Medical Response Research Articles

Who benefits from digital interventions for bipolar disorder? Stage of illness characteristics as predictors of changes in quality of life.

This study explored the potential role of stage-related variables in intervention outcomes in bipolar disorder (BD). Specifically, we aimed to identify which subgroups of individuals were most likely to experience improved quality of life following digitally delivered psychosocial interventions for BD. The study involved a secondary analysis of combined data from two randomised control trials (RCTs). Each trial assessed the effectiveness of digitally delivered interventions for improving quality of life, in late-stage (ORBIT RCT) or early-stage (BETTER RCT) BD. Three iterations of cluster analyses were performed, identifying subgroups of individuals based on (i) current phenomenology, (ii) course of illness and (iii) medication response. The resultant subgroups were compared with regard to changes in quality of life pre-post intervention, via repeated measures ANOVAs. In each cluster analysis, two clusters were found. The current phenomenology clusters reflected two impairment levels, 'moderate impairment' and 'low impairment'. The course of illness clusters reflected 'more chronicity' and 'less chronicity' and the medication response clusters reflected 'good medication response' and 'poor medication response'. Differences in changes in quality of life over time were observed between the two current phenomenology clusters and between the medication response clusters, while the course of illness subgroups did not respond differently. There are at least two distinct groups of treatment-seeking individuals with established BD, based on illness features with previously established links to different illness stages. Clusters within the current phenomenology and medication response domains demonstrated significantly different trajectories of QoL change over time in the context of our interventions, highlighting potential implications for treatment selection aligned with precision psychiatry.

Pharmacogenetic educational needs and the role of pharmacogenetics in primary care: a focus group study with multiple perspectives.

Pharmacogenomics (PGx) is a well-established concept of how genes impact medication response, with many studies demonstrating reductions in medication side effects, improved efficacy and cost effectiveness. Despite these benefits, implementation of PGx in daily practice remains limited. Studies on the implementation of PGx in clinical practice have previously found that inadequate knowledge is one of the main barriers. Details regarding specifically which educational needs exist among family medicine clinicians requires further study. The aim of this study was to identify both the perceived role that pharmacogenomics (PGx) could play in primary care practice, the knowledge gaps that family medicine clinicians experience, and the skills they require to use PGx in their daily practice. To achieve this aim, the attitudes, knowledge, barriers, skills needed, and preferred educational program were explored in a family medicine clinician focus group study via a semi-structured interview and knowledge quiz. Second, multidisciplinary focus groups provided information on the level of knowledge and necessary skills to use PGx in patient care. After gathering key recorded information from both focus groups, the perceived role pharmacogenomics could possibly play in primary care, the predominant knowledge gaps, and the most appropriate educational program was determined by qualitative analysis. Four themes emerged regarding the PGx educational needs and the role of PGx in family medicine: 1) need for PGx competences, 2) insight into the roles and responsibilities of PGx services, 3) optimization of PGx workflow through artificial intelligence integrated in the electronic health record, and 4) the ethical dilemmas and psychological effects related to PGx. These themes reflect a shift in the role of PGx in family medicine with implications for education. The results obtained from this study will help improve the implementation of PGx in daily practice, and consequently, may result in increased utilization of PGx, thereby resulting in improved medication efficacy and reduced side effects.

Biomarkers in the Diagnosis and Prediction of Medication Response in Depression and the Role of Nutraceuticals.

Depression continues to be a significant and growing public health concern. In clinical practice, it involves a clinical diagnosis. There is currently no defined or agreed upon biomarker/s for depression that can be readily tested. A biomarker is defined as a biological indicator of normal physiological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention that can be objectively measured and evaluated. Thus, as there is no such marker for depression, there is no objective measure of depression in clinical practice. The discovery of such a biomarker/s would greatly assist clinical practice and potentially lead to an earlier diagnosis of depression and therefore treatment. A biomarker for depression may also assist in determining response to medication. This is of particular importance as not all patients prescribed with medication will respond, which is referred to as medication resistance. The advent of pharmacogenomics in recent years holds promise to target treatment in depression, particularly in cases of medication resistance. The role of pharmacogenomics in routine depression management within clinical practice remains to be fully established. Equally so, the use of pharmaceutical grade nutrients known as nutraceuticals in the treatment of depression in the clinical practice setting is largely unknown, albeit frequently self-prescribed by patients. Whether nutraceuticals have a role in not only depression treatment but also in potentially modifying the biomarkers of depression has yet to be proven. The aim of this review is to highlight the potential biomarkers for the diagnosis, prediction, and medication response of depression.

