Satisfaction with telemedicine use during COVID-19 pandemic in the UK: a systematic review

Background: Telemedicine became a fundamental part of healthcare provision during COVID-19 pandemic. An evaluation of telemedicine-associated satisfaction helps the service develop more viable applications. This review evaluated the satisfaction of healthcare users and providers and their willingness to use this modality in future.Methods: The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A search on empirical articles published between March 2020 and December 2022 was performed on 'PubMed' and 'Scopus' databases. Findings that reported on satisfaction of patients, families and caregivers as well as clinicians were extracted and analysed. Quality of included studies was assessed. After applying inclusion and exclusion criteria, the review included 27 eligible studies.Results: Data was found from a variety of emergency and non-emergency departments of primary, secondary, and specialised healthcare. Almost all studies were undertaken within the NHS. There were many tools that measured satisfaction. Satisfaction was high among recipients of healthcare, scoring 9-10 on a scale of 0-10 or ranging from 73.3% to 100%. Convenience was rated high in every specialty examined. Satisfaction of clinicians was high throughout the specialities despite connection failure and concerns about confidentiality of information. Nonetheless, studies reported perception of increased barriers to accessing care and inequalities for vulnerable patients especially in older people. In general, willingness to use telemedicine in future was high in the recipients as well as the providers of healthcare.Conclusion: COVID-19 pandemic has transformed healthcare in the UK and promoted a revolution in telemedicine applications. Satisfaction was high among both recipient and provider of healthcare. Telemedicine managed to provide a continued care throughout the pandemic while maintaining social distance. The current review presented commendable evidence to encourage different specialities to engage in telemedicine application.

Clicks for credit: an analysis of healthcare professionals' social media use and potential for continuing professional development activities

BACKGROUND

Previous studies have examined social media habits and utilization patterns among various groups of healthcare professionals. However, very few studies have evaluated the use of social media to support continuing professional development activities. The goal of the 2023 Clinical Education Alliance social media survey was to explore how HCPs interact professionally with social media, describe utilization trends, and identify barriers to using social media to disseminate CPD content.

METHODS

We conducted an online anonymous, voluntary survey of healthcare professionals contained in the Clinical Education Alliance learner database from January to March 2023. The survey was distributed via email and all learners were invited to participate regardless of profession or specialty. This survey consisted of 16 questions and collected demographic information and social media utilization and habits of healthcare professionals.

RESULTS

Of the 2,615 healthcare professionals who completed the survey, 71.2% use social media. Most respondents were physicians (50.6%) practicing in an urban setting (59.6%) and have been practicing for more than 15 years (70.5%). The most widely used platform was Facebook (70.7%), but there were no significant differences among the different professions. Of the respondents who use social media, 44.5% used social media to access continuing professional development-certified activities. Surveyed learners preferred passive participation with social media content. Participant-reported concerns include issues with legitimacy of the information, privacy, time constraints, and institutional barriers.

DISCUSSION

As the continuing professional development community continues to evolve and seek new innovative strategies to reach healthcare professionals, the findings of this survey highlight the need to identify and enact social media-based strategies aimed to engage healthcare professionals and provide them with unbiased evidence-based education.

Research surveys and their evolution: Past, current and future uses in healthcare

Research surveys are believed to have originated in antiquity with evidence of them being performed in ancient Egypt and Greece. In the past century, their use has grown significantly and they are now one of the most frequently employed research methods including in the field of healthcare. Modern validation techniques and processes have allowed researchers to broaden the scope of qualitative data they can gather through these surveys such as an individual’s views on service quality to nationwide surveys that are undertaken regularly to follow healthcare trends. This article focuses on the evolution and current utility of research surveys, different methodologies employed in their creation, the advantages and disadvantages of different forms and their future use in healthcare research. We also review the role artificial intelligence and the importance of increased patient participation in the development of these surveys in order to obtain more accurate and clinically relevant data.

An immersive virtual reality communication skills training for dietitians: A feasibility study

Objective

The training of dietitians emphasizes the development of strong communication skills. Immersive virtual reality (IVR) has been successfully employed for various healthcare scenarios; however, it is yet understudied in dietetics education. Therefore, we conducted a feasibility study to investigate the usefulness of IVR for the purpose of communication skills training.

Methods

We designed a multi-user virtual hospital environment that enabled the training of professional conversations between dietitians and patients. Divided into groups of three, 30 dietetics students participated in a role-play task. Taking a qualitative approach to inquiry, we evaluated how participants assessed the benefits and limitations of the IVR training.

Results

Participants appreciated the authenticity of the training environment and mentioned various advantages of IVR (e.g., sense of privacy, better focus on conversation) over traditional modes of instruction. On the other side, participants frequently mentioned that the lack of avatars' facial expressions might present an obstacle for effective communication skills training.

Conclusion

IVR enables authentic communication skills trainings for dietitians. Special consideration should be given to providing ample social cues during training.

Innovation

The study demonstrates that the promising results from other healthcare professions regarding the usefulness of IVR training also apply to dietetics.

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data

Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.

Series: Public engagement with research. Part 2: GPs and primary care researchers working inclusively with minoritised communities in health research to help address inequalities

Public engagement in health research is vital for addressing health disparities and promoting inclusivity among minoritised communities who often face barriers to accessing healthcare. Minoritised communities are groups, which have been made minorities by a dominant culture, race, ethnic group and/or social class and may experience health inequalities as a result. By incorporating diverse perspectives and lived experiences of minoritised communities, this approach aims to achieve contextually relevant research outcomes that reduce health inequalities and improve overall well-being. However, underrepresentation and lack of inclusivity challenges persist, necessitating the establishment of inclusive partnerships and grassroots participatory methodologies.

To foster inclusive public engagement, it is important to overcome structural and cultural barriers, address socioeconomic challenges, and build trust with minoritised communities. This can be achieved by promoting a cultural shift that values inclusivity, providing comprehensive training to researchers, and collecting rigorous data on engagement demographics for transparency and accountability. Involving minoritised communities in decision-making through participatory research approaches enhances trust and yields successful outcomes. Additionally, allocating sufficient resources, collaborating in co-production, and prioritising the diverse needs and perspectives of stakeholders contribute to fostering inclusive public engagement in research.

Overall, inclusive engagement practices particularly in primary care research have the potential to reduce health inequalities and cater to the unique requirements of minoritised communities, thereby creating more impactful outcomes and promoting equitable healthcare access.

