The Current Status and Challenges of Prehospital Trauma Care in Low- and Middle-Income Countries: A Systematic Review.

BACKGROUND: This review aims to understand the present circumstances on the provision of prehospital trauma care in low- and middle-income countries (LMICs), particularly scoping the challenges experienced by LMICs in this regard. The objective is to systematically evaluate the currently available evidence on this topic. Based on the themes and challenges identified in the provision of prehospital trauma care in LMICs, we provide a series of recommendations and a knowledge base for future research in the field. METHODS: A systematic database search was conducted of original articles that explored and reported on prehospital trauma care in LMIC in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to March 2022. All original articles reporting on prehospital trauma care from 2010 to 2022 in LMICs were assessed, excluding case reports, small case series, editorials, abstracts, and pre-clinical studies; those with data inconsistencies that impede data extraction; and those with study populations fewer than ten. RESULTS: The literature search identified 2,128 articles, of which 29 were included in this review, featuring 27,848 participants from LMICs countries. Four main areas of focus within the studies were identified: (1) exploring emergency service systems, frameworks, and interconnected networks within the context of prehospital trauma care; (2) transportation of patients from the response site to hospital care; (3) medical education and the effects of first responder training in LMICs; and (4) cultural and social factors influencing prehospital trauma care-seeking behaviors. Due to overarching gaps in social and health care systems, significant barriers exist at various stages of providing prehospital trauma care in LMICs, particularly in injury identification, seeking treatment, transportation to hospital, and receiving timely treatment and post-intervention support. CONCLUSION: The provision of prehospital trauma care in LMICs faces significant barriers at multiple levels, largely dependent on wider social, geographic, economic, and political factors impeding the development of such higher functioning systems within health care. However, there have been numerous breakthroughs within certain LMICs in different aspects of prehospital trauma care, supported to varying degrees by international initiatives, that serve as case studies for widespread implementation and targets. Such experiential learning is essential due to the heterogenous landscapes that comprise LMICs.

Delivering medical leadership training through the Healthcare Leadership Academy: a four year analysis.

BACKGROUND: Formal leadership training is typically targeted at senior health professionals. The Healthcare Leadership Academy (HLA) was formed in 2016 to provide a leadership programme for students and early-career health professionals. This study analyses the effectiveness of the HLA scholarship programme as an intervention for improving interest in and preparing scholars for future leadership roles. METHODS: Survey data was used to assess the effectiveness of the HLA Scholarship program in cultivating leadership development. Questions required either multiple-choice, free text, ranking or Likert scale ('strongly agree', 'agree', 'neither agree nor disagree', 'disagree', 'strongly disagree) responses. Participants spanned six regions (London, Newcastle, Bristol, Belfast, Edinburgh, and Amsterdam) in four countries (England, Scotland, Northern Ireland, and the Netherlands). Descriptive statistical analyses were conducted, and insights were drawn from the open-ended survey questions using a leadership framework. RESULTS: Seventy participants who underwent the course between 2016 and 2020 completed the questionnaire. Nearly all (99%) found that the training provided on the programme had equipped them to be more effective leaders, with 86% of respondents stating that they were more likely to take on leadership roles. Nearly all (97.1%) found the course to be either of good or very good quality. Nineteen insights were identified from free text responses that fitted under one of the four themes of the leadership framework: "optimising", "resolving uncertainty", "enhancing adaptability", and "promulgating a vision". CONCLUSIONS: Healthcare leadership is a non-negotiable component of healthcare delivery in the 21st Century. As healthcare professionals, it is our duty to be effective leaders confident and competent in navigating the increasingly complex systems within which we operate for the benefit of ourselves, colleagues, and patients. By accounting for known shortcomings and developing ameliorative measures, the HLA Scholarship programme addresses unmet needs in a structured manner to support effective long-term healthcare leadership development.

Visualisation Approaches in Technology-Enhanced Medical Simulation Learning: Current Evidence and Future Directions.

