Background Pulmonary embolism (PE) is a condition with significant morbidity and mortality. Diagnostic imaging aims to identify patients with a PE so that they can receive anticoagulation. There is evidence, predominantly from the USA, that the number of diagnostic scans for PE has increased over time and the diagnostic yield (the percentage of positive scans) has decreased. There is limited evidence on whether this is also true in the UK. Aim To describe the pattern of diagnostic imaging for acute PE and investigate whether there has been a change in the diagnostic yield over the last 10 years. Methods All adult patients who underwent either computed tomography pulmonary angiogram (CTPA) or ventilation-perfusion (V/Q) scan as an investigation for acute PE at Cambridge University Hospitals NHS Foundation Trust (CUH) between 2015 and 2024 were included. This was a retrospective study using data extracted from the hospital electronic health record (EHR) system. Results 25,672 scans were included, comprising 23,908 CTPA and 1,764V/Q scans. The annual number of scans increased slightly over the time period, with an average increase of 1.2% per year (p < 0.05). The overall diagnostic yield was 17.0%, with no clear trend over time. There was also no clear trend in the proportion of subsegmental PE detected over time. Conclusions The overall number of scans performed and diagnostic yield for PE have remained relatively stable over the last decade. This is the largest study of its kind in the UK and, uniquely, also covers the entire timeline of the COVID-19 pandemic and its aftermath.
STUDY OBJECTIVE:Interest in medical research and clinical academic careers amongst medical students is concerningly low, posing a threat to the long-term sustainability of the clinical researcher workforce. A likely contributing factor is a lack of knowledge and teaching about medical research. To address this, we developed and evaluated the MEDICORE teaching programme; a dedicated clinical academia training programme designed to address these gaps. DESIGN:The MEDICORE programme consisted of six biweekly in-person sessions from January to March 2024, targeting UK medical students. The learning outcomes addressed educational gaps about research identified by a prior national survey and the GMC Outcomes for Graduates. MAIN OUTCOME MEASURES:Pre- and post-session assessments measured students' improvement in knowledge and attitudes towards medical research and clinical academic careers. RESULTS:534 student responses were collected overall, with on average 89 participants attending each session. Seven UK universities were represented, with 83.3% from one institution. Students reported that the MEDICORE programme addressed a curriculum gap in research training (81.5%). There was a significant increase in the number of students who felt confident completing a full research project and disseminating research through publications or presentations. Post-MEDICORE, students felt more confident in the structure of clinical academic training (mean Likert scale increase 1.45 ± 1.44, p < 0.0001). The number of students interested in pursuing a clinical academic career increased from 39.1% to 85.8% (p < 0.0001). CONCLUSION:MEDICORE demonstrates that dedicated research teaching is an effective intervention to promote research skills and the pursuit of future academic careers amongst medical students. Such efforts may help address predicted shortages in the clinical academic workforce. CLINICAL TRIAL NUMBER:Not applicable.
Foundation Year (FY) doctors consistently report feeling underprepared when applying intravenous fluid (IVF) guidelines due to inadequate training, resources and an underemphasis of evidence-based bedside assessment techniques. Given IVFs are the most commonly prescribed substance in the NHS, especially by FY doctors, inappropriate patient assessments and written prescriptions are a patient safety threat. Whilst the NICE IVF guideline exists, it is not without nuance and there remain challenges in delivering effective IVF education and sustainable learning interventions over time. From a FY doctor perspective discussed, senior clinicians demonstrating use of guidelines more explicitly may help address the gap between guidelines and clinical practice. Ultimately, given the widespread use of IVF, there is a need for improved education to develop FY doctors' clinical reasoning and ensure patient safety.
Artificial intelligence (AI) is increasingly embedded in healthcare delivery, yet its evaluation remains dominated by technical performance metrics that inadequately capture real-world system value. Clinical accuracy is necessary but insufficient to justify adoption in resource-constrained health systems facing workforce shortages and rising demand. AI technologies compete for limited financial, technical and cognitive resources, but there is no standardised framework to assess their effects on productivity, workflow, downstream utilisation or equity. This article argues that treating clinical validity as a proxy for value risks misallocating scarce resources and undermining trust in digital transformation. We propose an AI Productivity Index to complement existing safety, efficacy and economic assessments by evaluating operational impact, implementation burden, opportunity costs and post-deployment consequences. Embedding productivity measurement into procurement and governance processes could help align AI innovation with fiscal accountability, equitable access and sustainable healthcare delivery.
BACKGROUND:Accumulating evidence demonstrates associations between clonal hematopoiesis of indeterminate potential (CHIP) and increased risks of haematological and non-haematological health outcomes. Although adverse health consequences of CHIP have become well-documented, large knowledge gaps exist regarding the aetiology of CHIP. OBJECTIVE:To investigate risk factors for CHIP using large-scale phenotypic data and Mendelian randomisation analysis. METHODS:We utilised rich phenotypic and genomic data from the UK Biobank (UKB) to perform a cross-sectional study to systematically investigate risk factors of CHIP, including around 460,000 participants recruited from 2006 to 2010. We used Logistic regression to estimate odds ratios (ORs) of CHIP in relation to risk factors (sociodemographic factors, lifestyle factors, history of diseases, blood cell count, blood biochemistry parameters, proteomics biomarkers and metabolomics biomarkers). In addition, we conducted Mendelian Randomisation (MR) analyses to assess causality between the studied risk factors and CHIP, using publicly available summary statistics of genome-wide association studies (GWAS). RESULTS:We found that advanced age, smoking, history of several diseases (including breast cancer and leiomyoma of uterus), as well as several serum biomarkers (monocyte count, proteins LY75, SIGLEC6, SIRPB1 and CYB5R2) were associated with CHIP. CONCLUSION:Our findings expanded existing knowledge base by demonstrating novel risk factors of CHIP, especially history of several diseases and serum biomarkers. These findings advance understanding of the aetiology of CHIP.
