Field Of Health Care Research Articles

Validation of a didactic model evaluating the usability, usefulness and acceptability of psychological first aid teaching through simulation

PurposeTo evaluate the effectiveness of simulation as a means to teach Psychological First Aid in Operations (PFA-O). We propose to use a model built on research in didactics to assess the factors which may or may not encourage trainers to adopt and embrace this teaching technique.Material and methodWe submitted a questionnaire to all PFA-O trainers working in France, comprising 15 criteria divided into three dimensions: usability, usefulness and acceptability. Correlation and regression analyses were applied to assess the links between these criteria, teacher motivation, and actual implementation of training courses. We also wanted to test the relevance of these criteria to predict motivation in a multiple linear regression model.Results59 trainers, i.e. 52.68% of the national workforce, responded to the questionnaire. Correlation analysis showed good model consistency, with significant correlations between the 3 dimensions under evaluation (usability, usefulness and acceptability), between these dimensions and teacher motivation, and a strong correlation between teacher motivation and the number of introductory training sessions undertaken each year. The final model incorporating the 15 criteria showed an R2 coefficient of determination of 0.71 (i.e. 71%, F = 4.91, p < 0.001).ConclusionThis didactic model is relevant to assessing the motivational factors which contribute to trainers’ adherence to medical simulation and provides a methodological basis to evaluate the ergonomics of an educational tool in the field of health care. These findings suggest its applicability to a wider range of educational contexts beyond the military.

Development and research status of intelligent ophthalmology in China.

This paper analyzes the current status, technological developments, academic exchange platforms, and future challenges and solutions in the field of intelligent ophthalmology (IO) in China. In terms of technology, significant progress has been made in various areas, including diabetic retinopathy, fundus image analysis, quality assessment of medical artificial intelligence products, clinical research methods, technical evaluation, and industry standards. Researchers continually enhance the safety and standardization of IO technology by formulating a series of clinical application guidelines and standards. The establishment of domestic and international academic exchange platforms provides extensive collaboration opportunities for professionals in various fields, and various academic journals serve as publication platforms for IO research. However, challenges such as technological innovation, data privacy and security, lagging regulations, and talent shortages still pose obstacles to future development. To address these issues, future efforts should focus on strengthening technological research and development, regulatory framework construction, talent cultivation, and increasing patient awareness and acceptance of new technologies. By comprehensively addressing these challenges, IO in China is poised to further lead the industry's development on a global scale, bringing more innovation and convenience to the field of ophthalmic healthcare.

THE PUBLIC ADMINISTRATION OF INFORMATION TECHNOLOGIES IN HEALTH CARE FIELD UNDER THE CONDITIONS OF MARTIAL STATE

In the conditions of martial state, the need for fast and accurate processing of large volumes of information, especially in the field of health care, is increasing. This is due to a lack of resources, an increase in the number of victims of military operations, displaced persons, the complication of access to medical services in connection with hostilities, the loss of individual specialists and information arrays, as well as increased risks for information security and the entire process of public management of information technologies in the health care field. The public administration of information technologies in health care is becoming critical to ensure the stability of healthcare services, support communications between healthcare facilities and government agencies, and effectively process and protect healthcare data. The analysis of scientific works on the issue of the public administration of information technologies in health care under the conditions of martial state is insufficiently researched. The article highlights the main challenges of the industry related to the war: ensuring the continuity of medical services, the appropriateness of public administration decision-making in conditions of limited access to open information, problems with logistics, and stable power supply, information protection and cyber security, shortage of qualified personnel; adopted management decisions, and information technology tools, which were used by authorities at the state and regional levels to respond to new challenges, were investigated. In the article, we are offered some recommendations for the further development and management of information technologies in the healthcare field. The main ones are further measures to strengthen cybersecurity, namely the widespread implementation of the Digital Competency Framework for Healthcare Workers in various areas, the implementation of modern information protection systems, the implementation of an information security policy, measures to legalize and update software, deepening the integration of mHealth into the existing eHealth system to enable rapid and remote collection of medical information and the introduction of artificial intelligence elements into healthcare management processes.

