Aid Of Tools Research Articles

Substantial non-compliance of online pharmacy catalogues with guidelines.

Online pharmacies are popular worldwide. In Germany, there were 150 active online pharmacies in 2022. As the competition is therefore fierce and new customers need to be acquired, some of these online pharmacies create catalogues that advertise products in self-medication to attract the interest of potential customers. This study analyzed the allergy, cold, and gastrointestinal tract categories from the summer/spring 2023 catalogues of two different online pharmacies in Germany. A comparison of the active ingredients in both catalogues and an analysis of the product's conformity with the German AWMF (Association of the Scientific Medical Societies in Germany) guidelines as well as international guidelines were carried out. In medicine, guidelines serve as evidence-based decision-making aids tools to ensure adequate patient care. The online catalogue of online pharmacy 1 showed conformity of 65% with AWMF guidelines, and the online catalogue of online pharmacy 2 showed conformity of 59% with AWMF guidelines. The online catalogue of online pharmacy 1 showed conformity of 44% with international guidelines, and the online catalogue of online pharmacy 2 showed conformity of just only 17% with international guidelines. Thus, there is a substantial lack of conformity of online pharmacy self-medication catalogues with national and international guidelines. For several indication areas including market-relevant rhinosinusitis and irritable bowel syndrome, consensus between AWMF and international guidelines was poor. The lack of evidence-based presentation of online pharmacy catalogues constitutes a risk for consumer safety. This risk must be assessed in future studies, for example by studies on intoxications caused by self-medication that is non-compliant with guidelines. Our results also underline the necessity of a stricter surveillance of the German self-medication market.

Where to plan shared streets: Development and application of a multicriteria spatial decision support tool

In response to the growing recognition of the vital role played by streets as public spaces in enhancing the vibrancy of urban life, various concepts aiming at creating greener and more inclusive streets have gained popularity in recent years, especially in North America. Shared streets are one example of such concepts that have attracted the attention of citizens and of urban and transportation planning professionals alike. This was the case in the city of Sherbrooke (Quebec, Canada) where, in response to numerous citizens’ requests, a need was identified to develop decision aid tools to help evaluate and rank street segments based on their potential to become shared streets. To achieve this, an action-research project was initiated in which we conducted a socio-technical process based on MACBETH, a multicriteria evaluation method. The project led to the development of a spatial decision support tool, operationally used today by the city professionals. This tool ensures a more informed and transparent decision-making process and supports shared streets planning policy. The methods developed are generalizable and can be adapted to other cities facing similar planning problems.

3.E. Scientific session: Environmental Health Tracking Systems: a tool for planetary health

Abstract Planetary health offers a good framework for better understanding the interconnection between human health and environmental changes, promoting effective cross-sector actions and partnerships, and ensuring policy coherence. Effective implementation of this conceptual framework requires monitoring and reporting on indicators relevant to planetary health that capture the scope, spatial and temporal scales of changes in natural systems that affect human health and well-being. An approach in that sense are the environmental public health tracking systems (EPHT), which imply the ongoing collection, integration, analysis, and interpretation of data at the same spatiotemporal scale about environmental hazards, and related human exposure and health effects, maintaining appropriate data protection measures. It aims to provide public-health decision makers with timely, accurate and systematic data for quantifying health impacts related to environmental factors. EPHT system are proposed as an essential tool for designing and monitoring policies and actions that prevent and reduce environmental health burdens (EHB) efficiently and cost-effectively, and as an alerting system against present and future climatic and health crisis. Pursuing such goals requires the use of health information systems that enable the monitoring and assessing of environmental health in multiple dimensions, considering specificities across different geographical and cultural settings. EPHT system requires defining a rationale on how to select the dimensions and indicators to be included. Common challenges for defining those indicators are related to the availability of data recording, data standardization, and the linkage, integration and sharing of databases. In parallel, it is also important to define a decision aiding tools in which evidence and data are understandable and inform about the extent to which specific policy goals are attained, tailoring such tools to the specific settings. Present session attends to answers to those challenges by sharing experiences from different contexts and countries Key messages • EPHT systems represent an effective tool for progressing in achieving planetary health goals and as a surveillance alerting system for future health crisis. • Adjusting EH data quality recording and analysis in EPHT systems is essential for improving resources assignment, and the effectiveness of healthy policies in reducing EHB.

