Scarce Medical Resources Research Articles

Development of a metabolome-based respiratory infection prognostic during COVID-19 arrival.

In a new respiratory virus pandemic, optimizing allocation of scarce medical resources becomes an urgent challenge. Infection prognosis takes on particular importance when allocating scarce antiviral antibodies and drugs, which are most effective when administered before the onset of severe disease. During arrival of the COVID-19 pandemic to the United States in 2020, we conducted a prognostic biomarker discovery and validation effort based upon metabolomic profiling with a liquid-chromatography-mass spectrometer (LC-MS) type used clinically for rapid toxicology. We obtained urine specimens from 163 patients presenting for evaluation. We obtained LC-MS profiles in the initial cohort and used machine learning methods to define a simplified urine metabolomic signature associated with respiratory failure or death by 90 days. This signature was composed of three metabotypes linked to intestinal microbiome metabolism and anticonvulsant use, with a receiver-operator characteristic area under the curve (ROC AUC) of 89.4%. Blinded application of this signature to the subsequent validation cohort yielded a ROC AUC of 81.2%. A model trained on the two baseline metabotypes present before intubation exhibited similar performance in the validation cohort. This study demonstrates the plausibility and promise of rapid metabolome-based prognostic discovery and validation in the opening wave of a pandemic. The approach used here could be used to inform therapeutic and resource allocation decisions early in a future epidemic.IMPORTANCEIn a new respiratory virus pandemic, the ability to identify patients at greatest risk for severe disease is essential to direct scarce medical resources to those most likely to benefit from them. Tools to predict disease severity are best developed early in a pandemic, but laboratory-based resources to develop these may be limited by available technology and by infection precautions. Here, we show that an accessible metabolic profiling approach could identify a prognostic signature of severe disease in the initial wave of COVID-19, when patients presenting for care often exceeded the available doses of convalescent plasma and remdesivir. In a future pandemic, this approach, alongside efforts to identify clinical disease severity predictors, could improve patient outcomes and facilitate therapeutic trials by identifying individuals at high risk for severe disease.

Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo

Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detection of different cancer types and highlights the advancements of AI for in vivo cancer detection in smartphone-based imaging. However, these compact smartphone systems face challenges like low imaging quality and restricted computing power. The use of advanced AI algorithms to address the optical and computational limitations of smartphone-based imaging systems provides promising solutions. AI-based cancer detection also faces challenges. Transparency and reliability are critical factors in gaining the trust and acceptance of AI algorithms for clinical application, explainable and uncertainty-aware AI breaks the black box and will shape the future AI development in early cancer detection. The challenges and solutions for improving AI accuracy, transparency, and reliability are general issues in AI applications, the AI technologies, limitations, and potentials discussed in this paper are applicable to a wide range of biomedical imaging diagnostics beyond smartphones or cancer-specific applications. Smartphone-based multimodal imaging systems and deep learning algorithms for multimodal data analysis are also growing trends, as this approach can provide comprehensive information about the tissue being examined. Future opportunities and perspectives of AI-integrated smartphone imaging systems will be to make cutting-edge diagnostic tools more affordable and accessible, ultimately enabling early cancer detection for a broader population.

Healthcare workers’ opinions on non-medical criteria for prioritisation of access to care during the pandemic