'The Emperor's new clothes?' Healthcare professionals' perceptions of the nursing associate role in two UK National Health Service hospitals: A qualitative interview study

Background

The introduction of nursing associates in England in 2017 as a professional 'bridging' role aimed to mitigate chronic staffing shortages, enable career progression of healthcare assistants and release registered nurses to provide more complex care. Limited evidence exists about the alignment between the identity and purpose of nursing associate roles described by the UK independent regulator, the Nursing & Midwifery Council, and the expectations, obligations, and team dynamics encountered in practice.

Purpose

Investigate the perceptions of nursing associate roles through the views and experiences of role holders, registered nurses, and healthcare assistants.

Setting

Two British National Health Service (NHS) Hospital Trusts in London, England (UK).

Methods

For this registered service evaluation, data were collected via in-person, semi-structured interviews. Verbatim transcripts were coded inductively. An adapted framework analysis method, suitable for use with Excel, was applied to support the identification of cross cutting themes. We used the Standards for Reporting Qualitative Research checklist for reporting this study.

Results

Eleven registered nurses, five nursing associates, and five healthcare assistants participated. Their experiences seldom reflected the policy vision of the nursing associate role in practice. Several participants likened the nursing associate role to the fable of the 'Emperor's New Clothes' in which expectations and reality diverge. With this over-arching theme, four sub-themes were identified: (1) preparedness of organisational infrastructure to support this role; (2) credibility of the role in practice; (3) perceived organisational "blindness" to the ambiguities of the role and (4) increasing task orientation and segmentation in care delivery.

Conclusion

There is a discrepancy between the identity of the nursing associate role as imagined in the policy agenda and its reality in practice. There is a need for more protected and well-defined training, clear role boundaries, and accessible career progression pathways for nursing associates. Moreover, honest dialogue at an organisational and policy level must continue, so that the challenges and opportunities of the nursing associate role are properly realised.

Tweetable abstract

Emperor's new clothes! Experiences and views of new nursing associate roles in NHS (UK) acute hospitals @CarolynSpring3.

Virtual reality for preoperative patient education: Impact on satisfaction, usability, and burnout from the perspective of new nurses

BACKGROUND

Traditional paper-based preoperative patient education is a struggle for new nurses and requires extensive training. In this situation, virtual reality technology can help the new nurses. Despite its potential benefits, there are studies on patient satisfaction but there is limited information on the usability of virtual reality (VR) technology for new nurses in giving preoperative education to patients.

AIM

To investigate the impact on satisfaction, usability, and burnout of a system using VR technology in preoperative patient education.

METHODS

The study involved 20 nurses from the plastic surgery ward and 80 patients admitted between April and May 2019. Each nurse taught four patients: Two using traditional verbal education and two using virtual reality. The System Usability Scale, After-Scenario Questionnaire, and Maslach Burnout Inventory (MBI) were employed to evaluate the impact of these education methods.

RESULTS

The VR education groups showed a statistically higher satisfaction than the traditional verbal education groups. Among the three subscales of the MBI, emotional exhaustion and personal accomplishment improved statistically significantly. VR was also better in terms of usability.

CONCLUSION

This study suggests VR enhances usability and reduces burnout in nurses, but further research is needed to assess its impact on depersonalization and objective measures like stress and heart rate.

Candidaantarctica Lipase B mediated kinetic resolution: A sustainable method for chiral synthesis of antiproliferative β-lactams

Biocatalysis is a valuable industrial approach in active pharmaceutical ingredient (API) manufacturing for asymmetric induction and synthesis of chiral APIs. Herein, we investigated synthesis of a panel of microtubule-destabilising antiproliferative β-lactam enantiomers employing a commercially available immobilised Candida antarctica lipase B enzyme together with methanol and MTBE. The β-lactam ring remained intact during chiral kinetic resolution reactions, plausibly due to a bulky N-1 phenyl substituent on the β-lactam ring substrate. The predominant reaction mediated by CAL-B was methanol catalysed conversion of the β-lactam 3-acetoxy substituent to a 3-hydroxyl group, with preferential methanolysis of the 3S, 4S enantiomer. The unreacted substrate underwent progressive enantioenrichment to the 3R, 4R enantiomer. Substitution patterns on the B ring C3 meta position of the β-lactam scaffold greatly affected the rate of reaction. Halo substituents (fluoro-, chloro- and bromo-) reduced the rate of conversion compared to unsubstituted analogues, which in turn increased enantiomeric excess (ee). Ee values up to 86 % for the 3S, 4S 3-hydroxyl enantiomer were achieved. A double resolution approach for unreacted substrate yielded high ee values (>99 %) for the 3R, 4R 3-acetoxy enantiomer. CAL-B mediated methanolysis is a more sustainable method for resolution of racemic antiproliferative β-lactams compared to a previous technique of chiral diastereomeric resolution. Yields of β-lactams obtained using CAL-B are far superior than previously described, which will facilitate progression toward pre-clinical and clinical development. Biocatalysis is a useful tool in the toolbox of the medicinal chemist.

The advancements and prospective developments in anti-tumor targeted therapy

Cancer poses a major threat to human health worldwide. The development of anti-tumor materials provides new modalities for cancer diagnosis and treatment. In this review, we comprehensively summarize the research progress and clinical applications of anti-tumor materials. First, we introduce the etiology and pathogenesis of cancer, and the significance and challenges of anti-tumor materials research. Then, we classify anti-tumor materials and discuss their mechanisms of action. After that, we elaborate the research advances and clinical applications of anti-tumor materials, including those targeting tumor cells and therapeutic instruments. Finally, we discuss the future perspectives and challenges in the field of anti-tumor materials. This review aims to provide an overview of the current status of anti-tumor materials research and application, and to offer insights into future directions in this rapidly evolving field, which holds promise for more precise, efficient and customized treatment of cancer.

Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal optical coherence tomography (OCT) images of 7912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. We find benefits in both a low-data and high-data regime across tasks ranging from AMD stage and type classification to prediction of visual acuity. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining.

Artificial Intelligence Modeling and Priapism

PURPOSE OF REVIEW

This narrative review aims to outline the current available evidence, challenges, and future perspectives of Artificial Intelligence (AI) in the diagnosis and management of priapism, a condition marked by prolonged and often painful erections that presents unique diagnostic and therapeutic challenges.

