Language barriers between healthcare professionals and their patients remain a persistent challenge. Patients with limited proficiency in the primary language of the country where they receive care face higher risks of adverse events, misdiagnosis and unplanned readmissions.1 2 In linguistically diverse countries, services often fall short of meeting the needs of patients who speak minority languages. This leads to inequities in care across inpatient, outpatient and emergency settings. While concern for language discordance in healthcare is by no means a novel development, guidelines have rarely progressed beyond recommending implementation of professional interpreter services.3 In-person interpretation is generally considered the gold standard for addressing language barriers during direct care delivery4 5 and is in line with regulatory and ethical standards.6 Other modalities of interpretation, including telephone and video, are alternative options, although the feasibility of providing timely written...

Introduction

Machine translation of patient-specific information could mitigate language barriers if sufficiently accurate and non-harmful and may be particularly useful in healthcare encounters when professional translators are not readily available. We evaluated the translation accuracy and potential for harm of ChatGPT-4 and Google Translate in translating from English to Spanish, Chinese and Russian.

Methods

We used ChatGPT-4 and Google Translate to translate 50 sets (316 sentences) of deidentified, patient-specific, clinician free-text emergency department instructions into Spanish, Chinese and Russian. These were then back-translated into English by professional translators and double-coded by physicians for accuracy and potential for clinical harm.

Results

At the sentence level, we found that both tools were ≥90% accurate in translating English to Spanish (accuracy: GPT 97%, Google Translate 96%) and English to Chinese (accuracy: GPT 95%; Google Translate 90%); neither tool performed as well in translating English to Russian (accuracy: GPT 89%; Google Translate 80%). At the instruction set level, 16%, 24% and 56% of Spanish, Chinese and Russian GPT-translated instruction sets contained at least one inaccuracy. For Google Translate, 24%, 56% and 66% of Spanish, Chinese and Russian translations contained at least one inaccuracy. The potential for harm due to inaccurate translations was ≤1% for both tools in all languages at the sentence level and ≤6% at the instruction set level. GPT was significantly more accurate than Google Translate in Chinese and Russian at the sentence level; the potential for harm was similar.

Conclusion

These results support the potential of machine translation tools to mitigate gaps in translation services for low-stakes written communication from English to Spanish, while also strengthening the case for caution and for professional oversight in non-low-risk communication. Further research is needed to evaluate machine translation for other languages and more technical content.

Background

Healthcare patient safety investigations inappropriately focus on individual culpability and the target of recommendations is often on the behaviours of individuals, rather than addressing latent failures of the system. The aim of this study was to explore whether outcome bias might provide some explanation for this. Outcome bias occurs when the ultimate outcome of a past event is given excessive weight, in comparison to other information, when judging the preceding actions or decisions.

Methods

We conducted a survey in which participants were each presented with three incident scenarios, followed by the findings of an investigation. The scenarios remained the same, but the patient outcome was manipulated. Participants were recruited via social media and we examined three groups (general public, healthcare staff and experts) and those with previous incident involvement. Participants were asked about staff responsibility, avoidability, importance of investigating and to select up to five recommendations to prevent recurrence. Summary statistics and multilevel modelling were used to examine the association between patient outcome and the above measures.

Results

212 participants completed the online survey. Worsening patient outcome was associated with increased judgements of staff responsibility for causing the incident as well as greater motivation to investigate. More participants selected punitive recommendations when patient outcome was worse. While avoidability did not appear to be associated with patient outcome, ratings were high suggesting participants always considered incidents to be highly avoidable. Those with patient safety expertise demonstrated these associations but to a lesser extent, when compared with other participants. We discuss important comparisons between the participant groups as well as those with previous incident involvement, as victim or staff member.

Interpretation

Outcome bias has a significant impact on judgements following incidents and investigations and may contribute to the continued focus on individual culpability and individual focused recommendations observed following investigations.

