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Background: The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) has long recognized and championed increasing diversity within the surgical workplace. SAGES initiated the Fundamentals of Leadership Development (FLD) Curriculum to address these needs and to provide surgeon leaders with the necessary tools and skills to promote diversity, equity, and inclusion (DEI) in surgical practice. In 2019, the American College of Surgeons issued a request for anti-racism initiatives which lead to the partnering of the two societies. The primary goal of FLD was to create the first surgeon-focused leadership curriculum dedicated to DEI. The rationale/development of this curriculum and its evaluation/feedback methods are detailed in this White Paper. Methods: The FLD curriculum was developed by a multidisciplinary task force that included surgeons, education experts, and diversity consultants. The curriculum development followed the Analysis, Design, Development, Implementation and Evaluation (ADDIE) instructional design model and utilized a problem-based learning approach. Competencies were identified, and specific learning objectives and assessments were developed. The implementation of the curriculum was designed to be completed in short intervals (virtual and in-person). Post-course surveys used the Kirkpatrick's model to evaluate the curriculum and provide valuable feedback. Results: The curriculum consisted of interactive online modules, an online discussion forum, and small group interactive sessions focused in three key areas: (1) increasing pipeline of underrepresented individuals in surgical leadership, (2) healthcare equity, and (3) conflict negotiation. By focusing on positive action items and utilizing a problem-solving approach, the curriculum aimed to provide a framework for surgical leaders to make meaningful changes in their institutions and organizations. Conclusion: The FLD curriculum is a novel leadership curriculum that provided surgeon leaders with the knowledge and tools to improve diversity in three areas: pipeline improvement, healthcare equity, and conflict negotiation. Future directions include using pilot course feedback to enhance curricular effectiveness and delivery. [ABSTRACT FROM AUTHOR]

I n the context of an adequate health care organization, the figure of the neurologist as an emergency operator (in the emergency room—ER—and/or in a dedicated outpatient clinic) is crucial for an effective functional connection with the territory (and therefore with general practitioners), a reduction in inappropriate ER accesses, specific diagnostic and therapeutic approaches to neurological emergencies in the ER and a reduction in nonspecific or even unnecessary instrumental investigations. In this position paper of the Italian Association of Emergency Neurology (ANEU: Associazione Neurologia dell'Emergenza Urgenza), these issues are addressed, and two important organizational solutions are proposed: The Neuro Fast Track, as an outpatient organization approach strongly linked to general practitioners and non-neurological specialists and dedicated to cases with deferrable urgency (to be assessed within 72 h) The identification of an emergency neurologist, who is engaged in ER assessments as a consultant and involved in the management of the semi-intensive care unit of the emergency neurology and the stroke unit according to an appropriate rotation, as well as in consultations for patients with neurological emergencies in inpatient wards The possibility of computerizing the screening of patients with deferrable urgency in the Neuro Fast Track is described. A dedicated app represents an important tool that can facilitate the identification of patients for whom deferred assessment is appropriate, the scheduling of neurological examinations and reductions in the booking time through a more rapid approach to specialist assessment and subsequent investigations. [ABSTRACT FROM AUTHOR]

This paper explores the potential for a system to be a co-author on an academic paper based on the criteria proposed by the International Committee of Medical Journal Editors (ICMJE). We used a third generation generative pretrained transformer (GPT-3) to write a review paper on the topic of its choice: The Effects of Sleep Deprivation on Cognitive Function. The system was asked to fulfill the four main criteria for co-authorship as recommended by ICMJE, which includes contributions to the conception or design of the work; drafting the work or revising it critically for important intellectual content; final approval of the version to be published; and agreement to be accountable for all aspects of the work. Our results showed that the system was able to fulfill the criteria, with significant difficulties with accurate and reliable referencing. We also explored the ethical implications of using AI systems for research and found that it is important to take a cautious approach when considering its use for scientific authorship. This case study provides a methodology for further investigations into the possibilities and limitations of automated writing. [ABSTRACT FROM AUTHOR]

Background: Primary care practices are responding to calls to incorporate patients' social risk factors, such as housing, food, and economic insecurity, into clinical care. Healthcare likely relies on the expertise and resources of community-based organizations to improve patients' social conditions, yet little is known about the referral process.Objective: To characterize referrals to community-based organizations by primary care practices.Design: Qualitative study using semi-structured interviews with healthcare administrators responsible for social care efforts in their organization.Participants: Administrators at 50 diverse US healthcare organizations with efforts to address patients' social risks.Main Measures: Approaches used in primary care to implement social needs referral to community-based organizations.Results: Interviewed administrators reported that social needs referrals were an essential element in their social care activities. Administrators described the ideal referral programs as placing limited burden on care teams, providing patients with customized referrals, and facilitating closed-loop referrals. We identified three key challenges organizations experience when trying to implement the ideal referrals program: (1) developing and maintaining resources lists; (2) aligning referrals with patient needs; and (3) measuring the efficacy of referrals. Collectively, these challenges led to organizations relying on staff to manually develop and update resource lists and, in most cases, provide patients with generic referrals. Administrators not only hoped that referral platforms may help overcome some of these barriers, but also reported implementation challenges with platforms including inconsistent buy-in and use across staff; integration with electronic health records; management and prioritization of resources; and alignment with other organizations in their market.Conclusion and Relevance: Referrals to community-based organizations were used in primary care to improve patients' social conditions, but despite strong motivations, interviewees reported challenges providing tailored and up-to-date information to patients. [ABSTRACT FROM AUTHOR]