Research of the role of charitable foundations in the provision of medicines to the population

Introduction. Despite the positive developments in providing patients with orphan diseases, achieved through the implementation of a set of federal and regional programs, it has not yet been possible to achieve full and timely satisfaction of the target segment's need for medicines, and patients are forced to look for additional sources of funding for drug therapy, one of which is the budgets of charitable foundations. Objective: research the role and place of charitable foundations in the system of drug provision for patients with orphan diseases. Material and methods. Сontent analysis of legislative and regulatory acts of the Russian Federation in the field of healthcare and circulation of medicines, open data from the official website of the All-Russian Society of Orphan Diseases, statistical data of the Ministry of Health of the Russian Federation, open official reports of the Circle of Good Foundation and official websites of 57 charitable foundations. Results and discussion. The elaboration of mechanisms for including charitable foundations in the list of organizations that have the right to work with narcotic drugs and psychotropic substances in exceptional cases in terms of patients with orphan diseases seems to be another way to improve the quality of drug provision for privileged categories of patients. Conclusion. The elaboration of mechanisms for including charitable foundations in the list of organizations that have the right to work with narcotic drugs and psychotropic substances in exceptional cases in terms of patients with orphan diseases seems to be another way to improve the quality of drug provision for privileged categories of patients.

Public administration bodies in the sphere of health protection of Ukraine

Purpose. The purpose of the work is to study the current state of the activities of state bodies, which, according to their functional powers, are called to ensure the fulfillment of state tasks in the field of health care of Ukraine. Methodology. The methodology includes a comprehensive analysis and generalization of the available scientific and theoretical material and the formulation of relevant conclusions and recommendations. During the research, the following methods of scientific knowledge were used: logical-semantic, comparative-legal, system-structural and statistical, analysis and synthesis. The results. In the process of research, the system of public administration bodies in the sphere of health care of Ukraine is outlined. The author emphasizes that among a significant number of public and private law bodies which are designed to ensure the realization of the right of citizens to healthcare in Ukraine, the key subjects of administrative and legal regulation of relations in the healthcare sector are executive authorities and their officials. Without diminishing the role of private law entities, it is worth emphasizing that it is the executive authorities, as public law entities, which are entrusted with the main tasks of ensuring and practical implementation of administrative and legal regulation of the citizen’s right to healthcare. The author emphasizes that a significant number of subjects of administrative law can be attributed to public administration bodies in the field of healthcare in Ukraine. The author emphasizes that in the doctrine of administrative law, in addition to the category of “public administration”, such categories as “public management” and “public administration” are used, and there are also different points of view regarding their definition and correlation. The author analyzes the scientific opinions on the category of “public administration” and the activities of public administration entities in the healthcare sector. Bodies of special sectoral competence have been established, as well as the fact that the State Service of Ukraine for Food Safety and Consumer Protection is an interdisciplinary subject of public administration in the field of health care. Practical significance. The results of the study can be used in the scientific and research field – as a basis for further research related to the improvement of the activities of public administration bodies in the field of health care, in law enforcement activities – to improve the practical activities of subjects ensuring the implementation of the right to health care.

The role of AI-enabled health systems in the Jordanian context: the case ofdigital contact tracing apps.

Digital health or digital healthcare, is a broad, multidisciplinary concept that includes concepts from an intersection between technology and healthcare. Digital health applies digital transformation to the healthcare field, incorporating software, hardware and services. The usefulness of digital health technologies such as digital contact tracing apps (CTAs) depends deeply on the acceptance to use them. Accordingly, the main purpose of this study is to explore the usage of CTAs by integrating the protection motivation theory (PMT) and technology acceptance model (TAM) in the Jordanian context. Partial least squares structural equation modelling was employed to test empirically the integrated research model against data collected from 420 respondents, leading to the identification of the important determinants for the usage of CTAs. The results reveal that perceived severity and perceived vulnerability have a significant positive impact on the perceived need for CTAs, which, in turn, affects the individuals' intention to use CTAs. It also reveals that perceived trust in government, perceived privacy and perceived usefulness have a significant positive impact on individuals' intention to use CTAs, which, in turn, affects the usage of CTAs. In contrast, perceived ease of use did not find any significant relationship, and hence hypothesis H7 was not supported. Lastly, social media awareness has a significant positive impact on perceived usefulness and ease of use. This study is considered among the first studies in the Jordanian context that integrate the PMT and TAM to gain a better understanding of the critical determinants for the acceptance and usage of CTAs in the context of pandemics.