IntroductionThe COVID-19 pandemic generated overflow of healthcare systems in several countries. As the ethical debates focused on prioritisation for access to care with scarce medical resources, numerous recommendations were created. Late 2021, the emergence of the Omicron variant whose transmissibility was identified but whose vaccine sensitivity was still unknown, reactivated debates. Fears of the need to prioritise patients arose, particularly in France. Especially, a debate began about the role of vaccination status in the prioritisation strategy.Material and methodsThe Ethics Committee (EC) of the University Hospital of Bordeaux (UHB), France, identified prioritisation criteria in the literature (some recommended, such as being a healthcare worker (HCW) or having consented to research, while others were discouraged, such as age with a threshold effect or vaccination status). A survey was sent within the institution in January 2022 to explore frontline physicians' adherence to these prioritisation criteria. The decision making conditions were also surveyed.ResultsIn 15 days, 78/165 (47.3%) frontline physicians responded, and more widely 1286/12946 (9.9%) professionals. A majority of frontline physicians were opposed to prioritising HCWs (54/75, 72%) and even more opposed to participating in research (69/76, 89.6%). Conversely, the results were very balanced for non-recommended criteria (respectively 39/77, 50.7% and 34/69 49.3% in favour for age with a threshold effect and for vaccination status). Decisions were considered to be multi-professional and multi-disciplinary for 65/76, 85.5% and 53/77, 68.8% of frontline physicians. Responders expressed opposition to extending decision-making to representatives of patients, civil society or HCWs not involved in care.DiscussionPrioritisation recommendations in case of scarce medical resources were not necessarily approved by the frontline physicians, or by the other HCWs. This questions the way ethical recommendations should be communicated and discussed at a local scale, but it also questions these recommendations themselves. The article also reports the experience of seeking HCWs opinions on a sensitive ethical debate in a period of crisis.

Understanding the factors influencing nurses in managing patients with diabetic ketoacidosis in the emergency departments of referral hospitals in Dar es Salaam, Tanzania: A descriptive qualitative study.

The quality of nursing management of diabetic ketoacidosis (DKA) in emergency departments may be associated with either increased or decreased length of hospital stay. Despite that patient with DKA need intensive care unit (ICU) admission, effective initial nursing management of DKA patients at the emergency department is important. Regarding factors influencing the effective management when caring for patients with DKA, it is unknown how Tanzanian nurses perceive these issues. Therefore, this study was aimed to explore nurses' perceived factors influencing nursing management of DKA patients at emergency medical department (EMD) of two selected referral hospitals in Dar es Salaam, Tanzania. A descriptive qualitative study design was conducted to explore nurses' perspectives on the factors influencing nursing management of DKA patients. A total of twelve purposively selected nurses participated in in-depth interviews. The interview guide written in Kiswahili was used to collect data. All interviews were audio recorded and transcribed verbatim, and transcripts analyzed using qualitative content analysis. Two main themes emerged from the study including facilitators of DKA nursing management and barriers to DKA nursing management. Facilitators of DKA management encompassed three categories including nurses' knowledge of DKA, the availability of DKA management protocol, and nurses' skillset to enhance DKA management. On the other hand, barriers to DKA management had eight sub-categories including limited training on DKA management, lack of autonomy, decisions disagreement, delayed electrolyte results, scarcity of medical resources, shortage of nursing staffs, logistics in emergency care, and lack of specific-nursing management guideline. This study highlights facilitators and barriers to DKA management and underscores the need for comprehensive strategies to overcome these barriers and consolidate the facilitators to improve nurses' capacity in managing patients with DKA.

Abolishing age criterion to determine organ transplant recipients in Israel: A qualitative study of medical staff perceptions

IntroductionSince April 2014 the age limitation on candidates listed for organ transplantation in Israel was abolished following the recommendations of a Public Committee. In this research the new policy was examined in light of scarce medical resources and the increased rate of aging in Israel. MethodsThe opinions of twelve medical staff regarding the policy change were analyzed by a qualitative methodology, using semi-structured interviews. ResultsInterviews with medical staff members revealed three main themes: 1. positive acceptance of the new policy; 2. concerns and problems regarding the change; and 3. the influence of the policy change on the organ transplant allocation system and patient-doctor relationships. Discussion and ConclusionsThe medical staff expressed positive views towards the new policy, based on age-free, individually determined admission to transplant waiting lists. However, some concerns were raised regarding the medical implications of this policy, thus potentially hindering its full application.

Moral challenges and understanding of clinical ethics in Tanzanian hospitals: Perspectives of healthcare professionals.

