The integration of advanced nanotechnology solutions in healthcare presents unparalleled opportunities for transformative care and cost savings. However, market penetration remains hindered by regulatory, technological, and adoption barriers. This study explores business analytics strategies facilitating successful market integration of nanotechnology innovations in healthcare. A mixed-methods approach combining qualitative expert interviews, quantitative survey analysis, and data-driven modeling is employed.Key findings highlight the critical role of data-driven decision-making, collaborative partnerships, and tailored market segmentation in overcoming adoption hurdles. Specifically, predictive analytics and machine learning algorithms are identified as essential tools for optimizing nanotechnology product development, pricing strategies, and supply chain efficiency. Furthermore, strategic partnerships with healthcare stakeholders and tailored market segmentation enable targeted commercialization and accelerated diffusion.This research contributes to the existing literature by providing actionable insights for nanotechnology firms, policymakers, and healthcare administrators seeking to harness the transformative potential of nanotechnology in healthcare. The proposed business analytics framework offers a valuable roadmap for navigating the complex healthcare landscape and ensuring the sustainable market integration of advanced nanotechnology solutions.

Objective: This study aims to establish a link between primary healthcare service accessibility among rural populations and the principle of territorial equity, a pivotal aspect of regional development strategies. Methods: We conducted a comprehensive analysis of primary healthcare service accessibility in rural Quebec, integrating theoretical frameworks and empirical evidence on territorial, regional, and community development. Through this approach, we sought to comprehend the nuanced dynamics and mechanisms contributing to territorial equity enhancement. Results: Our findings indicate that refining developmental strategies through community-driven approaches and regional initiatives can foster territorial equity. Moreover, employing sophisticated methodologies to measure primary healthcare service accessibility can promote territorial equity, emphasizing community development. Conclusion: Enhancing access to primary healthcare services and employing advanced assessment methodologies are essential for advancing territorial equity and supporting regional development initiatives. This underscores the critical importance of prioritizing healthcare accessibility in rural areas to achieve equitable development outcomes.

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and behavior. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated.

India, a megadiverse nation, harbors significant biodiversity, particularly in tropical moist deciduous forests. However, these forests and valuable medicinal plants face habitat loss and overexploitation threats. While indigenous knowledge of medicinal plants exists, a comprehensive ethnomedicinal study still needs to be included, especially for Uttar Pradesh's Terai region. This study focuses on the Suhelwa Wildlife Sanctuary, a haven for medicinal plants within the Terai. We aim to document the knowledge of local healers regarding wild medicinal plants used in phytotherapy. We will employ quantitative indices and compare our findings with data from other regions to identify novel medicinal plant uses. This enriched database can serve as a springboard for future pharmacological research. Suhelwa's diverse geography and climate contribute to its rich medicinal flora, making it a prime candidate for exploring the potential of traditional knowledge for drug discovery and sustainable income generation for local communities.

Tribal healers and local practitioners possess knowledge about plant uses passed down through generations. However, habitat loss, unsustainable harvesting, and declining traditional knowledge threaten this valuable resource. India, a global biodiversity hotspot, is rich in medicinal plants. Documenting the traditional knowledge prevents it from being lost and ensures the preservation of this cultural heritage. Recording traditional practices helps bridge the gap between traditional and modern medicine. This study quantifies the ethnomedicinal uses of plants within the Sohag Wildlife Sanctuary. The findings contribute to preserving traditional knowledge and promoting the sustainable use of medicinal plants. The study documented 95 medicinal plant species from 39 families, highlighting the importance of ethnomedicinal studies for preserving traditional knowledge and ensuring the sustainable use of medicinal plants. In the future, scientific studies can be organized to understand their efficacy, leading to developing new, potentially safer, and more effective medications. Identifying and documenting medicinal plants is crucial in safeguarding cultural heritage, fostering scientific discovery, and promoting sustainable healthcare practices.