Interest in healthcare work post-pandemic: A study on social work students’ perspectives

ABSTRACT This study explored social work students’ attitudes toward healthcare responsibilities after the Coronavirus Disease 2019 (COVID-19) pandemic. Surveying n = 184 third and fourth-year Social Work students revealed a generally negative attitude toward working in healthcare. Factors such as age, marital status, academic level, personal or familial virus infection history, COVID-19 vaccination status, presence of chronic illness, or experiencing a COVID-19-related death did not significantly influence these attitudes. The challenges faced in healthcare during the pandemic, including psychological and physiological impacts, underscore the importance of equipping Social Work students with the necessary knowledge and skills for future healthcare roles. This research suggests that addressing students’ concerns and competencies in Social Work curricula may positively influence their future healthcare field engagement.

Voluntary Body Donation-Trends, People’s Perceptions, Beliefs and Practices: A Review

The Development of basic sciences in health care field depends on a strong foundation of Anatomy. Cadavers are required for training of students of health professionals. The source of these cadavers, in earlier days, were executed criminals, unclaimed bodies and voluntary body donations. Though the most ethical way of procuring cadavers for education and research purposes is body donation, the incidence of body donations varies across the world. The reasons could be different from person to person and from region to region. Factors that influence this opinion has been shown to be spiritual, religious and cultural/traditional beliefs. Other factors which might influence the willingness to donate are age, gender, education, occupation, economy and law of the country. Spirituality with the sense of detachment and altruistic characteristics promote body donation. Age, males, education and awareness of body donation, occupation and economy also affect the intent to body donations. The health care community who understand the benefits of body donation are mostly not willing to donate because of personalized opinions of being dissected by colleagues, disrespectful behavior towards cadavers. The law of the country which encourages body donations try to address the fears and concerns of the common man to facilitate body donation. This could be in the form abiding to cremate as per his will after the utilisation of the body, written consent for the same and prioritizing his wish over others, providing due respect to the deceased in the form of memorial services, religious ceremonies and public display and honour of the donors KEYWORDS: Voluntary Body Donation, Perceptions, Beliefs, Good Cadaver Practices.

Exploring How Older Adults Experience semAPP, a 360° Media-Based Tool for Memory Assessment: Qualitative Study.

Technology is already a part of our daily lives, and its influence is growing rapidly. This evolution has not spared the health care field. Nowadays, a crucial challenge is considering aspects such as design, development, and implementation, highlighting their functionality, ease of use, compatibility, performance, and safety when a new technological tool is developed. As noted in many works, the abandonment rate is usually higher when a user has a terrible experience with these instruments. It would be appropriate to incorporate the final users-whether they are patients, health care professionals, or both-in the stages of instrument design to understand their needs and preferences. Since most apps that fail did not include end users and health care professionals in the development phase, their involvement at all stages of app development may increase their commitment and improve integration, self-management, and health outcomes. This study aims (1) to develop semAPP (spatial and episodic memory assessment application), a 360° media-based tool, to assess memory in aging by simulating a real-life situation and (2) to test the usability of the app and the connected experience in an end-user population. A total of 34 older adults participated in the study: 16 (47%) healthy individuals and 18 (53%) patients with mild cognitive impairment. They used semAPP and completed qualitative and quantitative measures. The app includes 2 parts: object recognition and spatial memory tasks. During the first task, users have to navigate in an apartment freely and visit rooms, and then they must recognize the right map of the house. In the second task, users are immersed in a living room, and they have to encode and then recall some target objects, simulating a relocation. We deployed this app on an 11.2-inch iPad, and we tested its usability and the experience of users interacting with the app. We conducted descriptive analyses for both the entire sample and each subgroup; we also conducted parametric and correlation analyses to compare groups and to examine the relationship between task execution and the virtual experience, as well as the acceptance of technology. Both groups judged the app as an easy-to-use tool, and they were willing to use it. Moreover, the results match the idea that usability might be influenced by different factors depending on instrument and personal features, such as presentation, functionality, system performance, interactive behavior, attitudes, skills, and personality. The findings support the possibility of using semAPP in older patients, as well as the importance of designing and evaluating new technological tools, considering not only the general population but also the specific target ones.