RECENT FINDINGS

Recent advancements in AI offer promising solutions to face the challenges in diagnosing and treating priapism. AI models have demonstrated the potential to predict the need for surgical intervention and improve diagnostic accuracy. The integration of AI models into medical decision-making for priapism can also predict long-term consequences. AI is currently being implemented in urology to enhance diagnostics and treatment work-up for various conditions, including priapism. Traditional diagnostic approaches rely heavily on assessments based on history, leading to potential delays in treatment with possible long-term sequelae. To date, the role of AI in the management of priapism is understudied, yet to achieve dependable and effective models that can reliably assist physicians in making decisions regarding both diagnostic and treatment strategies.

Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques

Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques. The various types of AI like ML, NLP, RPA etc. are being used, which have streamlined and organised the Electronic Health Records (EHR) along with aiding the healthcare provider with decision making and sample and data analysis. This article also deals with the 3 major categories of diagnostic techniques - Imaging based, Pathology based and Preventive diagnostic techniques and what all changes and modifications were brought upon them, due to use of AI. Due to such a high demand, the investment in AI based healthcare techniques has increased substantially, with predicted market size of almost 188 billon USD by 2030. In India itself, AI in healthcare is expected to raise the GDP by 25 billion USD by 2028. But there are also several challenges associated with this like unavailability of quality data, black box issue etc. One of the major challenges is the ethical considerations and issues during use of medical records as it is a very sensitive document. Due to this, there is several trust issues associated with adoption of AI by many organizations. These challenges have also been discussed in this article. Need for further development in the AI based diagnostic techniques is also done in the article. Alongside, the production of such techniques and devices which are easy to use and simple to incorporate into the daily workflows have immense scope in the upcoming times. The increasing scope of Clinical Decision Support System, Telemedicine etc. make AI a promising field in the healthcare and diagnostics arena. Concluding the article, it can be said that despite the presence of various challenges to the implementation and usage, the future prospects for AI in healthcare is immense and work needs to be done in order to ensure the availability of resources for same so that high level of accuracy can be achieved and better health outcomes can be provided to patients. Ethical concerns need to be addressed for smooth implementation and to reduce the burden of the developers, which has been discussed in this narrative review article.

Addressing hidden tensions and grey areas of general practice: a qualitative study of the experiences of newly qualified GPs attending a course on generalist medicine

BACKGROUND

Generalist approaches can help address several challenges facing today's primary care. However, GPs report insufficient support to deliver advanced generalist medicine (AGM) in daily practice, struggling within a healthcare system that imposes strict adherence to single-disease focused guidelines.

AIM

To examine the professional and educational experiences of newly qualified GPs attending a course on AGM to understand how to redesign primary care systems to support their generalist work.

DESIGN AND SETTING

This was a qualitative study focusing on AGM in UK general practice (England), conducted in the context of the research evaluation of an online career development programme on AGM.

METHOD

We conducted 36 interviews and six focus groups with newly qualified GPs attending an online career development programme on AGM, and analysed data using framework analysis.

RESULTS

Three tensions experienced by the participants were identified: tension between realistic and idealistic practice; tension between different decision-making paradigms; and tension in the formation of the GPs' professional identities. These were owing to grey areas of practice deeply rooted in primary care systems - namely areas of work not adequately addressed by current education and service design.

CONCLUSION

Our findings have implications for tackling the general practice workforce crisis, highlighting that solutions targeting individual problems will not suffice by themselves. By making visible the grey areas of everyday general practice, we describe the changes needed to target tensions as described by the GPs in this study to ultimately enable, enhance and make visible the complex work of generalist medicine.

Exploring opportunities beyond clinical practice: The need for PA educators

ABSTRACT

Opportunities outside patient care are expanding for physician associates/assistants (PAs). Exploring new roles beyond clinical practice has the potential for improved career fulfillment as clinicians experience increasing levels of burnout and declining satisfaction with patient care. The decision to shift to a nonclinical role may be overwhelming without knowing what positions PAs are qualified to consider. PAs are increasingly found in the fields of healthcare consulting (as medical science liaisons), medical writing, healthcare insurance, research, and academia. With the rapid growth in PA programs, the need for educators is critical and this transition creates an interesting dynamic in which a proficient clinician becomes a novice educator. This article delves into these expectations and provides guidance on making the transition.

Desires and attitudes of outpatients and physicians regarding the use of teleurology during the pandemic: a prospective survey study

OBJECTIVE

It is not known to date how many patients admitted to urology outpatient clinics are willing to use telemedicine. We aimed to investigate knowledge levels and attitudes concerning the utilization of teleurology by patients applying to urology outpatient clinics.

METHODS

This prospective multicentre survey study included 334 patients aged 18-65 years who applied to an urology outpatient clinics. The patients were asked questions about their attitudes and expectations regarding teleurology. Diseases were divided into seven subgroups due to the broad spectrum of diagnoses. Physicians' and patients' opinions on whether it was possible to manage the current medical condition via teleurology were recorded.

RESULTS

69.5% of patients stated that they had sufficient technical skills to use teleurology by themselves for medical examination. 55.4% of patients and 78.4% of physicians responded that the existing complaints were suitable for teleurology. Both patients and physicians deemed genital system diseases and urinary tract infections suitable for teleurology (p<0.001, p<0.001 for physicians, and p<0.001, p<0.001 for patients), whereas urine transport, storage and emptying disorders (p=0.003) and benign prostatic hyperplasia (p=0.029) were deemed to be suitable for teleurology only by the physicians.

CONCLUSION

Our study shows that (i) the majority of our patient population has a telecommunications infrastructure suitable for teleurology, (ii) teleurology has aroused interest among patients, particularly during the pandemic period, and (iii) physicians and patients have high expectations that the problem can be solved with teleurology in suitable patients.

Eco-sustainability in ophthalmology

PURPOSE OF REVIEW

The purpose of this review is to evaluate the current state of eco-sustainability in ophthalmology, highlighting key practices, recent advancements, and areas requiring improvement. This review aims to provide ophthalmologists with a comprehensive overview of sustainable practices that can reduce environmental impact.