Technology-enhanced learning (TEL) has been proposed as an approach to minimise the healthcare workforce shortage preventing universal healthcare coverage. Simulation-based medical education is a well-established teaching method. Little is known about effective strategies to translate in-person medical simulation teaching into a virtual world. This work aimed to review the literature on approaches to visualisation in technology-enhanced medical simulation. A systematic search strategy was optimised using three databases: Embase, MEDLINE, and APA PsycInfo. Additional papers were identified through cross-referencing. The last date of this search was 3 January 2022. The articles were analysed qualitatively. The risk of bias was assessed using ROBINS-I and RoB 2 tools. The search yielded 656 results with 9 additional papers identified through cross-referencing. Following deduplication and exclusions, 23 articles were included in a qualitative synthesis of evidence. Offline and online computer-based modules with virtual patient cases or practical skills simulations were identified as the most prevalent clinical simulation teaching modalities. Visualisation approaches included text, images, animations, videos, and 3D environments. Significant heterogeneity of study designs with a moderate risk of bias was established. Based on the current data, the virtual patient scenarios should use natural language input interfaces enriched with video and voice recordings, 3D animations, and short text descriptions to make the patient management experience more lifelike and increase knowledge retention. However, there is no agreed framework for assessing the pedagogical value of these innovations. High-quality randomised controlled trials of TEL-based clinical simulation are essential to advance the field.

Leading Transformation in Medical Education Through Extended Reality.

Extended reality (XR) has exponentially developed over the past decades to incorporate technology whereby users can visualise, explore, and interact with 3-dimensional-generated computer environments, and superimpose virtual reality (VR) onto real-world environments, thus displaying information and data on various levels of the reality-virtuality continuum. In the context of medicine, VR tools allow for anatomical assessment and diagnosis, surgical training through lifelike procedural simulations, planning of surgeries and biopsies, intraprocedural guidance, and medical education. The following chapter aims to provide an overview of the currently available evidence and perspectives on the application of XR within medical education. It will focus on undergraduate and postgraduate teaching, medical education within Low-Middle Income Countries, key practical steps in implementing a successful XR programme, and the limitations and future of extended reality within medical education.

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.

BACKGROUND: The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. OBJECTIVE: In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. METHODS: Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals' perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning-enabled or non-rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. RESULTS: The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non-rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. CONCLUSIONS: Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non-rule-based clinical AI implementation. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/33145.

Present and future of machine learning in breast surgery: systematic review.

BACKGROUND: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications. METHODS: A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar. RESULTS: The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation. CONCLUSION: Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.

A cost-utility analysis comparing endovascular coiling to neurosurgical clipping in the treatment of aneurysmal subarachnoid haemorrhage.

Endovascular coiling (EC) has been identified in systematic reviews and meta-analyses to produce more favourable clinical outcomes in comparison to neurosurgical clipping (NC) when surgically treating a subarachnoid haemorrhage from a ruptured aneurysm. Cost-effectiveness analyses between both interventions have been done, but no cost-utility analysis has yet been published. This systematic review aims to perform an economic analysis of the relative utility outcomes and costs from both treatments in the UK. A cost-utility analysis was performed from the perspective of the National Health Service (NHS), over a 1-year analytic horizon. Outcomes were obtained from the randomised International Subarachnoid Aneurysm Trial (ISAT) and measured in terms of the patient's modified Rankin scale (mRS) grade, a 6-point disability scale that aims to quantify a patient's functional outcome following a stroke. The mRS score was weighted against the Euro-QoL 5-dimension (EQ-5D), with each state assigned a weighted utility value which was then converted into quality-adjusted life years (QALYs). A sensitivity analysis using different utility dimensions was performed to identify any variation in incremental cost-effectiveness ratio (ICER) if different input variables were used. Costs were measured in pounds sterling (£) and discounted by 3.5% to 2020/2021 prices. The cost-utility analysis showed an ICER of - £144,004 incurred for every QALY gained when EC was utilised over NC. At NICE's upper willingness-to-pay (WTP) threshold of £30,000, EC offered a monetary net benefit (MNB) of £7934.63 and health net benefit (HNB) of 0.264 higher than NC. At NICE's lower WTP threshold of £20,000, EC offered an MNB of £7478.63 and HNB of 0.374 higher than NC. EC was found to be more 'cost-effective' than NC, with an ICER in the bottom right quadrant of the cost-effectiveness plane-indicating that it offers greater benefits at lower costs. This is supported by the ICER being below the NICE's threshold of £20,000-£30,000 per QALY, and both MNB and HNB having positive values (> 0).