Smart Healthcare Liver Segmentation Image Analysis Using AI

With an emphasis on liver segmentation in medical pictures, this research investigates the use of artificial intelligence (AI) in the field of smart healthcare. In medical image analysis, liver segmentation is a crucial task, especially for liver disease diagnosis, surgical planning, and therapy monitoring. While AI-powered approaches, particularly those that make use of deep learning, offer the potential for increases in accuracy and efficiency, traditional image analysis methods are frequently slow and prone to errors. We provide a summary of the state-of-the-art techniques, difficulties, and developments in AI-based liver segmentation, with a focus on deep learning frameworks like transfer learning and convolutional neural networks (CNNs). The goal is to demonstrate how AI has the ability to transform liver disease detection and therapy by providing intelligent medical solutions. With an emphasis on diseases including pneumonia, lung cancer, and fractures, the suggested method uses Convolutional Neural Networks (CNNs) to precisely identify anomalies and patterns. The technology improves diagnostic accuracy, decreases human error, and expedites clinical workflows by automating the diagnostic process. Furthermore, the system can get better over time by adjusting to new datasets and medical procedures because to the incorporation of continuous learning techniques. By facilitating prompt intervention and increasing diagnosis speed and accuracy, AI-based liver segmentation holds the potential to revolutionize medical practice. When handling complex liver pictures, deep learning models particularly those that make use of transfer learning and 3D segmentation networks have demonstrated greater performance, frequently attaining higher accuracy than conventional not withstanding these difficulties, recent techniques. However, addressing the inherent diversity in liver pictures resulting from variations in patient anatomy, disease development, and imaging modality (CT, MRI, Ultrasound) presents a difficulty. Developments have shown that AI models can effectively generalize to various patient populations and imaging situations when trained on sizable and varied datasets.

Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging

This article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural Language Processing (NLP) and Machine Learning (ML) in the healthcare field, specifically in relation to Electronic Medical Records (EMRs). The review also examines the incorporation of Natural Language Processing (NLP) and Machine Learning (ML) in medical imaging as a supplementary field, emphasising the transformative impact of these technologies on the analysis of healthcare data and patient care. This review attempts to analyse both fields in order to offer insights into the current state of research and suggest potential chances for future advancements. The focus is on the use of these technologies in Electronic Medical Records (EMRs) and medical imaging. The review methodically detects, chooses, and assesses literature published between 2015 and 2023, utilizing keywords pertaining to natural language processing (NLP) and healthcare in databases such as SCOPUS. After applying precise inclusion criteria, 100 papers were thoroughly examined. The paper emphasizes notable progress in utilizing NLP and ML methodologies to improve healthcare decision-making, extract information from unorganized data, and evaluate medical pictures. The key findings highlight the successful combination of natural language processing (NLP) and image processing to enhance the accuracy of diagnoses and improve patient care. The study also demonstrates the effectiveness of deep learning-based NLP pipelines in extracting valuable information from electronic medical records (EMRs). Additionally, the research suggests that NLP has the potential to optimize the allocation of medical imaging resources. The identified gaps encompass the necessity for scalable and practical implementations, improved interdisciplinary collaboration, the consideration of ethical factors, the analysis of longitudinal patient data, and the customization of approaches for specific medical situations. Subsequent investigations should focus on these deficiencies in order to fully exploit the capabilities of natural language processing (NLP) and machine learning (ML) in the healthcare sector, consequently enhancing patient outcomes and the delivery of healthcare services.