Short Survey on Cardiopulmonary Resuscitation and Automated External Defibrillator Training in Rural British Columbia Schools: Preliminary Findings and Hypothesis-Generating Insights

BackgroundBritish Columbia (BC) faces more than 7000 out-of-hospital cardiac arrests annually, which disproportionately affect rural areas, owing to their slower emergency medical service response and limited specialized care. Despite the known benefits of automated external defibrillator (AED) access and cardiopulmonary resuscitation (CPR) training, their status in rural BC schools is poorly documented. MethodsWe used an online survey of principals and vice-principals of rural schools in BC. The survey assessed AED accessibility, prevalence of CPR and AED training, and obstacles to implementing such training. Questions covered school demographics, AED installation, and CPR and/or AED training for staff and students. ResultsWe recruited 23 elementary schools (kindergarten-grade 7; 46%), 6 middle schools (grades 6-8; 12%), and 21 high schools (grades 8- 12; 42%). A total of 72% (36 of 50) had at least one AED installed; 46% required staff CPR training; and 24% provided student CPR training. Significant gaps in training were noted for elementary and middle school students, compared to the training for high schools (P < 0.05). ConclusionsDisparities in AED and CPR training across rural schools in BC exist, highlighting a need for policy improvements and innovative solutions to enhance first-aid education. Barriers to implementing CPR and AED training included lack of funding, curricular priority, time constraints, and limited resources. Despite a 10.3% response rate, this study reveals significant disparities in AED and CPR training across school levels in rural BC, underscoring the need for targeted policies and educational strategies to enhance emergency preparedness and improve cardiac arrest outcomes in underserved areas.

The San Francisco Health Systems Collaborative: Public Health and Health Care Delivery Systems’ Response to the Covid-19 Pandemic

The Covid-19 pandemic challenged health care delivery systems worldwide. Many acute care hospitals in communities that experienced surges in cases and hospitalizations had to make decisions such as rationing scarce resources. Hospitals serving low-income communities, communities of color, and those in other historically marginalized or vulnerable groups reported the greatest operational impacts of surges. However, cross-institutional collaborations within jurisdictions offer unique opportunities to prevent or mitigate health disparities in resource utilization and access to care. In January 2020, in response to the emerging coronavirus epidemic, the San Francisco Department of Public Health (SFDPH) and local hospital and health systems partners convened to align and coordinate medical surge planning and response. Adopting a governance structure of mutual accountability and transparency, the San Francisco Health Systems Collaborative guided local medical and public health response in the areas of medical surge, vaccination administration, testing, and therapeutics. Four principles guided the collaborative response: (1) shared priorities, (2) clear governance and accountability, (3) data transparency, and (4) operational coordination. High-level priorities established included protecting vulnerable people, protecting health care workers, and maintaining health system capacity. The governance structure consisted of three layers: local hospital and health systems’ CEOs coordinating with SFDPH executives; hospital chief medical and nursing officers coordinating high-level surge capacity assessments and mitigation plans; and local clinical operational managers working with public health response operational leaders to coordinate scarce resource utilization. Fluctuating with the tempo of the disease indicators and medical surge, governance and coordination were maintained through a tiered meeting and reporting system. Data visibility and transparency were key principles facilitating operational decision-making and executive-level coordination of resources, including identifying additional surge bed capacity for use systemwide, as well as ensuring efficient and equitable vaccine distribution through implementation of five mass-vaccination sites with prioritized access for vulnerable communities. Applying these four principles of shared priorities, accountability, transparency, and operational coordination and pragmatism helped the public health and individual hospital systems make contributions to the overall response that were aligned with their unique strengths and resources. Publication here represents the first official public use of the name San Francisco Health Systems Collaborative (which had served as the term used internally to refer to the group) and the first time codifying this structure. Through this coordination, San Francisco achieved one of the lowest Covid-19 death rates and had one of the highest vaccination and booster rates, compared with rates across California or the United States. Similar principles and implementation methods can be adopted by other health jurisdictions for future emergency outbreak response.

