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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]

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]

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]

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]

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]

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]

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]

Family-centered care (FCC) is widely established as the standard professional practice model in pediatric hospital settings (Arabiat, D., Whitehead, L., Foster, M., Shields, L., & Harris, L. (2018). Parents' experiences of family centred care practices. Journal of Pediatric Nursing, 42, 39–44. https://doi.org/10.1016/j.pedn.2018.06.012; Espe-Sherwindt, M. (2008). Family-centred practice: Collaboration, competency and evidence. Support for Learning, 23(3), 136–143. https://doi.org/10.1111/j.1467-9604.2008.00384.x). It embraces social work values that promote self-determination and family empowerment by positioning family members as partners in healthcare treatment, delivery, and decision-making (Kuo, D. Z., Houtrow, A. J., Arango, P., Kuhlthau, K. A., Simmons, J. M., & Neff, J. M. (2012). Family-centered care: Current applica?tions and future directions in pediatric health care. Maternal and Child Health Journal, 16(2), 297–305. https://doi.org/10.1007/s10995-011-0751-7). To promote and advance FCC principles, healthcare organizations collaborate with patients and families as lived experience volunteers, or family leaders, to ensure program design and education reflect users' perspectives and experiences. Despite evidence for its efficacy and uptake (Arabiat, D., Whitehead, L., Foster, M., Shields, L., & Harris, L. (2018). Parents' experiences of family centred care practices. Journal of Pediatric Nursing, 42, 39–44. https://doi.org/10.1016/j.pedn.2018.06.012), translating FCC principles into practice can be challenging (McCarthy, E., & Guerin, S. (2022). Family-centred care in early intervention: A systematic review of the processes and out?comes of family-centred care and impacting factors. Child: Care Health and Development, 48(1), 1–32. https://doi.org/10.1111/cch.12901). In this paper, we introduce an innovative training program for family leaders that embodies the core principles of FCC. The training program was developed in collaboration with families, healthcare and higher education stakeholders, and engagement specialists by a social worker in a pediatric rehabilitation setting in Toronto, Ontario. The program prepares family leaders to engage in teaching and facilitation roles in healthcare education. The Teaching and Facilitation Course for Family as Faculty is the first program of its kind in Canada and was recently recognized by the Health Standards Organization of Canada as a leading practice in healthcare. Since launching in 2019, over 50 participants connected with healthcare organizations across Ontario have completed the course. Graduates have partnered with educators in designing and facilitating over 85 initiatives and workshops across healthcare and academic institutions. The outcomes of a utilization-focused program evaluation, including the strengths and lessons learned are discussed. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is emerging as an alternative solution in the healthcare sector, offering opportunities to enhance efficiency and optimize the utilization of precious resources. The nascent stage of AI application in the Indian healthcare sector has gained momentum during COVID-19, witnessing a surge in AI-based startups and companies specializing in diagnostic and prescriptive healthcare. This paper systematically analyzes barriers to AI application in the Indian context, incorporating the concept of flexibility, and proposes insights for fostering its adoption for resilient and sustainable healthcare practices. The identified barriers are drawn from literature and experts' inputs. These are further investigated using the decision-making trial and evaluation and laboratory methodology. This analysis not only categorizes barriers into cause-and-effect groups but also emphasizes the need for flexibility to adapt AI solutions to the dynamic healthcare sector. The paper underscores foundational barriers, including inadequate regulations, lack of awareness, high adaptation costs, and a scarcity of skilled AI expertise. In addition to managerial and social implications concerning regulation, implementation, economic viability, and data privacy, the study promotes flexibility as a key factor in addressing the evolving challenges in healthcare. [ABSTRACT FROM AUTHOR]