Strategizing AI in Healthcare: A Multidimensional Blueprint for Transformative Decision-Making in Clinical Settings

Abstract The integration of Artificial Intelligence (AI) into healthcare represents a transformative shift, offering opportunities for enhancing patient care, diagnostic accuracy, process optimization and treatment pathways. This research sets out to forge a strategic management decision support framework for leveraging AI within the healthcare sector, aimed at systematically exploring and integrating AI innovations to bolster the patient health outcomes. By creating a comprehensive categorization system, we attempt to navigate the complex array of possible AI applications within the field of healthcare, hence enabling the identification, selection, and advancement of AIdriven initiatives. Through a blend of systematic literature review and expert insights, this study maps possible AI applications across dimensions like ‘medical disciplines’, ‘healthcare processes’, ‘AI research areas’, and ‘user groups’. By reflecting the diverse perspectives, this system transcends mere classification and stands as a cornerstone for identifying, selecting, and developing AI-driven medical use cases to guide strategic implementations of AI within clinical settings. This multidimensional system offers a blueprint for healthcare entities to strategically navigate the AI landscape, enabling them to make informed decisions about technology adoption and change management processes, ultimately leading to improved patient care and operational efficiency.

AN EXAMINATION OF THE IMPLEMENTATION OF INTERNET OF THINGS IN HEALTHCARE UTILIZING SMARTWATCHES

Background: Smartwatches can use sensors to collect and send data to medical teams and family members through the platform of the Internet of things (IoT). The data are first analysed on the platform and the final results are used by the medical team. Aims: This paper reviews and categorises studies conducted in the field of the Internet of things based on smartwatches. Methods: The covered papers have been published over 13 years from 2010 to 2022. The search yielded 227 papers out of which 43 papers were reviewed after screening. The search keywords were “wearables, internet of things, smartwatches, smart bracelet, healthcare, and disease”. The search covered databases including PubMed, ScienceDirect, and IEEE. Results: Smartwatches are used in three fields of healthcare, including palliative care, speech therapy, diagnosis, disease prevention, rehabilitation, and health improvement. Conclusion: Smartwatches are not free of drawbacks and have not received the attention they deserve in the healthcare field. Given the potential of smartwatches, they can be useful in the health sector. Keywords: Disease, IOT, Smartwatch, Wearable

Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence.

Cardiovascular disease, which remains one of the main causes of death, can be prevented by early diagnosis of heart sounds. Certain noisy signals, known as murmurs, may be present in heart sounds. On auscultation, the degree of murmur is closely related to the patient's clinical condition. Computer-aided decision-making systems can help doctors to detect murmurs and make faster decisions. The Mel spectrograms were generated from raw phonocardiograms and then presented to the OpenL3 network for transfer learning. In this way, the signals were classified to predict the presence or absence of murmurs and their level of severity. Pitch level (healthy, low, medium, high) and Levine scale (healthy, soft, loud) were used. The results obtained without prior segmentation are very impressive. The model used was then interpreted using an Explainable Artificial Intelligence (XAI) method, Occlusion Sensitivity. This approach shows that XAI methods are necessary to know the features used internally by the artificial neural network then to explain the automatic decision taken by the model. The averaged image of the occlusion sensitivity maps can give us either an overview or a precise detail per pixel of the features used. In the field of healthcare, particularly cardiology, for rapid diagnostic and preventive purposes, this work could provide more detail on the important features of the phonocardiogram.

Measurement Guidance in Diffusion Models: Insight from Medical Image Synthesis.