Radiology as a specialty has been enormously successful since its beginnings, moving over time from an adjunct to clinical decision-making to a crucial component of multidisciplinary patient care. However, this increased centrality of radiology and reliance on our services carries within it dangers, prominent among them being the danger of our being viewed as deliverers of a commodity, and the risk of our becoming overwhelmed by increasing workload, unable to interact sufficiently with patients and referrers due to pressure of work. With this White Paper, the Board of Directors of the European Society of Radiology (ESR) seeks to briefly explain the position of the radiologist in the modern healthcare environment, considering our duties and contributions as doctors, protectors, communicators, innovators, scientists and teachers. This statement is intended to serve as a summary of the breadth of our responsibilities and roles, and to assist radiologists in countering misunderstanding of who we are and what we do. [ABSTRACT FROM AUTHOR]

Long-term management after stroke has not been standardized in contrast to acute and rehabilitative stroke care. The fragmented outpatient sector of healthcare allows a high degree of flexibility but also variability in the quality of care provided. The commission on long-term stroke care of the German Stroke Society developed a concept on how to provide standardized multiprofessional long-term stroke care with a focus on patient-centered comprehensive care. We propose a diagnostic work-up in stages, with an initial patient-reported screening, followed by an adapted in-depth assessment of affected domains. This includes internistic domains (management of risk factors) and also genuine neurological domains (spasticity, cognitive deficits etc.) that must be considered. This information is then merged with patient expectations and prioritization to a standardized treatment plan. Special challenges for the implementation of such a comprehensive care system are the intersectoral and intrasectoral communication between healthcare providers, patients and relatives, the need to create a compensation system for providers and the establishment of appropriate quality management services. Digital health applications are helpful tools to provide aspects of diagnostics, treatment and communication in long-term stroke care. [ABSTRACT FROM AUTHOR]

Over the years, survival of children with chronic diseases has significantly improved and a large proportion of them now are entering into adulthood. Transition of Care (ToC) of such patients with having childhood onset of chronic diseases to the adult health care system is well organized in developed countries, although it is an emerging concept in India. In situations where the systems for ToC are not in place, such cases are fraught with unsatisfactory health outcomes. With proper ToC in place, these patients are likely to receive uninterrupted care by the adult care physicians and hence reach their full potential. This document highlights the need, rationale and way forward for ToC of youth with special health care needs (YSHCN) across the country. It also describes the standard operating procedures to develop the ToC at a hospital level for clinicians and administrators. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development. This is especially relevant since IR is a highly advanced specialty that heavily relies on technology and thus is naturally susceptible to disruption by new technological developments. Disruption always means opportunity and interventionalists must therefore understand AI and be a central part of decision-making when such systems are developed, trained, and implemented. Furthermore, interventional radiologist must not only embrace but lead the change that AI technology will allow. The CIRSE position paper discusses the status quo as well as current developments and challenges. [ABSTRACT FROM AUTHOR]

Purpose: The prevention and early diagnosis of medication-related osteonecrosis of the jaw (MRONJ) is fundamental to reducing the incidence and progression of MRONJ. Many in the field believe that dental hygienists should play an integral role in primary and secondary MRONJ prevention. However, to date, very few publications in the literature have proposed standardised MRONJ protocols, which are dedicated to dental hygienists. The aim of this study was to provide guidance to the health care providers managing MRONJ.Methods: The expert opinion in this study was developed by dental hygienists from the main Italian technical-scientific associations (Italian Dental Hygienists Association, AIDI and National Union of Dental Hygienists, UNID) and authors of the latest Italian recommendations regarding MRONJ from the field of dentistry and maxillofacial surgery.Results: The oral care protocol outlined in this position paper is focused on the role of dental hygienist in patients at risk or affected by MRONJ, and it regards 3 main issues: primary prevention, secondary prevention and supporting the treatment of MRONJ. Each issue contains easy-to-apply indications and procedures, as described by the authors, regarding the role of the dental hygienist.Conclusion: Referring to the main issues under consideration (primary prevention, secondary prevention and the treatment of MRONJ), a clinical examination of periodontal tissue is critical in preventing MRONJ. It is the opinion of the authors of this study that the application of a periodontal screening score is fundamental in defining personalised strategies for patients at risk of MRONJ. By means of these basic procedures, a protocol for assisting the health care provider and the presentation of a practical approach for patients at risk or affected by MRONJ are described in this study. [ABSTRACT FROM AUTHOR]

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In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is lacking in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. [ABSTRACT FROM AUTHOR]

Aging causes the reduction in physical and physiological strength and an increase in the odds of affliction with chronic physical and mental diseases ultimately affecting the quality of life (QoL). So far, several primary studies have been conducted on the impact of religious-spiritual education and care on the QoL of the elderly. However, to the best of our knowledge, a study that compiles and represents these findings in a published paper is lacking. Therefore, the current study conducted a systematic review and meta-analysis to investigate the effect of religious-spiritual education and care on the QoL of the elderly population. To access studies related to the aim of the study, SID, MagIran, IranMedex, IranDoc, Embase, PubMed, Scopus, Web of Science, and Google Scholar databases were searched. The search was done using relevant and validated keywords with MeSH/Emtree without time limitations until December 2021. The qualitative appraisal of the papers was done using the JBI checklist. To estimate the heterogeneity of studies, I2 index was used, and to combine the data and carry out a meta-analysis, Random Effects model was used. In the primary research, 1065 studies were found. The omission of the redundant cases led to a total number of 12 papers with a sample size of 317 in the intervention group and 321 in the control to be included in the meta-analysis. After the intervention, the mean score of QoL showed a significant increase by 1.42 ± 0.31 with a Confidence Interval of 95% (P<0.001). Meta-regression results showed that the increase in the year of conducting the study, and the number of intervention sessions, that the standardized mean difference had a decreasing trend, and that with the increase in the average age of the elderly, there was a rising trend (P<0.001). Therefore, it seems that the application of religious-spiritual education and care could be part of a program to improve the QoL of the elderly. [ABSTRACT FROM AUTHOR]