Prediction Model of Spinal Osteoporosis Using Lumbar Spine X-Ray from Transfer Learning Deep Convolutional Neural Networks

Objective: Osteoporosis is highly prevalent among older adults and women. This condition leads to a deterioration in bone mineral density and microarchitecture, significantly increasing the risk of fractures. Additionally, osteoporosis commonly results in complications such as screw loosening and non-union during spinal surgery. Deep-learning algorithms have now achieved an accuracy comparable to the current human margin of error. Therefore, this study explored the potential of using transfer learning in deep learning algorithms to predict, diagnose, and screen for osteoporosis using commonly obtained sagittal spine X-rays from patients with spinal conditions.Methods: We retrospectively evaluated 2,300 consecutive patients who underwent dual energy X-ray absorptiometry (DXA) and lumbar sagittal plain X-ray exams between 2013 and 2021. The exclusion criteria included: (1) a gap of more than 1 year between the DXA and X-ray exams; (2) vertebrae that had undergone vertebroplasty; (3) lack of spine anterior-posterior DXA; and (4) images that were unassessable. Ultimately, 256 patients (images) were included in the study. Transfer learning was applied using convolutional neural network (CNN) techniques, specifically visual geometry group (VGG) 16, VGG 19, ResNet50, and Xception.Results: The most accurate CNN model in the training group was ResNet50, with an accuracy of 0.95. ResNet50 showed the best performance, with an accuracy of 0.82, precision of 0.80, recall of 0.86, and F1-score of 0.83. Additionally, its area under the curve (0.76) was higher than that other CNN models. The confusion matrix for ResNet50’s performance displayed the outcomes for images predicted as osteoporosis (n=12) among the test data osteoporosis images (n=14)Conclusion: Artificial intelligence (AI) technology employing deep learning techniques is significantly nearing human capabilities in the role of diagnostic assistance. The diagnosis of osteoporosis using bone mineral density is expected to evolve into a comprehensive diagnostic aid or decision-making tool with the integration of AI in the future.

Maximizing the "Functional" in the Functional External Memory Aid Tool for Patient-Centered Treatment Planning in Mild Cognitive Impairment.

The Functional External Memory Aid Tool (FEMAT) is an 11-item performance-based measure that simulates everyday tasks (e.g., medication management) to measure one's use of compensation (e.g., pill organizer). The FEMAT is not a norm-referenced diagnostic tool; rather, it is designed to provide clinicians with information about a patient's function and compensation in a standardized format. To provide further evidence for validity of the FEMAT, the purpose of this study was to conduct a mixed-methods analysis of FEMAT responses to operationalize the types of behaviors elicited during test administration. We adopted an embedded single-case study design to analyze the FEMAT administrations of 12 community-dwelling female participants, ages 73-90 years, who met criteria for mild cognitive impairment. These participants were part of the original validation sample during which they completed the FEMAT (Version 1.0) in a single session with an assessor. Sessions were video-recorded and transcribed. We analyzed the data qualitatively in two phases using theme and subtheme codes and then quantitatively analyzed the data for response trends. Our thematic analysis revealed four themes to operationalize participants' verbal and behavioral FEMAT responses: (a) used an external memory aid (EMA), (b) described using a strategy, (c) discussed memory and comprehension, and (d) discussed functional information. While completing the FEMAT, most participants described using a strategy (85%) and discussed their self-perceived memory and comprehension (33%). Several subthemes also emerged within each theme (e.g., self-perceived memory strengths and barrier). Our analysis suggests that beyond a 4-point item score, the FEMAT elicits behavioral (e.g., used an EMA) and verbal (e.g., discussion of health information) content that may be useful for person-centered treatment planning. This study is part of a comprehensive research agenda establishing the evidence for the validity of the FEMAT to support its use to inform person-centered treatment approaches.