In the field of healthcare, the acquisition of sample is usually restricted by multiple considerations, including cost, labor- intensive annotation, privacy concerns, and radiation hazards, therefore, synthesizing images-of-interest is an important tool to data augmentation. Diffusion models have recently attained state-of-the-art results in various synthesis tasks, and embedding energy functions has been proved that can effectively guide the pre-trained model to synthesize target samples. However, we notice that current method development and validation are still limited to improving indicators, such as Fréchet Inception Distance score (FID) and Inception Score (IS), and have not provided deeper investigations on downstream tasks, like disease grading and diagnosis. Moreover, existing classifier guidance which can be regarded as a special case of energy function can only has a singular effect on altering the distribution of the synthetic dataset. This may contribute to in-distribution synthetic sample that has limited help to downstream model optimization. All these limitations remind that we still have a long way to go to achieve controllable generation. In this work, we first conducted an analysis on previous guidance as well as its contributions on further applications from the perspective of data distribution. To synthesize samples which can help downstream applications, we then introduce uncertainty guidance in each sampling step and design an uncertainty-guided diffusion models. Extensive experiments on four medical datasets, with ten classic networks trained on the augmented sample sets provided a comprehensive evaluation on the practical contributions of our methodology. Furthermore, we provide a theoretical guarantee for general gradient guidance in diffusion models, which would benefit future research on investigating other forms of measurement guidance for specific generative tasks.

Exploring the potential of Tb and Bi-doped LaF3 as a UV absorber

Lanthanide-based nanophosphors are finding significant applicability in various fields such as LED, solar spectral convertors, lasers, and biological sensors, owing to their superior stability and luminescence properties. However, their function as a UV protector has not evolved, possibly because of their limited absorption cross-section in the UV spectral region. Hence, to overcome this limitation, in this work, a strategy to utilize Bi3+ ion as a co-dopant in LaF3:Tb3+ nanophosphor has been devised. These nanophosphors with an uniform morphology and narrow particle size distribution were synthesized using the hydrothermal method. Luminescence mechanism involving energy transfer from Bi3+ to Tb3+ was investigated. The excitation spectrum of a LaF3 nanophosphor, co-doped with Bi and Tb, reveals distinct absorption bands. Absorption ascribed to Bi3+ characterizes the UV range between 220 and 275 nm, whereas Tb3+ is associated with absorption in another UV range spanning 280 to 380 nm. This shows the potential applicability of the Bi and Tb co-doped LaF3 nanophosphors as a UV absorber. The cytotoxicity of these nanophosphors was tested on HaCaT cells, indicating their potential applicability in the healthcare field.

"All of us are capable, and all of us can be scientists." The impact of Scientist Spotlight assignments with undergraduates in physiology courses.

To advance ongoing efforts to diversify the healthcare field and promote inclusion in physiology education, the present study investigates the potential for an evidence-based intervention, Scientist Spotlight assignments, to highlight counterstereotypical representations of scientists in the context of majors and nonmajors physiology courses. Undergraduate students at an emerging Hispanic serving R1 institution completed six Scientist Spotlights assignments in their physiology courses. We conducted semistructured interviews and disseminated an established pre- and postsurvey protocol at the beginning and end of the courses. Our findings from interviews with 31 students from a range of marginalized backgrounds revealed that 1) the biographical information about counterstereotypical scientists deeply resonated with students by humanizing science, 2) the instructor's implementation of the assignments made a meaningful difference in their feelings of inclusion, and 3) the assignments supported students' beliefs about their content learning and understanding of physiological concepts. The results from the survey showed that regardless of being in a major (n = 159) or nonmajor (n = 117) course, students from a range of demographic groups can and do significantly shift in their relatability to and descriptions of scientists. We highlight implications for inclusive curricula like Scientist Spotlights for addressing the issue of representation in physiology textbooks, curriculum, and healthcare fields at large.NEW & NOTEWORTHY Scientist Spotlights significantly enhance undergraduate students' feelings of inclusion and learning in undergraduate physiology courses at an emerging Hispanic-serving institution. By engaging with assignments featuring counterstereotypical scientists, students in both majors and nonmajors physiology courses shifted in their relatability to and descriptions of scientists. These results suggest that an inclusive curriculum, combined with supportive instructor practices, can positively impact student success and representation in physiology education.