Sub-health state is a state of health and low quality between disease and health. The theoretical basis of children's sub-health is to start from the whole. The common clinical sub-health conditions cannot be explained by modern detection methods, and it can be screened and analyzed with the help of big data in medical informatization. The combination of "Internet + " and the health care model is an innovation in the construction of medical informatization. It can provide many considerate services to the masses in time and alleviate the anxiety of illness. Therefore, it is very necessary to carry out the efficacy evaluation of children's sub-health from the perspective of medical information. Therefore, this paper completes the following work with the help of AI neural network: (1) This paper proposes an improved AlexNet network evaluation method based on attention mechanism. In this study, attention mechanism is added to the original AlexNet model to weight each channel of the feature layer. At the same time, we improve the large convolution kernel of the previous layers of the original AlexNet network and use batch normalization instead of the local response normalization (LRN) layer in the original model. (2) This paper proposes an evaluation method based on improved residual network, which improves the original residual block of the residual model and widens the residual block. The residual block can effectively reduce the amount of network parameters and improve the efficiency of network training. (3) This paper proposes an evaluation method of multi-scale feature fusion (MSFF). The features extracted from the improved AlexNet and residual network are fused and then evaluated. At this time, the training time is greatly shortened, and the accuracy is higher than that of single model. [ABSTRACT FROM AUTHOR]

Green approaches remain little disseminated in the healthcare sector despite growing interest in recent years from practitioners and researchers. Big Data Analytics Capability (BDAC) can play a critical role in the integration of environmental concerns into operations and supply chain management (OSCM) and further strengthen the environmental performance of healthcare facilities. According to the literature, the integration of the environment into operations process remains insufficient to achieve high levels of performance and requires efforts in green process innovation. However, this relationship between BDAC and green process innovation remains poorly justified empirically. To address this theoretical gap, we investigated the relationship between BDAC, environmental process integration, green process innovation in OSCM and environmental performance. The main contribution of this study is the valuable knowledge on how BDAC influences environmental process integration and green process innovation to enhance environmental performance. Moreover, the study highlights the mediating role of green process innovation on environmental performance, a finding that has not been mentioned in the extant literature. The paper provides valuable insight for managers and stakeholders that can assist them in supporting the application of BDAC in healthcare OSCM to create sustainable value. [ABSTRACT FROM AUTHOR]

In 2021, a large urban university-based hospital transitioned to a new two-floor emergency department. Despite the new environment, there were usability and workflow challenges with the space. The authors of this paper created a multidisciplinary, human-centered design collaborative of clinicians, university faculty, and students in an effort to increase emergency department efficiency. After thorough design-research and clinician-focused collaboration, the authors and design team identified the need to improve medical supply retrieval time, which directly impacts patient care and clinician satisfaction. The primary interventions consisted of a redesign that is as follows: (a) created standardized icons related to organ system, (b) increased visibility of supply labels, and (c) reorganized supplies based on usage data. Although a successful project, it was not without several barriers discussed in this article, including design researcher and clinician-level setting and engagement, academic/institutional policies, and conflicting schedules. In addition, the lessons learned from implementing human-centered design concepts into clinical workflow sets forth future research opportunities and inspiration for other institutions to collaborate. [ABSTRACT FROM AUTHOR]

Multiple benefits (submitting healthcare bills to one or more organizations) and data breaches (tampering of healthcare data by healthcare organization authorities) are typical frauds happening due to the centralization of authority in healthcare systems. Preventing multiple benefits and data breaches can greatly lessen the huge losses to healthcare systems. In this paper, we propose an extended lightweight blockchain (ELB) based collaborative healthcare system for decentralizing the authority in healthcare organizations. The proposed architecture is rigorously tested in avoiding multiple benefits and data breaches and compared with the state-of-the-art methods. The experimental results evidenced that the proposed ELB architecture can effectively avoid multiple benefits and data breaches with less computation and communication overheads. [ABSTRACT FROM AUTHOR]