A large proportion of hospital inpatients are affected by cognitive impairment, posing challenges in the provision of their care in busy, fast-paced acute wards. Signs and symbols, known as visual identifiers, are employed in many U.K. hospitals with the intention of helping healthcare professionals identify and respond to the needs of these patients. Although widely considered useful, these tools are used inconsistently, have not been subject to full evaluation, and attract criticism for acting as a shorthand for a routinized response. In order for visual identifiers to be used effectively in acute care settings, thorough consideration must be given to the ethical and legal issues that are engaged in this context, and their potential benefits and harms must be weighed and balanced. This paper proposes a set of legal and ethical principles that can be used to guide the implementation of visual identifiers. Together, these principles provide a framework applicable in the design and implementation phases to systematically identify relevant considerations arising from the use of these tools. We outline some tensions that arise between principles and conclude that selecting a preferred moral framework could help to guide decision-making, as does clarity around the purpose and objectives of the identifier. [ABSTRACT FROM AUTHOR]

The rapid advancement of technology has created new opportunities to improve the accuracy and efficiency of medical diagnoses, treatments, and overall patient care in several medical domains, including mental health. One promising novel approach is federated learning, a machine learning approach that allows multiple devices to train a shared model without exchanging raw data. Instead of centralizing the data in one location, each device or machine holds a portion of the data and collaborates with other devices to update the shared model. In this way, federated learning enables training on more extensive and diverse datasets than would be possible with centralized training while preserving the privacy and security of individual data. In the mental health domain, federated learning has the potential to improve mental disorders' detection, diagnosis, and treatment. By pooling data from multiple sources while maintaining patient privacy by keeping data secure and ensuring that they are not used for unauthorized purposes. This literature survey reviews recent studies that have exploited federated learning in the psychiatric domain, covering multiple data resources and different machine-learning techniques. Furthermore, we formulate the gap in the current methodologies and propose new research directions. [ABSTRACT FROM AUTHOR]

With the rapid development of emerging network technologies such as cloud computing, the background server-side of public health information services is widely deployed on the untrusted cloud, which has become one of the main threats of user health privacy leakage. To this end, this paper proposes an agent-based algorithm for the protection for user privacy health topics based on identity replacement. Its basic idea is to deploy a group of intermediate agents between the server-side and the client-side, to replace the identity of each health service request issued by client users and then submit it to the server-side, thereby, making it difficult to identify the real user corresponding to each request, and then improving the security of user health topic privacy on the completely-untrusted server-sides. Then, this paper proposes a client-based algorithm for the selection of intermediate agents, which evenly distributes the request data issued by client users to all the agents after topic identification and privacy computation for the request data, to improve the security of user health topic privacy on the incompletely-trusted agent-side. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed method, i.e., it can effectively improve the security of user privacy health topics on the untrusted server-side, under the premises of no changes to user usage habits, server-side architecture, service algorithm, service accuracy and service efficiency, so as to provide a theoretical and technical foundation for building a privacy-preserving platform for public health information services. [ABSTRACT FROM AUTHOR]

The rapid development of healthcare big data has brought certain convenience to medical research and health management, but privacy protection of healthcare big data is an issue that must be considered in the process of data application. Access control is one of the methods for privacy protection, but traditional access control models cannot adapt to the dynamic, continuous, and real-time characteristics of healthcare big data scenarios. In this paper, we propose an access control model based on risk quantification and usage control (RQ-UCON). The model adds a risk quantification module to the traditional UCON model to achieve privacy protection of medical data. This module classifies risks into direct and indirect risks and quantifies them based on the physician's visit history. The model stores the quantified risk values as subject attributes. The RQ-UCON model uses an improved Exponentially Weighted Moving Average (EWMA) and penalty factors to predict risk value and to update the risk values of the subject attributes in real-time. The RQ-UCON model uses agglomerative hierarchical clustering to cluster the risk values of physicians within the department, resulting in risk intervals for each physician's operational behavior. Each risk interval is stored as a condition in the RQ-UCON model. Finally, according to the model whether the subject attributes meet the model conditions to determine whether the subject has the corresponding access rights, and according to the risk interval to grant the subject the corresponding access rights. Through the final experiment, it can be seen that the access control model proposed in this paper has a certain control on the excessive access behavior of doctors and has a certain limitation on the privacy leakage of healthcare big data. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models. Healthcare data democratization is often hampered by privacy constraints governing the sensitive healthcare data. Here, the authors show that encoding healthcare data could be a potential solution for achieving healthcare democratization within the context of deep learning. [ABSTRACT FROM AUTHOR]

The complex or heterogeneous nature of multimorbidity implies the need to provide integrated care for multimorbid patients. However, Implementing and Managing integrated care for Multimorbid patients (IMM) faces several challenges, which have led to several best practices in healthcare and Information Technology (IT). Thus, care programmes need to mutually and holistically adopt multiple best practices for providing integrated care to multimorbid patients. Yet this is not an easy/trivial task, due to the need to understand the contextual and holistic characteristics of specific best practices prior to adoption. Achieving this requires rational or systemic thinking about problems and solutions or best practices in providing integrated care for multimorbid patients. Thus, this paper answers the research question: How can challenges and best practices in IMM be rationally classified, and how can best practices be holistically aligned and prioritized for adoption? Answering this called for adoption of Soft Systems Methodology (SSM). Key research activities involved conducting a narrative review of literature on challenges and best practices in providing integrated care for multimorbid patients, and processing findings using SSM techniques. These yielded a model that specifies core strategic objectives and thematic actions, that can inform and guide the coherent alignment and implementation of healthcare and IT practices in providing integrated care for multimorbid patients. The strategic objectives and thematic actions can be perceived as requirements for a method that stakeholders can use to proactively assess the 'value addition' of specific healthcare and IT practices in a care programme for multimorbid patients. [ABSTRACT FROM AUTHOR]

