Alvogen, Inc. is voluntarily recalling one lot of Fentanyl Transdermal System 25 mcg/h transdermal patches to the consumer level. The reason for the recall is that there is a potential that patches could be multi-stacked, adhered one on top of the other, in a single product pouch. This transdermal s

This communication is part of the Communications Pilot to Enhance the Medical Device Recall Program

This communication is part of the Communications Pilot to Enhance the Medical Device Recall Program

This communication is part of the Communications Pilot to Enhance the Medical Device Recall Program

Mercury Medical recalls the Neo-Tee T-Piece Resuscitator due to the controller potentially not delivering the pressure levels needed for effective ventilation.

This communication is part of the Communications Pilot to Enhance the Medical Device Recall Program

This communication is part of the Communications Pilot to Enhance the Medical Device Recall Program

Olympus Corporation has announced a voluntary Advisory Notice for the Forceps/Irrigation Plug (Isolated Type) model MAJ-891 related to the risk of device contamination that may result from improper reprocessing of the MAJ-891 accessory.

StatStrip Hospital Glucose and Ketone Meters provide blood glucose and ketone readings in health care settings. A software issue may transmit inaccurate results to patient records.

The FDA is raising awareness about cybersecurity vulnerabilities with Contec CMS8000 and Epsimed MN-120 patient monitors.

Introduction

‘Hybrid’ interventions in which some intervention components are fixed across sites and others are flexible (locally created) are thought to allow for adaptation to the local context while maintaining fidelity. However, there is little evidence regarding the challenges and facilitators of implementing hybrid interventions. This paper reports on a process evaluation of a patient safety hybrid intervention called Your Care Needs You (YCNY). YCNY was tested in the Partners at Care Transitions (PACT) randomised controlled trial and aimed to enhance older patients and their families’ involvement in their care in order to achieve safer transitions from hospital to home.

Methods

The process evaluation took place across eight intervention wards taking part in the PACT trial. 23 interviews and 37 informal conversations were conducted with National Health Service (NHS) staff. Patients (n=19) were interviewed twice, once in hospital and once after discharge. Interviews with staff and patients concerned the delivery and experiences of YCNY. Ethnographic observations (n=81 hours) of relevant activities (eg, multidisciplinary team meetings, handovers, etc) were undertaken.

Results

The main finding relates to how staff understood and engaged with YCNY, which then had a major influence on its implementation. While staff broadly valued the aims of YCNY, staff from seven out of the eight wards taking part in the process evaluation enacted YCNY in a mostly task-based manner. YCNY implementation often became a hurried activity which concentrated on delivering fixed intervention components rather than a catalyst for culture change around patient involvement. Factors such as understaffing, constraints on staff time and the COVID-19 pandemic contributed towards a ‘taskification’ of intervention delivery, which meant staff often did not have capacity to creatively devise flexible intervention components. However, one ward with a sense of distributed ownership of YCNY had considerable success implementing flexible components.

Discussion

Hybrid interventions may allow aspects of an intervention to be adapted to the local context. However, the current constrained and pressured environment of the NHS left staff with little ability to creatively engage with devising flexible intervention components, despite recognising the need for and being motivated to deliver the intervention.

Background

Search engines often serve as a primary resource for patients to obtain drug information. However, the search engine market is rapidly changing due to the introduction of artificial intelligence (AI)-powered chatbots. The consequences for medication safety when patients interact with chatbots remain largely unexplored.

Objective

To explore the quality and potential safety concerns of answers provided by an AI-powered chatbot integrated within a search engine.

Methodology

Bing copilot was queried on 10 frequently asked patient questions regarding the 50 most prescribed drugs in the US outpatient market. Patient questions covered drug indications, mechanisms of action, instructions for use, adverse drug reactions and contraindications. Readability of chatbot answers was assessed using the Flesch Reading Ease Score. Completeness and accuracy were evaluated based on corresponding patient drug information in the pharmaceutical encyclopaedia drugs.com. On a preselected subset of inaccurate chatbot answers, healthcare professionals evaluated likelihood and extent of possible harm if patients follow the chatbot’s given recommendations.