Comparison of perceptions of the dental education environment among students from national dental schools in South Korea, Laos, and Vietnam

Purpose: This study seeks to analyze the educational environments of the national dental schools in South Korea, Laos, and Vietnam to develop targeted educational support strategies for Laos and Vietnam.Materials and methods: The survey, using the DREEM specific to the healthcare field, was conducted online, with 97, 109, 215 dental students from a single national university in South Korea (SKU), Laos (LU), and Vietnam (VU), respectively. The data allowed us to compare students’ perceptions of the educational environment across the three institutions and analyze differences by gender, academic achievement, and year of study.Results: VU, LU, and SKU reported scores of 133.47, 129.66, and 104.23, respectively, all within the "more positive than negative" range (101–150 points). SKU scored lowest in the category of "students’ perception of learning" (mean score of 1.82), while VU and LU had the lowest scores in category of "students’ perceptions of atmosphere" (mean scores of 2.49 and 2.57, respectively) and "students’ social self-perceptions" (mean scores of 2.49 and 2.52, respectively). In contrast to SKU, VU and LU showed no differences between achievement groups, with male students scoring lower than female students in some DREEM categories.Conclusion: We expect that analyzing the educational environment from the beneficiaries’ perspective and developing strategies based on this analysis will provide practical support to dental schools in Laos and Vietnam.

Decision-making tree to support sustainable waste management in optometric clinical practice and healthcare training programmes

ABSTRACT Clinical relevance Waste management strategies are well documented for employee safety, legal compliance and infection control measures; however, appropriate waste disposal is equally important to minimise adverse environmental impacts. Knowledge on best practices related to the disposal of healthcare-generated waste is crucial for environmental sustainability. Background Global healthcare contributes significantly to greenhouse gas emissions, thereby extending the pervasiveness of climate change. Urgent action is required by the healthcare sector to minimise these impacts. Proper waste management is an alleviation measure that can be easily implemented in healthcare practice. The aim of this study was to calculate the approximate mass of plastic waste generated through lens wear and develop an easy-to-use decision-making tree to support optometrists with sustainable waste management. Methods Data were analysed, from an online survey directed to ophthalmic lens wearers, to determine the mass of annual plastic waste associated with lens wear. Information collated from published studies and clinical experience of the researchers, supplemented by a literature search from online databases, EBSCOhost, Google Scholar, ScienceDirect and PubMed, were used to develop the waste decision-making tree. Results Approximately 48 kg of plastic waste was generated from annual lens wear, of which approximately 83% of recyclable materials were landfilled. The waste management decision-making tree was created to guide optometrists regarding sustainable waste management practices. Conclusion Although this study focused on Optometry, its findings and recommendations have applicability across the healthcare field. Environmental stewardship is a joint responsibility of all healthcare professionals. This study highlighted the importance of incorporating and promoting sustainable clinical measures as well as the embedding and regular updating of clinical evidence on waste management best practices into healthcare training programmes. In doing so, the broader healthcare sector can contribute significantly towards reducing healthcare waste and associated emissions and improving health outcomes, thereby safeguarding the planet and its inhabitants.

Reporting Guidelines for the Early-Phase Clinical Evaluation of Applications Using Extended Reality: RATE-XR Qualitative Study Guideline.

Extended reality (XR), encompassing technologies such as virtual reality, augmented reality, and mixed reality, has rapidly gained prominence in health care. However, existing XR research often lacks rigor, proper controls, and standardization. To address this and to enhance the transparency and quality of reporting in early-phase clinical evaluations of XR applications, we present the "Reporting for the early-phase clinical evaluation of applications using extended reality" (RATE-XR) guideline. We conducted a 2-round modified Delphi process involving experts from diverse stakeholder categories, and the RATE-XR is therefore the result of a consensus-based, multistakeholder effort. The guideline comprises 17 XR-specific (composed of 18 subitems) and 14 generic reporting items, each with a complementary Explanation & Elaboration section. The items encompass critical aspects of XR research, from clinical utility and safety to human factors and ethics. By offering a comprehensive checklist for reporting, the RATE-XR guideline facilitates robust assessment and replication of early-stage clinical XR studies. It underscores the need for transparency, patient-centeredness, and balanced evaluation of the applications of XR in health care. By providing an actionable checklist of minimal reporting items, this guideline will facilitate the responsible development and integration of XR technologies into health care and related fields.