Mental health is deteriorating far and fast globally post-COVID. Though there were already over one billion people living with mental disorders pre-pandemic, in the first year of COVID-19 alone, the prevalence of anxiety and depression soared by 25% worldwide. In light of the chronic shortages of mental health resources and talents, along with disruptions of available health services caused by pandemic-related restrictions, technology is widely believed to hold the key to addressing the rising mental health crises. However, hurdles such as fragmented and oftentimes suboptimal patient protection measures substantially undermine technology's potential to address the global mental health crises reliably and at scale. To shed light on these issues, this paper aims to discuss the post-pandemic mental health challenges and opportunities, and the strategies and solutions the global mental health community could leverage to protect and elevate society's mental health in the long run. [ABSTRACT FROM AUTHOR]

E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (n = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (g = 0.3). The effect was significant both for blended interventions (g = 0.42) and post-treatment EMH-based aftercare (g = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies. [ABSTRACT FROM AUTHOR]

This paper reports the results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support the implementation of a digital medicine intervention for persons with mild-to-moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (SM; n = 185), peer-supported use (PS; n = 186), or a clinically integrated model CI; (n = 187). Across all three groups, the percentage of self-reported heavy drinking days dropped from 38.4% at baseline (95% CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health and quality of life compared to the self-monitoring group (p = 0.011). However, higher attrition rates in the clinically integrated group warrant consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction. This study was prospectively registered at clinicaltrials.gov on 7/03/2019 (NCT04011644). [ABSTRACT FROM AUTHOR]

Background: In this paper, we estimate the long-lasting influence of the former Habsburg Empire's border on the territory of Romania, specifically on the prevalence of corrupt behaviour and practices in health services. Methods: Employing microdata from the 2016 Life in Transition Survey and applying ordered probit regression, we explore the hypothesis that the geographical proximity of respondents' residences to the former imperial border—restricting the analysis within a bandwidth of 50 km, 75 km or even 100 km on either side – significantly influences current individual tendencies towards corrupt behaviour. Results: The results indicate that individuals in Transylvania living in the immediate vicinity of the former border of the Habsburg Empire (no more than 75 km away) show a higher propensity towards corrupt behaviours, similar to those from Moldova and Wallachia who reside in the same bandwidth but to the east of the former historical border. Interestingly, on one hand, after a series of tests with various relevant factors, the contagion effect is observed from right to left, meaning from those in Moldova and Wallachia towards those in Transylvania, and not the other way around as might be expected based on other previous studies. On the other hand, individuals living more than 75 kms west of the former historical border show clear reluctance to engage in informal payments and gift-giving when interacting with the public health system as patients. Conclusion: By rigorously controlling for various variables that comprehensively show different legacies of the communist regime, our results confirm the persistence of these influences across different bandwidths, thereby corroborating the hypothesis of path dependence influenced by the former Habsburg Empire. [ABSTRACT FROM AUTHOR]

AI and robotic technologies attract much hype, including utopian and dystopian future visions of technologically driven provision in the health and care sectors. Based on 30 interviews with scientists, clinicians and other stakeholders in the UK, Europe, USA, Australia, and New Zealand, this paper interrogates how those engaged in developing and using AI and robotic applications in health and care characterize their future promise, potential and challenges. We explore the ways in which these professionals articulate and navigate a range of high and low expectations, and promissory and cautionary future visions, around AI and robotic technologies. We argue that, through these articulations and navigations, they construct their own perceptions of socially and ethically 'acceptable futures' framed by an 'ethics of expectations.' This imbues the envisioned futures with a normative character, articulated in relation to the present context. We build on existing work in the sociology of expectations, aiming to contribute towards better understanding of how technoscientific expectations are navigated and managed by professionals. This is particularly timely since the COVID-19 pandemic gave further momentum to these technologies. [ABSTRACT FROM AUTHOR]

We describe a computer-assisted program for conducting well-body assessments of children with neurodevelopmental disabilities attending a private special education school. Classroom instructors completed daily assessments to detect the presence of bodily injuries and other skin trauma, determine the need for medical treatment, and monitor healing status. Evaluations performed with the classroom instructors revealed that they implemented the program with high integrity and rated several procedural components favorably. Compared to "paper form" data recording, clinicians and supervisors judged the computer-assisted program to track student injuries more thoroughly and record student injuries more accurately. The importance of health monitoring among persons with neurodevelopmental disabilities, advantages of technology-based assessment methods, and practice considerations are discussed. [ABSTRACT FROM AUTHOR]

The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has already garnered considerable attention because of their diverse applications that include facilitating clinical documentation, obtaining insurance pre-authorization, summarizing research papers, or working as a chatbot to answer questions for patients about their specific data and concerns. While offering transformative potential, LLMs warrant a very cautious approach since these models are trained differently from AI-based medical technologies that are regulated already, especially within the critical context of caring for patients. The newest version, GPT-4, that was released in March, 2023, brings the potentials of this technology to support multiple medical tasks; and risks from mishandling results it provides to varying reliability to a new level. Besides being an advanced LLM, it will be able to read texts on images and analyze the context of those images. The regulation of GPT-4 and generative AI in medicine and healthcare without damaging their exciting and transformative potential is a timely and critical challenge to ensure safety, maintain ethical standards, and protect patient privacy. We argue that regulatory oversight should assure medical professionals and patients can use LLMs without causing harm or compromising their data or privacy. This paper summarizes our practical recommendations for what we can expect from regulators to bring this vision to reality. [ABSTRACT FROM AUTHOR]