Background

Natural hazards, such as earthquakes, pose a significant risk to both the public and healthcare professionals, jeopardising patient safety due to the disruption of healthcare systems and services. This study aimed to explore the lived experiences of healthcare professionals concerning patient safety during natural hazards, specifically earthquakes.

Methods

Employing a descriptive phenomenological approach, the study followed the Consolidated Criteria for Reporting Qualitative Research guidelines. 23 participants, including doctors, nurses and paramedics, were interviewed using purposive sampling. Data were gathered through semistructured interviews, which were audio recorded and transcribed. Ethical approval was obtained, and Colaizzi’s method was used for data analysis, with findings validated through researcher consensus and participant feedback.

Results

Nine overarching themes emerged, such as the emotional toll of communication breakdowns, struggles with patient identification, stress due to resource scarcity, operational chaos, ethical dilemmas and psychological impacts on both patients and staff. The study found that these factors collectively influenced patient safety during the earthquake.

Conclusion

The emotional strain caused by communication failures, patient identification issues and resource shortages compounded the challenges of providing safe care during the earthquake. Strengthening disaster preparedness through improved communication systems, resource management, psychological support, interagency coordination and regular realistic disaster drills is essential for safeguarding patient safety in future disasters.

Importance

Patients with poor glycaemic control have a high risk for major cardiovascular events. Improving glycaemic monitoring in patients with diabetes can improve morbidity and mortality.

Objective

To assess the effectiveness of a patient portal message in prompting patients with poorly controlled diabetes without a recent glycated haemoglobin (HbA1c) result to have their HbA1c repeated.

Design

A pragmatic, randomised clinical trial.

Setting

A large academic health system consisting of over 350 ambulatory practices.

Participants

Patients who had an HbA1c greater than 10% who had not had a repeat HbA1c in the prior 6 months.

Exposures

A single electronic health record (EHR)-based patient portal message to prompt patients to have a repeat HbA1c test versus usual care.

Main outcomes

The primary outcome was a follow-up HbA1c test result within 90 days of randomisation.

Results

The study included 2573 patients with a mean (SD) HbA1c of 11.2%. Among 1317 patients in the intervention group, 24.2% had follow-up HbA1c tests completed within 90 days, versus 21.1% of 1256 patients in the control group (p=0.07). Patients in the intervention group were more likely to log into the patient portal within 60 days as compared with the control group (61.2% vs 52.3%, p<0.001).

Conclusions

Among patients with poorly controlled diabetes and no recent HbA1c result, a brief patient portal message did not significantly increase follow-up testing but did increase patient engagement with the patient portal. Automated patient messages could be considered as a part of multipronged efforts to involve patients in their diabetes care.

Objective

Overconfidence is an important source of medical error. This review analyses experimental studies of confidence in medical diagnosis to identify factors affecting clinicians’ confidence in their diagnoses and how confidence impacts patient care.

Method

A scoping review of medical and psychological literature was conducted. Articles were categorised according to methodology and clinical specialty. Findings were analysed thematically. Our review methodology adheres to the JBI’s Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist.

Data sources

We searched SCOPUS, MEDLINE, PsycINFO and Global Health. We then performed citation tracking within these papers’ references to identify additional articles.

Eligibility criteria

Papers were included if they reported quantitative results from an empirical study in which participants reported their confidence or certainty during a diagnostic decision. Studies comprised several medical subdisciplines.

Results

77 articles met the inclusion criteria. Across these articles, confidence was not found to be well-calibrated to true diagnostic accuracy regardless of clinician experience. We organised articles under two main themes: the determinants of confidence and the uses of confidence during the patient’s care pathway. Confidence is found to be affected by several factors, including case complexity, early diagnostic differentials and the healthcare environment. Factors that affect confidence, but not accuracy, demonstrate how the two can become decoupled, resulting in overconfidence/underconfidence. Confidence is found to affect patient testing, medication administration and referral rates, among other clinical actions.

