Search

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]

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]

Objective: To study the factors associated with the subsequent (over next 9 years) full-text publication of papers presented at the 44 National Conference of Indian Academy of Pediatrics (PEDICON), 2007. Methods: All papers presented at PEDICON 2007 were searched for subsequent full-text publication over the next 9 years in English-language journals by an internet-based search. The published papers were compared with the conference-abstracts. Results: 74 (16%) of the 450 abstracts presented were subsequently published; 61 (82.4%) in Medline-indexed journals. Majority (50, 67.6%) of the papers was published within the first 36 mo in journals with mean (SD) impact factor of 2.62 (1.63). The factors significantly associated with subsequent publication were papers presented as award papers ( P<0.001), those reporting on Interventional trials ( P<0.001), and those from medical colleges ( P<0.05). On comparison of the conference abstracts with the subsequently published full-papers, 55% had a change in title; authors were changed in 65%, and participants' numbers were dissimilar in 8.6%. Conclusions: There is a need to identify the factors responsible for this low rate of subsequent publication, and interventions to improve it both at institutional and researchers' level. [ABSTRACT FROM AUTHOR]

The European Society of Radiology (ESR) established a Working Group on Value-Based Imaging (VBI WG) in August 2016 in response to developments in European healthcare systems in general, and the trend within radiology to move from volume- to value-based practice in particular. The value-based healthcare (VBH) concept defines 'value' as health outcomes achieved for patients relative to the costs of achieving them. Within this framework, value measurements start at the beginning of therapy; the whole diagnostic process is disregarded, and is considered only if it is the cause of errors or complications. Making the case for a new, multidisciplinary organisation of healthcare delivery centred on the patient, this paper establishes the diagnosis of disease as a first outcome in the interrelated activities of the healthcare chain. Metrics are proposed for measuring the quality of radiologists' diagnoses and the various ways in which radiologists provide value to patients, other medical specialists and healthcare systems at large. The ESR strongly believes value-based radiology (VBR) is a necessary complement to existing VBH concepts. The Society is determined to establish a holistic VBR programme to help European radiologists deal with changes in the evolution from volume- to value-based evaluation of radiological activities. Main Messages • Value-based healthcare defines value as patient's outcome over costs. • The VBH framework disregards the diagnosis as an outcome. • VBH considers diagnosis only if wrong or a cause of complications. • A correct diagnosis is the first outcome that matters to patients. • Metrics to measure radiologists' impacts on patient outcomes are key. • The value provided by radiology is multifaceted, going beyond exam volumes. [ABSTRACT FROM AUTHOR]

Background The Objective Structured Clinical Examination (OSCE) is a cornerstone in healthcare assessment. As a potential tool for providing learner-centred feedback on a large scale, the use of tablet devices has been proposed for the recording of OSCE marks, moving away from the traditional, paper-based checklist. Methods Examiner-recorded comments were collated from successive first year formative and summative OSCE examinations, with paper-based checklists used in 2012 and iPad-based checklists used in 2013. A total of 558 and 498 examiner-candidate interactions took place in the January OSCE examinations, and 1402 and 1344 for the May OSCE examination for 2012 and 2013 respectively. Examiner comments were analyzed for quantity and quality. A tool was developed and validated to assess the quality of the comments left by examiners for use as feedback (Kappa = 0.625). Results A direct comparison of paper-based checklists and iPad-recorded examinations showed an increase in the quantity of comments left from 41 to 51 % (+ 10 %). Furthermore, there was an increase in the number of comments left for students deemed 'borderline': + 22 %. In terms of the quality of the comments for feedback, there was a significant improvement (p < 0.001) between comments left in written-recorded and iPad-recorded examinations. Conclusions iPad-marked examinations resulted in a greater quantity and quality of examiner comment for use as feedback, particularly for students performing less well, enabling tutors to direct further learning for these students. [ABSTRACT FROM AUTHOR]