In a medical emergency situation, real-time patient data sharing may improve the survivability of a patient. In this paper, we explore how Digital Twin (DT) technology can be used for real-time data storage and processing in emergency healthcare. We investigated various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we found Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations. We propose a cloud-based DT storage solution, in which real-time medical data are transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings. [ABSTRACT FROM AUTHOR]

With the advent of novel and emerging technologies, long duration spaceflight will become more common; along with it, an increase in its inherent health risks. However, health-related ethical issues arising during long-duration spaceflight remain poorly characterized, uncertain and unpredictable. Medical ethics is defined as a set of moral principles, beliefs and values that guides choices about medical care. This set of principles, founded in our sense of right and wrong, helps us make fair and just decisions. The paper conceptually and analytically investigates the ethical issues likely to arise from medical complications during spaceflight, mapping unfilled gaps of the current status quo. Furthermore, this paper explores broad ethical themes of autonomy, nonmaleficence, beneficence and justice, while also delving deeper into specific scenarios within each theme. The manuscript represents an up-to-date review of the available literature in the field of space medical ethics and recommends guiding ethical principles and a framework for their application to negotiate the resolution of complex ethical scenarios during long-duration spaceflight. [ABSTRACT FROM AUTHOR]

Purpose: Low-grade glioma (LGG) patients may face health-related quality-of-life (HRQoL) impairments, due to the tumour, treatment and associated side-effects and prospects of progression. We systematically identified quantitative studies assessing HRQoL in adult LGG patients, for: aspects of HRQoL impacted; comparisons with non-cancer controls (NCC) and other groups; temporal trends; and factors associated with HRQoL. Methods: MEDLINE, CINAHL, Embase, PubMed, and PsycINFO were systematically searched from inception to 14th September 2021. Following independent screening of titles and abstracts and full-texts, population and study characteristics, and HRQoL findings were abstracted from eligible papers, and quality appraised. Narrative synthesis was conducted. Results: Twenty-nine papers reporting 22 studies (cross-sectional, n = 13; longitudinal, n = 9) were identified. Papers were largely good quality, though many excluded patients with cognitive and communication impairments. Comparators included high-grade gliomas (HGG) (n = 7); NCCs (n = 6) and other patient groups (n = 3). Nineteen factors, primarily treatment (n = 8), were examined for association with HRQoL. There was substantial heterogeneity in HRQoL instruments used, factors and aspects of HRQoL assessed and measurement timepoints. HRQoL, primarily cognitive functioning and fatigue, in adult LGG patients is poor, and worse than in NCCs, though better than in HGG patients. Over time, HRQoL remained low, but stable. Epilepsy/seizure burden was most consistently associated with worse HRQoL. Conclusion: LGG patients experience wide-ranging HRQoL impairments. HRQoL in those with cognitive and communication impairments requires further investigation. These findings may help clinicians recognise current supportive care needs and inform types and timings of support needed, as well as inform future interventions. [ABSTRACT FROM AUTHOR]

Resembling the morphology of Japanese polyp vessels, the classic form of Takotsubo cardiomyopathy is characterized by the presence of systolic dysfunction of the mid-apical portion of the left ventricle associated with basal hyperkinesia. It is believed that this may be due to a higher density of β-adrenergic receptors in the context of the apical myocardium, which could explain the greater sensitivity of the apex to fluctuations in catecholamine levels. The syndrome is precipitated by significant emotional stress or acute severe pathologies, and it is increasingly diagnosed during the perioperative period. Indeed, surgery, induction of general anaesthesia and critical illness represent potential harmful trigger of stress cardiomyopathy. No universally accepted guidelines are currently available, and, generally, the treatment of TTS relies on health care personal experience and/or local practice. In our daily practice, anaesthesiologists can be asked to manage patients with the diagnosis of new-onset Takotsubo before elective surgery or an emergent surgery in a patient with a concomitant stress cardiomyopathy. Even more, stress cardiomyopathy can arise as a complication during the operation. In this paper, we aim to provide an overview of Takotsubo syndrome and to discuss how to manage Takotsubo during surgery and in anaesthesiologic special settings. [ABSTRACT FROM AUTHOR]

Healthcare has emerged as a key setting where expectations are rising for the potential benefits of artificial intelligence (AI), encompassing a range of technologies of varying utility and benefit. This paper argues that, even as the development of AI for healthcare has been pushed forward by a range of public and private actors, insufficient attention has been paid to a key contradiction at the center of AI for healthcare: that its pursuit to improve health is necessarily accompanied by environmental costs which pose risks to human and environmental health—costs which are not necessarily directly borne by those benefiting from the technologies. This perspective paper begins by examining the purported promise of AI in healthcare, contrasting this with the environmental costs which arise across the AI lifecycle, to highlight this contradiction inherent in the pursuit of AI. Its advancement—including in healthcare—is often described through deterministic language that presents it as inevitable. Yet, this paper argues that there is need for recognition of the environmental harm which this pursuit can lead to. Given recent initiatives to incorporate stakeholder involvement into decision-making around AI, the paper closes with a call for an expanded conception of stakeholders in AI for healthcare, to include consideration of those who may be indirectly affected by its development and deployment. [ABSTRACT FROM AUTHOR]