Suboptimal multidisciplinary team collaboration is a barrier to effective health care provision for patients with chronic kidney disease (CKD) associated with type 2 diabetes mellitus (T2DM). We describe an example practice model of a clinical practice called Baptist Health Deaconess, based in Madisonville, Kentucky, USA, where a small multidisciplinary team consisting of an endocrinologist, nurse practitioner, and pharmacist (authors of this article) work collaboratively in an ambulatory care setting to provide health care to the patients they serve. Many of the patients who receive care at Baptist Health Deaconess are on a low income, have poor health literacy, and do not have a primary care physician. The presence of a pharmacist in the team allows for insurance/access investigations to assess drug choice and affordability; such aspects can be performed quickly with a pharmacist in the office. Plain Language Summary: Health care professionals (HCPs) supporting people living with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) may work at one or more health care settings such as a health care practice, a hospital, a pharmacy, or they might support people in their own homes. When HCPs do not work well together this negatively affects the quality of care that people visiting them receive. This paper gives the point of view of three HCPs (a nurse practitioner, a pharmacist, and a diabetes specialist [an endocrinologist]) who work at a health care practice called Baptist Health Deaconess situated in Kentucky, USA. The three HCPs describe how many of the people who visit them at their practice do not have much money, they do not really understand what their conditions are about or how they can be best treated, and they do not have a primary care physician. The HCP team believe that having a pharmacist on their team (which is not common throughout the USA) means that they work better together because it saves money, people with T2DM and CKD visiting get the best treatment recommendations for them, and this is all done quicker compared to having no pharmacist on the team. [ABSTRACT FROM AUTHOR]

Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers' mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol's attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol's vulnerabilities. We propose many RDoS attack's techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks' network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks. [ABSTRACT FROM AUTHOR]

Over the course of this year, more than a billion people have been afflicted by the COVID-19 outbreak. As long as individuals maintain their social distance, they should all be secure at this period. Because of this, there has been a rise in the usage of different online technologies, but at the same time, there has also been a rise in the likelihood of different cyber-attacks. A DDoS assault, the most prevalent and deadly of them all, impairs an online resource for its users. Thus, in this paper, we have proposed a filtering approach that can work efficiently in the COVID-19 scenario and detect the DDoS attack. We base our proposed approach on statistical methods like packet score and entropy variation for the identification of DDoS attack traffic. We have implemented our proposed approach on Omnet++ and for testing its efficiency we have checked it with different test cases. Our proposed approach detects the DDoS attack traffic with 96% accuracy and can also clearly have differentiated the DDoS attack traffic from the flash crowd. [ABSTRACT FROM AUTHOR]

Prior trauma exposure significantly increases the risk of developing PTSD following medical stressors and may contribute to the development of medically induced PTSD. However, healthcare systems often overlook the interaction between prior trauma and current medical stressors, contributing to negative psychosocial and health-related outcomes for patients. Integration of both trauma-informed and trauma-focused practices into psychosocial programming in medical settings may be key to effectively addressing the needs of trauma-exposed patients. Yet, there is a lack of practical guidance on how clinical psychologists in medical settings can respond to trauma's effects in routine clinical practice. This paper aims to provide an overview of trauma-focused theory, assessment, and treatment considerations within medical settings, emphasizing the importance of incorporating trauma-focused intervention into integrated psychosocial programming to address prior trauma and its impacts on care in order to improve patient outcomes. [ABSTRACT FROM AUTHOR]

The concepts of metacognitive reflection, reflection, and metacognition are distinct but have undergone shifts in meaning as they migrated into medical education. Conceptual clarity is essential to the construction of the knowledge base of medical education and its educational interventions. We conducted a theoretical integrative review across diverse bodies of literature with the goal of understanding what metacognitive reflection is. We searched PubMed, Embase, CINAHL, PsychInfo, and Web of Science databases, including all peer-reviewed research articles and theoretical papers as well as book chapters that addressed the topic, with no limitations for date, language, or location. A total of 733 articles were identified and 87 were chosen after careful review and application of exclusion criteria. The work of conceptually and empirically delineating metacognitive reflection has begun. Contributions have been made to root metacognitive reflection in the concept of metacognition and moving beyond it to engage in cycles of reflection. Other work has underscored its affective component, transformational nature, and contextual factors. Despite this merging of threads to develop a richer conceptualization, a theory of how metacognitive reflection works is elusive. Debates address whether metacognition drives reflection or vice versa. It has also been suggested that learners evolve along on a continuum from thinking, to task-related reflection, to self-reflection, and finally to metacognitive reflection. Based on prior theory and research, as well as the findings of this review, we propose the following conceptualization: Metacognitive reflection involves heightened internal observation, awareness, monitoring, and regulation of our own knowledge, experiences, and emotions by questioning and examining cognition and emotional processes to continually refine and enhance our perspectives and decisions while thoughtfully accounting for context. We argue that metacognitive reflection brings a shift in perspective and can support valuable reconceptualization for lifelong learning. [ABSTRACT FROM AUTHOR]