Conclusions

Improving the calibration of confidence should be a priority for medical education and clinical practice (eg, via decision aids). We propose a theoretical model of factors that affect diagnostic confidence/certainty. Such a model can inform future work on how appropriate diagnostic confidence can be prompted and communicated among clinicians.

Statistical process control (SPC) charts provide a natural approach to analysing time series data for healthcare quality improvement (QI) initiatives. A problem arising in practice is that having established baseline control limits, there is no accepted objective and transparent approach to deciding when to establish new control limits for a given chart. We present the Stable Shift Algorithm, a new algorithm to aid analysts by identifying when control limits should be re-established, partitioning a control chart of time series data into distinct time periods. The algorithm aims to achieve this while (1) using only the theory of SPC, familiar to many QI practitioners, (2) avoiding re-establishing limits prematurely and (3) remaining flexible to choice of basic parameters of typical control chart use in QI. This is achieved through the commonly used shift rule of control charts, applied to establish whether shifts warrant new control limits or not. We conducted a simulation study to evaluate the effectiveness of the algorithm in achieving its aims, and a case study demonstrating application of the algorithm to 557 time series of accident and emergency care measures for providers in England and Scotland. Simulation results show that the algorithm avoids premature re-establishment of limits more often than simply re-establishing at every shift rule break. Application of the algorithm to the accident and emergency measures demonstrates this is not achieved at the cost of excessive additional rule breaks that might indicate control limits do not represent the underlying process. The Stable Shift Algorithm offers a potentially highly valuable tool for QI practitioners and researchers undertaking SPC analyses, providing an automated, consistent and rigorous approach facilitating large-scale analyses.

Introduction

The role of quality and safety professionals and leaders in realising the potential and managing the risks of artificial intelligence (AI) tools has not been well defined. We suggest these leaders focus on three areas: using quality, safety and implementation sciences to increase the likelihood of beneficial AI adoption; using AI to enhance and support the methods of quality and safety management; and serving as experts and champions for AI tool use that promotes health and equity (figure 1).

Background and approach

We used 90-day research and development cycles1 to examine AI topics and the role of quality and safety leaders with respect to AI integration, including applying AI tools for quality improvement (QI) use cases,2 the broad application of AI in quality management systems, the implications of AI for patient safety3 and the use of AI...

Trividia recommends that device users who receive an E-5 error code and are experiencing symptoms of high glucose should seek medical care immediately  

Medline updates use instructions for Electronic Homecare Beds due to risk of electrical fires and entrapment.

AirLife is removing Broselow Rainbow Tapes due to incorrectly printed medication dosing information.

FT. LAUDERDALE, FL) – February 6, 2026 – Trividia Health, Inc., announced today that it is initiating a labeling correction which requires a modification of the Owner’s Booklets/System Instructions for Use for all TRUE METRIX, TRUE METRIX AIR, TRUE METRIX GO, and TRUE METRIX PRO Blood Glucose Monit

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Certain Abbott Diabetes Care Continuous Glucose Monitor sensors may incorrect or lower than actual glucose readings

Certain Abbott Diabetes Care Continuous Glucose Monitor sensors may incorrect or lower than actual glucose readings

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Integra is removing all MediHoney Wound and Burn products and certain lots of CVS Wound Gel that may have breached sterile barriers

Despite decades of progress in global child health, neonatal mortality remains high, accounting for nearly half of all under-five deaths worldwide.1 Most of these deaths occur in low- and middle-income countries and are preventable with timely, high-quality care for small and sick newborns.2 The WHO has called for every newborn to receive essential, high-impact interventions,3 yet the challenge lies not only in knowing what works, but in implementing those interventions at scale, with quality, and within real-world health systems. Quality improvement (QI) and implementation science (IS) offer complementary strategies to address this challenge. QI focuses on local, iterative problem solving to adapt and improve evidence-based or locally generated care processes,4 5 while IS provides structured, theory-driven methods to promote their uptake and sustainability.6 7 Yet too often, these fields operate independently rather than in a...