Evaluation of Implicit Bias Training in Continuing Medical and Nursing Education to Address Racial Bias in Maternity Health Care Settings

OBJECTIVES

Implicit bias can affect clinical decisions that influence the care received by patients whose ancestors had been subjected to unfair medical and social practices. However, literature describing the effects of implicit bias training as part of continuing medical and nursing education is scarce. We conducted a longitudinal evaluation of a training for maternal health care clinical and nonclinical staff.

METHODS

A total of 80 staff members at 2 clinical sites in Cleveland, Ohio, participated in the training and evaluation in 2020 and 2021. We used a mixed-methods evaluation to capture changes in knowledge, awareness of bias, and application of strategies to reduce biased behavior by conducting pre- and posttraining surveys immediately after training and interviews at 3 and 6 months posttraining. We conducted univariate and bivariate analyses of the surveys and recorded, transcribed, and analyzed interviews for themes.

RESULTS

Using a threshold of answering 3 of 5 knowledge questions correctly, 50 of 80 (62.5%) trainees who engaged in the evaluation passed the pretraining knowledge questions and 67 (83.8%) passed the posttraining knowledge questions. Of the 80 participants, 75 (93.8%) were women. Interviewees (n = 11) said that low staff-to-patient ratios, lack of racial and ethnic diversity in leadership, inadequate training on implicit bias, and lack of institutional consequences for poor behavior exacerbated bias in maternity care. Interviewees reported having heightened awareness of bias and feeling more empowered after the training to advocate for themselves and patients to prevent and mitigate bias in the hospital.

CONCLUSION

Additional study describing the effect of implicit bias training as part of continuing medical education should be conducted, and administrative and management changes should also be made to prevent bias and improve quality of care.

Carbon Footprint Reduction Associated With Multidisciplinary Pediatric Airway Clinics: A Program Evaluation Study

Objective

Health care is a significant contributor to the climate crisis. Multidisciplinary clinics (MDC) may reduce carbon emissions by combining multiple appointments into one. This is the first program evaluation study to quantify the carbon footprint associated with multidisciplinary pediatric airway clinics.

Study Design

Retrospective.

Setting

Children's Hospital at London Health Sciences Center, London, Canada.

Methods

Pediatric airway MDC allows patients to see otolaryngology and respirology in one appointment. The carbon and financial savings (Canadian Dollars) of all patients attending the MDC from January 1, 2018 to December 31, 2022 were calculated. Patient postal codes and institutional parking rates were inputted into the CASCADES carbon accounting tool. Total distance was divided into unsustainable (vehicles) and sustainable (transit, walking, cycling) transportation to calculate carbon emissions. Travel costs included cost/kilometer for vehicles (maintenance, license/registration, insurance, fuel) and costs/ride for transit.

Results

A total of 560 MDC appointments for 300 patients saved 77,785 km. Total carbon emissions saved from travel averted was 16.21 tonnes. The total carbon emissions saved, minus public transit, was 15.60 tonnes. Using the Natural Resources Canada Greenhouse Gas Equivalencies Calculator, 16.21 tonnes are approximately equivalent to 5 passenger vehicles, 6906 L of gasoline, 3.8 homes' energy, and 10.8 homes' electricity use for one year, 36.6 barrels of oil consumed, and 675 propane cylinders. Travel costs of $28,891.83 (no parking), $30,519.40 ($4 minimum parking fee), or $33,774.55 ($12 maximum parking fee) were saved.

Conclusion

MDC effectively reduced carbon emissions and offered patients financial savings. Similar models can be adapted across institutions to help mitigate climate change.

Can generative artificial intelligence pass the orthopaedic board examination?

Background

Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education. ChatGPT, an LLM launched in late 2022 has demonstrated the ability to produce accurate, detailed answers, potentially enabling it to aid in medical education and clinical decision-making. The purpose of this study is to evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations using Self-Assessment Exams from the AAOS database and approved literature as a proxy for the Orthopaedic Board Examination.

Methods

301 SAE questions from the AAOS database and associated AAOS literature were input into ChatGPT's interface in a question and multiple-choice format and the answers were then analyzed to determine which answer choice was selected. A new chat was used for every question. All answers were recorded, categorized, and compared to the answer given by the OITE and SAE exams, noting whether the answer was right or wrong.

Results

Of the 301 questions asked, ChatGPT was able to correctly answer 183 (60.8%) of them. The subjects with the highest percentage of correct questions were basic science (81%), oncology (72.7%, shoulder and elbow (71.9%), and sports (71.4%). The questions were further subdivided into 3 groups: those about management, diagnosis, or knowledge recall. There were 86 management questions and 47 were correct (54.7%), 45 diagnosis questions with 32 correct (71.7%), and 168 knowledge recall questions with 102 correct (60.7%).

Conclusions

ChatGPT has the potential to provide orthopedic educators and trainees with accurate clinical conclusions for the majority of board-style questions, although its reasoning should be carefully analyzed for accuracy and clinical validity. As such, its usefulness in a clinical educational context is currently limited but rapidly evolving.

Clinical relevance

ChatGPT can access a multitude of medical data and may help provide accurate answers to clinical questions.

Climate-conscious sedation: how can we sustainably manage dental anxiety using inhalation sedation?

Climate change represents an urgent global threat. Without action, rising temperatures resulting from human activity will increasingly affect our health and wellbeing through changing patterns of disease, extreme weather events and availability of resources. Expedient decarbonisation of the UK economy is an ambitious goal to which we must all contribute.The NHS aims to be the world's first net-zero health service and reach carbon-neutral status by 2040. Dental services are particularly resource-intensive. Some dental anxiety management techniques have a disproportionately high impact on the environment relative to their usage. Inhalation sedation with nitrous oxide is one such example.Nitrous oxide is a greenhouse gas almost 300 times more potent than carbon dioxide, but its utility to facilitate dental treatment for anxious and vulnerable patients is well-documented. This paper balances the health utility with environmental and social harm of continuing to use nitrous oxide and suggests evidence-based methods we can apply to limit the environmental impact of sedation services.

Barriers and Enablers Affecting the Uptake of Biosimilar Medicines Viewed Through the Lens of Actor Network Theory: A Systematic Review

BACKGROUND AND OBJECTIVE

Biosimilars represent an opportunity to realise savings against the costs of innovative medicines. Despite efforts made by stakeholders, there are numerous barriers to the uptake of biosimilars. To realise the promise of biosimilars reducing costs, barriers must be identified, understood, and overcome, and enablers magnified. The aim of this systematic review is to summarise the enablers and barriers affecting uptake of biosimilars through the application of a classification system to organise them into healthcare professional (HCP), patient, or systemic categories.