Autism diagnostic aid (ADA) tool for use in adults with intellectual disability: development, face and content validation

Purpose Individuals with intellectual disability have an increased predisposition to other co-morbid physical health conditions, mental illness and neurodevelopmental conditions including autism spectrum condition. There are challenges in diagnosing autism in adults with intellectual disability, and these are often attributes to the complexities in symptoms. The purpose of this paper is to describe the development of the autism diagnostic aid (ADA) tool and face and content validation of its properties. Design/methodology/approach The ADA tool was developed following a detailed scoping exercise which included literature reviews, observations and discussion with wider multi-disciplinary team members. A face and content validation after consultation with eight experts who are trained and routinely perform assessments for autism spectrum disorders. The experts also have expertise in working with individuals with intellectual disability. Findings The experts agreed that the ADA tool is appropriate to the targeted construct and assessment objectives as a diagnostic aid in the assessment of autism in adults with intellectual disability. They also determined that the content of ADA tool was valid for its use as a diagnostic aid in the diagnosis of adults with intellectual disability. Research limitations/implications The ADA tool was considered a valid tool from the expert’s perspective. It was identified to have a good face and content validity. To the best of the authors’ knowledge, the ADA is first of its kind tool, developed specifically to aid clinicians with autism assessments in adults with intellectual disability. Practical implications This tool could potentially help with timely assessment and help with providing appropriate intervention but would require further exploration. Originality/value To the best of the authors’ knowledge, this is the first of its kind tool for the assessment of autism in adults with intellectual disability.

Systematic Analysis of Packaging Production in the Electric Motors Industry: A Multi-Criteria Approach through the SAPEVO-M Method

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes.

The PROMETHEE-GAIA: A multi-criteria decision-making method for identifying best conservation agricultural practices

In a first, we used Preference Ranking Organization Method and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) tool in agricultural research for identifying the best management decision with weakening factors for cultivation of mustard following rice under fifteen regimes of conservation agriculture (CA) practices, because of its versatility, simplicity and usefulness. We created the regime of CA practices based on energy spent for raising the crop combining different tillage practices viz., reduced tillage and zero tillage, and quantity of crop residues and fertilizer application. The performance of the CA regimes was evaluated over the conventional ones using conflicting criteria (31) related to soil, agronomy, plant protection, energy use and economics. With the PROMETHEE-II method, we found zero tillage with 100 % residue plus 75 % NPK (ZERO3) as the best performing alternative, while GAIA analysis identified ZERO3 and zero tillage with 50 % residue plus 100 % NPK (ZERO4). On integration, PROMETHEE-GAIA helped the decision makers to segregate the effects of the criteria on the outcome creating a scope for maneuvering the weak links for optimizing the performance of mustard crop under different CA-regimes. The tool has a huge potential for use in multi-factorial agricultural research.

Pertinence of finite element method for impulse localization in urban environments

Although urban combat is nothing new, recent conflicts have shed light on Western armies' difficulties when operating in such environments. Thus, new surveillance and hazard detection challenges in complex environments are addressed. A new decision aid tool for acoustic sensor positioning has been developed to improve reconnaissance performance. The software's sensor location optimization kernel is based on a genetic algorithm developed by our partners from FKIE. It resolves successive localization problems using pre-computed Times Of Arrival (TOA) between an impulsive acoustic source and various surveillance microphones. The present study discusses the pertinence of the Time-Domain Finite-Element Method (TD-FEM) to simulate wave propagation within an urban area to obtain TOA and resolve localization problems. The approach is compared to experimental measurements in an urban area, and the pros and cons of the TD-FEM for this configuration are discussed.