Healthcare professionals encounter many moral challenges in their daily clinical practice. However, there have been few studies on the subject matter in Tanzania. This study aims to provide an account of moral challenges faced by healthcare professionals in Tanzanian hospitals, their understanding of clinical ethics, and the ethics education they have received. Many participants reported receiving some kind of ethics training through formal education and on-the-job training. Some participants understood ethics in healthcare settings as adherence to established laws, regulations, guidelines, procedures, norms, and rules essential in clinical practice. Analysis of the data identified four themes of moral challenges. These challenges are related to 1) decision-making and communication in clinical practice, 2) scarcity of medical resources and prioritization in clinical practice, 3) withdrawal of curative treatment, and 4) conflicts between professional judgment, religious convictions and adherence to alternative treatments. Based on the findings, we suggest a context-sensitive form of clinical ethics training to prepare healthcare professionals to recognize and address these moral challenges.

Nursing Care and Ethical Challenges in the COVID-19 Pandemic: A Review

The COVID-19 pandemic has placed unprecedented pressures on healthcare systems across the globe, with nurses playing a pivotal role in managing patient care during this crisis. As front-line caregivers, nurses were not only responsible for delivering essential medical care but also found themselves navigating a series of ethical challenges that were often fraught with complexity. This review delves into the multifaceted role of nursing during the pandemic, focusing on both the clinical care provided and the significant ethical dilemmas faced by nurses. One of the most pressing ethical challenges was the allocation of scarce medical resources. The surge in COVID-19 cases led to critical shortages of ventilators, ICU beds, personal protective equipment (PPE), and other medical supplies. Nurses, often collaborating with multidisciplinary teams, were forced to make difficult decisions regarding which patients would receive these limited resources, raising profound ethical concerns about fairness and equity in healthcare. The review highlights the need for transparent, consistent decision-making frameworks that prioritize justice and equity. Such frameworks help ensure that resource allocation is guided by ethical principles rather than subjective judgments, thus fostering a fairer and more consistent approach to patient care. Another significant ethical issue addressed in this review is the balance between personal safety and professional duty. Nurses were often placed in situations where they had to weigh their commitment to patient care against the risks posed to their own health, particularly when faced with shortages of adequate PPE. This balancing act between personal safety and professional obligation introduced ethical dilemmas, such as the extent to which nurses were adequately informed of the risks they faced and whether healthcare institutions fulfilled their responsibility to provide a safe working environment. The review underscores the importance of developing robust infection control measures and ensuring the continuous supply of PPE to safeguard the health and well-being of healthcare workers. Patient autonomy and consent were also critical concerns during the pandemic. The urgency of care for critically ill COVID-19 patients, combined with isolation protocols that restricted family involvement, made it challenging to uphold patients' decision-making rights. The review explores how informed consent processes were affected by the rapid progression of illness and the necessity for immediate medical interventions, often with limited patient or family input. This issue further underscores the need for ethical guidelines that protect patient rights while ensuring timely and effective care. The psychological impact of the pandemic on nurses is another key focus of this review. Nurses faced not only physical exhaustion from long hours of work but also significant emotional strain from witnessing high mortality rates and making difficult ethical decisions. The review emphasizes the need for mental health resources, such as counseling and support groups, to help nurses cope with the emotional toll of their work. Addressing issues such as moral injury, compassion fatigue, and burnout is essential to maintaining a resilient nursing workforce. In conclusion, this review provides a comprehensive examination of the ethical challenges faced by nurses during the COVID-19 pandemic. It highlights the critical need for clear ethical guidelines, transparent resource allocation frameworks, protective measures, and mental health support systems. The lessons learned from this pandemic are invaluable for shaping future responses to global health crises, ensuring that nurses and other healthcare professionals are better equipped to navigate the ethical complexities of emergency care.

Possibility of diagnosing rotator cuff tears in areas with scarce medical resources: can non-standard anteroposterior radiographs accurately predict rotator cuff tears?