METHODS

A systematic literature search was performed in PubMed, Scopus, CINAHL, eConlit, and Embase. Included were primary research studies published in English between Jan 2017 through June 2023 focused on enablers and barriers affecting uptake of biosimilars. Excluded studies comprised comparisons of biosimilar efficacy and safety versus the reference biologic. One reviewer extracted data that included classification of barriers or enablers, the sub-classification, and the identification of the degree of agency associated with the actor through their role and associations as a mediator within their network, through the application of Actor Network Theory. The data were validated by a second reviewer (PV).

RESULTS

Of the 94 studies included, 59 were cross-sectional, 20 were qualitative research, 12 were cohort studies, and three were economic evaluations. Within the review, 51 of the studies included HCP populations and 35 included patients. Policies and guidelines were the most cited group of enablers, overall. Systemic enablers were addressed in 29 studies. For patients, the most frequently cited enabler was positive framing of a biosimilar, while for HCPs, cost benefit was the most frequently noted enabler. The most frequently discussed systemic barrier to biosimilar acceptance was lack of effective policies or guidelines, followed by lack of financial incentives, while the most significant barriers for HCPs and patients, respectively, were their lack of general knowledge about biosimilars and concerns about safety and efficacy. Systemic actors and HCPs most frequently acted with broad degree of agency as mediators, while patient most frequently acted with a narrow degree of agency as mediators within their networks.

CONCLUSIONS

Barriers and enablers affecting uptake of biosimilars are interconnected within networks, and can be divided into systemic, HCP, and patient categories. Understanding the agency of actors within networks may allow for more comprehensive and effective approaches. Systemic enablers in the form of policies appear to be the most effective overall levers in affecting uptake of biosimilars, with policy makers advised to give careful consideration to appropriately educating HCPs and positively framing biosimilars for patients.

GP perceptions of informal peer support in primary care: a qualitative study

BACKGROUND

Burnout is on the rise among GPs in the UK. One approach to mitigating burnout in GPs is through informal peer support (IPS). This refers to GPs informally supporting each other on an informational (advice) and emotional (venting and reflection) basis.

AIM

To explore GPs' perceptions of how IPS manifests in the primary care setting and what factors influence effective GP engagement with IPS.

DESIGN & SETTING

A qualitative study utilising semi-structured interviews to develop an in-depth understanding of GPs' perceptions of IPS, based on their experiences in practices across England.

METHOD

Fifteen GPs were purposively sampled to include the views of locum, salaried, and trainee GPs and GP partners. Semi-structured interviews were conducted, recorded, and transcribed verbatim. Transcripts were analysed using inductive thematic analysis.

RESULTS

Four types of IPS were identified relating to emotional support, professional advice, sharing of workload, and mentorship, which reflect existing literature. The frequency and efficacy of IPS was found to be influenced by several factors categorised into individual traits, practice culture, and occupation.

CONCLUSION

The results highlight where efforts should be directed to improve GP engagement with IPS. Specifically, GP leaders have an important role in shaping practice culture and fostering an environment for IPS to occur. Practices may also benefit from introducing professional development measures targeted at training GPs to better support each other based on their individual traits.

Providing Adolescents with Access to Online Patient Portals: Interviews with Parent-Adolescent Dyads

OBJECTIVE

To identify perceived benefits, problems, facilitators, and barriers to adolescent online patient portal use.

STUDY DESIGN

Qualitative, semi-structured interviews with dyads of parents and adolescents with or without chronic illness. The study team purposively sampled for racial and ethnic minorities and fathers. Three team members then performed thematic analysis of the transcripts, with subsequent dyadic analysis of themes represented by related parents and adolescents.

RESULTS

We performed 102 interviews with 51 dyads of parents and adolescents (26 with chronic illness, 25 without chronic illness). Nearly all participants believed that adolescents should be permitted portal access. We identified 4 themes related to portal benefits: improves adolescent's knowledge of health; supports medical self-management and autonomy; strengthens communication and relationships; and supports parental influence. We identified 4 themes related to portal problems: misunderstanding or confusion; emotional distress; strain on relationships; and irresponsible use of portal. Facilitators of portal use included severity of illness, adolescent's curiosity, and ease of technology use. Barriers included lack of awareness or interest, complexity of information, and access difficulties. Twenty adolescents (39%) did not know they could access the portal, and 23 (45%) lacked interest in portals. Parents and adolescents seldom used the portal as a collaborative tool, and instead were engaging with the portal independently.

CONCLUSION

Parents and adolescents perceive several benefits and problems with portal use, but many adolescents lack interest in using portals. Adolescent portals represent an underutilized resource to engage adolescents in their care.

Characterising trends in the initiation, timing, and completion of recommended summary plan for emergency care and treatment (ReSPECT) plans: Retrospective analysis of routine data from a large UK hospital trust

AIM

To assess patient socio-demographic and disease characteristics associated with the initiation, timing, and completion of emergency care and treatment planning in a large UK-based hospital trust.

METHODS

Secondary retrospective analysis of data across 32 months extracted from digitally stored Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) plans within the electronic health record system of an acute hospital trust in England, UK.

RESULTS

Data analysed from ReSPECT plans (n = 23,729), indicate an increase in the proportion of admissions having a plan created from 4.2% in January 2019 to 6.9% in August 2021 (mean = 8.1%). Forms were completed a median of 41 days before death (a median of 58 days for patients with capacity, and 21 days for patients without capacity). Do not attempt cardiopulmonary resuscitation was more likely to be recorded for patients lacking capacity, with increasing age (notably for patients aged over 74 years), being female and the presence of multiple disease groups. 'Do not attempt cardiopulmonary resuscitation' was less likely to be recorded for patients having ethnicity recorded as Asian or Asian British and Black or Black British compared to White. Having a preferred place of death recorded as 'hospital' led to a five-fold increase in the likelihood of dying in hospital.

CONCLUSION

Variation in the initiation, timing, and completion of ReSPECT plans was identified by applying an evaluation framework. Digital storage of ReSPECT plan data presents opportunities for assessing trends and completion of the ReSPECT planning process and benchmarking across sites. Further research is required to monitor and understand any inequity in the implementation of the ReSPECT process in routine care.

Enhancing student understanding of rheumatic disease pathologies through augmented reality: findings from a multicentre trial

OBJECTIVE

The possibility of combining real and virtual environments is driving the increased use of augmented reality (AR) in education, including medical training. The aim of this multicentre study was to evaluate the students' perspective on the AR-based Rheumality GO!® app as a new teaching concept, presenting six real anonymized patient cases with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA).

METHODS

The study encompassed 347 undergraduate medical students (232 women and 115 men) from four medical universities in Germany (Jena, Bad Nauheim/Gießen, Nuremberg, Erlangen). The course was divided into a theoretical refresher lecture followed by six AR-based cases in each of the three indications presented in the Rheumality GO! app. All participants evaluated the course after completion, assessing the benefit of the app from a student's perspective using a questionnaire with 16 questions covering six subject areas.

