adoption.

Results: The LLM had scores of 94% mean agreement among reviewers in identifying problems, and 91.5% and 83.3% agreement in assigning 'parent' and 'child' category labels, respectively. The model identified previously hidden patterns of patient safety issues. Stakeholders described LLM-generated insights as clear, appropriate and valuable for their work. They perceived few barriers to adoption and believed the model could expedite manual report reviews and support system-level quality improvement.

Conclusion: LLMs offer an approach to capture and analyse multidimensional concepts in patient safety reports. By extracting problem summaries and categorising them into an accepted taxonomy, they can expose previously unidentified trends and provide visibility into system-level risks. LLMs could effectively augment and expedite manual reviews of safety events, while guiding prioritisation of quality and safety interventions.

Background: While the incidence of hospital adverse events appeared to be declining before 2019, the COVID-19 pandemic may have changed its course. This study aimed to evaluate adverse event incidence rates and trends during the pandemic and analyse differences in patient outcomes.

Methods: This retrospective electronic chart review included a random sample of adult patients admitted to four acute care hospitals in Calgary between 2017 and 2022. 18 adverse events and patient information were extracted. We calculated the observed and risk-standardised incidence rates of adverse events. Interrupted time series analysis was employed to determine the impact of COVID-19 on adverse events trends. Outcome differences were evaluated using mixed-effects logistic regression and negative binomial models.

Results: Among 10 673 patient admissions, 2310 adverse events were identified, resulting in an incidence rate of 21.64 (95% CI 20.77 to 22.54) per 100 patient admissions, or 26.85 (95% CI 25.77 to 27.97) per 1000 patient days. After adjusting for patient characteristics, seasonal variations and overall trends, the adverse event incidence rate increased by 14% (incidence rate ratio (IRR) 1.14, 95% CI 1.01 to 1.29) during the COVID-19 pandemic. In multivariable mixed-effects models, adverse events were associated with significantly longer hospital stays (IRR 3.13, 95% CI 2.97 to 3.30), increased odds of 30-day readmission (OR 1.4, 95% CI 1.17 to 1.68) and in-hospital death (OR 1.72, 95% CI 1.43 to 2.08).

Conclusion: The incidence of adverse events was high but relatively stable in acute healthcare settings before the COVID-19 pandemic and increased during the pandemic. Strengthening healthcare resilience and prioritising patient safety initiatives are crucial as we transition into the post-pandemic era.

Variability and persistent gaps in reporting have been consistently observed across studies evaluating adverse events in healthcare, dating back to the early days of the patient safety movement. Incomplete descriptions-particularly missing details regarding study design, conduct, methodologies and applied definitions-impede interpretation, hinder critical appraisal and limit reproducibility. These deficiencies not only obstruct a shared understanding of findings but also diminish the overall impact such studies may have on patient safety initiatives and policy development.To address these challenges and enhance the quality of reporting, we developed the SESAME (Standard Elements in Studies of Adverse Events and Medical Error) reporting guideline. SESAME is a 44-item checklist designed to support comprehensive and standardised reporting in studies evaluating potential patient harm. The guideline was developed over a 2-year period by an international, multidisciplinary panel through a consensus-driven process aligned with Enhancing the QUAlity and Transparency Of Health Research Network standards.

Background: Reducing hospitalisations in heart failure (HF) requires organisational models for rapid optimisation, education and structured follow-up. Evidence on complex programmes in outermost or underserved regions and on the mechanisms through which they achieve their effects remains limited.

Methods: We conducted a convergent mixed-methods evaluation of an anonymised, culturally adapted, pharmacist-supported HF day-hospital programme implemented across two sites in an insular remote European region. The quantitative component was a self-controlled before-after study including all adults attending at least one visit between 1 January 2017 and 31 December 2021 (n=424). The primary outcome was 12-month HF rehospitalisation after versus before enrolment. Secondary outcomes were HF-related bed-days, extended 24-month utilisation, overall survival and optimisation of guideline-directed medical therapy, including quadruple therapy. The qualitative component comprised semi-structured interviews with 10 stakeholders, analysed thematically and integrated with quantitative findings.

Results: Using a person-time approach with post-index follow-up censored at death and capped at 12 months, HF rehospitalisation incidence rates decreased from 0.427 (95% CI 0.367 to 0.494) to 0.164 (95% CI 0.127 to 0.207) events per patient-year (incidence rate ratio (IRR) 0.38, 95% CI 0.30 to 0.48). HF bed-days rates decreased from 4.96 (95% CI 4.75 to 5.18) to 1.77 (95% CI 1.64 to 1.90) bed-days per patient-year (IRR 0.43, 95% CI 0.28 to 0.64). Overall survival was 96.0% at 12 months and 91.5% at 24 months. The programme achieved substantial optimisation of core HF therapies and uptake of quadruple therapy. Qualitative findings highlighted enabling mechanisms-multidisciplinary coordination, dedicated infrastructure, pharmacist-supported and nurse-supported titration, bilingual education and engaged leadership-and vulnerabilities including workforce constraints, telemonitoring instability and variable hospital-community integration.

Conclusions: In this high-risk, remote setting, this pharmacist-supported, culturally adapted HF day-hospital model was associated with major reductions in HF rehospitalisations and strong implementation of contemporary therapy, supporting integrated, context-sensitive organisational models to improve HF care quality and equity.