Due to the scarcity and high cost of MRI in resource-constrained regions, prompt diagnosis and treatment of rotator cuff tears remain problematic in these areas. Therefore, extensive research has been conducted to predict rotator cuff tears using simple and affordable anteroposterior radiographs. It remains unclear whether non-standard anteroposterior radiographs would have a notable impact on the preciseness of the diagnosis. We analyzed patients treated for shoulder pain at hospitals. These patients underwent shoulder joint MRI and standard anteroposterior radiographs, were categorized into those with rotator cuff tears and a control group. We assessed whether the radiographs were standard anteroposterior radiographs using classification criteria from previous studies. Three assessors independently measured the acromiohumeral interval, upwards migration index, acromion index, critical shoulder angle, and double-circle radius ratio in radiographic images. The intraclass correlation coefficient and receiver operating characteristic curves were used to assess measurement reliability and predictive capabilities of each predictive method for rotator cuff tears. This study included 102 non-standard radiographs that met the research criteria for the measurement and analysis. The intragroup correlation coefficients for the acromiohumeral interval, upwards migration index, and double-circle radius ratio were above 0.7 (0.77, 0.71, 0.76), while those for the acromion index and critical shoulder angle exceeded 0.8 (0.86 and 0.87). In non-standard radiographs, the double-circle radius ratio reliably predicted rotator cuff tears (p < 0.05), contrary to the other methods (p > 0.05). The areas under the receiver operating characteristic curves of the double-circle radius ratio, estimated by the three researchers for rotator cuff tears. This study found that non-standard radiographs significantly impaired the diagnostic performance of the acromiohumeral interval, upwards migration index, acromion index, and critical shoulder angle. Only the double-circle radius ratio maintained its predictive power (although this diminished capability may fall short of clinical relevance) and demonstrated high applicability. These findings indicate the need for researchers to prioritize the quality of radiographs and focus on reducing the sensitivity of the prediction method in relation to radiograph quality. The capability exhibited by the double-circle radius ratio warrants further investigation, to facilitate a simplified diagnosis of rotator cuff tears.

Comparison of Two Symptom Checkers (Ada and Symptoma) in the Emergency Department: Randomized, Crossover, Head-to-Head, Double-Blinded Study.

Emergency departments (EDs) are frequently overcrowded and increasingly used by nonurgent patients. Symptom checkers (SCs) offer on-demand access to disease suggestions and recommended actions, potentially improving overall patient flow. Contrary to the increasing use of SCs, there is a lack of supporting evidence based on direct patient use. This study aimed to compare the diagnostic accuracy, safety, usability, and acceptance of 2 SCs, Ada and Symptoma. A randomized, crossover, head-to-head, double-blinded study including consecutive adult patients presenting to the ED at University Hospital Erlangen. Patients completed both SCs, Ada and Symptoma. The primary outcome was the diagnostic accuracy of SCs. In total, 6 blinded independent expert raters classified diagnostic concordance of SC suggestions with the final discharge diagnosis as (1) identical, (2) plausible, or (3) diagnostically different. SC suggestions per patient were additionally classified as safe or potentially life-threatening, and the concordance of Ada's and physician-based triage category was assessed. Secondary outcomes were SC usability (5-point Likert-scale: 1=very easy to use to 5=very difficult to use) and SC acceptance net promoter score (NPS). A total of 450 patients completed the study between April and November 2021. The most common chief complaint was chest pain (160/437, 37%). The identical diagnosis was ranked first (or within the top 5 diagnoses) by Ada and Symptoma in 14% (59/437; 27%, 117/437) and 4% (16/437; 13%, 55/437) of patients, respectively. An identical or plausible diagnosis was ranked first (or within the top 5 diagnoses) by Ada and Symptoma in 58% (253/437; 75%, 329/437) and 38% (164/437; 64%, 281/437) of patients, respectively. Ada and Symptoma did not suggest potentially life-threatening diagnoses in 13% (56/437) and 14% (61/437) of patients, respectively. Ada correctly triaged, undertriaged, and overtriaged 34% (149/437), 13% (58/437), and 53% (230/437) of patients, respectively. A total of 88% (385/437) and 78% (342/437) of participants rated Ada and Symptoma as very easy or easy to use, respectively. Ada's NPS was -34 (55% [239/437] detractors; 21% [93/437] promoters) and Symptoma's NPS was -47 (63% [275/437] detractors and 16% [70/437]) promoters. Ada demonstrated a higher diagnostic accuracy than Symptoma, and substantially more patients would recommend Ada and assessed Ada as easy to use. The high number of unrecognized potentially life-threatening diagnoses by both SCs and inappropriate triage advice by Ada was alarming. Overall, the trustworthiness of SC recommendations appears questionable. SC authorization should necessitate rigorous clinical evaluation studies to prevent misdiagnoses, fatal triage advice, and misuse of scarce medical resources. German Register of Clinical Trials DRKS00024830; https://drks.de/search/en/trial/DRKS00024830.