RESULTS

The use of the AR-based app Rheumality GO! improved the understanding of pathologies in RA, PsA and axSpA for 99% of the participants. For 98% of respondents, the concept of AR with real patient data has made a positive impact on the teaching environment. On the other hand, 82% were in favour of the use of virtual tools (e.g. AR) in addition to this conventional approach.

CONCLUSION

The results of our survey showed that from the medical students' perspective, an AR-based concept like the Rheumality GO! app can complement rheumatology teaching in medical school as an effective and attractive tool though not replace bedside teaching.

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines.

Physician perceptions of medically unexplained symptoms in adolescent patients presenting to the emergency department

INTRODUCTION

Adolescents presenting with medically unexplained symptoms (MUS) in non-mental healthcare settings, particularly Emergency Departments (EDs), pose diagnostic challenges necessitating a comprehensive bio-psycho-social approach. Amid the youth mental health crisis, recognising psychological distress is imperative. This study delved into physicians' perceptions and diagnostic tendencies regarding such cases, exploring the potential overshadowing of psychosomatic presentations by medicalized diagnoses in EDs.

METHODS

Our study involved 74 physicians, representing 82% of eligible respondents in the Paediatric Emergency Medicine Department, and was conducted using an online questionnaire examining perceptions of case scenarios with psychosomatic presentations.

RESULTS

Results disclosed a prevalent inclination toward medical diagnoses, with less than 10% of physicians considering psychosomatic conditions in specific scenarios. Interestingly, psychosomatic diagnoses were more probable for symptoms like headaches, shortness of breath, and chest pain. The study uncovered a possible bias among physicians towards medical diagnoses in EDs for adolescents with MUS, possibly stemming from physicians' focus on physical care, diagnostic uncertainties, cognitive biases, and concerns about stigmatisation.

CONCLUSION

Adolescents with MUS seeking assistance in non-mental health settings may encounter delayed mental health diagnoses and interventions. Psychosomatic symptoms could signify stressors or underlying mental health disorders. Recognising psychosocial distress early on is crucial for optimal mental health outcomes. Consequently, the study advocates for a paradigm shift towards a holistic bio-psychosocial approach in both medical education and practice.

Implementation of a geriatric in-reach service improves acute surgical unit outcomes; a retrospective before-and-after study

BACKGROUND

Australia's ageing population is challenging for surgical units and there is a paucity of evidence for geriatric co-management in acute general surgery. We aimed to assess if initiating a Geriatric Medicine in-reach service improved outcomes for older adults in our Acute Surgical Unit (ASU).

METHODS

The Older Adult Surgical Inpatient Service (OASIS) was integrated into ASU in 2021. We retrospectively reviewed all patients over age 65 admitted to ASU over a 12-month period before and after service integration with a length of stay (LOS) greater than 24 h. There was no subsequent truncation or selection. Primary outcomes were 30-day mortality, LOS, and 28-day readmissions. Secondary outcomes were discharge disposition, in-hospital mortality, and hospital-acquired complications (HACs).

RESULTS

1339 consecutive patients were included in each group, with no differences in baseline characteristics. There was a significant decrease in 28-day readmissions from 20.2% to 16.0% (P < 0.05), greatest in patients undergoing non-EL operative procedures (21.9% pre-OASIS vs. 12.6% post-OASIS; P < 0.05). Trends towards reduced 30-day mortality (7.17% vs. 5.90%; P = 0.211), in-hospital mortality (3.88% vs. 2.91%; P = 0.201), permanent care placement (7.77% vs. 7.09%; P = 0.843) and HACs (8.14% vs. 7.62%; P = 0.667) were seen, although statistical significance was not demonstrated. LOS remained unchanged at 4 days (P = 0.653).

CONCLUSION

The addition of a geriatric in-reach service to a tertiary ASU led to a significant reduction in 28-day readmissions. Downtrends were seen in mortality, permanent care placement, and HAC rates, while LOS remained unchanged.

Synthetic, non-person related panoramic radiographs created by generative adversarial networks in research, clinical, and teaching applications

OBJECTIVES

Generative Adversarial Networks (GANs) can produce synthetic images free from personal data. They hold significant value in medical research, where data protection is increasingly regulated. Panoramic radiographs (PRs) are a well-suited modality due to their significant level of standardization while simultaneously displaying a high degree of personally identifiable data.

METHODS

We produced synthetic PRs (syPRs) out of real PRs (rePRs) using StyleGAN2-ADA by NVIDIA©. A survey was performed on 54 medical professionals and 33 dentistry students. They assessed 45 radiological images (20 rePRs, 20 syPRs, and 5 syPRcontrols) as real or synthetic and interpreted a single-image syPR according to the image quality (0-10) and 14 different items (agreement/disagreement). They also rated the importance for the profession (0-10). A follow-up was performed for test-retest reliability with >10 % of all participants.

RESULTS

Overall, the sensitivity was 78.2 % and the specificity was 82.5 %. For professionals, the sensitivity was 79.9 % and the specificity was 82.3 %. For students, the sensitivity was 75.5 % and the specificity was 82.7 %. In the single syPR-interpretation image quality was rated at a median of 6 and 11 items were considered as agreement. The importance for the profession was rated at a median score of 7. The Test-retest reliability yielded a value of 0.23 (Cohen's kappa).

CONCLUSIONS

The study marks a comprehensive testing to demonstrate that GANs can produce synthetic radiological images that even health professionals can sometimes not differentiate from real radiological images, thereby being genuinely considered authentic. This enables their utilization and/or modification free from personally identifiable information.

CLINICAL SIGNIFICANCE

Synthetic images can be used for university teaching and patient education without relying on patient-related data. They can also be utilized to upscale existing training datasets to improve the accuracy of AI-based diagnostic systems. The study thereby supports clinical teaching as well as diagnostic and therapeutic decision-making.

A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.

Level of Evidence

Level V.

Artificial intelligence in perinatal mental health research: A scoping review

The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.

Adjunctive Testing Using Biospectral Emission Sequencing: Bioregulatory Intelligence Technology in Parallel With the Goals of Artificial Intelligence in Medicine

The many advancements in medical technology of the last century have continually sought to improve the sensitivity of testing and the specificity of treatment of human maladies. Conventional physical and pharmaceutical treatment is largely an imprecise process, stimulating the impetus for the advancement of machine learning-enhanced artificial intelligence (AI) medical technologies. Biospectral Emission Sequencing (BES) is a bioregulatory intelligence (BI) technology already in use as an adjunct to conventional testing. Biospectral Emission Sequencing provides a functional system of dynamic real-time adjunctive testing and treatment selection. This paper discusses the parallel technologies of present and future AI and BI technologies in medicine.