RECENT FINDINGS

Recent studies have identified several areas within ophthalmology where sustainable practices can be implemented including multidose eyedrops, adjusting surgical packs, making package inserts available electronically, and reusing surgical instruments. Moreover, there is an increasing emphasis on waste reduction both in the surgical and clinical setting, by decreasing the use of unnecessary materials and implementing more efficient use of recycling programs. Telemedicine has also emerged as a sustainable option, reducing the carbon footprint associated with patient travel. Despite these advancements, significant challenges remain, particularly in balancing the cost and accessibility of sustainable options with traditional practices.

SUMMARY

Eco-sustainability in ophthalmology is gaining momentum as the medical community aims to reduce its environmental impact. While progress has been made, further efforts are required to overcome financial and logistical barriers to widespread implementation. Continued research, education, and policy development are essential to advancing eco-sustainable practices, ensuring that environmental considerations become an integral part of ophthalmic care.

Development of neonatal Apgar scoring training program utilizing contactless hand tracking in immersive virtual reality

BACKGROUND

The rapid and accurate assessment of neonatal asphyxia is critical to preventing potentially fatal outcomes. Therefore, nursing students must acquire the skills to assess newborn conditions immediately after birth and implement appropriate interventions. Virtual reality (VR) simulation education has emerged as a promising tool for nursing education, offering repetitive and customizable clinical training while ensuring patient safety and overcoming spatiotemporal limitations.

AIM

This study investigated the effects of a contactless hand-tracking-based immersive VR neonatal Apgar scoring program, adapted from experiential learning theory.

DESIGN

A non-randomized controlled trial with a pre-post-test, quasi-experimental design was conducted.

SETTINGS

The study was conducted at two nursing schools from July to October 2023.

PARTICIPANTS

Participants comprised nursing students holding bachelor's degrees in nursing, with three or four years of experience and successful completion of a neonatal nursing theory course. Additionally, individuals with at least six months of experience working in a neonatal ward or delivery room before enrolling in nursing school were eligible.

METHODS

The participants were divided into three groups: the VR group (n = 27) received contactless hand-tracking-based immersive VR neonatal Apgar scoring training; the simulation group (n = 28) received face-to-face Apgar scoring simulation training; and the control group (n = 26) received instruction on the Apgar scoring criteria. Changes in scores among the VR, simulation, and control groups were statistically compared using ANOVA with SPSS-WIN 27.0.

RESULTS

The VR group exhibited significant improvements in knowledge, learning satisfaction, self-confidence, immersion, and motivation compared to the simulation and control groups. Moreover, satisfaction was significantly higher in the VR group than in the simulation group.

CONCLUSIONS

The hand-tracking-based immersive VR neonatal Apgar scoring program represents an innovative and effective educational tool, prioritizing the privacy and rights of mothers and infants. It can potentially replace traditional delivery-room clinical training, which is observation-based and limited.

Benefits and Barriers of Using Virtual Reality in Teaching Undergraduate Nursing Students: A Scoping Review

BACKGROUND

Although studies have been conducted using virtual reality (VR) in nursing education, evidence distinguishing the benefits and barriers of adopting VR pedagogical methods to teach undergraduate nursing students is not well-defined in nursing literature.

PURPOSE

To identify current evidence on the benefits and barriers of adopting VR as a pedagogical method in nursing education.

METHODS

A scoping review of 6 databases using PRISMA-ScR reporting guidelines. Data were managed using Covidence® software. Table of Evidence illustrates the themes.

RESULTS

Seventeen articles identified 5 thematic benefits of VR (knowledge acquisition, student engagement, improved self-confidence, teaching strategy, and cost factors) and 4 barriers (physical discomfort, learning outcome challenges, challenges to integrating into curricula, and technology challenges).

CONCLUSIONS

VR is an effective teaching-learning strategy to reinforce nursing concepts and build competencies. It is not a substitute for clinical education.

Insights from explainable AI in oesophageal cancer team decisions

BACKGROUND

Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).

METHODS

Retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.

RESULTS

Amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75-85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.

CONCLUSION

XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.

Breast calcifications on mammography from systemic amyloidosis: A case report

Calcifications on mammography from systemic disease at times meet diagnostic criteria for histologic sampling to exclude malignancy. We present a case of bilateral groups of new calcifications on mammography that yielded amyloidosis on core biopsy. Awareness of our patient's known diagnosis of systemic light chain amyloidosis (AL) prompted use of Congo red staining to confirm the histologic diagnosis. Knowledge of systemic diseases with possible manifestations on mammography can facilitate cogent and clinically relevant radiology-pathology correlation.

Impact of digital health on the quadruple aims of healthcare: A correlational and longitudinal study (Digimat Study)

BACKGROUND

Digital healthcare aims to deliver on the quadruple aim: enhance patient experiences, improve population health, reduce costs and improve provider experiences. Despite large investments, it is unclear how advancing digital health enables these healthcare aims.

OBJECTIVE

Our objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes.

MATERIALS AND METHODS

We undertook two studies: 1) Digital health correlational study investigating the association among healthcare system capability and healthcare aims, and 2) Digital hospital longitudinal study investigating outcomes pre and post electronic medical record implementation.

RESULTS

Digital health capability was associated with lower staff turnover. Digitising healthcare services was associated with decreased medication errors, decreased nosocomial infections, increased hospital activity, and a transient increase in staff leave.

DISCUSSION

These results suggest positive impacts on the population health and healthcare costs aim, minimal impacts on the provider experience aim and no observed impacts to the patient experience aim.

CONCLUSION

These findings should provide confidence to healthcare decision-makers investing in digital health.

A Fulbright International Exchange to Expand the Physician Assistant/Associate Curriculum in Geriatrics

ABSTRACT

The international growth of the physician assistant/associate (PA) profession provides opportunities for global exchange that PA faculty and clinicians should seek out and cultivate. Physician assistant/associate programs can benefit from the experiences and expertise of faculty educators from different backgrounds and countries. The Fulbright International Scholars Program is a well-established and recognized organization dedicated to supporting international exchange in higher education. This study describes the experience of an American PA who received a Fulbright Specialist Program award that provided the opportunity to collaborate with a Dutch PA program on improvement of their geriatric medicine didactic curriculum.

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

BACKGROUND

Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention.

METHODS

This analysis followed systematic review procedures, registered with the Prospective Register of Systematic Reviews. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the Quality Assessment of Diagnostic Accuracy Studies-2 tool for evaluating the quality and robustness of the included studies.