Mitral valve surgery assisted by virtual and augmented reality: Cardiac surgery at the front of innovation.

BACKGROUND: Given the variety in mitral valve (MV) pathology and associated surgical techniques, extended reality (XR) holds great potential to assist MV surgeons. This review aims to systematically evaluate the currently available evidence investigating the use of XR and associated technologies in MV surgery. METHODS: A systematic database search was conducted of original articles and case reports that explored the use of XR and MV surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar, from inception to February 2022. RESULTS: Our search yielded 171 articles, of which 15 studies were included in this review, featuring 328 patients. Two main areas of application were identified: (i) pre-operative planning and (ii) predicting post-operative outcomes. The articles reporting outcomes relating to pre-operative planning were further categorised as exploring themes relevant to (i) mitral annular assessment; (ii) training; (iii) evaluation of surgical technique; (iv) surgical approach or plan and (v) selecting ring size or type. Preoperatively, XR has been shown to evaluate mitral annular pathology more accurately than echocardiography, informing the surgeon about the optimal surgical technique, approach and plan for a particular patient's MV pathology. Furthermore, XR could simulate and aid ring size/type selection for MV annuloplasty, creating a personalized surgical plan. Additionally, XR could estimate the postoperative MV biomechanical and physiological characteristics, predicting and pre-empting post-operative complications. CONCLUSION: XR demonstrated promising applications for assisting MV surgery, enhancing outcomes and patient-centred care, nevertheless, there remain the need for randomized studies to ascertain its feasibility, safety, and validity in clinical practice.

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review.

INTRODUCTION: Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth. EVIDENCE ACQUISITION: A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases. EVIDENCE SYNTHESIS: Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD). CONCLUSIONS: The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.

Autologous breast reconstruction in low- and middle-income countries (LMICs): a systematic review of current practices and challenges.

INTRODUCTION: Efforts to improve global healthcare persist, yet LMICs face challenges accessing surgical care, especially breast reconstruction amidst rising breast cancer cases. This review evaluates the present state and challenges of autologous breast reconstruction in low- and middle-income countries (LMICs). EVIDENCE ACQUISITION: Utilizing the PRISMA guidelines and the Cochrane Collaboration's standards, databases such as EMBASE, MEDLINE, Cochrane, PubMed, and Google Scholar were examined for studies on breast reconstruction in LMICs (based on the World Bank's 2022-2023 definitions) up to August 2022. Articles and case reports focusing on autologous reconstruction following breast cancer surgery in these regions were incorporated. EVIDENCE SYNTHESIS: From an initial 288 articles, 19 met the criteria after thorough assessment. These articles documented 4899 patient cases from LMICs, with the breakdown being: 11 on LD flaps, nine on TRAM flaps, eight on DIEP flaps, two on TDAP flaps, and one on TMG flap. Flap necrosis emerged as the prevalent complication in four studies. CONCLUSIONS: While autologous breast reconstruction presents superior aesthetic benefits without notable long-term economic setbacks, its adoption in LMICs is limited. This is partly due to the domination of implant-based methods among patients and surgeons, selected due to convenience. The scarcity of concrete evidence and standardized metrics in LMICs clouds the understanding of this procedure. Despite its advantages, awareness is low, necessitating more training and awareness campaigns. Uniform reporting, quality data, and financial analysis can provide a comprehensive LMIC understanding, aiding future research.

Efficacy of pharmacological and mechanical cervical priming methods for induction of labour and their applicability for outpatient management: A systematic review of randomised controlled trials.