A low-cost digital first aid tool to reduce psychological distress in refugees: a multi-country randomized controlled trial of Self-Help Online in the first months after the invasion of Ukraine

Armed conflicts increase distress levels among affected populations, particularly impacting refugees who often face barriers to accessing psychological support. We evaluate an online version of a previously tested in-person and endorsed for online adaptation by the WHO Self-Help Plus (SH+) program among Ukrainian refugees dispersed across 17 countries, internally displaced and not displaced Ukrainians. This is the first randomized controlled trial to test an online psychological intervention simultaneously on refugees, internally displaced, and non-displaced conflict-affected populations. This study is an online two-arm, individually randomized controlled trial among participants above 18 years old in Ukraine or EU countries who were randomly assigned to receive either the Self-Help Online (SHO) intervention and passive informational resource or the passive informational resource alone. We recruited 652 participants starting the program on July 7th, 2022. The analysis focused on 292 participants who completed the final survey one week after the end of the program. Results indicated significant distress reduction among refugees (β −2.16, 95% CI −4.17 to −0.16; p=0.03; d −0.47) but not among internally displaced in Ukraine (β 0.56, 95% CI −1.1 to 2.99; p=0.17; d 0.2) or non-displaced participants in Ukraine (β 0.2, 95% CI −0.95 to 1.35; p=0.73; d 0.08). The effect size in stress reduction for refugees was comparable to other similar interventions but with lower average costs. The average cost per participant was €11, with €46.16 for each benefiting (refugee) participant, suggesting cost-effectiveness for scale-up. These findings suggest that Self-Help Online is an effective psychological intervention for reducing stress among geographically dispersed refugees at a low cost. We also find that the online delivery format of psychological interventions is feasible for internally displaced and non-displaced conflict-affected populations.

An experimental analysis of the relationship between the evaluations of artificial intelligence and pre-service teachers

One of the potential benefits of AI is that it may allow optimizing teachers' tasks, enabling them to be more efficient in their work. This study aimed to analyze potential differences between the evaluations given by pre-service teachers and the evaluations given by different generative AIs. A total of 507 pre-service teachers participated, who were provided with a rubric to evaluate 12 texts of different types and qualities. The results showed how the performance of generative AIs in evaluating written tasks replicated the functioning of pre-service teachers quite accurately, with ChatGPT being the AI that best replicated the behavior of pre-service teachers, with an accuracy close to 70% of the evaluation provided by humans. Similarly, there were minimal differences in the evaluations given by pre-service teachers based on their gender and academic year. Likewise, generative AI overestimated scores provided for texts. However, this overestimation decreased as the performance of pre-service teachers improved. Thus, assessments provided by high-performing pre-service teachers were more aligned with those provided by generative AI compared to students with lower performance. These results are useful as they highlight how generative AI could be an aid tool guiding the pedagogical knowledge of pre-service teachers in digital assessment tasks.

The Comparative Performance of Large Language Models on the Hand Surgery Self-Assessment Examination.

Generative artificial intelligence (AI) models have emerged as capable of producing human-like responses and have showcased their potential in general medical specialties. This study explores the performance of AI systems on the American Society for Surgery of the Hand (ASSH) Self-Assessment Exams (SAE). ChatGPT 4.0 and Bing AI were evaluated on a set of multiple-choice questions drawn from the ASSH SAE online question bank spanning 5 years (2019-2023). Each system was evaluated with 999 questions. Images and video links were inserted into question prompts to allow for complete AI interpretation. The performance of both systems was standardized using the May 2023 version of ChatGPT 4.0 and Microsoft Bing AI, both of which had web browsing and image capabilities. ChatGPT 4.0 scored an average of 66.5% on the ASSH questions. Bing AI scored higher, with an average of 75.3%. Bing AI outperformed ChatGPT 4.0 by an average of 8.8%. As a benchmark, a minimum passing score of 50% was required for continuing medical education credit. Both ChatGPT 4.0 and Bing AI had poorer performance on video-type and image-type questions on analysis of variance testing. Responses from both models contained elements from sources such as PubMed, Journal of Hand Surgery, and American Academy of Orthopedic Surgeons. ChatGPT 4.0 with browsing and Bing AI can both be anticipated to achieve passing scores on the ASSH SAE. Generative AI, with its ability to provide logical responses and literature citations, presents a convincing argument for use as an interactive learning aid and educational tool.

