Communities of practice (CoPs) are defined as "groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis". They are an effective form of knowledge management that have been successfully used in the business sector and increasingly so in healthcare. In May 2023 the electronic databases MEDLINE and EMBASE were systematically searched for primary research studies on CoPs published between 1st January 1950 and 31st December 2022. PRISMA guidelines were followed. The following search terms were used: community/communities of practice AND (healthcare OR medicine OR patient/s). The database search picked up 2009 studies for screening. Of these, 50 papers met the inclusion criteria. The most common aim of CoPs was to directly improve a clinical outcome, with 19 studies aiming to achieve this. In terms of outcomes, qualitative outcomes were the most common measure used in 21 studies. Only 11 of the studies with a quantitative element had the appropriate statistical methodology to report significance. Of the 9 studies that showed a statistically significant effect, 5 showed improvements in hospital-based provision of services such as discharge planning or rehabilitation services. 2 of the studies showed improvements in primary-care, such as management of hepatitis C, and 2 studies showed improvements in direct clinical outcomes, such as central line infections. CoPs in healthcare are aimed at improving clinical outcomes and have been shown to be effective. There is still progress to be made and a need for further studies with more rigorous methodologies, such as RCTs, to provide further support of the causality of CoPs on outcomes.

Background: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation.Methods: A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021.Results: Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk.Conclusion: ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.

Spondylodiscitis is a potentially life-threatening infection of the intervertebral disk and adjacent vertebral bodies, with a mortality rate of 2-20%. Given the aging population, the increase in immunosuppression, and intravenous drug use in England, the incidence of spondylodiscitis is postulated to be increasing; however, the exact epidemiological trend in England remains unknown. The Hospital Episode Statistics (HES) database contains details of all secondary care admissions across NHS hospitals in England. This study aimed to use HES data to characterise the annual activity and longitudinal change of spondylodiscitis in England. The HES database was interrogated for all cases of spondylodiscitis between 2012 and 2019. Data for the length of stay, waiting time, age-stratified admissions, and 'Finished Consultant Episodes' (FCEs), which correspond to a patient's hospital care under a lead clinician, were analysed. In total, 43135 FCEs for spondylodiscitis were identified between 2012 and 2022, of which 97.1% were adults. Overall admissions for spondylodiscitis have risen from 3 per 100,000 population in 2012/13 to 4.4 per 100,000 population in 2020/21. Similarly, FCEs have increased from 5.8 to 10.3 per 100,000 population, in 2012-2013 and 2020/21 respectively. The highest increase in admissions from 2012 to 2021 was recorded for those aged 70-74 (117% increase) and aged 75-59 (133% increase), among those of working age for those aged 60-64 years (91% increase). Population-adjusted admissions for spondylodiscitis in England have risen by 44% between 2012 and 2021. Healthcare policymakers and providers must acknowledge the increasing burden of spondylodiscitis and make spondylodiscitis a research priority.

Lumbar disc herniation (LDH) is a leading cause of radiculopathy and low back pain, contributing significantly to global disability. Management strategies include conservative and surgical treatments, but clinical decision-making lacks standardization, particularly in surgical indications, timing, and criteria for transitioning from conservative management. What are the surgical indications, criteria for transitioning from conservative to surgical management in LDH, and what role do motor deficits play? Following PRISMA guidelines, a systematic search across major databases identified 20 studies. Risk of bias was assessed using the Newcastle Ottawa Scale and RoB 1 tools. A qualitative synthesis was conducted, and the Index of Qualitative Variation (IQV) quantified variability in indications. Among the studies that reported specific indications, imaging-confirmed nerve root compression (reported in 18/20 studies) and severe/refractory pain (reported in 17/20 studies) were the most consistent indications, while thresholds for sensory deficits (reported in 8/20 studies) varied widely. Early surgery (48 h–6 weeks) was associated with superior recovery, particularly for mild/moderate motor deficits graded ≤ MRC 3/4, achieving >90 % recovery rates. Delayed surgery (>6 weeks) resulted in prolonged symptoms and poorer outcomes, especially in severe cases. Transition criteria included a patient-specific combination of failure of conservative therapy (n = 12) after a most frequently 4–6-week trial, neurological progression, and worsening imaging findings. Significant heterogeneity was observed in thresholds for motor and sensory deficits, with high IQV scores for definitions of conservative treatment failure (IQV = 0.96) and motor deficit (IQV = 0.96). Significant heterogeneity in surgical indications, timing, and decision-making highlights the urgent need for standardized, evidence-based guidelines to optimize clinical decisions and improve outcomes in LDH management.

Artificial intelligence (AI) models have shown potential for diagnosing and prognosticating traumatic spinal cord injury (tSCI), but their clinical utility remains uncertain. ology: The primary aim was to evaluate the performance of AI algorithms in diagnosing and prognosticating tSCI. Subsequent systematic searching of seven databases identified studies evaluating AI models. PROBAST and TRIPOD tools were used to assess the quality and reporting of included studies (PROSPERO: CRD42023464722). Fourteen studies, comprising 20 models and 280,817 pooled imaging datasets, were included. Analysis was conducted in line with the SWiM guidelines. For prognostication, 11 studies predicted outcomes including AIS improvement (30%), mortality and ambulatory ability (20% each), and discharge or length of stay (10%). The mean AUC was 0.770 (range: 0.682–0.902), indicating moderate predictive performance. Diagnostic models utilising DTI, CT, and T2-weighted MRI with CNN-based segmentation achieved a weighted mean accuracy of 0.898 (range: 0.813–0.938), outperforming prognostic models. AI demonstrates strong diagnostic accuracy (mean accuracy: 0.898) and moderate prognostic capability (mean AUC: 0.770) for tSCI. However, the lack of standardised frameworks and external validation limits clinical applicability. Future models should integrate multimodal data, including imaging, patient characteristics, and clinician judgment, to improve utility and alignment with clinical practice.