RESULTS

Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management.

CONCLUSIONS

This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis

AIM

Machine learning may be a tool with the potential for obesity prediction. This study aims to review the literature on the performance of machine learning models in predicting obesity and to quantify the pooled results through a meta-analysis.

DATA SYNTHESIS

A systematic review and meta-analysis were conducted, including studies that used machine learning to predict obesity. Searches were conducted in October 2023 across databases including LILACS, Web of Science, Scopus, Embase, and CINAHL. We included studies that utilized classification models and reported results in the Area Under the ROC Curve (AUC) (PROSPERO registration: CRD42022306940), without imposing restrictions on the year of publication. The risk of bias was assessed using an adapted version of the Transparent Reporting of a multivariable prediction model for individual Prognosis or Diagnosis (TRIPOD). Meta-analysis was conducted using MedCalc software. A total of 14 studies were included, with the majority demonstrating satisfactory performance for obesity prediction, with AUCs exceeding 0.70. The random forest algorithm emerged as the top performer in obesity prediction, achieving an AUC of 0.86 (95%CI: 0.76-0.96; I2: 99.8%), closely followed by logistic regression with an AUC of 0.85 (95%CI: 0.75-0.95; I2: 99.6%). The least effective model was gradient boosting, with an AUC of 0.77 (95%CI: 0.71-0.82; I2: 98.1%).

CONCLUSION

Machine learning models demonstrated satisfactory predictive performance for obesity. However, future research should utilize more comparable data, larger databases, and a broader range of machine learning models.

Shaping the future of healthcare in British Columbia: Establishing provincial clinical governance for responsible deployment of artificial intelligence tools

As healthcare embraces the transformative potential of Artificial Intelligence (AI), it is imperative to safeguard patient and provider safety, equity, and trust in the healthcare system. This article outlines the approach taken by the British Columbia (BC) Provincial Health Services Authority (PHSA) to establish clinical governance for the responsible deployment of AI tools in healthcare. Leveraging its province-wide mandate and expertise, PHSA establishes the infrastructure and processes to proactively and systematically intake, assess, prioritize, and evaluate AI tools. PHSA proposes a coordinated approach in AI tool deployment in collaboration with regional health authorities to prevent duplication of efforts and ensure equitable access to existing and emerging AI tools across the province of BC, incorporating principles of anti-Indigenous racism, cultural safety, and humility. The proposed governance structure underscores the identification of clinical needs, proactive ethics review, rigorous risk assessment, data validation, transparent communication, provider training, and ongoing evaluation to ensure success.

Large language models and generative AI in telehealth: a responsible use lens

OBJECTIVE

This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI.

MATERIALS AND METHODS

Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions.

RESULTS

Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration.

CONCLUSION

The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

Patient Factors Associated with Missed Otolaryngology Appointments at an Urban Safety-Net Hospital

OBJECTIVE

To determine if patient factors related to ethnicity, socioeconomic status (SES), medical comorbidities, or appointment characteristics increase the risk of missing an initial adult otolaryngology appointment.

METHODS

This study is a retrospective case control study at Boston Medical Center (BMC) in Boston, Massachusetts, that took place in 2019. Patient demographic and medical comorbidity data as well as appointment characteristic data were collected and compared between those that attended their initial otolaryngology appointment versus those who missed their initial appointment. Chi-square and ANOVA tests were used to calculate differences between attendance outcomes. Multivariate analysis was used to compare the odds of missing an appointment based on various patient- and appointment-related factors.

RESULTS

Patients who were more likely to miss their appointments were more often female, of lower education, disabled, not employed, Black or Hispanic, and Spanish-speaking. Spring and Fall appointments were more likely to be missed. When a multivariate regression was conducted to control for social determinants of health (SDOH) such as race, insurance status, employment, and education status, the odds of females, Spanish-speaking, students, and disabled patients missing their appointment were no longer statistically significant.

CONCLUSION

A majority of patients at BMC come from lower SES backgrounds and have multiple medical comorbidities. Those who reside closer to BMC, often areas of lower average income, had higher rates of missed appointments. Interventions such as decreasing lag time, providing handicap-accessible free transportation, and increasing accessibility of telemedicine for patients could help improve attendance rates at BMC.

LEVEL OF EVIDENCE

IV Laryngoscope, 134:4003-4010, 2024.

Exploring potential drivers of patient engagement with their health data through digital platforms: A scoping review

BACKGROUND

Patient engagement when providing patient access to health data results from an interaction between the available tools and individual capabilities. The recent digital advancements of the healthcare field have altered the manifestation and importance of patient engagement. However, a comprehensive assessment of what factors contribute to patient engagement remain absent. In this review article, we synthesised the most frequently discussed factors that can foster patient engagement with their health data.

METHODS

A scoping review was conducted in MEDLINE, Embase, and Google Scholar. Relevant data were synthesized within 7 layers using a thematic analysis: (1) social and demographic factors, (2) patient ability factors, (3) patient motivation factors, (4) factors related to healthcare professionals' attitudes and skills, (5) health system factors, (6) technological factors, and (7) policy factors.

RESULTS

We identified 5801 academic and 200 Gy literature records, and included 292 (4.83%) in this review. Overall, 44 factors that can affect patient engagement with their health data were extracted. We extracted 6 social and demographic factors, 6 patient ability factors, 12 patient motivation factors, 7 factors related to healthcare professionals' attitudes and skills, 4 health system factors, 6 technological factors, and 3 policy factors.

CONCLUSIONS

Improving patient engagement with their health data enables the development of patient-centered healthcare, though it can also exacerbate existing inequities. While expanding patient access to health data is an important step towards fostering shared decision-making in healthcare and subsequently empowering patients, it is important to ensure that these developments reach all sectors of the community.

Cost of Carbon in the Total Cost of Healthcare Procedures: A Methodological Challenge

Economic evaluations aim to compare the costs and the results of health strategies to guide the public decision-making process. Cost estimation is, thus, a cornerstone of this approach. At present, few national evaluation agencies recommend incorporating the cost of greenhouse gas (GHG) emissions from healthcare actions into the calculation of healthcare costs. Our main goal is to describe and discuss the methodology for integrating the cost of GHG emissions into the field of applied economic evaluations. To estimate this cost, three steps are required: (1) identifying and quantifying the physical flows linked to the production and management of the outputs of healthcare interventions, (2) estimating the quantity of GHG that can be attributed to each physical flow, and (3) valuing these GHG emissions in monetary terms. Integrating the cost of GHG emissions into the calculation of the costs of healthcare interventions is both useful and relevant from a perspective of collective intergenerational well-being. This approach has been made possible thanks to the existence of accounting and monetary valuation methods for emissions. Agencies specialized in health economic evaluations could take up this issue to resolve ongoing questions, thus providing researchers with a methodological framework and public decision-makers with some key insights.