Generalistic Assessments of the Potential of Medical Drones in Urban Environment

At the current development stage, the lower airspace above urban areas is only used to a very limited extent. Recent developments in the drone industry are making this area more accessible. The leading use case for drone applications is currently seen in the medical sector. Individual evidence shows that the use of drones to transport medical personnel for first medical response brings significant improvements in terms of cost and response time. Advantages in urban applications are seen as promising by some research projects, but this is not scientifically proven yet. This study deals with the simulation of transportation times of medical proposals by ambulance and drone in the metropolitan region of Stavanger and the comparison of transport times. The proposed methodology develops transferable results from concrete use-cases. By using a drone 80% of the expected operations can benefit from a reduction in transport time of up to 10 minutes with a variation of +/- 4 minutes through adjusted flight speeds due to weather conditions and variations in ambulance travel times due to different traffic volumes. The data set cleaned for the local special cases shows potential for a reduction of up to 20 minutes for the remaining operations, while the extracted individual cases even showing improvements of up to 60 minutes.

ChatGPT vs Medical Professional: Analyzing Responses to Laboratory Medicine Questions on Social Media.

The integration of ChatGPT, a large language model (LLM) developed by OpenAI, into healthcare has sparked significant interest due to its potential to enhance patient care and medical education. With the increasing trend of patients accessing laboratory results online, there is a pressing need to evaluate the effectiveness of ChatGPT in providing accurate laboratory medicine information. Our study evaluates ChatGPT's effectiveness in addressing patient questions in this area, comparing its performance with that of medical professionals on social media. This study sourced patient questions and medical professional responses from Reddit and Quora, comparing them with responses generated by ChatGPT versions 3.5 and 4.0. Experienced laboratory medicine professionals evaluated the responses for quality and preference. Evaluation results were further analyzed using R software. The study analyzed 49 questions, with evaluators reviewing responses from both medical professionals and ChatGPT. ChatGPT's responses were preferred by 75.9% of evaluators and generally received higher ratings for quality. They were noted for their comprehensive and accurate information, whereas responses from medical professionals were valued for their conciseness. The interrater agreement was fair, indicating some subjectivity but a consistent preference for ChatGPT's detailed responses. ChatGPT demonstrates potential as an effective tool for addressing queries in laboratory medicine, often surpassing medical professionals in response quality. These results support the need for further research to confirm ChatGPT's utility and explore its integration into healthcare settings.

Study on the intervention effect of HCH integrated SMG health management model on community high-risk group of cardiovascular disease.

This study aimed to evaluate the integration of the Hospital-Community-Home (HCH) model with the Self-Mutual-Group (SMG) health management model for high-risk populations with cardiovascular disease in the Yuhua community of Shijiazhuang city. The study focused on implementing care interventions (HCH, SMG) with a specific emphasis on SMG to promote beneficial views/behaviors, enhance self-efficacy/agency, and address detrimental determinants of health, ultimately leading to durable changes and healthier lifestyles. Comparing the HCH model with the combined SMG model helps to comprehensively assess the strengths and weaknesses of different health management approaches. This comparison contributes to theoretical innovation and practical development in the field of health management, as well as improving patients' health outcomes and quality of life. This study employed a quasi-experimental design. Using stratified sampling, individuals who underwent health examinations in Community A and Community B from Shijiazhuang city between May 2023 and August 2023 were randomly selected. After informing the participants about the study and obtaining informed consent via telephone, high-risk patients with cardiovascular disease were screened based on their medical examination reports. Data on lifestyle behaviors, self-efficacy, medical responses, quality of life, and readmission rates were collected and compared before and after the intervention. A total of 526 eligible participants were included, with 241 in the control group and 285 in the study group. After the intervention, there was no significant change in the proportions of smokers, alcohol consumers, and individuals engaging in leisure exercises in the control group. However, in the study group, the proportions of smokers and alcohol consumers significantly decreased, while the proportion of individuals engaging in leisure exercises significantly increased. After the intervention, both the study group and the control group showed significant increases in scores on the General Self-Efficacy Scale-Schwarzer (GSES) and the Seattle Angina Questionnaire (SAQ), with the study group scoring significantly higher than the control group. Avoidance and surrender scores significantly increased after the intervention, with the study group scoring significantly lower than the control group. Confrontation scores significantly increased after the intervention, with the study group scoring significantly higher than the control group. During the follow-up period, the study group had a significantly lower readmission rate than the control group. The integration of HCH with SMG health management model can significantly improve lifestyle behaviors, optimize medical responses, enhance self-efficacy and quality of life, and significantly reduce readmission rates among high-risk populations with cardiovascular disease.