To explore the existing literature on the effect of Interprofessional Education (IPE) on the work environment of health professionals. The research question was systematized according to the PCC (Population, Concept, and Context) format. A scoping review was performed. A search of multiple bibliographic databases identified 407 papers, of which 21 met the inclusion criteria. The populations of the 21 studies reviewed were composed of professionals in the fields of medicine, nursing, psychology, occupational therapy, physiotherapy, and social work, among others. The study contexts were both academic and nonacademic hospitals, mental health institutions, and community settings, and the topics examined were organizational climate, organizational culture, organizational attachment and job satisfaction. The findings from the reviewed studies showed positive effects of IPE interventions on organizational climate and culture, but the results on job satisfaction and organizational attachment were mixed (i.e., positive and no effects following IPE interventions). Research on IPE is worth more attention as IPE could be an effective alternative for the fulfillment of the Quadruple Aim and achieving the third of the United Nations Sustainable Development Goals, aimed at improving health and well-being. It seems critical for IPE to be positioned as a trend in global health, aiming at boosting human health resources as one of its building blocks and calling the attention of health decision-makers. [ABSTRACT FROM AUTHOR]

Cable-driven manipulators find applications in medical care, housekeeping services, and various other scenarios. However, the flexible joints of cable-driven manipulators exhibit rigid-flexible coupling characteristics, posing challenges for their modeling and accurate control. In this paper, a cable-driven serial-parallel manipulator is analyzed, and the rigidflexible coupling characteristics and dynamic modeling of its flexible joints are studied. Based on the coupling problem between joint flexibility deformation and end motion of the manipulator, a rigid-flexible coupling dynamics model was established. Through derivation of the coupling matrix, the coupling factor was defined to characterize the influence of flexible joint deformation on end motion of the manipulator. Finally, the dynamics coupling factor was simulated, and the influence of the joint cable-wheel reduction ratio and the end load on rigid-flexible characteristics of the manipulator was revealed, which lays a theoretical basis for the accurate control of cable-driven manipulators. [ABSTRACT FROM AUTHOR]

People from refugee and asylum seeker backgrounds resettling in Australia often experience intersecting risks for poor mental and physical health. Physical activity can promote better health outcomes, however there are limited programs tailored for this population. Therefore, understanding how to support refugees and asylum seekers to engage in physical activity is crucial. This paper aims to describe how the experience-based co-design (EBCD) process was used to identify priorities for a new physical activity service for refugees and asylum seekers. Using an EBCD framework we conducted qualitative interviews and co-design workshops with service users (refugees and asylum seekers living in the community) and service providers at a community Centre in Sydney, Australia. Sixteen participants, including eight service users and eight service providers engaged in the EBCD process over 12-months. The interviews revealed common themes or 'touchpoints' including barriers and enablers to physical activity participation such as access, safety and competing stressors. Subsequent co-design focus groups resulted in the establishment of five fundamental priorities and actionable strategies; ensuring cultural and psychological safety, promoting accessibility, facilitating support to access basic needs, enhancing physical activity literacy and fostering social connection. Using EBCD methodology, this study used the insights and lived experiences of both service users and providers to co-design a physical activity service for refugees and asylum seekers which is safe, supportive, social and accessible. The results of the implementation and evaluation of the program are ongoing. [ABSTRACT FROM AUTHOR]