Identifying Patterns of Primary Care In-Person and Telemedicine Use in the Veterans Health Administration: A Latent Class Analysis

BACKGROUND

The Veterans Health Administration increased synchronous telemedicine (video and telephone visits) in primary care in response to the COVID-19 pandemic.

OBJECTIVE

Our objective was to determine veteran use patterns of in-person and telemedicine primary care when all modalities were available.

DESIGN

A retrospective cohort analysis. We performed a latent class analysis of primary care visits over a 1-year period to identify veteran subgroup (i.e., class) membership based on amount of primary care use and modality used. Then, we used multinomial logistic regression with a categorical outcome to identify patient characteristics associated with class identification.

PARTICIPANTS

A random national sample consisting of 564,580 primary care empaneled veterans in June 2021.

MAIN MEASURES

Latent class membership.

KEY RESULTS

We identified three latent classes: those with few primary care visits that were predominantly telephone-based (45%), intermediate number of visits of all modalities (50%), and many visits of all modalities (5%). In an adjusted model, characteristics associated with the "few" visits class, compared to the intermediate class, were older age, male sex, White race, further driving distance to primary care, higher Gagne, optimal internet speed, and unmarried status (OR 1.002, 1.52, 1.13, 1.004, 1.04, 1.05, 1.06, respectively; p < .05). Characteristics associated with membership in the "many" visits class, compared to the intermediate class, were Hispanic race, higher JEN Frailty Index and Gagne (OR 1.12, 1.11, 1.02, respectively; p < .05), and higher comorbidity by Care Assessment Need score quartile (Q2 1.73, Q3 2.80, Q4 4.12; p < 0.05).

CONCLUSIONS

Veterans accessing primary care in-person or via telemedicine do so primarily in three ways: (1) few visits, predominantly telephone; (2) intermediate visits, all modalities, (3) many visits, all modalities. We found no groups of veterans receiving a majority of primary care through video.

Characteristics of Patients Receiving Complex Case Management in an Acute Care Hospital

BACKGROUND

Improving transitions in care is a major focus of health care planning. In the research team's prior intervention study, the length of stay (LOS) was reduced when patients at high risk for readmission were identified early in their acute care stay and received complex management.

OBJECTIVE

This study will describe the characteristics of patients receiving complex case management in an urban acute care hospital.

PRIMARY PRACTICE SETTING

Acute care hospital.

METHODOLOGY AND SAMPLE

This was a retrospective chart review of patients in a previous quality assurance study. A random selection of patients who previously underwent high-risk screening using the LACE (Length of stay; Acuity of the admission; Comorbidity of the patient; Emergency department use) index and received complex case management (the intervention group) were reviewed. The charts of a random selection of patients from the previous comparison group were also reviewed. Patient characteristics were collected and compared using descriptive statistics.

RESULTS

In the intervention group, more patients had their family physicians (FPs) documented (93.1% [81/87] vs. 89.2% [66/74]). More patients in the intervention group (89.7% [77/87] vs. 85.1% [63/74]) lived at home prior to admission. More patients in the intervention group had a family caregiver involved (44.8% [39/87] vs. 41.9% [31/74]). At discharge, more patients in the intervention group (87.1% [74/85]) were discharged home compared with the comparison group (78.4% [58/74]).

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

(1) Having an identified FP, living at home, and having family caregiver(s) characterized those with lower LOS and discharged home. (2) Case management, risk screening, and discharge planning improve patient outcomes. (3) This study identified the importance of having a FP and engaged family caregivers in improving care outcomes.

Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions

In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is on the brink of profound transformation due to these digital strides. While EHRs have unified patient data, challenges related to data privacy, interoperability, and access persist. In response, we present Pelvic Health Place (PHPlace) - a multilingual, patient-centric application. Purposefully designed to bolster patient engagement, PHPlace provides clinicians with essential pre-consultation insights, streamlines the consent process, vividly delineates surgical pathways, and assures comprehensive long-term monitoring. This platform also establishes a foundation for global data amalgamation, promising to invigorate research and potentially harness artificial intelligence (AI) capabilities. With AI integration, we anticipate a more tailored treatment approach and enriched patient education, signaling a pivotal shift in urogynecology and emphasizing the imperative for ongoing academic inquiry.

No more government-imposed societal-level COVID-19 control measures but still significant self-experienced burden for severely immunocompromised individuals - A cross-sectional survey in the Netherlands

Objectives

In March 2023, all societal-level COVID-19 control measures were lifted by the Dutch government. This study was performed to understand the self-experienced burden of this new phase of COVID-19 on the perspectives and behaviors of severely immunocompromised individuals.

Methods

This is an observational, descriptive, cross-sectional study in The Netherlands. An online survey was completed by severely immunocompromised individuals, to capture their general well-being (score from 1 = worst to 10 = best), mental and physical health, and daily and social activities during survey conduct and retrospectively for before onset of COVID-19. The survey was open for completion from May 24th until August 7nd, 2023.

Results

Of the 236 respondents, 96.6 % had been vaccinated against COVID-19 and 24.6 % were shielding to avoid COVID-19 during survey conduct. The general well-being score for all respondents was 7.5 (±1.2 SD) before onset of the COVID-19 pandemic and 6.9 (±1.6 SD) during survey conduct (P<0.001). For the shielding group (n = 58), these scores were 7.6 (±1.0 SD) and 5.7 (±1.6 SD), respectively (P<0.001). Generally, for all questions about mental and physical health and daily and social activities, there was a trend towards more negative answers during survey conduct, compared with before onset of the COVID-19 pandemic, which was more pronounced for the shielding group.

Conclusions

Despite absence of government-imposed societal measures, COVID-19 avoidance still had a self-experienced burden on perspectives and behaviors of immunocompromised individuals in The Netherlands, with a significantly lower general well-being during survey conduct, compared with before onset of COVID-19.