Antiplatelet Therapy and Platelet Activity Testing for Neurointerventional Procedures

The management of antiplatelet medications in neurointerventional procedures remains a subject of considerable variability and debate. This review article explores the diverse clinical practices and the impact of different antiplatelet regimens and platelet activity testing on patient outcomes in neurointerventional treatments. While much of the evidence around antiplatelet therapies largely stems from randomized trials in cardiac and peripheral vascular diseases, their application in neurointerventional settings requires nuanced consideration. Various assays exist to assess individual platelet function, yet the optimal assay, thresholds, and agents remain uncertain due to interpatient variability in medication responsiveness. Expert consensus groups have attempted to standardize antiplatelet management, which is summarized for elective and emergent neurointerventional procedures. Clopidogrel, a commonly used antiplatelet, faces challenges such as genetic variability in metabolism and drug–drug interactions, impacting its effectiveness. Other agents, such as ticagrelor and prasugrel, offer alternatives with different mechanisms of action and potential advantages. Additionally, short‐acting intravenous P2Y 12 inhibitors, such as cangrelor, and glycoprotein IIb/IIIa inhibitors provide options for acute bridging therapy in neurointerventional cases. Despite advancements, significant gaps persist in understanding the optimal antiplatelet management for neurovascular procedures. While platelet function testing is commonly used, its clinical utility and standardization remain an area of investigation. This review underscores the need for further multicenter studies to delineate best practices and optimize patient outcomes in neurointerventional settings.

Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN)

Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interactions. Identifying and addressing adverse effects caused by polypharmacy is crucial to ensure patient safety and improve healthcare results. This paper introduces a new method using Graph Convolutional Networks (GCN) to identify polypharmacy side effects. Our strategy involves developing a medicine interaction graph in which edges signify drug-drug intuitive predicated on pharmacological properties and hubs symbolize drugs. GCN is a well-suited profound learning procedure for graph-based representations of social information. It can be used to anticipate the probability of medicate unfavorable impacts and to memorize important representations of sedate intuitive. Tests were conducted on a huge dataset of patients’ pharmaceutical records commented on with watched medicate unfavorable impacts in arrange to approve our strategy. Execution of the GCN show, which was prepared on a subset of this dataset, was evaluated through a disarray framework. The perplexity network shows the precision with which the show categories occasions. Our discoveries demonstrate empowering advance within the recognizable proof of antagonistic responses related with polypharmaceuticals. For cardiovascular system target drugs, GCN technique achieved an accuracy of 94.12%, precision of 86.56%, F1-Score of 88.56%, AUC of 89.74% and recall of 87.92%. For respiratory system target drugs, GCN technique achieved an accuracy of 93.38%, precision of 85.64%, F1-Score of 89.79%, AUC of 91.85% and recall of 86.35%. And for nervous system target drugs, GCN technique achieved an accuracy of 95.27%, precision of 88.36%, F1-Score of 86.49%, AUC of 88.83% and recall of 84.73%. This research provides a significant contribution to pharmacovigilance by proposing a data-driven method to detect and reduce polypharmacy side effects, thereby increasing patient safety and healthcare decision-making.

Evaluation of the quality and quantity of artificial intelligence-generated responses about anesthesia and surgery: using ChatGPT 3.5 and 4.0.

The large-scale artificial intelligence (AI) language model chatbot, Chat Generative Pre-Trained Transformer (ChatGPT), is renowned for its ability to provide data quickly and efficiently. This study aimed to assess the medical responses of ChatGPT regarding anesthetic procedures. Two anesthesiologist authors selected 30 questions representing inquiries patients might have about surgery and anesthesia. These questions were inputted into two versions of ChatGPT in English. A total of 31 anesthesiologists then evaluated each response for quality, quantity, and overall assessment, using 5-point Likert scales. Descriptive statistics summarized the scores, and a paired sample t-test compared ChatGPT 3.5 and 4.0. Regarding quality, "appropriate" was the most common rating for both ChatGPT 3.5 and 4.0 (40 and 48%, respectively). For quantity, responses were deemed "insufficient" in 59% of cases for 3.5, and "adequate" in 69% for 4.0. In overall assessment, 3 points were most common for 3.5 (36%), while 4 points were predominant for 4.0 (42%). Mean quality scores were 3.40 and 3.73, and mean quantity scores were - 0.31 (between insufficient and adequate) and 0.03 (between adequate and excessive), respectively. The mean overall score was 3.21 for 3.5 and 3.67 for 4.0. Responses from 4.0 showed statistically significant improvement in three areas. ChatGPT generated responses mostly ranging from appropriate to slightly insufficient, providing an overall average amount of information. Version 4.0 outperformed 3.5, and further research is warranted to investigate the potential utility of AI chatbots in assisting patients with medical information.