Aims: The present study assessed the transitioning process of young adults with type 1 diabetes mellitus (T1D) in Italy. Materials and methods: We asked Pediatric Diabetes Centers (PDC) and Adult Diabetes Centers (CAD) to fill in a web-based survey on the current state of services, the number of transitioning adolescents with T1D within the last year, observations on limitations, and future directions. Results: 93 centers (46 PDCs, 47 CADs) joined the study. The total number of subjects with T1D being followed by a PDC was 16,261 (13,779 minors and 2483 young adults), while CADs had 25,500 patients. The survey showed an uneven situation. Only some services had a dedicated diabetes team (78% of PDCs, 64% of CADs). 72% of PDCs and 58% of CADs reported a protocol dedicated to transition. The median age for transition was 19 (range 16–25 years); the time required for preparing transition, indicated by both PDCs and CADs, was 5.5 months. A high percentage of CADs (80%) confirmed receiving sufficient clinical information, mainly through paper or computerized reports. The transition process is hampered by a lack of resources, logistical facilities, and communication between services. While some services have a protocol, monitoring of results is only carried out in a few cases. Most specialists expressed the need to enhance integration and continuity of treatment. Conclusions: The current situation could be improved. Applying standard guidelines, taking into consideration both clinicians' and patients' necessities, would lead to a more successful transition process. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of the role of smart materials, specifically triboelectric and piezoelectric materials, in the context of human-integrated healthcare systems. The study begins by elucidating the fundamentals of smart materials in healthcare, providing a detailed definition and characteristics of these materials and underscoring their paramount importance in advancing healthcare technologies. The research approach employed in this study involves a combination of literature review, experimental investigation, and computational modeling to analyze the potential of smart materials in healthcare applications. The subsequent sections delve into the individual contributions of triboelectric and piezoelectric materials in healthcare systems, outlining their unique attributes and potential applications. Eventually, real-world implementation challenges, such as scaling p production, ensuring stability, and establishing ethical deployment protocols, are meticulously examined. [ABSTRACT FROM AUTHOR]

The smart healthcare system advancements have introduced the Internet of Things, enabling technologies to improve the quality of medical services. The main idea of these healthcare systems is to provide data security, interaction between entities, efficient data transfer, and sustainability. However, privacy concerning patient information is a fundamental problem in smart healthcare systems. Many authentications and critical management protocols exist in the literature for healthcare systems, but ensuring security still needs to be improved. Even if security is achieved, it still requires fast communication and computations. In this paper, we have introduced a new secure privacy-enhanced fast authentication key management scheme that effectively applies to lightweight resource-constrained devices in healthcare systems to overcome the issue. The proposed framework is applicable for quick authentication, efficient key management between the entities, and minimising computation and communication overheads. We verified our proposed framework with formal and informal verification using BAN logic, Scyther simulation, and the Drozer tool. The simulation and tool verification shows that the proposed system is free from well-known attacks, reducing communication and computation costs compared to the existing healthcare systems. [ABSTRACT FROM AUTHOR]

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle. [ABSTRACT FROM AUTHOR]

Discussion around the increasing use of AI in healthcare tends to focus on the technical aspects of the technology rather than the socio-technical issues associated with implementation. In this paper, we argue for the development of a sustained societal dialogue between stakeholders around the use of AI in healthcare. We contend that a more human-centred approach to AI implementation in healthcare is needed which is inclusive of the views of a range of stakeholders. We identify four key areas to support stakeholder involvement that would enhance the development, implementation, and evaluation of AI in healthcare leading to greater levels of trust. These are as follows: (1) aligning AI development practices with social values, (2) appropriate and proportionate involvement of stakeholders, (3) understanding the importance of building trust in AI, (4) embedding stakeholder-driven governance to support these activities. [ABSTRACT FROM AUTHOR]