Implications for Nash Equilibrium in Intelligent Transportation Systems and Allocation of Scarce Medical Resources

Implications for Nash Equilibrium in Intelligent Transportation Systems and Allocation of Scarce Medical Resources

On the Morality of Reallocating Life-Sustaining Interventions in Times of Scarcity

Abstract As coronavirus disease 2019 made its way across the world in 2019, health systems began to develop guidelines to allocate what was expected to be a scarcity of medical resources. Considerable attention was given to triaging intensive care resources such as ventilators. While there was general agreement among bioethicists and policymakers that it may be permissible to withhold life-sustaining interventions from patients with poor prognoses in order to make them available to patients with better prognoses, there was disagreement about the permissibility of withdrawing such interventions for this purpose. Some maintained that the doctrine of double effect (DDE) revealed the intrinsic wrongness of such acts of reallocation. This article argues that so long as there is transparency and effective ongoing communication, the DDE may permit the discontinuation of life-sustaining interventions from patients who may still benefit from them in order to make them available to patients with more favorable prognoses.

Long-term outcome following liver transplantation of patients with ACLF grade 3

Utility, a major principle for allocation in the context of transplantation, is questioned in patients with acute-on chronic liver failure grade 3 (ACLF-3) who undergo liver transplantation (LT). We aimed to explore long-term outcomes of patients included in a three-centre retrospective French study published in2017. All patients with ACLF-3 (n= 73), as well as their transplanted matched controls with ACLF-2 (n= 145), 1 (n= 119) and no ACLF (n= 292), who participated in the Princeps study published in 2017 were included. We explored 5- and 10-year patient and graft survival rates, causes of death and their predictive factors. Median follow-up of patients with ACLF-3 was 7.5 years. At LT, median MELD was 40. In patients with ACLF-3, 2, 1 and no ACLF, 5-year patient survival rates were 72.6% vs. 69.7% vs. 76.4% vs. 77.0%, respectively (p= 0.31). Ten-year patient survival for ACLF-3 was 56.8% and was not different to other groups (p= 0.37). Leading causes of death in patients with ACLF-3 were infections (33.3%) and cardiovascular events (23.3%). After exclusion of early death, UCLA futility risk score, age-adjusted Charlson comorbidity index and CLIF-C ACLF score were independently associated with 10-year patient survival. Long-term graft survival rates were not different across the groups. Clinical frailty scale and WHO performance status improved over time in patients alive after 5 years. 5- and 10-year patient and graft survival rates were not different in patients with ACLF-3 compared to matched controls. 5-year patient survival is higher than the 50%-70% threshold defining the utility of a liver graft. Efforts should focus on candidate selection based on comorbidities, as well as the prevention of infection and cardiovascular events. While short-term outcomes following liver transplantation in the most severely ill patients with cirrhosis (acute-on-chronic liver failure grade 3 [ACLF-3]) are known, long-term data are limited, raising questions about the utility of graft allocation in the context of scarce medical resources. This study provides a favourable long-term update, confirming no differences in 5- and 10-year patient and graft survival following liver transplantation in patients with ACLF-3 compared to matched patients with ACLF-2, ACLF-1, and no-ACLF. The study highlights the risk of dying from infection and cardiovascular causes in the long-term and identifies scores including comorbidity evaluation, such as the age-adjusted Charlson comorbidity index, as independently associated with long-term survival. Therefore, physicians should consider the cumulative burden of comorbidities when deciding whether to transplant these patients. Additionally, after transplantation, the study encourages mitigating infectious risk with tailored immunosuppressive regimens and tightly managing cardiovascular risk over time.

Rational resistance amidst gentle breeze and mild rain: Examining online collective behavior among the Chinese public using the elaborated social identity model.