Post-Acute COVID-19 Syndrome and Stroke

This review explores the multifaceted strategies to mitigate the risk of cerebrovascular impairment in young adults recovering from COVID-19. The pandemic has illuminated an increased incidence of stroke in populations traditionally considered at lower risk, underscoring the need for targeted preventative measures. Given the complex interplay between SARS-CoV-2 infection and stroke risk, this article synthesizes current evidence and recommendations across lifestyle modifications, medical interventions, and proactive health monitoring. Key recommendations include adopting a heart-healthy diet, engaging in regular physical activity, and implementing stress reduction techniques. The management of existing cardiovascular risk factors, such as hypertension and diabetes, is emphasized as crucial for minimizing stroke risk post-COVID. Moreover, education on recognizing stroke symptoms is vital for ensuring timely medical response. The review also discusses the importance of avoiding smoking, limiting alcohol intake, and, for specific high-risk individuals, the judicious use of anticoagulants. Regular medical follow-ups for monitoring post-COVID symptoms and addressing any ongoing complications are advocated to identify and manage potential risks early. In conclusion, a comprehensive approach involving lifestyle changes, vigilant management of pre-existing conditions, education on stroke symptoms, and regular healthcare engagement is essential for reducing the risk of cerebrovascular events in young adults recovering from COVID-19. This review underscores the importance of continued research and public health advocacy in addressing the long-term effects of the pandemic on cerebrovascular health.

An Overview of Radiation Countermeasure Development in Radiation Research from 1954 to 2024.

Preparation for medical responses to major radiation accidents, further driven by increases in the threat of nuclear warfare, has led to a pressing need to understand the underlying mechanisms of radiation injury (RI) alone or in combination with other trauma (combined injury, CI). The identification of these mechanisms suggests molecules and signaling pathways that can be targeted to develop radiation medical countermeasures. Thus far, the United States Food and Drug Administration (U.S. FDA) has approved seven countermeasures to mitigate hematopoietic acute radiation syndrome (H-ARS), but no drugs are available for prophylaxis and no agents have been approved to combat the other sub-syndromes of ARS, let alone delayed effects of acute radiation exposure or the effects of combined injury. From its inception, Radiation Research has significantly contributed to the understanding of the underlying mechanisms of radiation injury and combined injury, and to the development of radiation medical countermeasures for these indications through the publication of peer-reviewed research and review articles.

Increasing cost-effectiveness of AEDs using algorithms to optimise location

ObjectivesVolunteer responder systems (VRSs) aim to decrease time to defibrillation by dispatching trained volunteers to automated external defibrillators (AEDs) and out-of-hospital cardiac arrest (OHCA) victims. AEDs are often underutilized due to poor placement. This study provides a cost-effectiveness analysis of adding AEDs at strategic locations to maximize quality-adjusted life years (QALYs). MethodsWe simulated combined volunteer, police, firefighter, and emergency medical service response scenarios to OHCAs, and applied our methods to a case study of Amsterdam, the Netherlands. We compared the competing strategies of placing additional AEDs, using steps of 40 extra AEDs (0, 40, …, 1480), in addition to the existing 369 AEDs. Incremental cost-effectiveness ratios (ICERs) were calculated for each increase in additional AEDs, from a societal perspective. The effect of AED connection and time to connection on survival to hospital admission and neurological outcome at discharge was estimated using logistic regression, using OHCA data from Amsterdam from 2006 to 2018. Other model inputs were obtained from literature. ResultsPurchasing up to 1120 additional AEDs (ICER €75,669/QALY) was cost-effective at a willingness-to-pay threshold of €80,000/QALY, when positioned strategically. Compared to current practice, adding 1120 AEDs resulted in a gain of 0.111 QALYs (95% CI 0.110–0.112) at an increased cost of €3792 per OHCA (95% CI €3778-€3807). Health benefits per AED diminished as more AEDs were added. ConclusionsOur study identified cost-effective strategies to position AEDs at strategic locations in a VRS. The case study findings advocate for a substantial increase in the number of AEDs in Amsterdam.