Purpose: To give an overview of the multicenter Prostate Cancer Outcomes (PCO) study, involving paper-based and web-based collection of patient-reported outcome measures (PROM) in patients undergoing local treatment for prostate cancer in certified centers in Germany. The PCO study is part of the larger Movember-funded TrueNTH Global Registry. The article reports on the study's design and provides a brief progress report after the first 2 years of data collection. Methods: Prostate cancer centers (PCCs) certified according to German Cancer Society requirements were invited to participate in collecting patient-reported information on symptoms and function before and at least once (at 12 months) after treatment. The data were matched with disease and treatment information. This report describes progress in patient inclusion, response rate, and variations between centers relative to online/paper use, and also data quality, including recruitment variations relative to treatment in the first participating PCCs. Results: PCC participation increased over time; 44 centers had transferred data for 3094 patients at the time of this report. Patient recruitment varied widely across centers. Recruitment was highest among patients undergoing radical prostatectomy. The completeness of the data was good, except for comorbidity information. Conclusions: The PCO study benefits from a quality improvement system first established over 10 years ago, requiring collection and harmonization of a predefined clinical dataset across centers. Nevertheless, establishing a PROM routine requires substantial effort on the part of providers and constant monitoring in order to achieve high-quality data. The findings reported here may be useful for guiding implementation in similar initiatives. [ABSTRACT FROM AUTHOR]

Background: Patients with disabilities often require healthcare accommodations in order to access high-quality, equitable healthcare services. While attention has been paid to accommodation needs in specific disability populations, limited research to date has explored healthcare accommodations that cross-cut diverse disability populations.Objective: To identify a deeper understanding regarding accommodations in healthcare settings that could apply across disability populations and promote equitable healthcare.Design: We conducted qualitative focus groups with patients with disabilities and caregivers to understand their experiences and preferences for healthcare accommodations.Participants: We recruited patients and caregivers across all major disability categories to participate in focus groups. Participants were recruited through advocacy organizations and healthcare settings in Southeastern Minnesota.Approach: A total of eight focus groups were conducted with 56 participants. Participants described their healthcare experiences and desires for healthcare accommodations. The multidisciplinary research team recorded, transcribed verbatim, and coded all focus groups. The team thematically coded transcripts using content analysis within and across focus groups to identify major themes.Key Results: Patients identified four challenges and corresponding steps healthcare team could take to promote equitable care: (1) consistent documentation of disabilities and needed accommodations in the medical record; (2) allowance for accommodations to the environment, including adapting physical space, physical structures, and scheduling and rooming processes; (3) provide accommodations for administrative tasks, such as completing paper or electronic forms; and (4) adapt communication during interactions, such as speaking slower or using terms that patients can easily understand.Conclusion: These identified themes represent specific opportunities for healthcare teams to effectively provide accessible care to patients with disabilities. Many of the accommodations require minimal financial investment, but did require behavioral changes by the healthcare team to ensure equitable healthcare. [ABSTRACT FROM AUTHOR]

Objectives: To develop a paper-and-pencil semi-adaptive test for 5 domains of health-related quality of life (PAT-5D-QOL) based on item response theory (IRT). Methods: The questionnaire uses items from previously developed item banks for 5 domains: (1) walking, (2) handling objects, (3) daily activities, (4) pain or discomfort, and (5) feelings. For each domain, respondents are initially classified into 4 functional levels. Depending on the level, they are instructed to respond to a different set of 5 additional questions. IRT scores for each domain and overall health utility scores are obtained using a simple spreadsheet. The questions were selected using psychometric and conceptual criteria. The format of the questionnaire was developed through focus groups and cognitive interviews. Feasibility was tested in two population surveys. A simulation study was conducted to compare PAT-5D-QOL with a computerized adaptive test (CAT-5D-QOL) and a fixed questionnaire, developed from the same item banks, in terms of accuracy, bias, precision, and ceiling and floor effects. Results: Close to 90 % of the participants in feasibility studies followed the skip instructions properly. In a simulation study, scores on PAT-5D-QOL for all domains tended to be more accurate, more precise, less biased, and less affected by a ceiling effect than scores on a fixed IRT-based questionnaire of the same length. PAT-5D-QOL was slightly inferior to a fully adaptive instrument. Conclusions: PAT-5D-QOL is a novel, semi-adaptive, IRT-based measure of health-related quality of life with a broad range of potential applications. [ABSTRACT FROM AUTHOR]

