NHS Forth Valley is one of the fourteen regions of NHS Scotland. It provides healthcare services in the Clackmannanshire, Falkirk and Stirling area. NHS Forth Valley is headquartered in Castle Business Park, Stirling.
Purpose - The purpose of this study is to evaluate the positive behavioural support (PBS) pathway in the NHS Forth Valley Adult Learning Disability Service by exploring the characteristics of referrals, the adherence to the pathway and staff experiences of the pathway to identify areas of good practice and areas for improvement. Design/methodology/approach - Routinely collected data were extracted from online systems and analysed using descriptive statistics in line with the aims of the audit. Findings - A total of 77 referrals were made to the PBS pathway in the timeframe. There were some areas of strength in terms of adherence to the pathway, with some areas for improvement, including for admin processes, data capturing and implementation. Practical implications - A number of suggested improvements for the service are highlighted, which may be applicable to wider service contexts using a tiered model of PBS. Originality/value - This is the first, to the best of the authors' knowledge, audit of the pathway since its development and highlights the use of PBS in a real-world context in an NHS setting, with recommendations for areas of improvement.
Chronic pain is a highly prevalent and disabling condition, yet its true population burden remains poorly characterised. This is partly due to the lack of validated case-finding methods within routinely collected electronic health records that adequately reflect people’s lived experiences. Existing algorithms for identifying chronic pain are limited by poor validation, insufficient involvement of people with chronic pain, and low diagnostic accuracy. The C-PICTURE study aims to develop, refine, and validate an algorithm capable of accurately identifying individuals living with chronic pain within primary care electronic health records. This mixed-methods study comprises four phases, preceded by a pilot algorithm. Six diverse GP practices are purposively selected across Scotland to reflect variation in geography, population demographics, and socioeconomic context. Phase A involves review of 1,200 electronic medical records across the six practices to create a reference dataset for algorithm comparison. Phase B includes a patient-reported outcomes survey sent to approximately 6,200 adults, collecting data on chronic pain presence, severity, impact, and management strategies. Phase C consists of semi-structured interviews and focus groups with people with chronic pain and healthcare providers to explore discrepancies between coding-based and self-reported chronic pain, understand coding practices, and examine how people use healthcare services to manage needs. Phase D pilots the algorithm across the six practices using the Primary Care Intelligence Service, comparing algorithm performance with medical record review and patient-reported outcomes data. Sensitivity, specificity, predictive values, and Area Under the Receiver Operating Characteristic Curve are estimated, with cross-validation to assess internal validity. Data linkage across phases enables refinement and validation of the final algorithm. The C-PICTURE study will address evidence gaps by producing the first validated population-based method for identifying chronic pain using Scottish primary care data. Integrating clinical records, patient-reported data, and qualitative insights ensures the algorithm reflects both biomedical and lived experiences of chronic pain. Findings will have implications for policy, practice, and research, including highlighting limitations in current coding practices, supporting surveillance, and informing national planning for pain management services. The validated algorithm could also be adapted for use across the UK and internationally, supporting epidemiological monitoring and enabling future chronic pain research. This study is registered with the UK’s Clinical Study Registry (ISRCTN reference number: 37628569; Registered on 27 February 2024; DOI: https://doi.org/10.1186/ISRCTN37628569).
Abstract Aims This study compares the efficacy and safety of carotid artery stenting (CAS) and carotid endarterectomy (CEA) in high-risk patients and identifies gaps in the current evidence regarding clinical outcomes. Methods Randomised controlled trials and non-randomised comparative studies evaluating CAS versus CEA in high-risk patients were systematically reviewed. High-risk status was defined by anatomical, physiological, or co-morbid factors associated with increased surgical risk. A search of electronic databases (PubMed, Medline, Ovid, Cochrane). Nine Eligible studies were selected, data extracted, and findings synthesised narratively. Results Nine studies including 4,198 patients were analysed. Periprocedural stroke rates were higher with CAS (1.1%–13.1%) than with CEA (2.1%–5.9%), suggesting increased stroke risk with CAS in symptomatic high-risk patients (p < 0.05). Myocardial infarction occurred less frequently after CAS (0%–2.4%) than after CEA (up to 6.1%), indicating a protective effect of CAS against perioperative MI (p < 0.01). Cranial nerve injury occurred in 0% of CAS cases and up to 4.9% of CEA cases, confirming a significant reduction with CAS (p < 0.001). Restenosis rates were low for CAS (2%–4.3%) and CEA (0.6%–2%), with no significant difference (p > 0.05). Conclusions Several trials included mixed populations without stratified outcomes, and available data do not demonstrate statistically significant differences in perioperative stroke (CAS 13.1% vs. CEA 5.9%, p = 0.08). Thus no definitive conclusions can be drawn, highlighting the need for future studies with further stratification and statistical analysis.
Clinicians often face workflow problems that are perceived as either too bespoke or low stakes to attract commercial attention. Historically, most do not have the technical knowledge to address these problems, but the recent emergence of "vibe coding" presents a transformative opportunity. Vibe coding refers to the co-development of software using natural language prompts to large language models. It offers a pathway to create simple tools that address these real-world pain points, or to prototype more complex ideas. In this review, written by a group of early adopter clinicians with a range of programming expertise, we introduce vibe coding for clinicians (especially those with no or minimal coding experience) as a way of democratising innovation from the front lines. We discuss foundational skills, outline some common challenges, provide a practical step-by-step playbook, and illustrate this approach with some case examples, taking care to consider caveats and guardrails for deployment. We propose that vibe coding is more than a technical shortcut for beginners and is not a replacement for professional software developers. Instead, it can bridge the gap between clinical insight and technical execution, equipping clinicians with the ability to rapidly prototype digital health solutions most reflective of clinical realities.