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

Medical Technologists: Are They Ready to Play Their Fair Share to Facilitate the Organ Donation and Transplantation Process in India?

Abstract Background: Organ shortage is a global health crisis. Health-care professionals are supposed to be frontrunners in the campaign for organ donation (OD) by being in a good position to liaise with patients and their families for obtaining consent for the OD process. We propose to highlight the current state of awareness, knowledge, attitudes, and practices (KAP) of Medical Technologists, a burgeoning relatively new branch of allied health professionals, in order to assess their readiness for facilitating OD/transplantation process in India. Objective: We aimed to study the current state of awareness, KAP of OD among Medical Technologists. Methods: A cross-sectional, survey-based study utilizing online Google forms for gathering medical technologist responses was conducted. Two surveys (Part A and Part B) were used to collect the requisite data. 146 and 114 participants responded to Part A and Part B, respectively, out of which 99 answered both surveys. Descriptive statistics using Excel and statistical analysis using SPSS version 22 were carried out. Results: Majority of participants were male (61%) within the 20–30 years’ age group, with 82.2% interacting with patients on a daily basis. The mean awareness, knowledge, death, legal, and attitude score were 4.89 ± 1.55 (54.3%), 3.925 ± 1.9514 (43.8%), 2.123 ± 1.328 (42.6%), 1.798 ± 1.00 (59.9%), and 6.11 ± 4.718 (87.3%) (mean ± standard deviation) (% of mean/total score), respectively. Among participants, 23.7% had already donated or signed donor cards, whereas 30.4% of those not yet donated are willing to donate when asked or sometime in the future. Majority of medical technologists showed positive attitudes leaning toward OD and supported further education and awareness. There was no significant difference in scores between donated versus not-donated groups (P &gt; 0.05); however, attitudes scores were significantly higher for the willingness to donate group among current nondonors (P = 0.002). The death score was higher in the postgraduate group (P = 0.045) and knowledge (P = 0.016) and death scores (P = 0.032) were significantly correlated with educational qualification. Furthermore, medical technologists working in different specializations (i.e. areas of work) possessed significant difference in scores (knowledge (P = 0.003), death scores (P = 0.008), legal (P = 0.028), and attitude (P = 0.01) scores) while specifically legal score was significantly different between genders (P = 0.013). Interestingly, the legal score was associated with and moderately correlated with attitude score (P = 0.437). Knowledge score was associated with daily interaction (P = 0.034), death score was associated with area of work (P = 0.021); and legal score was associated with gender (P = 0.003) and area of work (P = 0.043). Conclusion: Since the medical technologist cadre in India can serve as an important bridge between patients and health-care organizations supporting OD; upgrading their knowledge, converting their attitudes into pro-OD practices, and honing their skills through training interventions to facilitate OD definitely needs to be invested in. Such investments in training interventions/courses implemented now can pay rich dividends in the years to come.

A National Disaster Medicine Quality Management Tool in an International Context – A Theoretical Study

Abstract The impact of disasters is changing, with disaster events becoming more complex and often affecting multiple countries simultaneously. Despite this, quality management systems have not yet been established in the field of disaster medicine. The German research project QUARZ-SAND, developed a national disaster medicine quality management system and an online registry. This theoretical study ascertains the current state of quality management in disaster medicine and compares existing initiatives to QUARZ-SAND and highlights advantages and synergies in the pooling of incident data, in view of improving resilience to national and transboundary disaster events. A systematic literature research was conducted on December 18, 2020 in the databases PubMed and LIVIVO. Existing disaster medicine quality management tools were identified and compared with QUARZ-SAND. It was determined that a lot of effort is still needed to get to an internationally accepted common standard and a tool such as a registry for evaluating medical disaster responses. A first step toward the establishment of a registry is the development of a standardized data set.