Background: Regulation plays a central role in health and social care systems, particularly in ensuring quality, safety and accountability. However, there is limited understanding of how organisations effectively implement and adhere to these regulatory requirements. In particular, little is known about how providers of residential care facilities for people with disabilities (RCF-D) navigate and apply statutory care regulations.

Methods: We conducted semistructured interviews with managers of RCF-D. Participant recruitment followed a purposive maximum variation sampling approach. 19 participants were interviewed, representing 22 RCF-D and 16 provider organisations. Interview data were analysed using a mixed deductive-inductive approach.

Results: Most managers were supportive of regulatory goals, creating a more favourable environment for successful implementation. By making sense of regulatory requirements and sharing insights across their organisations, managers facilitated smoother implementation. Crucially, building strong internal and external networks played a pivotal role in driving success. Collaborative relationships with inspectors, centred on a shared commitment to improving residents' lives, further strengthened the implementation process.

Conclusion: Managers of RCF-D devised a range of strategies to manage compliance, balancing regulatory demands with problem-solving and relationship-building. These efforts were supported by a collaborative approach to working with inspectors, which fostered a shared commitment to improving residents' lives. Our findings offer practical guidance for organisations seeking to improve regulatory compliance through effective relationship management and resource alignment. Future research could investigate how framing regulation as an adaptive intervention could further enhance implementation and sustain compliance.

Background: There is increasing interest in how patient complaint material can be used to highlight areas requiring quality improvement (QI) in healthcare. However, knowledge of using complaint material to initiate or monitor QI is limited.

Objectives: This review explored the use of complaint material in QI by identifying problems related to substandard care that were addressed by QI initiatives, exploring how complaint material was used before or after a QI initiative, and mapping changes in complaint material after QI initiatives.

Methods: This scoping review followed the Joanna Briggs Institute methodology and adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews reporting guideline.

Eligibility criteria: Studies were included if a QI initiative was initiated or monitored using complaint material. Eligible designs included observational studies, QI projects, pre-intervention and post-intervention studies and randomised controlled trials. Audio, online and symptom-based complaints were excluded.

Information sources: A systematic search was conducted on 10 December 2024 in Embase, Medline, CINAHL and Web of Science, and additional sources, with no language or date limitations.

Synthesis of results: Substandard problems targeted by QI initiatives were categorised using the Healthcare Complaints Analysis Tool by two independent coders. Findings were synthesised narratively and summarised using frequency analyses where applicable.

Results: We identified 58 QI initiatives, most frequently targeting safety (n=39). Before QI, complaint material was usually analysed through review (n=19), counts (n=17), content categorisation (n=9) or root cause analysis (n=2). After QI, analyses included counts (n=34), rates (n=20), content categorisation (n=7) and review (n=4). Reviewing or categorisation methods were often unspecified. Among studies using complaints as an outcome, most reported complaint reductions (n=43), while a few reported increases (n=2) or mixed results (n=4).

Discussion: The QI initiatives primarily targeted patient safety and applied simple quantitative analyses. Some studies relied on reviews or categorisations without reporting the validation or reliability of the used tools. Improved reporting standards are needed to strengthen learning. Furthermore, while QI initiatives appear to have the potential to change complaint patterns, this finding should be interpreted with caution, as this is based on a scoping review.

Other: Preregistered protocol: https://osf.io/6g4qw.

Background: Improving small and sick newborn care (SSNC) is crucial in resource-limited settings. Newborn Essential Solutions and Technologies (NEST360), a multicountry alliance, aims to reduce newborn mortality through evidence-based interventions. NEST360 developed a multipronged approach to improving quality. We use implementation research (IR) to describe this approach and report emerging implementation outcomes.

Methods: The implementation research logic model (IRLM) was applied to link contextual factors, implementation strategies, mechanisms and implementation outcomes, capturing successes and challenges of the improving quality approach. Data sources included programme data, peer-reviewed publications and team input. Contextual factors were organised by the NEST360-UNICEF SSNC implementation toolkit. Strategies were grouped by the Expert Recommendations for Implementation Change list, and implementation outcomes were measured using Proctor's implementation outcomes.

Results: We developed an IRLM to describe the implementation of NEST360's improving quality model. This IRLM included 33 contextual factors; 42% were barriers, 42% were facilitators, and 15% were both a barrier and facilitator. Additionally, we identified 10 implementation strategies that NEST360 used. The logic model also describes the connections between the contextual factors, the strategies that address them, and the preliminary implementation outcomes. Examples of the outcomes measured include Reach with 100% of units logging into the NEST360-Implementation Tracker (NEST-IT) at least once (October 2023 to March 2024), Adoption with 100% of units conducting a quality improvement (QI) project (April 2024 to June 2024), and Feasibility with 93% of units reporting NEST-IT data in their QI project documentation (April 2024 to June 2024). Finally, this study identified sustainability strategies as a critical need.

Conclusions: Integrating IR and QI enhances SSNC in resource-limited settings. Addressing barriers, leveraging facilitators and using structured IR frameworks advanced QI efforts, thereby improving intervention reach, adoption and feasibility while building scalable systems for high-quality healthcare.