Results

Of 500 generated chatbot answers, overall readability implied that responses were difficult to read according to the Flesch Reading Ease Score. Overall median completeness and accuracy of chatbot answers were 100.0% (IQR 50.0–100.0%) and 100.0% (IQR 88.1–100.0%), respectively. Of the subset of 20 chatbot answers, experts found 66% (95% CI 50% to 85%) to be potentially harmful. 42% (95% CI 25% to 60%) of these 20 chatbot answers were found to potentially cause moderate to mild harm, and 22% (95% CI 10% to 40%) to cause severe harm or even death if patients follow the chatbot’s advice.

Conclusions

AI-powered chatbots are capable of providing overall complete and accurate patient drug information. Yet, experts deemed a considerable number of answers incorrect or potentially harmful. Furthermore, complexity of chatbot answers may limit patient understanding. Hence, healthcare professionals should be cautious in recommending AI-powered search engines until more precise and reliable alternatives are available.

Background

The raison d’etre of healthcare profession regulators across the globe is to protect patients and the public from the risk of harm. In cases of serious misconduct, remediation is deemed to be an important factor when considering the risk of harm from a practitioner under investigation. Yet, we know very little about how regulators account for remediation in their decision-making, and whether it is consistent with the aim of risk reduction. This paper explores the role of remediation in decision-making in cases of serious misconduct before UK healthcare regulators.

Methods

We conducted interviews with 21 participants from across eight of the nine UK healthcare profession regulators, covering a range of roles in the decision-making process in misconduct cases. Interviews were conducted remotely by video call and digitally transcribed. Data were analysed using the framework analysis method. The initial framework was developed from existing literature and guidance documents from the regulators, and was subsequently refined through the various rounds of coding.

Results

Remediation influenced decision-making in three ways: (1) Some types of misconduct were deemed more inherently remediable than others. In cases involving dishonesty or sexual misconduct, remediation was less likely to serve as a mitigating factor. (2) Decision-makers often view remediation as a proxy indicator of practitioner insight. (3) Whether a practitioner had demonstrated their commitment to change through undergoing remediation was more likely to feed into decision-making at the point where current impairment was under consideration.

Conclusions

Remediation plays a key role in decision-makers’ judgements in cases of misconduct, particularly when these cases relate to clinical misconduct. In such cases, remediation informs judgements on the levels of practitioner insight and the risk of such misconduct being repeated. Our results suggest a need to develop remediation interventions that are explicitly geared towards the regulatory function of developing practitioner insight. Regulators should also consider the structure of their fitness to practise processes and whether there are appropriate opportunities for judgements on remediation to feed into decisions and to facilitate balanced and proportionate outcomes.

Introduction

Quality improvement (QI) efforts are critical to promoting health equity and mitigating disparities in healthcare outcomes. Equity-focused QI (EF-QI) interventions address the unique needs of equity-deserving groups and the root causes of disparities. This scoping review aims to identify themes from EF-QI interventions that improve the health of equity-deserving groups, to serve as a resource for researchers embarking on QI.

Methods

In adherence with Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines, several healthcare and medical databases were systematically searched from inception to December 2022. Primary studies that report results from EF-QI interventions in healthcare were included. Reviewers conducted screening and data extraction using Covidence. Inductive thematic analysis using NVivo identified key barriers to inform future EF-QI interventions.

Results

Of 5,330 titles and abstracts screened, 36 articles were eligible for inclusion. They reported on EF-QI interventions across eight medical disciplines: primary care, obstetrics, psychiatry, paediatrics, oncology, cardiology, neurology and respirology. The most common focus was racialised communities (15/36; 42%). Barriers to EF-QI interventions included those at the provider level (training and supervision, time constraints) and institution level (funding and partnerships, infrastructure). The last theme critical to EF-QI interventions is sustainability. Only six (17%) interventions actively involved patient partners.

Discussion

EF-QI interventions can be an effective tool for promoting health equity, but face numerous barriers to success. It is unclear whether the demonstrated barriers are intrinsic to the equity focus of the projects or can be generalised to all QI work. Researchers embarking on EF-QI work should engage patients, in addition to hospital and clinic leadership in the design process to secure funding and institutional support, improving sustainability. To the best of our knowledge, no review has synthesised the results of EF-QI interventions in healthcare. Further studies of EF-QI champions are required to better understand the barriers and how to overcome them.

Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage. We applied our classification system to two generative AI applications to assess its effectiveness in categorising safety issues: patient-facing conversational large language models (LLMs) and an ambient digital scribe (ADS) system for clinical documentation. In the LLM analysis, we identified 45 errors across 27 patient medical queries, with omission being the most common (42% of errors). Of the identified errors, 50% were categorised as low clinical significance, 25% as moderate clinical significance and 25% as high clinical significance. Similarly, in the ADS simulation, we identified 66 errors across 11 patient visits, with omission being the most common (83% of errors). Of the identified errors, 55% were categorised as low clinical significance and 45% were categorised as moderate clinical significance. These findings demonstrate the classification system’s utility in categorising output errors from two different AI healthcare applications, providing a starting point for developing a robust process to better understand AI-enabled errors.

The healthcare delivered in high-income countries is riddled with defects in value. One in 10 patients experiences harm when receiving medical care, while nearly 13% of health expenditures are spent managing that harm.1 Half of patients with chronic disease are not on recommended therapy and suffer avoidable hospitalisations and ED visits, all while healthcare costs continue to increase as a percentage of GDP.2 3

Policymakers, health plans and health systems have responded to these challenges by working to improve value. While these efforts continue to mature, physicians are running up against the efficiency-thoroughness trade-off: to complete an increasing number of tasks in service of hitting quality metrics across their entire attributed population, they must decrease the time spent caring for each individual patient or increase the total amount of time they spend working. This paradox, however, is itself a product of how our...

Healthcare systems are currently buckling under the pressure of trying to manage the increasing demand for services. Nowhere is this pressure more acute than in intensive care units (ICUs). Technological developments, an ageing population, increased comorbidities and societal expectations about healthcare delivery and services have all driven demand for critical care resources to exceed capacity.1 ICUs amalgamate all medical and surgical specialties and support services to provide the best care for the most vulnerable and sickest hospital patients; they have been referred to as the ‘heart of the hospital’.2 Because of their pivotal role in providing complex care to different patient cohorts, ICUs require a flexible, nimble and adaptable workforce because when demand for ICU increases, the need for staff surges to meet this demand.3 Responding to resource challenges, increasing bed demands and the need for skilled and experienced staff requires significant adaptability...

In this issue of BMJ Quality & Safety, Hampton and colleagues report a process evaluation of an intervention trial intended to encourage older patients’ involvement in their hospital care.1

The logic of the intervention, Your Care Needs You (YCNY), was that more patient involvement in aspects of care in hospital will carry over to home after discharge, preventing avoidable repeat admissions. YCNY was described as a ‘hybrid’ intervention. Ward-level staff were obliged to deliver ‘fixed’ components—a booklet, and advice sheet and a video. But they were also invited to design and deliver ‘flexible’ components, that is, any other components that the ward team thought would also encourage patients to take part in the selected aspects of their care (some examples were offered by the investigators). One of their eight wards went all in, embracing the challenge of designing flexible components. But the others chose differently, keeping with...

The last 5 years have seen a rapid growth in research applying artificial intelligence or machine learning to improve the quality and safety of healthcare. This coincides with the release of web interfaces (such as ChatGPT from OpenAI and Copilot from Microsoft) that have enabled the general public (including health professionals and researchers) to easily access the latest generation of large language models (LLMs).

LLMs have fundamentally changed how machine learning is used across domains. Unlike previous generation systems that required careful data curation for specific tasks before training, modern LLMs work well with just a few examples or a simple problem description. This progress is mainly due to training on large volumes of web data that allows them to develop an ‘understanding’ of both language and general knowledge which they can then apply to a wide range of tasks.1

To fully comprehend the capabilities and associated...

Performance problems among healthcare professionals can have significant implications for patient safety. Estimates suggest approximately 6–12% of physicians experience performance issues,1 while about one in three healthcare professionals report encountering a poorly performing colleague within the past year.2 Performance problems can arise from individual-level causes including physical illness, substance use disorders, cognitive impairment, mood or personality disorders, and failure to acquire or maintain the knowledge and skills necessary to safely carry out their responsibilities.3 Furthermore, broader systemic issues, including excessive workloads, inadequate resources, lack of institutional support and poor workplace culture, can contribute to or exacerbate performance problems.4 The performance of healthcare professionals is generally evaluated against a set of standards or core competencies of a particular profession that commonly require health professionals to maintain the knowledge, procedural proficiencies, communication skills and professionalism to effectively care for patients. Deficiencies in any...