At the Washington DC Pharmacogenomics in Drug Development and Regulatory Decision-Making: Workshop III – Three Years of Promise, Proposals and Progress on Optimizing the Benefit/Risk of Medicines (11–13 April 2005), one break-out session (Track 2) focused on co-development of therapeutic drug and diagnostic products. The Food and Drug Administration (FDA) released a draft concept paper shortly before the workshop was to convene. Track 2 was a forum for initial discussion of the content of the concept paper, and industry's initial reactions. After the workshop, formal commentaries on the co-development concept paper were submitted by several trade associations (e.g., Pharmaceutical Research and Manufacturers of America (PhRMA), Advanced Medical Technology Association (AdvaMed), American Association for Clinical Chemistry) and individual companies to FDA's Docket No. 2004N-0279. This paper includes a summary of the key features of the draft concept paper, the discussion in Track 2 of the April, 2005 meeting and highlights of the industry comments submitted to the FDA docket following the meeting.The Pharmacogenomics Journal (2006) 6, 375–380. doi:10.1038/sj.tpj.6500392; published online 2 May 2006 [ABSTRACT FROM AUTHOR]

Explores the role of nurse practitioners (NP) in the emergency department (ED) in Canada. Definition of NP; Special groups commonly referred to as NPs; International perspectives on ED nurse practitioners; Educational requirements for ED service; Benefits and pitfalls of ED nurse practitioners; Real imperative for introducing nurse practitioners in the ED; Barriers to the introduction of NPs into the ED.

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]

We describe the development of software that allows and automates the routine inclusion of nondigital paper-based data directly into DICOM examinations. No human intervention is required. The software works by allowing the direct faxing of nondigital paper-based patient data directly into DICOM imaging examinations and is added as the first series in the examination. The software is effective in any typical PACS/DICOM server environment. [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]

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]

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]

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]

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]

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]

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]

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]

Since 2000 several papers have examined the efficiency of healthcare delivery systems worldwide. These papers have extended the literature using drastically different input and output combinations from one another, with little theoretical or empirical support backing these specifications. Issues arise that many of these inputs and outputs are available for a subset of OECD countries each year. Using a common estimator and the different specifications proposed leads to the result that efficiency rankings across papers can diverge quite significantly, with several countries being highly efficient in one specification and highly inefficient in another. Broad input-output measures that are collected annually provide consistent efficiency rankings across specifications, compared to specifications that utilize specific measures collected infrequently. This paper also finds that broad output measures that are not quality-adjusted, such as life expectancy, seem to be a suitable alternative for infrequently collected quality-adjusted output measures, such as disability adjusted life years. [ABSTRACT FROM AUTHOR]

Presents a series of articles which deal with the persistence of racial and ethnic disparities in health care.

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]

Background: Colorectal cancer (CRC) is prevalent in the developed world. Favourable survival rates highlight the need to better understand CRC survivors' experiences of long-term impacts of treatment, which can in turn inform decision making. This systematic review aimed to identify and synthesise CRC survivors' experiences of long-term impacts on health-related quality of life. Methods: We searched Medline, Embase and PsychINFO from inception to January 2019. Qualitative studies describing CRC survivors' experiences at least 1-year post-treatment were included. Study eligibility, quality assessment (COREQ guidelines), and data synthesis was performed independently by two reviewers and discussed with the study team. Results: Of 1363 papers retrieved, 20 reporting 15 studies met eligibility. Thematic synthesis produced 12 themes: symptoms, physical, social, psychological and sexual functioning, impact on relationships, informal care needs provided by family/friend, supportive care needs provided by healthcare professional, health care experiences, health behaviour, financial toxicity and occupational experiences. Stoma problems (e.g. leakage, skin irritation) were common in ostomates. Survivors with no/reversed stoma experienced unexpected, long-term altered and unpredictable bowel functioning. Survivors often regulated timing, amount and foods consumed to manage bowel functioning. Less common symptoms included fatigue, impaired sleep and anal pain. Stoma problems and altered bowel functioning impaired survivors' physical, social, sexual and psychological functioning. Cognitive functioning and heredity issues were not reported in any paper. Conclusion: CRC survivors experience ongoing symptoms and functioning impairments more than 1-year post-treatment completion. Many survivors find their own ways to manage symptoms rather than seek professional help. Follow-up care for CRC survivors should integrate screening for long-term effects and provide targeted supportive care. [ABSTRACT FROM AUTHOR]

