Background: Resident Doctors (RDs) in the UK must balance clinical training with non-clinical administrative tasks. Concerns increase over how these tasks impact their professional development, job satisfaction and patient care. The shift toward consultant-delivered care has led to a redistribution of responsibilities, increasing administrative burden on RDs. Aims: This study aims to quantify how RDs allocate time between patient-facing and non-patient-facing tasks and assess impact on job satisfaction. Design: National multicentre observational cohort study. Methodology: The Time Allocation in Clinical Training study is a multicentre, observational cohort study conducted over seven months (January–July 2024) at secondary NHS centres. 137 RDs, from Foundation Year 1 to Specialty Training Year 8, were observed for 4-hour periods, with time tracked using stopwatches for five task categories. An optional survey assessed job satisfaction, rated on a five-point Likert scale. Results: RDs spent 73.0% of their time on non-patient-facing tasks, and 17.9% on patient-facing activities. Women spent more time on non-patient-facing tasks compared to men (75.0% vs. 69.9%, P = 0.03). Junior RDs (Foundation years and Core trainees) spent significantly less time on patient-facing tasks compared to senior RDs (ST6-8) (38.4% vs. 17.8%; P = 0.004). Most RDs (62%) reported dissatisfaction with the administrative burden. Electronic health record users spent significantly more time on administrative tasks than paper-based record users (44.1% vs. 37.3%; P = 0.02). Conclusions: The study reveals a significant imbalance between clinical and administrative tasks, with excessive administrative workload contributing to RD dissatisfaction. Streamlining administrative duties and integrating digital solutions is crucial for improving job satisfaction, clinical development and healthcare delivery in the NHS.

Motility disorders, particularly gastroparesis, are prevalent complications following solid organ transplantation, significantly impacting quality of life, nutritional status, graft survival, and mortality. This comprehensive review synthesises evidence from PubMed, Scopus, and Embase databases on pathophysiology, clinical manifestations, diagnosis, management, and prognostic factors across transplant types. Mechanisms include vagal nerve injury (highest in lung transplants, prevalence 40–91%), immunosuppressive effects (e.g., tacrolimus accelerates motility; mycophenolate impairs it), surgical trauma, microbiome dysbiosis (reduced Firmicutes/Bacteroidetes ratio), and metabolic factors like post-transplant diabetes (OR 5.17 in kidney recipients). Pediatric and thoracic recipients face the highest risks, with lung transplant gastroparesis conferring a 2.7-fold increased mortality/retransplantation hazard (p < 0.05). Diagnosis relies on gastric emptying scintigraphy (gold standard, sensitivity 85–95%) and wireless motility capsules (100% sensitivity for delay), while management encompasses prokinetics (60–80% response), endoscopic G-POEM (85% success), gastric electrical stimulation (100% quality-of-life improvement in series), and nutritional support. Prognostic factors include younger age (better intervention response), aetiology (anatomical worse than metabolic), and early therapy success. Outcomes vary: lung recipients experience severe impacts on chronic allograft dysfunction (83% oesophageal motility abnormalities correlate with 66–67% rejection). Future directions emphasise microbiome therapies, AI predictive models (AUC 0.85), and wearables for continuous monitoring. Multidisciplinary approaches are essential to balance immunosuppression with GI management, addressing ethical dilemmas like drug interactions and access disparities. Ultimately, early screening and personalised interventions can mitigate complications, enhancing long-term transplant success.