Use of social media for academic and professional purposes by gynecologic oncologists

OBJECTIVE

To investigate the prevalence and patterns of social media use among gynecologic oncologists for professional and academic purposes.

METHODS

A prospective online survey between November and December 2022 targeted gynecologic oncology practitioners (gynecologic oncologists, surgical oncologists, medical oncologists, radiation/clinical oncologists, and onco-pathologists/pathologists). The survey, distributed via various social media platforms, included 40 questions to capture qualitative and quantitative data on social media use.

RESULTS

Of 131 respondents from 32 countries, 106 (80.9%) were gynecologic oncologists and affiliated with academic institutions (84.7%). Facebook (n=110, 83.9%), Twitter (n= 108, 82.4%), and Instagram (n=100, 76.3%) were the most used platforms. Respondents used social media to stay updated (n=101, 77.1%), network (n=97, 74%), learn about conferences and webinars (n=97, 74%), and engage in academic discussions (n=84, 64.1%). Following the COVID-19 pandemic, 100/129 (77.5%) reported increased social media use. However, only 32 (24.4%) used it to connect with patients, and concerns were raised about privacy and the need for separate professional and personal accounts. A quarter of respondents hesitated to share their opinions on social media due to the fear of controversy, with 26 (20%) experiencing cyberbullying, yet 120/130 (92.3%) believed it enabled junior professionals to express their views. Concerns about differentiating valid content, information reliability, and the professional perception of sourcing knowledge from social media were noted. Gender, age, specialty, and income level influenced patterns of social media use, with variations in preferences for platforms, content engagement, and purposes, highlighting a complex landscape of social media interaction among gynecologic oncologists.

CONCLUSION

While the use of social media among gynecologic oncologists is prevalent, particularly for academic and professional development, challenges such as cyberbullying, privacy concerns, and the need for formal training in social media navigation persist. Tailored training programs and guidelines could enhance social media's effective and ethical use in this field, promoting a safe environment for professional expression and engagement.

Leadership to Accelerate Healthcare's Digital Transformation: Evidence From 33 Health Systems

GOAL

The COVID-19 pandemic, healthcare market disruptors, and new digital healthcare technologies have made a substantial impact on the delivery of healthcare services, highlighting the critical roles of leaders in hospitals and health systems. This study sought to understand the evolving roles of CEOs, CIOs, and other executive leaders in the postpandemic era and highlight the adaptability and strategic vision of executives in shaping the future of healthcare delivery.

METHODS

Between October 2022 and May 2023, 51 interviews were conducted with CEOs, CIOs, and other executives responsible for delivering technology solutions for 33 nonprofit health systems in the United States. They were asked to describe their backgrounds; how information solutions and technologies were viewed within their organizations' strategy, operations, and governance; and the key characteristics of executive leaders.

PRINCIPAL FINDINGS

The study has found that effective CEOs have an authentic belief in technology's role in achieving their organization's mission and that contemporary CIOs are strategic executive partners who align strategy with culture to improve care. This study examines how healthcare systems are creating digitally savvy executive leadership teams that operate in a new, integrated model that unites previously siloed functions.

PRACTICAL APPLICATIONS

Some healthcare CIOs are unprepared for current and future business challenges, and some CEOs are unsure how to leverage digital technologies and C-suite expertise to transform their organizations. This research provides insights into how the nation's health systems are building and sustaining leadership teams capable of adapting to the healthcare environment and accelerating organizational transformation.

Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

How debunking biases in research and development decisions could lead to more equitable healthcare?

Decades of research have demonstrated that a variety of cognitive biases can affect our judgment and ability to make rational decisions in personal and professional environments. The lengthy, risky, and costly nature of pharmaceutical research and development (R&D) makes it vulnerable to biased decision-making. Moreover, cognitive biases can play a role in regulatory and clinical decision-making, the latter impacting diagnostic and treatment decisions in the therapeutic use of medicines. These inherent and/or institutionalized biases (e.g., in assumptions, data, or decision-making practices) could conceivably contribute to health inequities. In this mini-review, we provide a broad perspective on how cognitive biases can affect pharmaceutical R&D, regulatory evaluation, and therapeutic decision-making. Example approaches to mitigate the effect of common biases in the development, approval, and use of new therapeutics, such as quantitative decision criteria, multidisciplinary reviews, regulatory and treatment guidelines, and evidence-based clinical decision support systems are illustrated. Mitigating the impact of cognitive biases could increase pharma R&D efficiency, change the perspective and prioritization of unmet medical needs, increase representativeness and quality of evidence generated through clinical trials and real-world research, leading to higher quality insights and more effective medication use, and as such could eventually contribute to more equitable healthcare.

Global Validation of a Model to Predict Reduced Estimated GFR in People With Type 2 Diabetes Without Diagnosis of CKD

Introduction

A minimal-resource model for predicting reduced kidney function among people with type 2 diabetes and no diagnosis of chronic kidney disease (CKD) stages 3 to 5 was previously developed in a UK population to pre-screen for undiagnosed CKD. This study aims to evaluate the performance of the model on a global population and assess its adequacy with and without regional adjustment.

Methods

A retrospective observational study was performed using data collected from the iCaReMe global registry (NCT03549754) and the DISCOVER study (NCT02322762 and NCT02226822). Patients were grouped by their World Health Organization classified region. An estimated glomerular filtration rate (eGFR) <60 ml/min per 1.73 m2 was the marker of reduced kidney function. A regional-intercept recalibration was applied to adjust for regional variation. Discrimination and calibration were evaluated for the UK-developed and recalibrated models.

Results

A total of 14,180 patients (46 countries, 6 regions) were identified with type 2 diabetes, no previous diagnosis of CKD stages 3 to 5, and had a serum creatinine measurement or eGFR recorded. The UK model underestimated risk when applied globally and was deemed inadequate. The model with regional adjustment achieved the target sensitivity (80.5%; 95% confidence interval [CI]: 78.8%-82.3%) and demonstrated a relative improvement of 51.5% (95% CI: 48.1%-55.1%) in the positive predictive value (PPV), compared to a screen-all approach.

Conclusion

The regional-adjusted model demonstrated adequate performance globally. Incorporating the model within practice could help clinicians to risk-stratify and prioritize patients at high risk. This could enable improved efficiency via risk-tailored screening, particularly in lower-middle-income countries (LMICs).

Effect of virtual reality-based seizure management education program for parents (VR-ESMEPP) on seizure management: A randomized controlled trial

OBJECTIVE

This study evaluated the efficacy of Virtual Reality-Based Seizure Management Education Program for Parents (VR-ESMEPP) that was designed to improve parents' knowledge-skill percentage about epileptic seizure, and motivation levels about educational material.