Multi-GlaucNet: A multi-task model for optic disc segmentation, blood vessel segmentation and glaucoma detection

Glaucoma is a common and severe ocular disease that often leads to vision loss. The information on the optic disc (OD) and blood vessels in fundus images can significantly aid in glaucoma detection. In addition, the use of deep learning models for glaucoma detection is a highly effective approach. We propose a multi-task deep learning model called Multi-GlaucNet that can simultaneously segment the OD and blood vessels, thereby assisting doctors in diagnosing glaucoma. Multi-GlaucNet consists of three modules: the OD segmentation module, blood vessel segmentation module and glaucoma detection module. The OD segmentation module and blood vessel segmentation module both adopt an encoder–decoder structure to segment OD and blood vessels, respectively. The segmentation module is constructed using bottleneck layers in the encoding process and uses Pixel Shuffle and channel attention mechanisms in the decoding process. The detection module uses the ResNet50 network to perform glaucoma detection based on the features extracted from the segmentation modules. Multi-GlaucNet demonstrates outstanding performance in three areas. It achieves a Dice coefficient of 96.7% for OD segmentation on the ORIGA dataset, accuracy of 0.9798 and F1 score of 0.8562 for blood vessel segmentation on a mixed dataset. For glaucoma detection on the REFUGE dataset, it attains the highest accuracy of 0.967 and an area under the curve (AUC) of 0.950. These results validate the effectiveness of Multi-GlaucNet for glaucoma detection. The model’s ability to perform multiple tasks with high accuracy and efficiency demonstrates that it is a valuable aiding tool for glaucoma diagnosis.

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking

Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have been developed to address these issues and increase walking performance. While these devices have shown promise in their ability to increase musculature engagement of the lower limbs when used on a treadmill, most have not been developed or validated for overground walking and community use. Overground walking may limit the effectiveness of robotic devices due to differences in gait characteristics between walking terrains and reduced user engagement. The purpose of this study was to validate our multimodal robotic gait training system for overground walking in individuals with neuromuscular gait impairments. This untethered wearable robotic device can provide an ankle resistive torque proportional to the users’ biological ankle torque. The device can also provide audio biofeedback based on users’ plantar pressure intending to increase ankle power and muscle activity of the plantar flexors. Seven individuals with cerebral palsy participated. Participants walked overground and on a treadmill with our robotic gait training system in a single testing session. Results showed all seven participants to increase peak plantar flexor muscle activity, 10.3% on average, when walking with the gait trainer overground compared to treadmill. When compared to typical baseline overground walking, overground gait trainer use caused individuals to have slightly less knee joint excursion (3°) and moderately more ankle joint excursion (7°). This work supports our vision of using the wearable robotic device as a gait aid and rehabilitation tool in the home and community settings.

Making moral decisions with artificial agents as advisors. A fNIRS study

Artificial Intelligence (AI) is on the verge of impacting every domain of our lives. It is increasingly being used as an advisor to assist in making decisions. The present study aimed at investigating the influence of moral arguments provided by AI-advisors (i.e., decision aid tool) on human moral decision-making and the associated neural correlates. Participants were presented with sacrificial moral dilemmas and had to make moral decisions either by themselves (i.e., baseline run) or with AI-advisors that provided utilitarian or deontological arguments (i.e., AI-advised run), while their brain activity was measured using an fNIRS device. Overall, AI-advisors significantly influenced participants. Longer response times and a decrease in right dorsolateral prefrontal cortex activity were observed in response to deontological arguments than to utilitarian arguments. Being provided with deontological arguments by machines appears to have led to a decreased appraisal of the affective response to the dilemmas. This resulted in a reduced level of utilitarianism, supposedly in an attempt to avoid behaving in a less cold-blooded way than machines and preserve their (self-)image. Taken together, these results suggest that motivational power can led to a voluntary up- and down-regulation of affective processes along moral decision-making.