Aims: Artificial intelligence (AI) applied to a single-lead electrocardiogram (AI-ECG) can detect impaired left ventricular systolic dysfunction [LVSD: left ventricular ejection fraction (LVEF) ≤ 40%]. This study aimed to determine if AI-ECG can also predict the two-year risk of major adverse cardiovascular events (MACE) and all-cause mortality independent of LVSD. Methods and results: Clinical outcomes after two-year follow-up were collected on patients who attended for routine echocardiography and received simultaneous single-lead-ECG recording for AI-ECG analysis. MACE and all-cause mortality were compared by Cox regression, measured against the classification of LVEF > or ≤40%. A subgroup analysis was performed on patients with echocardiographic LVEF ≥ 50%. With previously established thresholds, ‘positive’ AI-ECG was defined as an LVEF-predicted ≤40%, and negative AI-ECG signified an LVEF-predicted >40%; 1007 patients were included for analysis (mean age, 62.3 years; 52.4% male). 339 (33.7%) had an AI-ECG-predicted LVEF ≤ 40% and had a higher MACE rate (LVEF ≤ 40% vs. >40%: 34.2% vs.11.9%; adjusted hazard ratio (aHR) 1.93; 95% CI, 1.39–2.69; P < 0.001), primarily driven by increased mortality (23% vs. 9.6%; P < 0.001; aHR 1.56; 95% CI, 1.06–2.29; P = 0.0239). In patients with echocardiographic LVEF ≥ 50%, there was a higher incidence of MACE in those with an AI-ECG ‘false positive’ prediction of LVEF ≤ 40% (27.2% vs.11.9%; P < 0.001; aHR 1.71 and 95% CI, 1.11–2.47) and all-cause mortality (20.4% vs. 9.6%; P < 0.001; aHR 1.59, 95% CI, 1.09–2.42). Conclusion: An AI-ECG algorithm designed to detect LVEF ≤ 40% can also identify patients at risk of MACE and all-cause mortality from single-lead ECG recording—independent of actual LVEF on echo. This requires further evaluation as a point-of-care risk stratification tool.

Aims: Health systems face increasing waiting times for transcatheter aortic valve implantation (TAVI), incurring excess deaths and morbidity. To determine whether remote patient monitoring (RPM) using connected technologies can mitigate these risks by prioritizing patients awaiting TAVI, we aimed to measure the clinical safety and effectiveness of an RPM-based prioritization programme. Methods and results: Prospective observational cohort study of all patients awaiting TAVI at Imperial College Healthcare NHS Trust, London, UK, between 24th April 2023 and 15th November 2023. An RPM pathway was implemented for all patients accepted for TAVI. These patients responded to a weekly symptom questionnaire via web, smartphone RPM platform or telephone monitoring; with rule-based clinical escalation. The primary endpoint was the rate of adverse events (defined as emergency department presentation, unplanned hospitalization, or death), compared with a propensity score-matched (PSM) historical control group. Secondary endpoints included pathway performance characteristics for detection of deterioration. 200 patients met inclusion criteria. Despite growth of the waiting list, responsible for longer waiting times experienced by the RPM group [median 104 days (IQR 61.00–176.00) vs. 75 days (IQR 38.75–118.00)], there was no difference in rates of adverse events between RPM-patients and historical controls (Log rank P = 0.9). The RPM pathway had high sensitivity for prediction of waiting list death (100%). Patients deemed at high-risk of deterioration experienced shorter waiting times to treatment. Conclusion: RPM for patients awaiting TAVI is feasible and may mitigate the adverse effects of longer waiting times through accurate detection of deterioration and by informing prioritization decisions.

Legislative moves to legalise assisted dying in the UK have renewed discussion on future clinicians’ preparedness for new potential responsibilities. This study examined medical students’ ethical attitudes, legal understanding and curricular exposure. VOICE was a national cross-sectional survey of UK and Ireland medical students in 2025 (n = 896). The questionnaire assessed ethical views, confidence, legal knowledge and teaching exposure. Descriptive statistics, thematic analysis and multivariable logistic regression explored predictors of ethical agreement, curriculum coverage and legal knowledge. Most students (64.5%) believed that assisted dying can be ethically justified, yet legal knowledge was limited; only 7.6% correctly identified all eligibility criteria. Nearly 70% reported minimal curricular coverage. Formal teaching was associated with higher confidence and greater ethical agreement. Regional variation and concerns about coercion and inequalities were common. Students show broad ethical support but have very limited confidence, legal understanding and curricular preparation, highlighting the need for structured education.

Cardiovascular disease remains the leading cause of death in both the United Kingdom (UK) and worldwide 1. For several coronary and valvular conditions, timely surgical or catheter-based interventions improve survival and quality of life 2,3. Yet the UK National Health Service (NHS) faces an unprecedented backlog of over 370,000 patients (in a population of 67 million) awaiting critical cardiac procedures, leading to over 30,000 excess deaths 4. In this Comment, we describe a novel strategy for digitally-supported prioritization for critical cardiovascular procedures. We then examine potential ethical concerns in this new clinical and population health pathway and evaluate programmatic features that may address these hazards while promoting effective technology adoption within the service provision for these most vulnerable patients.