This research examines internet collective behavior in mainland China during the COVID-19 pandemic, focusing on the factors and characteristics that drive such behavior. The Chinese government initially implemented a conservative and biased policy to contain the spread of the virus, but the sudden lifting of lockdown measures in late 2022 resulted in a surge in infections and scarcity of medical resources. This policy shift led many Chinese internet users to perceive the government's actions as hasty and harsh, prompting them to engage in collective online behavior. The study employed a survey-based approach, collecting 1,626 valid questionnaires, which underwent reliability testing, descriptive statistical analysis, and a difference-in-differences test. A structural equation model (SEM) was then constructed and applied to comprehensively analyze the mediating and moderating effects of latent variables. Ethical considerations were prioritized, with informed consent obtained from all participants, who were provided with detailed information about the study and given sufficient time to review and ask questions. The research yielded three primary conclusions: the Chinese public demonstrated a perception of fairness and exhibited obedience, respect, and cooperation with the government during the epidemic; the observed online collective behavior can be characterized as a moderate and rational form of resistance, explained by the elaborated social identity model (ESIM); and the middle class consistently adopted a self-vulnerability strategy, positioning themselves as beneficiaries of protection to maximize their own interests in epidemic prevention and control. This study shows notable insights into internet collective behavior in mainland China during the COVID-19 pandemic, highlighting perceptions, resistance, and strategies adopted by different segments of the population.

Discussion on the Management Strategy of Imaging Medical Equipment under the New Situation

In the realm of healthcare, particularly in the management of medical imaging equipment, unprecedented challenges and opportunities are encountered. This is especially pronounced in the niche domain of gastrointestinal endoscopy, where innovative management strategies are pivotal in the face of evolving circumstances. With the rapid iteration of technology, the escalating scarcity of medical resources, alongside the rising costs of management and maintenance, coupled with the ongoing flux of regulations and standards, these factors collectively constitute the intricate landscape confronting current management of medical imaging equipment. Addressing the pace of technological advancement, employing proactive technological foresight and flexible updating strategies are paramount. This entails not only technological upgrades of the equipment itself but also continuous training and education for operators to ensure their alignment with technological advancements. Given the scarcity of medical resources, enhancing the utilization and efficiency of existing equipment becomes imperative. This may involve optimizing equipment allocation and scheduling, reducing idle time, while also exploring the application of emerging technologies such as telemedicine to expand service coverage. This article will delve into how, amidst new circumstances, effective management strategies can optimize the utilization and maintenance of gastrointestinal endoscopy to cope with challenges such as the rapid pace of technological evolution, scarcity of medical resources, increasing management and maintenance costs, as well as changes in regulations and standards.

Revolutionizing Heart Monitoring with Oximetry

Innovative medical gadgets are now critically needed to monitor and analyze the situation, especially in light of the COVID-19 pandemic. In light of this, a portable, affordable, IOT-based heart rate and SpO2/level sensor device with effective performance has been developed. In order to publish data to a software web page and display data on the OLED, the suggested system has an integrated LED with basic internet access. Its mobility, user-friendliness, and distinctive method of sending collected data are its key qualities. data online, enabling it to perform better than the solutions that are currently on offer in a number of ways. The project is designed in such a way that the analogue quantities which are to be recorded continuously are taken and converted into corresponding digital values using a six-channel ADC. Here we consider temperature and monitor the heart rate. The processed data from ADC is sent to the microcontroller. With the help of a six-channel ADC, the project's architecture transforms the analogue numbers that must be continuously recorded into equivalent digital values. Here, we keep an eye on the heart rate and take the temperature into account. The microcontroller receives the data from the ADC that has been processed. The microcontroller is designed to continuously monitor the status of the sensors. To transfer sensor data to the server web page in the form of a database, the microcontroller and the Wifi Module will be interfaced using serial connection. Monitoring physiological data, such as electrocardiogram (ECG) signals, offers a new avenue for ease of control and prevention in modern healthcare. Consequently, a new era of proactive, intelligent healthcare would emerge—especially considering the major barrier of scarce medical resources. It was shown how to use a mobile ECG monitoring service hosted in the cloud. With the use of a pulse oximeter and a wireless transmission method like WI-FI, these may identify ECG signals and display them on a screen. IOT based on ECG monitoring has been the subject of various works.