Framework of the strengths and challenges of clinically integrated trials: An expert panel report

The limitations of the explanatory clinical trial framework include the high expense of implementing explanatory trials, restrictive entry criteria for participants, and redundant logistical processes. These limitations can result in slow evidence generation that is not responsive to population health needs, yielding evidence that is not generalizable. Clinically integrated trials, which integrate clinical research into routine care, represent a potential solution to this challenge and an opportunity to support learning health systems. The operational and design features of clinically integrated trials include a focused scope, simplicity in design and requirements, the leveraging of existing data structures, and patient participation in the entire trial process. These features are designed to minimize barriers to participation and trial execution and reduce additional research burdens for participants and clinicians alike. Broad adoption and scalability of clinically integrated trials are dependent, in part, on continuing regulatory, healthcare system, and payer support. This analysis presents a framework of the strengths and challenges of clinically integrated trials and is based on a multidisciplinary expert "Think Tank" panel discussion that included representatives from patient populations, academia, non-profit funding agencies, the U.S. Food and Drug Administration, and industry.

Metrics used in quality improvement publications addressing environmental sustainability in healthcare: A scoping review protocol

Quality improvement approaches are increasingly being used to address the problem of healthcare's climate and ecological impact. While sustainability is increasingly recognized as a domain of quality, consensus is lacking on the most appropriate measures and metrics for those looking to reduce ecological impacts through quality improvement initiatives. We propose a scoping review to summarize approaches for selecting and quantifying ecological impacts in the published quality improvement literature. We will search multiple electronic databases (MEDLINE, EMBASE, CINAHL, and Scopus) from 2000 onwards, to identify published quality improvement initiatives in the human healthcare setting intended to address ecological impact with at least one quantitative measure of ecological impact, such as kilograms of carbon dioxide equivalent greenhouse gas. Two independent reviewers working in parallel will screen studies for inclusion and abstract study data, including publication, study, and ecological impact characteristics. Charted data will be synthesized narratively as well as with descriptive tables, figures, and summary statistics. In doing so, we will map areas of relative focus as well as gaps in the measurement of ecological impact across quality improvement initiatives. This map can in turn be used to raise awareness of ecological impacts requiring broader consideration, encouraging holistic and clinically relevant approaches to measuring ecological impact in future quality improvement work.

"Visualization matters" - stereoscopic visualization of 3D graphic neuroanatomic models through AnaVu enhances basic recall and radiologic anatomy learning when compared with monoscopy

BACKGROUND

The authors had previously developed AnaVu, a low-resource 3D visualization tool for stereoscopic/monoscopic projection of 3D models generated from pre-segmented MRI neuroimaging data. However, its utility in neuroanatomical education compared to conventional methods (specifically whether the stereoscopic or monoscopic mode is more effective) is still unclear.

METHODS

A three-limb randomized controlled trial was designed. A sample (n = 152) from the 2022 cohort of MBBS students at Government Medical College, Thiruvananthapuram (GMCT), was randomly selected from those who gave informed consent. After a one-hour introductory lecture on brainstem anatomy and a dissection session, students were randomized to three groups (S - Stereo; M - Mono and C - Control). S was given a 20-min demonstration on the brainstem lesson module in AnaVu in stereoscopic mode. M was given the same demonstration, but in monoscopic mode. The C group was taught using white-board drawn diagrams. Pre-intervention and post-intervention tests for four domains (basic recall, analytical, radiological anatomy and diagram-based questions) were conducted before and after the intervention. Cognitive loads were measured using a pre-validated tool. The groups were then swapped -S→ M, M →S and C→S, and they were asked to compare the modes.

RESULTS

For basic recall questions, there was a statistically significant increase in the pre/post-intervention score difference of the S group when compared to the M group [p = 0.03; post hoc analysis, Bonferroni corrections applied] and the C group [p = 0.001; ANOVA test; post hoc analysis, Bonferroni corrections applied]. For radiological anatomy questions, the difference was significantly higher for S compared to C [p < 0.001; ANOVA test; post hoc analysis, Bonferroni corrections applied]. Cognitive load scores showed increased mean germane load for S (33.28 ± 5.35) and M (32.80 ± 7.91) compared with C (28.18 ± 8.17). Subjective feedbacks showed general advantage for S and M compared to C. Out of the S and M swap cohorts, 79/102 preferred S, 13/102 preferred M, and 6/102 preferred both.

CONCLUSIONS

AnaVu tool seems to be effective for learning neuroanatomy. The specific advantage seen when taught with stereoscopy in basic recall and radiological anatomy learning shows the importance of how visualization mode influences neuroanatomy learning. Since both S and M are preferred in subjective feedbacks, these results have implications in choosing methods (stereoscopic - needs 3D projectors; monoscopic - needs web based or hand-held devices) to scale AnaVu for anatomy teaching in medical colleges in India. Since stereoscopic projection is technically novel and cost considerations are slightly higher compared to monoscopic projection, the specific advantages and disadvantages of each are relevant in the Indian medical education scenario.

Patient safety during transfers from critical care: developing and assessing a checklist

Critically ill patients often need to be transferred from the intensive care unit (ICU) to the imaging department. This can compromise their safety, not only because of the inherent risk of deterioration but also because of the potential for incidents due to the inadequate preparation of medicines, equipment and monitoring. Using a patient transfer checklist can reduce the risk of human factor errors. This article reports on a quality improvement project conducted at the ICU of an acute hospital trust in the Midlands to develop and evaluate a patient transfer checklist. The checklist was developed based on guidance from the Intensive Care Society and evaluated using retrospective incident reports, an audit of staff compliance and a user survey. Using a transfer checklist in the ICU is likely to reduce patient safety incidents during transfers, but a shift in workplace culture may be needed to enhance incident reporting.

The AI Revolution in Glaucoma: Bridging Challenges with Opportunities

Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

Accuracy of surgical death information: comparison of data held by Victorian Audit of Surgical Mortality and Victorian Agency for Health Information

BACKGROUND

The Victorian Audit of Surgical Mortality (VASM) investigates all surgically related deaths in Victoria. The Victorian Admitted Episodes Database (VAED) is an administrative dataset maintained by the Health Department's Victorian Agency of Health Information (VAHI). We have reviewed clinical records to assess the correlation between deaths reported to the databases.