Variational autoencoders for generative modeling of drug dosing determinants in renal, hepatic, metabolic, and cardiac disease states

AbstractPhysiological determinants of drug dosing (PDODD) are a promising approach for precision dosing. This study investigates the alterations of PDODD in diseases and evaluates a variational autoencoder (VAE) artificial intelligence model for PDODD. The PDODD panel contained 20 biomarkers, and 13 renal, hepatic, diabetes, and cardiac disease status variables. Demographic characteristics, anthropometric measurements (body weight, body surface area, waist circumference), blood (plasma volume, albumin), renal (creatinine, glomerular filtration rate, urine flow, and urine albumin to creatinine ratio), and hepatic (R‐value, hepatic steatosis index, drug‐induced liver injury index), blood cell (systemic inflammation index, red cell, lymphocyte, neutrophils, and platelet counts) biomarkers, and medical questionnaire responses from the National Health and Nutrition Examination Survey (NHANES) were included. The tabular VAE (TVAE) generative model was implemented with the Synthetic Data Vault Python library. The joint distributions of the generated data vs. test data were compared using graphical univariate, bivariate, and multidimensional projection methods and distribution proximity measures. The PDODD biomarkers related to disease progression were altered as expected in renal, hepatic, diabetes, and cardiac diseases. The continuous PDODD panel variables generated by the TVAE satisfactorily approximated the distribution in the test data. The TVAE‐generated distributions of some discrete variables deviated from the test data distribution. The age distribution of TVAE‐generated continuous variables was similar to the test data. The TVAE algorithm demonstrated potential as an AI model for continuous PDODD and could be useful for generating virtual populations for clinical trial simulations.

Future Perspective of Risk Prediction in Aesthetic Surgery: Is Artificial Intelligence Reliable?

Artificial intelligence (AI) techniques are showing significant potential in the medical field. The rapid advancement in artificial intelligence methods suggests their soon-to-be essential role in physicians' practices. In this study, we sought to assess and compare the readability, clarity, and precision of medical knowledge responses provided by 3 large language models (LLMs) and informed consent forms for 14 common aesthetic surgical procedures, as prepared by the American Society of Plastic Surgeons (ASPS). The efficacy, readability, and accuracy of 3 leading LLMs, ChatGPT-4 (OpenAI, San Francisco, CA), Gemini (Google, Mountain View, CA), and Copilot (Microsoft, Redmond, WA), was systematically evaluated with 14 different prompts related to the risks of 14 common aesthetic procedures. Alongside these LLM responses, risk sections from the informed consent forms for these procedures, provided by the ASPS, were also reviewed. The risk factor segments of the combined general and specific operation consent forms were rated highest for medical knowledge accuracy (P < .05). Regarding readability and clarity, the procedure-specific informed consent forms, including LLMs, scored highest scores (P < .05). However, these same forms received the lowest score for medical knowledge accuracy (P < .05). Interestingly, surgeons preferred patient-facing materials created by ChatGPT-4, citing superior accuracy and medical information compared to other AI tools. Physicians prefer patient-facing materials created by ChatGPT-4 over other AI tools due to their precise and comprehensive medical knowledge. Importantly, adherence to the strong recommendation of ASPS for signing both the procedure-specific and the general informed consent forms can avoid potential future complications and ethical concerns, thereby ensuring patients receive adequate information.

Development of AI-generated medical responses using the ChatGPT for cancer patients

Background and objectiveTo develop a healthcare chatbot service (AI-guided bot) that conducts real-time conversations using large language models to provide accurate health information to patients. MethodsTo provide accurate and specialized medical responses, we integrated several cancer practice guidelines. The size of the integrated meta-dataset was 1.17 million tokens. The integrated and classified metadata were extracted, transformed into text, segmented to specific character lengths, and vectorized using the embedding model. The AI-guide bot was implemented using Python 3.9. To enhance the scalability and incorporate the integrated dataset, we combined the AI-guide bot with OpenAI and the LangChain framework. To generate user-friendly conversations, a language model was developed based on Chat-Generative Pretrained Transformer (ChatGPT), an interactive conversational chatbot powered by GPT-3.5. The AI-guide bot was implemented using ChatGPT3.5 from Sep. 2023 to Jan. 2024. ResultsThe AI-guide bot allowed users to select their desired cancer type and language for conversational interactions. The AI-guided bot was designed to expand its capabilities to encompass multiple major cancer types. The performance of the AI-guide bot responses was 90.98 ± 4.02 (obtained by summing up the Likert scores). ConclusionsThe AI-guide bot can provide medical information quickly and accurately to patients with cancer who are concerned about their health.