This paper discusses the key role medical regulators have in setting standards for doctors who use artificial intelligence (AI) in patient care. Given their mandate to protect public health and safety, it is incumbent on regulators to guide the profession on emerging and vexed areas of practice such as AI. However, formulating effective and robust guidance in a novel field is challenging particularly as regulators are navigating unfamiliar territory. As such, regulators themselves will need to understand what AI is and to grapple with its ethical and practical challenges when doctors use AI in their care of patients. This paper will also argue that effective regulation of AI extends beyond devising guidance for the profession. It includes keeping abreast of developments in AI-based technology and considering the implications for regulation and the practice of medicine. On that note, medical regulators should encourage the profession to evaluate how AI may exacerbate existing issues in medicine and create unintended consequences so that doctors (and patients) are realistic about AI's potential and pitfalls when it is used in health care delivery. [ABSTRACT FROM AUTHOR]

Background: In 2016, England launched the largest nationwide diabetes mellitus prevention programme, the NHS Diabetes Prevention Programme (NHS DPP). This paper seeks to evaluate the long-term cost-effectiveness of this programme. Methods: A Markov cohort state transition model was developed with a 35-year time horizon and yearly cycles to compare referral to the NHS DPP to usual care for individuals with non-diabetic hyperglycaemia. The modelled cohort of individuals mirrored the age profile of referrals received by the programme by April 2020. A health system perspective was taken, with costs in UK £ Sterling (price year 2020) and outcomes in terms of quality-adjusted life-years (QALYs). Probabilistic analysis with 10,000 Monte Carlo simulations was used. Several sensitivity analyses were conducted to explore the uncertainty surrounding the base case results, particularly varying the length of time for which the effectiveness of the programme was expected to last. Results: In the base case, using only the observed effectiveness of the NHS DPP at 3 years, it was found that the programme is likely to dominate usual care, by generating on average 40.8 incremental QALYs whilst saving £135,755 in costs for a cohort of 1000. At a willingness to pay of £20,000 per QALY, 98.1% of simulations were on or under the willingness-to-pay threshold. Scaling this up to the number of referrals actually received by the NHS DPP prior to April 2020, cost savings of £71.4 million were estimated over the 35-year time horizon and an additional 21,472 QALYs generated. These results are robust to several sensitivity analyses. Conclusion: The NHS DPP is likely to be cost-effective. Indeed, in the majority of the simulations, the NHS DPP was cost-saving and generated greater QALYs, dominating usual care. This research should serve as evidence to support the continued investment or recommissioning of diabetes prevention programmes. [ABSTRACT FROM AUTHOR]

Over the past decade, healthcare systems have started to establish control centres to manage patient flow, with a view to removing delays and increasing the quality of care. Such centres—here dubbed Healthcare Capacity Command/Coordination Centres (HCCCs)—are a challenge to design and operate. Broad-ranging surveys of HCCCs have been lacking, and design for their human users is only starting to be addressed. In this review we identified 73 papers describing different kinds of HCCCs, classifying them according to whether they describe virtual or physical control centres, the kinds of situations they handle, and the different levels of Rasmussen's [1] risk management framework that they integrate. Most of the papers (71%) describe physical HCCCs established as control centres, whereas 29% of the papers describe virtual HCCCs staffed by stakeholders in separate locations. Principal functions of the HCCCs described are categorised as business as usual (BAU) (48%), surge management (15%), emergency response (18%), and mass casualty management (19%). The organisation layers that the HCCCs incorporate are classified according to the risk management framework; HCCCs managing BAU involve lower levels of the framework, whereas HCCCs handling the more emergent functions involve all levels. Major challenges confronting HCCCs include the dissemination of information about healthcare system status, and the management of perspectives and goals from different parts of the healthcare system. HCCCs that take the form of physical control centres are just starting to be analysed using human factors principles that will make staff more effective and productive at managing patient flow. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the development of an integrated patient-centred cardiac care registry spanning the initial 5 years (September 2017 to December 2022). The Netherlands Heart Registration facilitates registration committees in which mandated cardiologists and cardiothoracic surgeons structurally evaluate quality of care using real-world data. With consistent attendance rates exceeding 60%, a valuable network is supported. Over time, the completeness level of the registry has increased. Presently, four out of six quality registries show over 95% completeness in variables that are part of the quality policies of cardiology and cardiothoracic surgery societies. Notably, 93% of the centres voluntarily report outcomes related to open heart surgery and (trans)catheter interventions publicly. Moreover, outcomes after implantable cardioverter-defibrillator and pacemaker procedures are transparently reported by 26 centres. Multiple innovation projects have been initiated by the committees, signalling a shift from publishing outcomes transparently to collaborative efforts in sharing healthcare processes and investigating improvement initiatives. The next steps will focus on the entire pathway of cardiac care for a specific medical condition instead of focusing solely on the outcomes of the procedures. This redirection of focus to a comprehensive assessment of the patient pathway in cardiac care ultimately aims to optimise outcomes for all patients. [ABSTRACT FROM AUTHOR]

The nature and the complex intermingling of the difficulties encountered by Inuit youth calls for a close collaboration between health and social professionals from different fields, the families of youth and their communities. Promising practices within Indigenous communities are described as being collaborative, community-driven and culturally grounded. Ideally most professionals would be of Indigenous heritage, and families would have access to Indigenous healing approaches if desired. Despite the documented importance of collaboration and self-determination of services, very little is known regarding how collaboration takes place in Indigenous communities, and what the facilitators and barriers to collaboration may be. This paper presents results from a study that took place in Nunavik Quebec between 2014 and 2015 and is based on qualitative interviews conducted with 42 key informants including community workers, social workers, doctors, teachers, family members and more. Using a combination of network and qualitative analyses, we explore the principal characteristics of health and social networks in child and youth mental health in Nunavik, Québec. Our findings show that while collaboration is desired by all participants, at the time of the interviews, non-Inuit and institutional actors were more central in collaborations than informal and community ressources, even if they were considered essential elements in service delivery. Participants spoke of many ways to foster collaboration; however, our project shows that collaboration is often hindered by structural and colonial challenges. [ABSTRACT FROM AUTHOR]