METHODS

The study was conducted at a university hospital's pediatric neurology clinic in Turkey and involved both a VR-trained group and a control group. The parents' knowledge-skill percentage about epileptic seizure, and motivation levels about educational material were assessed before, after, and at 15 days after participating in VR-ESMEPP.

RESULTS

The parents' knowledge-skill percentage about epileptic seizure increased in the group that participated in the VR-ESMEPP. There was no such increase in the control group. Examination of the scores of the Instructional Materials Motivation Survey (IMMS) for the parents showed that while there was a significant increase between the pre-test and post-test within the group that participated in the VR-ESMEPP, there was no significant difference in the scores of the control group. However, the high IMMS scores obtained by all parents indicate the motivating nature of the education material.

SIGNIFICANCE

The study established the efficacy of VR-ESMEPP and demonstrated its ability to enhance parents' knowledge-skill percentage about epileptic seizure. Despite the absence of a difference in motivation levels between the groups, the high scores obtained by all participants indicate that the program was indeed motivating.

Identifying, Understanding, and Minimizing Unconscious Cognitive Biases in Perioperative Crisis Management: A Narrative Review

Rapid clinical decision-making behavior is often based on pattern recognition and other mental shortcuts. Although such behavior is often faster than deliberative thinking, it can also lead to errors due to unconscious cognitive biases (UCBs). UCBs may contribute to inaccurate diagnoses, hamper interpersonal communication, trigger inappropriate clinical interventions, or result in management delays. The authors review the literature on UCBs and discuss their potential impact on perioperative crisis management. Using the Scale for the Assessment of Narrative Review Articles (SANRA), publications with the most relevance to UCBs in perioperative crisis management were selected for inclusion. Of the 19 UCBs that have been most investigated in the medical literature, the authors identified 9 that were judged to be clinically relevant or most frequently occurring during perioperative crisis management. Formal didactic training on concepts of deliberative thinking has had limited success in reducing the presence of UCBs during clinical decision-making. The evolution of clinical decision support tools (CDSTs) has demonstrated efficacy in improving deliberative clinical decision-making, possibly by reducing the intrusion of maladaptive UCBs and forcing reflective thinking. Anesthesiology remains a leader in perioperative crisis simulation and CDST implementation, but spearheading innovations to reduce the adverse impact of UCBs will further improve diagnostic precision and patient safety during perioperative crisis management.

Nipah virus disease: what can we do to improve patient care?

The year 2023 marked the 25th anniversary of the first detected outbreak of Nipah virus disease. Despite Nipah virus being a priority pathogen in the WHO Research and Development blueprint, the disease it causes still carries high mortality, unchanged since the first reported outbreaks. Although candidate vaccines for Nipah virus disease exist, developing new therapeutics has been underinvested. Nipah virus disease illustrates the typical market failure of medicine development for a high-consequence pathogen. The unpredictability of outbreaks and low number of infections affecting populations in low-income countries does not make an attractive business case for developing treatments for Nipah virus disease-a situation compounded by methodological challenges in clinical trial design. Nipah virus therapeutics development is not motivated by commercial interest. Therefore, we propose a regionally led, patient-centred, and public health-centred, end-to-end framework that articulates a public health vision and a roadmap for research, development, manufacturing, and access towards the goal of improving patient outcomes. This framework includes co-creating a regulatory-compliant, clinically meaningful, and context-specific clinical development plan and establishing quality standards in clinical care and research capabilities at sites where the disease occurs. The success of this approach will be measured by the availability and accessibility of improved Nipah virus treatments in affected communities and reduced mortality.

Clinical Integration of Artificial Intelligence for Breast Imaging

This article describes an approach to planning and implementing artificial intelligence products in a breast screening service. It highlights the importance of an in-depth understanding of the end-to-end workflow and effective project planning by a multidisciplinary team. It discusses the need for monitoring to ensure that performance is stable and meets expectations, as well as focusing on the potential for inadvertantly generating inequality. New cross-discipline roles and expertise will be needed to enhance service delivery.

Reflexivity and positionality applied to medical practice: a study on implicit gender bias with medical students in a Swiss university

BACKGROUND

An array of evidence shows how the presence of implicit bias in clinical encounters can negatively impact provider-patient communication, quality of care and ultimately contribute to health inequities. Reflexive practice has been explored as an approach to identify and address implicit bias in healthcare providers, including medical students. At the Lausanne School of Medicine, a clinically integrated module was introduced in 2019 to raise students' awareness of gender bias in medical practice using a reflexivity and positionality approach. The purpose of this study is to describe the gender bias that were identified by medical students, analysing their types, places and modes of emergence during a clinical encounter. It further explores how positionality supported students' reflection on the way in which social position modulates their relationship to patients.

METHODS

As part of the teaching activity, medical students individually reflected on gender bias in a specific clinical encounter by answering questions in their electronic portfolio. The questionnaire included a section on positionality. We qualitatively analysed the students' assignments (n=76), applying a thematic analysis framework.

RESULTS

Medical students identified and described gender biases occurring at different moments of the clinical encounter (anamnesis (i.e. patient history), physical exam, differential diagnosis, final management). They causally associated these biases with wider social phenomena such as the gendered division of labour or stereotypes around sexuality and gender. Analysing students' reflections on how their position influenced their relationship with patients, we found that the suggested exercise revealed a major contradiction in the process of medical enculturation: the injunction to be neutral and objective erases the social and cultural context of patients and impedes an understanding of gender bias.

CONCLUSION

Gender biases are present in the different steps of a clinical consultation and are rooted in broader gendered social representations. We further conclude that the tension between a quest for objectivity and the reality of social encounters should be made explicit to students, because it is constitutive of medical practice.

Hospital-Based Health Professionals' Perceptions of Frailty in Older People

BACKGROUND AND OBJECTIVES

There is a high prevalence of frailty amongst older patients in hospital settings. Frailty guidelines exist but implementation to date has been challenging. Understanding health professional attitudes, knowledge, and beliefs about frailty is critical in understanding barriers and enablers to guideline implementation, and the aim of this study was to understand these in rehabilitation multidisciplinary teams in hospital settings.

RESEARCH DESIGN AND METHODS

Twenty-three semistructured interviews were conducted with health professionals working in multidisciplinary teams on geriatric and rehabilitation wards in Adelaide and Sydney, Australia. Interviews were audio recorded, transcribed, and coded by 2 researchers. A codebook was created and interviews were recoded and applied to the Framework Method of thematic analysis.

RESULTS

Three domains were developed: diagnosing frailty, communicating about frailty, and managing frailty. Within these domains, 8 themes were identified: (1) diagnosing frailty has questionable benefits, (2) clinicians don't use frailty screening tools, (3) frailty can be diagnosed on appearance and history, (4) frailty has a stigma, (5) clinicians don't use the word "frail" with patients, (6) frailty isn't always reversible, (7) there is a lack of continuity of care after acute admission, and (8) the community setting lacks resources.