Development of Promising Interventions to Improve Human Papillomavirus Vaccination in a School-Based Program in Quebec, Canada: Results From a Formative Evaluation Using a Mixed Methods Design.

Despite the availability of school-based human papillomavirus (HPV) vaccination programs, disparities in vaccine coverage persist. Barriers to HPV vaccine acceptance and uptake include parental attitudes, knowledge, beliefs, and system-level barriers. A total of 3 interventions were developed to address these barriers: an in-person presentation by school nurses, an email reminder with a web-based information and decision aid tool, and a telephone reminder using motivational interviewing (MI) techniques. Here we report on the development and formative evaluation of interventions to improve HPV vaccine acceptance and uptake among grade 4 students' parents in Quebec, Canada. In the summer of 2019, we conducted a formative evaluation of the interventions to assess the interventions' relevance, content, and format and to identify any unmet needs. We conducted 3 focus group discussions with parents of grade 3 students and nurses. Interviews were recorded, transcribed, and analyzed for thematic content using NVivo software (Lumivero). Nurses received training on MI techniques and we evaluated the effect on nurses' knowledge and skills using a pre-post questionnaire. Descriptive quantitative analyses were carried out on data from questionnaires relating to the training. Comparisons were made using the proportions of the results. Finally, we developed a patient decision aid using an iterative, user-centered design process. The iterative refinement process involved feedback from parents, nurses, and experts to ensure the tool's relevance and effectiveness. The evaluation protocol and data collection tools were approved by the CHU (Centre Hospitalier Universitaire) de Québec Research Ethics Committee (MP-20-2019-4655, May 16, 2019). The data collection was conducted from April 2019 to March 2021. Following feedback (n=28) from the 3 focus group discussions in June 2019, several changes were made to the in-person presentation intervention. Experts (n=27) and school nurses (n=29) recruited for the project appreciated the visual and simplified information on vaccination in it. The results of the MI training for school nurses conducted in August 2019 demonstrated an increase in the skills and knowledge of nurses (n=29). School nurses who took the web-based course (n=24) filled out a pretest and posttest questionnaire to evaluate their learning. The rating increased by 19% between the pretest and posttest questionnaires. Several changes were made between the first draft of the web-based decision-aid tool and the final version during the summer of 2019 after an expert consultation of experts (n=3), focus group participants (n=28), and parents in the iterative process (n=5). More information about HPV and vaccines was added, and users could click if more detail is desired. We developed and pilot-tested 3 interventions using an iterative process. The interventions were perceived as potentially effective to increase parents' knowledge and positive attitudes toward HPV vaccination, and ultimately, vaccine acceptance. Future research will assess the effectiveness of these interventions on a larger scale.

Amplifying Chinese physicians' emphasis on patients' psychological states beyond urologic diagnoses with ChatGPT-A multi-center cross-sectional study.

Artificial intelligence (AI) technologies, particularly large language models (LLMs), have been widely employed by the medical community. In addressing the intricacies of urology, ChatGPT offers a novel possibility to aid in clinical decision-making. This study aimed to investigate the decision-making ability of LLMs in solving complex urology-related problems and assess its effectiveness in providing psychological support to patients with urological disorders. This study evaluated the clinical and psychological support capabilities of ChatGPT 3.5 and 4.0 in the field of urology. A total of 69 clinical and 30 psychological questions were posed to the AI models, and their responses were evaluated by both urologists and psychologists. As a control, clinicians from Chinese medical institutions provided responses under closed-book conditions. Statistical analyses were conducted separately for each subgroup. In multiple-choice tests covering diverse urological topics, ChatGPT 4.0, performed comparably to the physician group, with no significant overall score difference. Subgroup analyses revealed variable performance, based on disease type and physician experience, with ChatGPT 4.0 generally outperforming ChatGPT 3.5 and exhibiting competitive results against physicians. When assessing the psychological support capabilities of AI, it is evident that ChatGPT4.0 outperforms ChatGPT3.5 across all urology-related psychological problems. The performance of LLMs in dealing with standardized clinical problems and providing psychological support has certain advantages over clinicians. AI stands out as a promising tool for potential clinical aid.