Delivering Dialectical Behavior Therapy (DBT) with adherence to the manual is a critical aspect of quality assurance and leads to better outcomes for clients. However, DBT is a challenging treatment to deliver adherently as it requires therapists to learn many strategies and to know when and how to apply them effectively. To date, little is known about the degree to which therapists in routine care deliver DBT adherently or what adherence challenges may be most common in these settings. This paper presents data from two studies in which the DBT Adherence Coding Scale (Linehan, M. M., & Korslund, K. E. (2003). Dialectical behavior therapy adherence coding scale manual. University of Washington.) was used to evaluate 158 DBT individual therapy sessions delivered by 63 therapists to 78 clients in routine practice (non-academic or research) settings. Nearly all therapists (98.4%) had read the DBT manual and attended DBT workshops (M = 17.9 days). Across studies, therapists delivered DBT individual therapy slightly below adherence on average (M = 3.96, SD = 0.18; cut-off for adherence = 4.00). Overall, 54.4% of sessions were adherent and 45.6% of sessions were non-adherent. On average, therapists exhibited highest adherence for acceptance-oriented strategies, moderate adherence for structural strategies, and lowest adherence for change-oriented and dialectical strategies. Informed by these results, we provide recommendations for how to improve adherence for the strategies most frequently delivered non-adherently as well as how to increase use of underutilized strategies. [ABSTRACT FROM AUTHOR]

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020–2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes"). [ABSTRACT FROM AUTHOR]

The healthcare system aims to provide a reliable and organized solution to enhance the health of human society. Studying the history of patients can help physicians to consider patients' needs in healthcare system designing and offering service, which leads to an increase in patient satisfaction. Therefore, healthcare is becoming a growing contesting market. With this significant growth in healthcare systems, such challenges as huge data volume, response time, latency, and security vulnerability are raised. Therefore, fog computing, as a well-known distributed architecture, could help to solve such challenges. In fog computing architecture, processing components are placed between the end devices and cloud components, and they execute applications. This architecture is suitable for such applications as healthcare systems that need a real-time response and low latency. In this paper, a systematic review of available approaches in the field of fog-based healthcare systems is proposed; the challenges of its application in healthcare are explored, classified, and discussed. First, the fog computing approaches in healthcare are categorized into three main classes: communication, application, and resource/service. Then, they are discussed and compared based on their tools, evaluation methods, and evaluation metrics. Finally, based on observations, some open issues and challenges are highlighted for further studies in fog-based healthcare. [ABSTRACT FROM AUTHOR]

In the healthcare sector, the growth in technology has had a huge effect. Besides, when introduced to the world of healthcare, the Internet of Things (IoT) will simplify the transition by helping physicians closely track their patients, allowing rapid recovery. Aged patients/people should be intensively checked, and their loved ones must be aware of their wellbeing periodically. Therefore, using IoT in healthcare will simplify the lives of physicians and patients alike. Hence, this study explored a comprehensive review of intelligent IoT-based embedded healthcare systems. The papers around intelligent IoT-based healthcare systems printed until Dec-2022 are studied, and some research lines are suggested for the upcoming researchers. Thus, this study's innovation will apply healthcare systems based on IoT to include certain strategies for the future deployment of new generations of IoT-based health technology. The findings revealed that IoT is beneficial for governments to strengthen society's health and economic relations. Besides, owing to novel functional principles, IoT needs modern safety infrastructure. This study is helpful for prevalent and useful electronic healthcare services, health experts, and clinicians. [ABSTRACT FROM AUTHOR]

The COVID-19 virus caused a global pandemic leading to a swift policy response. While this response was designed to prevent the spread of the virus and support those with COVID-19, there is growing evidence regarding measurable impacts on non-COVID-19 patients. The paper uses a large dataset from administrative records of the Brazilian public health system (SUS) to estimate pandemic spillover effects in critically ill health care delivery, i.e. the additional mortality risk that COVID-19 ICU hospitalizations generate on non-COVID-19 patients receiving intensive care. The data contain the universe of ICU hospitalizations in SUS from February 26, 2020 to December 31, 2021. Spillover estimates are obtained from high-dimensional fixed effects regression models that control for a number of unobservable confounders. Our findings indicate that, on average, the pandemic increased the mortality risk of non-COVID-19 ICU patients by 1.296 percentage points, 95% CI 1.145–1.448. The spillover mortality risk is larger for non-COVID patients receiving intensive care due to diseases of the respiratory system, diseases of the skin and subcutaneous tissue, and infectious and parasitic diseases. As of July 2023, the WHO reports more than 6.9 million global deaths due to COVID-19 infection. However, our estimates of spillover effects suggest that the pandemic's total death toll is much higher. [ABSTRACT FROM AUTHOR]