DISCUSSION AND IMPLICATIONS

Implementation of frailty guidelines will remain challenging while staff avoid using the term "frail," don't perceive benefit of using screening tools, and focus on the individual aspects of frailty rather than the syndrome holistically. Clinical champions and education about frailty identification, reversibility, management, and communication techniques may improve the implementation of frailty guidelines in hospitals.

Predictive analytics support for complex chronic medical conditions: An experience-based co-design study of physician managers' needs and preferences

PURPOSE

The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND).

METHODS

This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care.

RESULTS

Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation.

CONCLUSIONS

The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals' involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care.

ChatGPT Responses to Common Questions About Slipped Capital Femoral Epiphysis: A Reliable Resource for Parents?

BACKGROUND

We sought to evaluate the ability of ChatGPT, an AI-powered online chatbot, to answer frequently asked questions (FAQs) regarding slipped capital femoral epiphysis (SCFE).

METHODS

Seven FAQs regarding SCFE were presented to ChatGPT. Initial responses were recorded and compared with evidence-based literature and reputable online resources. Responses were subjectively rated as "excellent response requiring no further clarification," "satisfactory response requiring minimal clarification," "satisfactory response requiring moderate clarification," or "unsatisfactory response requiring substantial clarification."

RESULTS

ChatGPT was frequently able to provide satisfactory responses that required only minimal clarification. One response received an excellent rating and required no further clarification, while only 1 response from ChatGPT was rated unsatisfactory and required substantial clarification.

CONCLUSIONS

ChatGPT is able to frequently provide satisfactory responses to FAQs regarding SCFE while appropriately reiterating the importance of always consulting a medical professional.

'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting

OBJECTIVE

This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden.

METHODS

We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis.

RESULTS

We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one.

CONCLUSION

The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.

Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.

COVID-19 and the Impact on Delirium Care of Hospitalized Older Adults: A Qualitative Study

BACKGROUND

Older adult patients with COVID-19 and delirium experience higher rates of adverse outcomes. Early recognition of at-risk patients and implementation of management strategies improve outcomes, though understanding barriers to acute care nurses implementing these strategies is limited.

PURPOSE

This study's purpose was to understand the experiences of acute care nurses providing care to older adults with COVID-19 and delirium. Experiences explored included assessment, nursing management interventions, and barriers to care.

METHODS

Purposive sampling to recruit nurses for semistructured focus groups was performed, and thematic analysis was generated by 4 members of the research team.

RESULTS

Twenty-one nurses participated in focus groups. Thematic analysis revealed themes of increased patient social isolation, barriers to delirium assessment and prevention, increased staff demands, and stressful work environments.

CONCLUSION

Rich findings reveal the profound impact of the pandemic on assessment for delirium and implementation of strategies for prevention and management in older adult patients.

Predictors of in-hospital outcomes for diverticular bleeding patients: a retrospective analysis of National Inpatient Sample data (2016-2020)

Background

Diverticular bleeding is the leading cause of lower gastrointestinal bleeding, affecting 3-5% of patients with diverticulosis. Current management protocols include resuscitation, diagnosis via direct visualization, computed tomography imaging, endoscopic interventions, angioembolization, and surgery when needed. However, predictive factors for outcomes and optimal interventions remain ambiguous.

Methods

This retrospective cohort study analyzed data from the National Inpatient Sample (NIS) database (2016-2020) to determine predictors of adverse in-hospital outcomes in diverticular bleeding patients without perforation or abscess. Demographic and clinical data were extracted, and multivariate regression models were applied. Analysis was conducted using R statistical software (version 4.1.3), with significance set at P<0.05.

Results

A total of 28,269 patients hospitalized for diverticular bleeding were identified. Age >85 years, moderate to severe Charlson Comorbidity Index, hypovolemic shock, blood transfusion requirement, and requirement for colectomy were significantly associated with greater in-hospital mortality. Factors such as late colonoscopy timing and colon resection led to longer hospital stays, while arterial embolization was predicted by older age, Black race, hypovolemic shock, and blood transfusion. Predictors of colon resection included advanced age, presence of colon cancer, and hypovolemic shock.

Conclusions

Our retrospective study identified significant predictors of in-hospital outcomes among patients with diverticular bleeding, informing risk stratification and management strategies. Further research is warranted to validate these findings and refine management algorithms for improved patient care. Integrating these insights into clinical practice may enhance outcomes and guide personalized interventions in diverticular bleeding management.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

Stress and Its Consequences-Biological Strain

Understanding the role of stress in pregnancy and its consequences is important, particularly given documented associations between maternal stress and preterm birth and other pathological outcomes. Physical and psychological stressors can elicit the same biological responses, known as biological strain. Chronic stressors, like poverty and racism (race-based discriminatory treatment), may create a legacy or trajectory of biological strain that no amount of coping can relieve in the absence of larger-scale socio-behavioral or societal changes. An integrative approach that takes into consideration simultaneously social and biological determinants of stress may provide the best insights into the risk of preterm birth. The most successful computational approaches and the most predictive machine-learning models are likely to be those that combine information about the stressors and the biological strain (for example, as measured by different omics) experienced during pregnancy.

Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology

The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.

The role of extended reality in optometry education: a narrative review

The evolution of digitally based pedagogies, such as extended reality (XR) - a group of simulated learning environments that include virtual simulation, virtual reality, and augmented reality - has prompted optometry educators to seek evidence to guide the implementation of these teaching and learning activities within their curricula. Looking more broadly across the medical and allied health fields, there is a wealth of evidence to guide the incorporation of XR, as it is increasingly being integrated into the curricula of other select health professions disciplines. Educators from these disciplines continue to explore and embed XR in practice. This narrative review summarises the findings and appraises the literature on the use of XR in optometry education. It identifies the learning domains in which XR has been implemented in optometry education and proposes areas for further investigation. The review questions the technology-focused approach that has driven the literature within the review and calls for richer pedagogical foundations with suggestions for future research agendas. As such, this narrative review provides optometry educators with new ways of understanding XR and its relationship with the curriculum.

Clinical trial results in context: comparison of baseline characteristics and outcomes of 38,510 RECOVERY trial participants versus a reference population of 346,271 people hospitalised with COVID-19 in England

BACKGROUND

Randomised trials are essential to reliably assess medical interventions. Nevertheless, interpretation of such studies, particularly when considering absolute effects, is enhanced by understanding how the trial population may differ from the populations it aims to represent.

METHODS

We compared baseline characteristics and mortality of RECOVERY participants recruited in England (n = 38,510) with a reference population hospitalised with COVID-19 in England (n = 346,271) from March 2020 to November 2021. We used linked hospitalisation and mortality data for both cohorts to extract demographics, comorbidity/frailty scores, and crude and age- and sex-adjusted 28-day all-cause mortality.