To what extent do lay people and healthcare providers differ in the allocation of scarce medical resources in the context of the COVID-19 pandemic?

Studying the most ethical way to allocate scarce medical resources has been of interest within the last year, due to shortages associated with the COVID-19 pandemic. This study aimed to establish differences between what healthcare providers (HCP) and laypeople consider to be the most ethical way to prioritise the distribution of scarce resources. Healthcare providers ( n = 100) and laypeople ( n = 102) were asked to rank ethical principles from most to least ethical for the allocation of ICU beds in a COVID-19 outbreak, COVID-19 vaccinations and organ transplantation, and the rankings from each scenario across the two groups were compared. Results indicated that HCPs preferred utilitarian principles informed by medical knowledge, such as ‘prognosis’, whereas laypeople preferred less situation-specific and prioritarian principles, such as ‘sickest first’, replicating pre-pandemic findings. Laypeople showed increased in-group agreement when ranking principles, potentially due to the collective experience of the pandemic. Demographic variables such as older age, identifying as Black, Asian or minority ethnic and greater experience with COVID-19, were also associated with a preference for ‘prognosis’.

The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research.

Development and Validation of a Risk Prediction Model for Female Stress Urinary Incontinence in Rural Fujian, China.

With China's rapidly aging population and the rising proportion of obese people, an increase in the number of women suffering from urinary incontinence (UI) is to be expected. In order to identify high-risk groups before leakage occurs, we aimed to develop and validate a model to predict the risk of stress UI (SUI) in rural women. This study included women aged 20-70 years in rural Fujian who participated in an epidemiologic survey of female UI conducted between June and October 2022. Subsequently the data was randomly divided into training and validation sets in a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify independent risk factors as well as to further construct a nomogram for risk prediction. Finally, concordance index (C-index), calibration curve and decision curve analysis were applied to evaluate the performance of the predictive models. A total of 5290 rural females were enrolled, of whom 771 (14.6%) had SUI. Age, body mass index (BMI), postmenopausal status, number of vaginal deliveries, vaginal delivery of large infant, constipation and family history of pelvic organ prolapse (POP) and SUI were included in the nomogram. C-index of this prediction model for the training and validation sets was 0.835 (95% confidence interval [CI] = 0.818-0.851) and 0.829 (95% CI = 0.796-0.858), respectively, and the calibration curves and decision analysis curves for both the training and validation sets showed that the model was well-calibrated and had a positive net benefit. This model accurately estimated the SUI risk of rural women in Fujian, which may serve as an effective primary screening tool for the early identification of SUI risk and provide a basis for further implementation of individualized early intervention. Moreover, the model is concise and intuitive, which makes it more operational for rural women with scarce medical resources.

Game Theory-Based Decision Making for the Allocation of Scarce Medical Resources in the COVID-19 Situation

Game Theory-Based Decision Making for the Allocation of Scarce Medical Resources in the COVID-19 Situation

Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System.

Background: The scarcity of medical resources and personnel has worsened due to COVID-19. Telemedicine faces challenges in assessing wounds without physical examination. Evaluating pressure injuries is time consuming, energy intensive, and inconsistent. Most of today's telemedicine platforms utilize graphical user interfaces with complex operational procedures and limited channels for information dissemination. The study aims to establish a smart telemedicine diagnosis system based on YOLOv7 and large language model. Methods: The YOLOv7 model is trained using a clinical data set, with data augmentation techniques employed to enhance the data set to identify six types of pressure injury images. The established system features a front-end interface that includes responsive web design and a chatbot with ChatGPT, and it is integrated with a database for personal information management. Results: This research provides a practical pressure injury staging classification model with an average F1 score of 0.9238. The system remotely provides real-time accurate diagnoses and prescriptions, guiding patients to seek various medical help levels based on symptom severity. Conclusions: This study establishes a smart telemedicine auxiliary diagnosis system based on the YOLOv7 model, which possesses capabilities for classification and real-time detection. During teleconsultations, it provides immediate and accurate diagnostic information and prescription recommendations and seeks various medical assistance based on the severity of symptoms. Through the setup of a chatbot with ChatGPT, different users can quickly achieve their respective objectives.