Acupressure is a noninvasive method in traditional Chinese medicine to increase blood circulation, relieve discomfort, promote health, and prevent disease. This paper presents a multifunctional acupoint application system that not only provides real-time visualization of acupoints on mobile devices for users' self-acupressure but also provides health care, education, and entertainment through acupressure. The system allows users to obtain acupoint information to relieve specific discomfort or symptoms. It automatically marks the requested acupoints on a real-time camera stream to guide users to locate acupoints so that even users unfamiliar with acupoints can easily find acupoints for self-acupressure. Three core technologies are used: acupoint localization and knowledge, real-time image recognition, and augmented reality. This paper details the system design and implementation. This system uses Google's machine learning kit for facial detection to extract the user's facial features and contours in real time on a mobile device. These contour points are used as landmarks with specific displacements to locate acupoints. As there are many acupoints and various positioning methods, the acupoint location is traditionally described in words through specific feature points and corresponding displacements related to each person's physiological characteristics. This work proposes a novel acupoint positioning and marking mechanism based on the standard acupoint localization, which formalizes various acupoint positioning methods so that positions can be processed and stored in an acupoint database established in this system. This positioning information allows the proposed mechanism to fulfill real-time visualization of acupoints in augmented reality on the camera stream. Experimental results show that the proposed system locates and marks acupoints with low latency and high accuracy. Moreover, the system also provides recommendations for further medical treatment and a mini-game that tests knowledge of acupoints, which enhances users' experience of acupoint massage. [ABSTRACT FROM AUTHOR]

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread. [ABSTRACT FROM AUTHOR]

Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs' weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process. [ABSTRACT FROM AUTHOR]

E-health has become a top priority for healthcare organizations focused on advancing healthcare services. Thus, medical organizations have been widely adopting cloud services, resulting in the effective storage of sensitive data. To prevent privacy and security issues associated with the data, attribute-based encryption (ABE) has been a popular choice for encrypting private data. Likewise, the attribute-based access control (ABAC) technique has been widely adopted for controlling data access. Researchers have proposed electronic health record (EHR) systems using ABE techniques like ciphertext policy attribute-based encryption (CP-ABE), key policy attribute-based encryption (KP-ABE), and multi authority attribute-based encryption (MA-ABE). However, there is a lack of rigorous comparison among the various ABE schemes used in healthcare systems. To better understand the usability of ABE techniques in medical systems, we performed a comprehensive review and evaluation of the three popular ABE techniques by developing EHR systems using knowledge graphs with the same data but different encryption mechanisms. We have used the MIMIC-III dataset with varying record sizes for this study. This paper can help healthcare organizations or researchers using ABE in their systems to comprehend the correct usage scenario and the prospect of ABE deployment in the most recent technological evolution. [ABSTRACT FROM AUTHOR]

Background and objective: Political, economic, communicative and cultural borders still limit the accessibility of acute healthcare services for patients so that they frequently have to accept longer distances to travel to the next provider within their own country. In this paper, we analyze the impact of borders and opening of borders on acute medical care in hospitals and on patients in border regions. Methods: We develop a conceptual framework model of cross-border healthcare and apply it to the Polish–German border area. The model combines the distance decay effect, a catchment area analysis, economies of scale and the learning curve. Results: Borders have a major impact on acute medical care in hospitals and on patients. Setting of new borders will reduce the accessibility of health facilities for patients or require the establishment of new hospitals. Reopening borders might induce a vicious circle leading to the insolvency of a hospital which might result in poorer health for some patients. Conclusion: Strong effort should be invested to overcome political and cultural borders to improve the health of the population in border regions. Similarly, increased cross-border acute healthcare must be seen in the context of rural health and the special situation of small rural hospitals in rural peripheral areas. [ABSTRACT FROM AUTHOR]

This paper aims to apply a multi-criteria decision-making (MCDM) framework to evaluate the environmental performance of the medical service supply chain using a case study in India. An application of MCDM method, the fuzzy TOPSIS (Technique for order of preference by similarity to ideal solution) model, is used for Medical Support Service Provide firms (MSSPF) environmental performance evaluation. We require multiple and conflicting criteria to fix real-life decision-making challenges, which in turn led to the inception of (MCDM). However, MCDM is contracted with various contradictory requirements in which the knowledge of decision-makers is generally vague and thereby more challenging. In this situation, inclusions of fuzzy set theory in MCDM Methods would be good to solve the MCDM problem. In this study, the authors used a fuzzy TOPSIS MCDM model to solve the evaluation framework. Data were collected from the experts from the MSSPF about the more perfect criteria to assess environmental performance using a questionnaire. Case assesses the environmental performance, based on defined criteria, of three MSSPF by the experts. Fuzzy TOPSIS Method ranks three MSSPF according to environmental performance in the following order: Firm B, Firm A and Firm C. The result suggested that 'Firm B' performed superior environmental performance followed by 'Firm A' and 'Firm C'. This evaluation framework will help MSSPF to identify potential areas of further improvement in Environmental performance, and thereby creates strategies to overcome and formulate new innovations. Notably, this is one of the few studies to have used this method to gauge the environmental performance of the medical service supply chain in India. [ABSTRACT FROM AUTHOR]

The General Medical Council (GMC) instructs doctors to act 'reasonably' in obtaining consent from patients. However, the GMC does not explain what it means to be reasonable: it is left to doctors to figure out the substance of this instruction. The GMC relies on the Supreme Court's judgment in Montgomery v Lanarkshire Health Board; and it can be assumed that the judges' idea of reasonability is adopted. The aim of this paper is to flesh out this idea of reasonability. This idea is commonly personified as the audience that has to be satisfied by the doctor's justification for offering, or withholding, certain treatments and related information. In case law, this audience shifted from a reasonable doctor to a 'reasonable person in the patient's position'; and Montgomery expands the audience to include 'particular' patients, too. Senior judges have clarified that the reasonable person is a normative ideal, and not a sociological construct; but they do not set out the characteristics of this ideal. John Rawls has conceived the reasonable person-ideal as one that pursues fair terms of co-operation with other members of society. An alternative ideal can be inferred from the feminist ethic of care. However, the reasonable patient from Montgomery does not align with either theoretical ideal; but, instead, is an entirely rational being. Such a conception conflicts with both real-life constraints on rationality and the doctor's duty to care for the patient, and it challenges the practice of medicine. [ABSTRACT FROM AUTHOR]