RESULTS

Demographics of RECOVERY participants were broadly similar to the reference population, but RECOVERY participants were younger (mean age [standard deviation]: RECOVERY 62.6 [15.3] vs reference 65.7 [18.5] years) and less frequently female (37% vs 45%). Comorbidity and frailty scores were lower in RECOVERY, but differences were attenuated after age stratification. Age- and sex-adjusted 28-day mortality declined over time but was similar between cohorts across the study period (RECOVERY 23.7% [95% confidence interval: 23.3-24.1%]; vs reference 24.8% [24.6-25.0%]), except during the first pandemic wave in the UK (March-May 2020) when adjusted mortality was lower in RECOVERY.

CONCLUSIONS

Adjusted 28-day mortality in RECOVERY was similar to a nationwide reference population of patients admitted with COVID-19 in England during the same period but varied substantially over time in both cohorts. Therefore, the absolute effect estimates from RECOVERY were broadly applicable to the target population at the time but should be interpreted in the light of current mortality estimates.

TRIAL REGISTRATION

ISRCTN50189673- Feb. 04, 2020, NCT04381936- May 11, 2020.

Association between deep neural network-derived electrocardiographic-age and incident stroke

Background

Stroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified. Recently, deep neural networks (DNNs) have been used to determine age from ECGs, otherwise known as the electrocardiographic-age (ECG-age), which predicts clinical outcomes. However, the relationship between ECG-age and stroke has not been well studied. We hypothesized that ECG-age is associated with incident stroke.

Methods

In this study, UK Biobank participants with available ECGs (from 2014 or later). ECG-age was estimated using a deep neural network (DNN) applied to raw ECG waveforms. We calculated the Δage (ECG-age minus chronological age) and classified individuals as having normal, accelerated, or decelerated aging if Δage was within, higher, or lower than the mean absolute error of the model, respectively. Multivariable Cox proportional hazards regression models adjusted for age, sex, and clinical factors were used to assess the association between Δage and incident stroke.

Results

The study population included 67,757 UK Biobank participants (mean age 65 ± 8 years; 48.3% male). Every 10-year increase in Δage was associated with a 22% increase in incident stroke [HR, 1.22 (95% CI, 1.00-1.49)] in the multivariable-adjusted model. Accelerated aging was associated with a 42% increase in incident stroke [HR, 1.42 (95% CI, 1.12-1.80)] compared to normal aging. In addition, Δage was associated with prevalent stroke [OR, 1.28 (95% CI, 1.11-1.49)].

Conclusions

DNN-estimated ECG-age was associated with incident and prevalent stroke in the UK Biobank. Further investigation is required to determine if ECG-age can be used as a reliable biomarker of stroke risk.

Healthcare Sustainability to Address Climate Change: Call for Action to the Infectious Diseases Community

The US healthcare system's contribution to greenhouse gas emissions and climate change is disproportionately high and harms the public. Several medical specialties are now reassessing how they can mitigate healthcare's harmful environmental impact. Healthcare sustainability is broadly defined as measures to decrease greenhouse gas emissions, waste, and other pollutants generated during the healthcare delivery process. Prior efforts and programs by infectious diseases (ID) professionals, such as antimicrobial stewardship and infection prevention and control can form a framework for ID professionals to help apply this expertise to healthcare environmental sustainability more broadly. This call to action proposes strategies for ID societies and professionals to incorporate climate change education for trainees, increase research and funding opportunities in healthcare sustainability, and calls for action by ID societies to champion system changes to decrease greenhouse gas emissions.

The feasibility and usability of mixed reality teaching in a hospital setting based on self-reported perceptions of medical students

BACKGROUND

Clinical teaching during encounters with real patients lies at the heart of medical education. Mixed reality (MR) using a Microsoft HoloLens 2 (HL2) offers the potential to address several challenges: including enabling remote learning; decreasing infection control risks; facilitating greater access to medical specialties; and enhancing learning by vertical integration of basic principles to clinical application. We aimed to assess the feasibility and usability of MR using the HL2 for teaching in a busy, tertiary referral university hospital.

METHODS

This prospective observational study examined the use of the HL2 to facilitate a live two-way broadcast of a clinician-patient encounter, to remotely situated third and fourth year medical students. System Usability Scale (SUS) Scores were elicited from participating medical students, clinician, and technician. Feedback was also elicited from participating patients. A modified Evaluation of Technology-Enhanced Learning Materials: Learner Perceptions Questionnaire (mETELM) was completed by medical students and patients.

RESULTS

This was a mixed methods prospective, observational study, undertaken in the Day of Surgery Assessment Unit. Forty-seven medical students participated. The mean SUS score for medical students was 71.4 (SD 15.4), clinician (SUS = 75) and technician (SUS = 70) indicating good usability. The mETELM Questionnaire using a 7-point Likert Scale demonstrated MR was perceived to be more beneficial than a PowerPoint presentation (Median = 7, Range 6-7). Opinion amongst the student cohort was divided as to whether the MR tutorial was as beneficial for learning as a live patient encounter would have been (Median = 5, Range 3-6). Students were positive about the prospect of incorporating of MR in future tutorials (Median = 7, Range 5-7). The patients' mETELM results indicate the HL2 did not affect communication with the clinician (Median = 7, Range 7-7). The MR tutorial was preferred to a format based on small group teaching at the bedside (Median = 6, Range 4-7).

CONCLUSIONS

Our study findings indicate that MR teaching using the HL2 demonstrates good usability characteristics for providing education to medical students at least in a clinical setting and under conditions similar to those of our study. Also, it is feasible to deliver to remotely located students, although certain practical constraints apply including Wi-Fi and audio quality.

Calibration trial of an innovative medical device (NEVVA© ) for the evaluation of pain in non-communicating patients in the intensive care unit

Background

Pain management is an essential and complex issue for non-communicative patients undergoing sedation in the intensive care unit (ICU). The Behavioral Pain Scale (BPS), although not perfect for assessing behavioral pain, is the gold standard based partly on clinical facial expression. NEVVA© , an automatic pain assessment tool based on facial expressions in critically ill patients, is a much-needed innovative medical device.

Methods

In this prospective pilot study, we recorded the facial expressions of critically ill patients in the medical ICU of Caen University Hospital using the iPhone and Smart Motion Tracking System (SMTS) software with the Facial Action Coding System (FACS) to measure human facial expressions metrically during sedation weaning. Analyses were recorded continuously, and BPS scores were collected hourly over two 8 h periods per day for 3 consecutive days. For this first stage, calibration of the innovative NEVVA© medical device algorithm was obtained by comparison with the reference pain scale (BPS).

