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...

Background

Healthcare systems are operating under substantial pressures. Clinicians and managers are constantly having to make adaptations, which are typically improvised, highly variable and not coordinated across teams. This study aimed to identify and describe the types of everyday pressures in intensive care and the adaptive strategies staff use to respond, with the longer-term aim of developing practical and coordinated strategies for managing under pressure.

Methods

We conducted qualitative semi-structured interviews with 20 senior multidisciplinary healthcare professionals from intensive care units (ICUs) in 4 major hospitals in the UK. The interviews explored the everyday pressures faced by intensive care staff and the strategies they use to adapt. A thematic template analysis approach was used to analyse the data based on our previously empirically developed taxonomy of pressures and strategies.

Results

The principal source of pressure described was a shortage of staff with the necessary skills and experience to care for the increased numbers and complexity of patients which, in turn, increased staff workload and reduced patient flow. Strategies were categorised into anticipatory (in advance of anticipated pressures) and on the day. The dynamic and unpredictable demands on ICUs meant that strategies were mostly deployed on the day, most commonly by flexing staff, prioritisation of patients and tasks and increasing modes of communication and support.

Conclusions

ICU staff use a wide variety of adaptive strategies at times of pressure to minimise risk and maintain a reasonable standard of care for patients. These findings provide the foundation for a portfolio of strategies, which can be flexibly employed when under pressure. There is considerable potential for training clinical leaders and teams in the effective use of adaptive strategies.