METHODS

Data July 2019 to June 2020 were compared. All hospitals that had surgical deaths not reported to VASM were asked to provide clinical summaries which were analysed to assess the deaths should have been reported. Case note review of 280 deaths only reported to VAHI through VAED was undertaken to establish whether these should have been reported to VASM.

RESULTS

VASM received 1763 reported surgical deaths with VAHI recording 1907. Using individual patient identifiers, 517 (29.3%) of the VASM deaths were not reported to VAHI but subsequent data analysis revealed they were correctly identified as reportable deaths in 385 (74.5%). There were 914 (47.9%) VAED recorded deaths that were not reported to VASM. 280/914 (30.6%) were reviewed from 21 hospitals. Only 24 (8.6%) of these had a surgical procedure in the patient's final admission and should have been reported. This suggests that the current method of VASM capture of surgical mortality only missed 4% cases for the 2019/20 year.

CONCLUSION

There were major differences between surgical deaths reported to VASM and those reported by health services and recorded in VAED. Less than 5% of cases are not reported to VASM and thus not subjected to peer review.

Role and contribution of the nurse in caring for patients with palliative care needs: A scoping review

BACKGROUND

The provision of high-quality palliative care is important to nursing practice. However, caring for palliative care patients and their families is challenging within a complex everchanging health environment. Nonetheless the caring, artistic role of the nurse is fundamental to the care of the patient and family. However, this role is currently being overshadowed by the technical and scientific elements of nursing.

METHODS

A scoping review was conducted utilising Arksey and O'Malley's framework to identify the role and contribution of nurses in caring for patients with palliative care needs. An open time period search of eight electronic databases (MEDLINE, CINAHL, Academic Search Complete, PsycINFO, EMBASE, Web of Science, Scopus and Cochrane Library) was conducted on the 8th of March 2023 and updated on the 30th of April 2024. Screening was performed independently by two reviewers against eligibility criteria with meetings between authors to discuss included papers and form a consensus. Data was extracted relating to palliative care nursing, methodology, key findings, and recommendations. The analysed and summarised data was mapped onto Oldland et al seven domains framework: (a) medical/nursing and technical competence, (b) person centred care, (c) positive interpersonal behaviours, (d) clinical leadership and governance, (e) promotion of safety, (f) management of the environment, and (g) evidence-based practice.

RESULTS

Fifty-five papers met the criteria for this review which describes the role and contribution of nurses in caring for palliative patients across all domains of professional practice. The review found the leading areas of nurse contribution were person centred, interpersonal and nursing care aspects, with leadership, managing the environment, patient safety and evidence-based practice evident but scoring lower. The contribution of the nurse in palliative care supports a biopsychosocial-educational approach to addressing the physical, emotional and social needs of patients with palliative care needs and their families across the care continuum.

CONCLUSION

Nurses in palliative care engage in a wide range of roles and responsibilities in caring for patients and their families with palliative care needs. However, there remains minimal evidence on the assessment, intervention, and evaluation strategies used by nurses to highlight the importance of their role in caring for patients and their families in this area. The findings of this review suggest that the artistic element of nursing care is being diluted and further research with a focus on evidencing the professional competence and artistic role of the nurse in the provision of palliative care is required. In addition, research is recommended that will highlight the impact of this care on patient and family care outcomes and experiences.

ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: A comparative pilot study

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes.

Assessing the Readability, Reliability, and Quality of AI-Modified and Generated Patient Education Materials for Endoscopic Skull Base Surgery

BACKGROUND

Despite National Institutes of Health and American Medical Association recommendations to publish online patient education materials at or below sixth-grade literacy, those pertaining to endoscopic skull base surgery (ESBS) have lacked readability and quality. ChatGPT is an artificial intelligence (AI) system capable of synthesizing vast internet data to generate responses to user queries but its utility in improving patient education materials has not been explored.

OBJECTIVE

To examine the current state of readability and quality of online patient education materials and determined the utility of ChatGPT for improving articles and generating patient education materials.

METHODS

An article search was performed utilizing 10 different search terms related to ESBS. The ten least readable existing patient-facing articles were modified with ChatGPT and iterative queries were used to generate an article de novo. The Flesch Reading Ease (FRE) and related metrics measured overall readability and content literacy level, while DISCERN assessed article reliability and quality.

RESULTS

Sixty-six articles were located. ChatGPT improved FRE readability of the 10 least readable online articles (19.7 ± 4.4 vs. 56.9 ± 5.9, p < 0.001), from university to 10th grade level. The generated article was more readable than 48.5% of articles (38.9 vs. 39.4 ± 12.4) and higher quality than 94% (51.0 vs. 37.6 ± 6.1). 56.7% of the online articles had "poor" quality.

CONCLUSIONS

ChatGPT improves the readability of articles, though most still remain above the recommended literacy level for patient education materials. With iterative queries, ChatGPT can generate more reliable and higher quality patient education materials compared to most existing online articles and can be tailored to match readability of average online articles.

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings, detected by our pipeline, provide valuable insights into these diseases.

IMPORTANCE

Assessing disease similarity is an essential initial step preceding a disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual diseases to understand their unique characteristics, which by design excludes comorbidities in individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilizes both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.

Wider health needs in attention deficit hyperactivity disorder from lived and professional experience: a qualitative framework analysis

OBJECTIVES

This study aimed to explore the perspectives of people with attention deficit hyperactivity disorder (ADHD), their supporters and primary care professionals (PCPs), on the wider physical and mental health needs of people with ADHD and the support currently available.

DESIGN

Qualitative semi-structured interviews, analysed using reflexive thematic analysis.

SETTING

Five general practice surgeries across England.

PARTICIPANTS

Participants with lived experience (people with ADHD and their supporters (n=11)) and PCPs (n=9) (eg, general practitioners and practice managers), recruited via clinical academic networks and previous work packages of this study.

RESULTS

We generated three major themes in relation to ADHD, using reflexive thematic analysis: understanding health, barriers to health and addressing health. Within these, participants reflected on mental and physical health challenges, as well as wider social difficulties and variability in support offered/accessed.

CONCLUSIONS

This study highlights that health problems in ADHD are complex and rooted both in individual factors (eg, mental health) and social factors (eg, support). This study also highlights the differences in expectations and fulfilment of healthcare.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.