Results

Thirty participants were enrolled between March and July 2022. To assess the acute severity of illness, the Sequential Organ Failure Assessment (SOFA) and the Simplified Acute Physiology Score (SAPS II) were recorded on ICU admission and were 9 and 47, respectively. All participants had deep sedation, assessed by a Richmond Agitation and Sedation scale (RASS) score of less than or equal to -4 at the time of inclusion. One thousand and six BPS recordings were obtained, and 130 recordings were retained for final calibration: 108 BPS recordings corresponding to the absence of pain and 22 BPS recordings corresponding to the presence of pain. Due to the small size of the dataset, a leave-one-subject-out cross-validation (LOSO-CV) strategy was performed, and the training results obtained the receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.792. This model has a sensitivity of 81.8% and a specificity of 72.2%.

Conclusion

This pilot study calibrated the NEVVA© medical device and showed the feasibility of continuous facial expression analysis for pain monitoring in ICU patients. The next step will be to correlate this device with the BPS scale.

Does a junior doctor focused 'Bootcamp' improve the confidence and preparedness of newly appointed ENT registrars to perform their job roles?

BACKGROUND

To assess changes in confidence and preparedness after conducting a 2-day induction bootcamp for novice Ear Nose and Throat (ENT) first year specialty trainee registrars (ST3s) in the United Kingdom (UK). The bootcamp covered common ENT presentations on the ward, and in the elective and emergency settings.

METHODS

A total of 32 trainees (ST3 or research fellow) voluntarily registered via an online application form to the Southern ST3 accelerated learning course bootcamp through ENT UK. ENT UK is a membership body that supports ENT trainees throughout their careers. They completed a two-day bootcamp that was hosted at St Mary's Hospital, London and 10 skills sessions were delivered by either a senior ENT registrar or an ENT consultant. A pre-session questionnaire was distributed to all participants and a post-session questionnaire was provided that assessed the changes in confidence and preparedness of the participants, if any. The responses were scored by a 10-point Likert scale. Only participants who fully completed the pre and post questionnaire were included, which was 29 in total.

RESULTS

Participants self-reported a significant increase in confidence (p < 0.001) and preparedness (p < 0.001) following the bootcamp course. The greatest improvements in comparison to all other stations were self-preparedness in the rigid bronchoscopy station and self-confidence in the sphenopalatine artery (SPA) ligation station.

CONCLUSION

The use of a two-day bootcamp improved confidence and preparedness of managing common ENT presentations in the ward, elective and emergency settings for ENT ST3s. It provides a useful adjunct in the acquisition of technical and non-technical skills alongside the traditional surgical apprenticeship. In the future, more work is required to assess the impact of bootcamps on patient outcomes and long-term benefits on trainees' skill retention and clinical proficiency.

Should physician-assisted suicide (PAS) be legalised in the UK? An innovative workshop exploring medical students' perspectives

This workshop aimed to investigate students' perspectives on physician-assisted suicide (PAS) and its potential legalisation. A two-pronged strategy was used - a goldfish bowl roleplay simulation and a facilitated group discussion. The roleplay enabled students to engage with practical and emotional challenges related to responding to a PAS request, while the discussion encouraged open dialogue on the ethical complexities of legalising PAS. Students showed nuanced changes in perspectives on PAS by actively participating in roleplay and discussions, demonstrating the potential for these approaches to promote deeper understanding.

Training as imagined? A critical realist analysis of Scotland's internal medicine simulation programme

BACKGROUND

Evaluating the impact of simulation-based education (SBE) has prioritised demonstrating a causal link to improved patient outcomes. Recent calls herald a move away from looking for causation to understanding 'what else happened'. Inspired by Shorrock's varieties of human work from patient safety literature, this study draws on the concept of work-as-done versus work-as-imagined. Applying this to SBE recognises that some training impacts will be unexpected, and the realities of training will never be quite as imagined. This study takes a critical realist stance to explore the experience and consequences, intended and unintended, of the internal medicine training (IMT) simulation programme in Scotland, to better understand 'training-as-done'.

METHODS

Critical realism accepts that there is a reality to uncover but acknowledges that our knowledge of reality is inevitably our construction and cannot be truly objective. The IMT simulation programme involves three courses over a 3-year period: a 3-day boot camp, a skills day and a 2-day registrar-ready course. Following ethical approval, interviews were conducted with trainees who had completed all courses, as well as faculty and stakeholders both immersed in and distant from course delivery. Interviews were audio-recorded, transcribed verbatim and analysed using critical realist analysis, influenced by Shorrock's proxies for work-as-done.

RESULTS

Between July and December 2023, 24 interviews were conducted with ten trainees, eight faculty members and six stakeholders. Data described proxies for training-as-done within three broad categories: design, experience and impact. Proxies for training design included training-as-prescribed, training-as-desired and training-as-prioritised which compete to produce training-as-standardised. Experience included training-as-anticipated with pre-simulation anxiety and training-as-unintended with the valued opportunity for social comparison as well as a sense of identity and social cohesion. The impact reached beyond the individual trainee with faculty development and inspiration for other training ventures.

CONCLUSION

Our findings highlight unintended consequences of SBE such as social comparison and feeling 'valued as a trainee, valued as a person'. It sheds light on the fear of simulation, reinforcing the importance of psychological safety. A critical realist approach illuminated the 'bigger picture', revealing insights and underlying mechanisms that allow this study to present a new framework for conceptualising training evaluation.

Benchmarking Sweden's Digitalization Transformation Strategy-Is It a Good Fit for the UAE?

Background: The ongoing revolution in health care, driven by wearable technology, virtual reality, and the Internet of Things, is reshaping both health care operations and our daily lives. This digital transformation ensures broader access to health care options, fosters patient-centered care and affects both health care institutions and individuals. In Sweden, health care is undergoing a digital shift, with initiatives like personal health management, remote monitoring, and virtual care enhancing patient involvement. This article reviews Sweden's health care digital transformation and compares it with the United Arab Emirates (UAE's) initiatives to assess viability. Methods: Using systematic literature review methods, databases from 2011 to 2023 were searched, supplemented by reference lists. Results: Database searches identified 761 records. A total of 480 articles were screened on basis of title and abstract, yielding 184 that were assessed for eligibility, leading to 40 academic studies to be included and 12 grey literature. Conclusions: The findings highlight Sweden's success in empowering patients through enhanced connectivity with clinical teams, knowledge sharing, and care management. However, due to contextual differences, the UAE should not blindly replicate Sweden's strategy. In conclusion, Sweden's efforts have positively engaged patients in health care, but challenges such as emerging technologies, demographic shifts, and budget constraints persist. Proactive planning and adaptation are crucial, with lessons applicable to the UAE market. Establishing a clear regulatory framework for digital care is imperative for future resilience.