This paper elucidates and rationalizes the ethical governance system for healthcare AI research, as outlined in the 'Research Ethics Guidelines for AI Researchers in Healthcare' published by the South Korean government in August 2023. In developing the guidelines, a four-phase clinical trial process was expanded to six stages for healthcare AI research: preliminary ethics review (stage 1); creating datasets (stage 2); model development (stage 3); training, validation, and evaluation (stage 4); application (stage 5); and post-deployment monitoring (stage 6). Researchers identified similarities between clinical trials and healthcare AI research, particularly in research subjects, management and regulations, and application of research results. In the step-by-step articulation of ethical requirements, this similarity benefits from a reliable and flexible use of existing research ethics governance resources, research management, and regulatory functions. In contrast to clinical trials, this procedural approach to healthcare AI research governance effectively highlights the distinct characteristics of healthcare AI research in research and development process, evaluation of results, and modifiability of findings. The model exhibits limitations, primarily in its reliance on self-regulation and lack of clear delineation of responsibilities. While formulated through multidisciplinary deliberations, its application in the research field remains untested. To overcome the limitations, the researchers' ongoing efforts for educating AI researchers and public and the revision of the guidelines are expected to contribute to establish an ethical research governance framework for healthcare AI research in the South Korean context in the future. [ABSTRACT FROM AUTHOR]

Nowadays, smart healthcare and Electronic Health Record (EHR) play a significant role in simplifying the experience of medical treatment and cuts down the trivial work of paramedics. However, this poses significant challenges for organizations, including the management, transfer and protection of EHR which is vital for correct detection. To overcome these challenges, cloud computing presented which offers various attractive features for EHR management such as data availability, elasticity, and ubiquitous computation. However, the use of cloud computing introduces confidentiality and privacy concerns which can lead to human health threat. A trivial solution for this problem is encryption which makes searching a desired document infeasible, more specifically in big data scenarios. Although searchable encryption is a candidate solution, it suffers from keyword guessing attacks, malicious service providers, lack of trust, data integrity, and fairness. In this paper, we propose BACASE-SH framework that leverages blockchain technology to EHR integrity and availability insurance, fair payment, and establish trust among the involved parties. Moreover, we present a certificate-less authenticated asymmetric searchable encryption scheme to address challenges such as data owner (patient) and data user (physician) identity and keyword guessing attacks. The scheme is implemented using a smart contract, and the evaluation results show its efficiency. [ABSTRACT FROM AUTHOR]

In this paper we aim to discuss a theoretical explanation for the positive relationship between patients' knowledge and their trust in healthcare personnel. Our approach is based on John Dewey's notion of continuity. This notion entails that the individual's experiences are interpreted as interrelated to each other, and that knowledge is related to future experience, not merely a record of the past. Furthermore, we apply Niklas Luhmann's theory on trust as a way of reducing complexity and enabling action. Anthony Giddens' description and analysis of the high modern society provides a frame for discussing the preconditions for patient-healthcare personnel interaction. High modernity is dominated by expert systems and demands trust in these. We conclude that patient knowledge and trust in healthcare personnel is related because both knowledge and trust are future- and action-oriented concepts. The traits of high modernity provides opportunities and challenges as the personnel can and must perform discretion. This discretion must be made in a context where knowledge is considered uncertain and preliminary. [ABSTRACT FROM AUTHOR]

In response to the growing maturation of technological level in the modern medical industry, this paper proposes the PZLMVF-VIKOR method to tackle uncertain multi-attribute group decision-making in smart healthcare. First, a novel Pythagorean Z-linguistic modified value function based on regret theory is defined to consider the impact of DMs' emotions on decision outcomes. Then, a combined weighting method is used to obtain the final attribute weight vector by the closeness degree, with the expert weight determination based on consensus measures. Subsequently, we propose an extended VIKOR method in Pythagorean Z-linguistic environment to solve the evaluation problem of the development in smart healthcare. Finally, a practical case and some comparative analysis is conducted to illustrate the justifiability and effectiveness of this method. [ABSTRACT FROM AUTHOR]