Existing Digital Health Technology Index Summary Report for Older Adults Living with Neurocognitive Disorders (Mild and Major) and Their Informal Caregivers: An Environmental Scan.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers. (1) Background: It is important to obtain a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will aid the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences based on trustable information. (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in the grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized the findings. (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed for identifying current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

OTHR-03. DEVELOPMENT OF PANORAMA: AN ASSESSMENT TOOL FOR PEDIATRIC NEURO-ONCOLOGY SERVICE DELIVERY CAPACITY

Abstract BACKGROUND To enhance the quality of care available for children with central nervous system (CNS) tumors across the world, a systematic evaluation of service delivery capacity is needed to identify gaps in pediatric neuro-oncology and prioritize interventions. To that end, we created the PediAtric Neuro-Oncology Resource AssesMent Aid (PANORAMA) tool. METHODS PANORAMA development consisted of three phases: operationalization, consensus building, and piloting. PANORAMA was designed to have domains that capture the elements of the pediatric neuro-oncology care continuum using assessments which were weighted according to their importance. Responses were ordinally scored to reflect adequacy of satisfaction. PANORAMA was revised based on feedback at various phases to improve its relevance, usability, and clarity. RESULTS The operationalization phase identified thirteen domains using 252 questions. The consensus phase included validating the identified domains, questions, and scoring by fifteen experts (six pediatric oncologists, three radiation oncologists, two neurosurgeons, two radiologists, and two pathologists). The validated PANORAMA domains were national context, hospital infrastructure, organization and service integration, human resources, financing, laboratory, neurosurgery, diagnostic imaging, pathology, chemotherapy, radiotherapy, supportive care, and patient outcomes. Seventy-six questions were added during the expert review. PANORAMA was piloted at thirteen institutions in twelve countries, representing diverse patient care contexts. Face validity was assessed using the correlation between the estimated score by respondents and calculated PANORAMA scores for each domain (r = 0.67, p <0.0001). Piloting eliminated two questions. CONCLUSIONS PANORAMA was developed following a systematic approach, leading to a relevant tool to evaluate the core elements of pediatric neuro-oncology service capacity. PANORAMA will be distributed to facilitate quantitative evaluation of institutional services, allowing for prioritization and benchmarking with centers at the local, regional, and international levels.

Can ChatGPT generate practice question explanations for medical students, a new faculty teaching tool?

Introduction Multiple-choice questions (MCQs) are frequently used for formative assessment in medical school but often lack sufficient answer explanations given time-restraints of faculty. Chat Generated Pre-trained Transformer (ChatGPT) has emerged as a potential student learning aid and faculty teaching tool. This study aims to evaluate ChatGPT’s performance in answering and providing explanations for MCQs. Method Ninety-four faculty-generated MCQs were collected from the pre-clerkship curriculum at a US medical school. ChatGPT’s accuracy in answering MCQ’s were tracked on first attempt without an answer prompt (Pass 1) and after being given a prompt for the correct answer (Pass 2). Explanations provided by ChatGPT were compared with faculty-generated explanations, and a 3-point evaluation scale was used to assess accuracy and thoroughness compared to faculty-generated answers. Results On first attempt, ChatGPT demonstrated a 75% accuracy in correctly answering faculty-generated MCQs. Among correctly answered questions, 66.4% of ChatGPT's explanations matched faculty explanations, and 89.1% captured some key aspects without providing inaccurate information. The amount of inaccurately generated explanations increases significantly if the questions was not answered correctly on the first pass (2.7% if correct on first pass vs. 34.6% if incorrect on first pass, p < 0.001). Conclusion ChatGPT shows promise in assisting faculty and students with explanations for practice MCQ’s but should be used with caution. Faculty should review explanations and supplement to ensure coverage of learning objectives. Students can benefit from ChatGPT for immediate feedback through explanations if ChatGPT answers the question correctly on the first try. If the question is answered incorrectly students should remain cautious of the explanation and seek clarification from instructors.