BACKGROUND: A systematic review to determine the efficacy and safety of prostaglandins (PG) and Foley catheter (FC) for cervical priming in the outpatient setting. Various methods are available to achieve cervical ripening prior to induction of labour (IOL). In this systematic review, we will report the literature to date, and investigate the efficacy and safety of using the Foley catheter balloon or prostaglandins for cervical ripening, comparing both methods with each other, and discuss the implications of these findings for midwifery led units. METHODS: English peer-reviewed journals were systematically searched in the databases PubMed, MEDLINE, EMCARE, EMBASE and CINAHL, for studies investigating cervical ripening using the FC or PGs. Additional randomised controlled trials (RCTs) and non-RCTs were identified by a manual search. Search terms included: cervix dilatation effacement, cervix ripening, outpatient, ambulatory care, obstetric patients, pharmacological preparations, and Foley catheter. Only RCTs of FC versus PG or either intervention versus placebo or intervention in the in-patient Vs. outpatient setting were included. 15 RCTs were included. RESULTS: The results of this review show that both FC and PG analogues are equally effective cervical ripening agents. When compared to FC, PGs lead to a reduced requirement for oxytocin augmentation and a shorter intervention to delivery interval. However, PG use is also associated with an increased risk of hyperstimulation, cardiotocographic monitoring abnormalities and negative neonatal outcomes. CONCLUSIONS: FC cervical ripening is an effective method of outpatient cervical priming, which is safe, acceptable, and cost-effective and thus has a potential role in both resource-rich and resource-poor countries. With appropriate dosing, some PG analogues also appear to offer similar outcomes.

Simulation training for obstetric emergencies in low- and lower-middle income countries: A systematic review.

BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the effectiveness of simulation-based training (SBT) in emergency obstetrics care (EmOC) in Low- and Lower-Middle Income Countries (LMIC). Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field. METHODS: A systematic database search was conducted of original articles that explored the use of simulation-based training for EmOC in LMIC in EMBASE, MEDLINE, Cochrane database and Google Scholar, from inception to January 2022. RESULTS: The literature search identified 1,957 articles of which a total of 15 studies were included in this review, featuring 8,900 healthcare professionals from 18 countries. The SBT programmes varied in the reviewed studies. The most common training programme consisted of the PRONTO programme implemented by four studies, comprising of 970 participants across four different countries. In general, programmes consisted of lectures, workshops and simulations of emergency obstetric scenarios followed by a debrief of participants. There were thirteen studies, comprising of 8,332 participants, which tested for improvements in clinical knowledge in post-partum haemorrhage, neonatal resuscitation, pre-eclampsia, shoulder dystocia and sepsis. All the included studies reported improvements in clinical knowledge following the simulation of scenarios. Changes in teamwork, improvement in leadership and in communication skills were also widely reported. CONCLUSION: The use of SBT programmes is not only sustainable, feasible and acceptable in LMIC, but could also improve clinical knowledge, communication, and teamwork among healthcare providers, thus directly addressing the UN Sustainable Development Goals.

Medical students' views about Modernising Medical Careers: a questionnaire survey of London students.

A survey of London medical students asked for their views of the changes to postgraduate medical education starting in August 2005. The majority had clear ideas about their career plans and did not want their career held back by the introduction of an extra year. They overwhelmingly preferred to start their early training in or reasonably near to where they had undertaken their medical studies.

Cyclic AMP enhances progesterone action in human myometrial cells.

Cyclic AMP (cAMP) has been shown to promote progesterone and glucocorticoid action in a variety of cellular settings. In this study, we have used human myometrial cells to investigate whether cAMP potentiates the ability of progesterone to repress IL-1ß-driven COX-2 expression. We found that forskolin enhanced progesterone-repression of IL-1ß-driven COX-2 expression in association with delayed IL-1ß-induced nuclear phospho-p65 entry and reduced NF-κB binding to the COX-2 promoter. Further, forskolin enhanced the progesterone-induced expression of FKBP5 and 11ßHSD1, progesterone-driven activity of a progesterone response element (PRE) and progesterone receptor (PR)-B binding to a transfected PRE. In addition, forskolin treatment increased PR-B levels and reduced the PR-A:PR-B ratio while acutely decreasing the association between PR and nuclear receptor co-repressor (NCoR) and reducing NCoR levels after 6h. These findings are of importance in situations where enhancing progesterone activity is desirable, for example in the management of endometrial cancer, the promotion of endometrial receptivity or the maintenance of myometrial quiescence during pregnancy.