Annually, lots of research papers are published in scientific journals around the world. The knowledge of the status of research is a prerequisite for research planning and policy making. This type of knowledge could be gained through a scientometrics study on the published literature that analyzes research products in a scientific field. Always healthcare was a permanent concern of researchers and also rapidly expanding field of Big Data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It leads attracting attention from academia, industry and even governments around the world to "Big data in Healthcare". Therefore, this paper has done a meta-analysis on published researches methodology in this field in the period of 2008-2018. Statistical finding shows the "Meta-analysis and evidence" is the most used methodology in published papers. We applied data mining techniques for predicting using methodologies in the various databases to achieving knowledge discovery in the field. Naïve Bayes classifier in RapidMiner has been applied and results show eight main categories for words used in papers while "Developing methods to evaluate of care" averagely is the most intended using methodology for publishing papers and "Agent-based modeling" in nature is most using methodology and could be better predicted. [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]

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]

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]

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain. [ABSTRACT FROM AUTHOR]

Focuses on the nature of a community-based participatory research. Involvement of community representatives and organizations in all stages and aspects of the research process; Contribution of participatory research on the improvement of care and outcomes of populations at risk; Extension of participatory research in clinical and health services.

Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources. [ABSTRACT FROM AUTHOR]

Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390). [ABSTRACT FROM AUTHOR]

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models. [ABSTRACT FROM AUTHOR]

Background: In 2015, one of the largest healthcare companies in the world committed to phasing out from all its products and processes worldwide any substances declared by the European Chemicals Agency to be of very high concern. Since then, extended practical experience was gained in the phasing out of substances of concern. Results: We report in detail on a company-wide programme to phase out substances of concern, the challenges of and the approaches to the phase-out. The paper provides concrete ideas of how to address the legitimate urge to remove substances of concern from economic activities in a holistic way, taking into account the broad diversity of aspects of sustainability. The present paper also reviews the regulatory and societal environment in which substances of concern are being phased out. The paper attempts to contribute to the ongoing discussion of how to improve the chemicals policy in Europe and beyond. Conclusions: Phasing out substances of concern, substance selection for new products and processes, and the avoidance of regrettable substitutions while maintaining the pace of genuine innovation will stay a major challenge for our industry in the years ahead whereby not only toxicological and ecotoxicological hazards have to be considered in the selection and deselection of substances, but also other sustainability criteria characterising the entire life cycle will play an increasingly important role. Legislators and industry need to pay more attention to how this is implemented while avoiding slowing down innovation, making essential products unnecessarily more expensive, and further pushing material production out of highly regulated countries. [ABSTRACT FROM AUTHOR]

In this paper we analyze the impact of vaccinations on spread of the COVID-19 virus for different age groups. More specifically, we examine the deployment of vaccines in the Nordic countries in a comparative analysis where we focus on factors such as healthcare stress level and severity of disease through new infections, hospitalizations, intensive care unit (ICU) occupancy and deaths. Moreover, we analyze the impact of the various vaccine types, vaccination rate on the spread of the virus in each age group for Denmark, Finland, Iceland, Norway and Sweden from the start of the vaccination period in December 2020 until the end of September 2021. We perform a threefold analysis: (i) frequency analysis of infections and vaccine rates by age groups; (ii) rolling correlations between vaccination strategies, severity of COVID-19 and healthcare stress level and; (iii) we also employ the epidemic Renormalization Group (eRG) framework. The eRG is used to mathematically model wave structures, as well as the impact of vaccinations on wave dynamics. We further compare the Nordic countries with England. Our main results outline the quantification of the impact of the vaccination campaigns on age groups epidemiological data, across countries with high vaccine uptake. The data clearly shows that vaccines markedly reduce the number of new cases and the risk of serious illness. [ABSTRACT FROM AUTHOR]

Background: Millions of people pass through U.S. jails annually. Conducting research about these public institutions is critical to understanding on-the-ground policies and practices, especially health care services, affecting millions of people. However, there is no existing database of the number, location, or contact information of jails. We created the National Jails Compendium to address this gap. In this paper, we detail our comprehensive methodology for identifying jail locations and contact information. We then describe the first research project to use the Compendium, a survey assessing jails' treatment practices for incarcerated pregnant people with opioid use disorder. Results: This study sent surveys electronically or in paper form to all 2986 jails in the Compendium, with 1139 surveys returned. We outline the process for using the Compendium, highlighting challenges in reaching contacts through case examples, cataloging responses and non-responses, and defining what counts as a jail. Conclusion: We aim to provide tools for future researchers to use the Compendium as well as a pathway for keeping it current. The Compendium provides transparency that aids in understanding jail policies and practices. Such information may help devise interventions to ensure humane, evidence-based treatment of incarcerated people. [ABSTRACT FROM AUTHOR]

Crowdfunding is emerging as an alternative form of funding for medical purposes, with capital being raised directly from a broader and more diverse audience of investors. In this paper, we have systematically researched and reviewed the literature on medical crowdfunding to determine how crowdfunding connects with the health care industry. The health care industry has been struggling to develop sustainable research and business models for economic systems and investors alike, especially in pharmaceuticals. The research results have revealed a wealth of evidence concerning the way crowdfunding is applied in real life. Patients and caregivers utilize web platform–based campaigns all over the world to fund their medical expenses, generally on a spot basis, using donation-based or even reward-based schemes, regardless of the health care system archetype (public, private insurance-based or hybrid). Academics have also focused on funding campaigns and the predictors of success (which range from social behaviour and environment to the basic demographics of the campaigners and their diseases) and on social and regulatory concerns, including heightened social inequality and stigma. While equity crowdfunding is disrupting the way many ventures/businesses seek capital in the market, our research indicates that there are no relevant or consistent data on the practice of medical equity crowdfunding in health care, apart from a few anecdotal cases. [ABSTRACT FROM AUTHOR]

Disposal of healthcare waste is a key issue of environmental sustainability in the world. The amount of healthcare waste is increasing every day, and it is necessary to adequately dispose of this kind of waste. There are various treatments for healthcare waste disposal, of which incineration of healthcare waste is one of the solutions. This paper suggests a model for selection of the type of incinerators that best solve the problem of healthcare waste in secondary healthcare institutions in Bosnia and Herzegovina. In the selection of incinerators, extended sustainability criteria were applied. Basic sustainability criteria: environmental, economic, and social criteria, were extended with the technical criterion. To assess which of the incinerators best meets the needs for healthcare waste collection, multi-criteria decision-making was used. For this purpose, a combination of two MCDA methods was applied in this paper, namely full consistency method (FUCOM) and compromise ranking of alternatives from distance to ideal solution (CRADIS). The FUCOM method was applied to determine the weights of the criteria, while the CRADIS method was applied to rank the alternatives. The best alternative of the six alternatives used is A2 (I8-M50), followed by alternative A1 (I8-M40), while the worst ranked alternative is A5 (I8-M100). These results were confirmed by applying the other six methods of multi-criteria analysis and the performed sensitivity analysis. The contribution of this paper is reflected through a new method of multi-criteria analysis that was used to solve decision-making problems. This method has shown simplicity and flexibility in operation and can be used in all problems when it is necessary to make a multi-criteria selection of alternatives. [ABSTRACT FROM AUTHOR]

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes. [ABSTRACT FROM AUTHOR]

Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain. [ABSTRACT FROM AUTHOR]

In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call "Caring in the In-Between". Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical perspective of Science and Technology Studies and empirical research, "Caring in the In-Between" is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the "Robot Embedded in a Network"); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, "Caring in the In-Between" implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the "in-between" dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions. [ABSTRACT FROM AUTHOR]

Uncertainty is prevalent in various health contexts. It is imperative to understand how health-related uncertainty can impact individuals' healthcare experiences and health decision making. The purpose of the present paper is to provide five overarching recommendations from an interdisciplinary team of experts to address gaps in the literature on health-related uncertainty. We present a case study of health-related uncertainty within the specific context of alcohol use to demonstrate these gaps and provide context for the recommendations. The five recommendations concerning health-related uncertainty include: (1) use common, consistent terminology to discuss uncertainty, (2) clarify measures of individual differences in response to uncertainty, (3) increase research on uncertainty and affect, (4) investigate the impact of the channel through which uncertainty is communicated, and (5) develop theory-driven interventions to improve uncertainty management. We conclude by reviewing health contexts in which health-related uncertainty exists and note how our recommendations complement existing reviews and data. [ABSTRACT FROM AUTHOR]