(1) Background: Burnout syndrome results from chronic stress in the work-place. The COVID-19 pandemic has imposed a heavy workload on health professionals, doctors and nurses to respond to first-line health care during the first two years of an emergency caused by this virus. The aim of this study was to evaluate the prevalence of burnout in healthcare personnel, doctors and nurses during the COVID-19 pandemic. (2) Methods: This was a review of reviews on the prevalence of professional burnout syndrome in healthcare personnel, doctors and nurses before or during the COVID-19 pandemic, without language restrictions or missing data from the Cochrane Library electronic databases. In total, 48,657 articles were obtained from Pub-Med/MEDLINE and the Web of Science. (3) Results: After completing the examination, 20 articles were included in the review. Of these articles, 7% included nurses in their study population, 72% interviewed doctors from different specialties, and the remaining 21% reported personnel data. Healthcare as a whole: In total, 624 articles with 233,087 participants were included. Regarding the prevalence by subscale, a PD of 33% (95% CI; 33%- 34%), an AE of 37% (95% CI; 37%- 37%) and a PR of 29% (95% CI) were found. %; 29% and 29%). ; (4) Conclusions: The incidences of DP and AE are high, and that of RP is low. These findings demonstrate the high prevalence of burnout according to subscale. Burnout is a problem of great magnitude, and as such, interventions must be implemented immediately at the individual and group levels by institutions and health systems.

The world has changed. New technologies, new trends, new protocols, new efficient algorithms, and systems have been used widely by civilized countries to have better and more convenient living. In this research, there have been introduced many new terms, technologies, and mechanisms. Moreover, threats, attacks, and vulnerabilities have been studied and a novel scenario has been proposed in order to overcome all these violations that have been presented in this paper. The scenario is based on a security model that has been proposed for critical sectors, such as hospitals, industries, etc., which need efficient solutions to go one step further and improve the secure and efficient living and treatment of people. The aim of this research has been to understand through research the IoT security issues and propose the appropriate solutions based on the most suitable security algorithms. The results, which have been based on the complexity, the throughput, the power consumption, the strength, and the memory usage of the algorithms studied, have been hopeful and helpful for future research and experimentation.

Healthcare-Associated infections (HAIs) affect more than 100 million patients each year worldwide. These infections are mainly caused by multi-resistant bacterial strains (MDR) such as Acinetobacter Baumanii which is responsible of high mortality rate in immunocompromised individuals. However, the treatment is further aggravated by the abuse of antibiotics, the lack of vaccine and the emergence of resistance posing a huge challenge for the discovery of new therapeutic drugs to overcome this nosocomial infection. In this context, antimicrobial peptides with potential antibacterial proprieties could be an alternative. In this research, we describe the synthesis and the bioactivity of dermaseptins and their derivatives against Acitenobacter baumanii. The cytotoxicity of these DS was investigated on the HEp-2 cell line by the MTT cell viability assay. Thereafter, we studied morphological alterations caused by the action of one of the active peptides on the bacterial membrane using Atomic Force Microscopy (AFM). The cytotoxicity of dermaseptins was concentration-dependent at microgram concentrations. It was observed that all tested analogs exhibit antibacterial activity with MICs ranging from 3.125 to 12.5 μg/mL and MBCs ranging from 6.25 to 25 μg/mL. Microscopic images obtained by AFM revealed morphological changes on the surface of treated bacteria caused by K4S4(1-16), as well as significant surface alterations. Overall, these findings demonstrate that DS might constitute new lead structures for the development of potent antibacterial agents against Acinetobacter infections.

Attack on health has become a significant concern for non-belligerents of war, including healthcare personnel and facilities as witnessed in the ongoing Sudan conflict. About 1.5 billion people living in fragile and conflict-affected settings (FCAS) have a heightened need for essential health services. Conflicts often lead to the disruption of building blocks of health systems, lack of access to health facilities, failure of essential medical supply chains, the collapse of political, social and economic systems, migration of health care workers, and upsurges in illness. While health indicators often decline in conflict, health can also bring peace and harmony among communities. Investment in health systems and health diplomacy is a neutral starting point for bringing peace and mitigating conflicts. The international commitment towards Sustainable Development Goals (SDGs) provides the impetus to emphasize the relationship between health and peace with the amalgamation of SDG 3, SDG 16, and SDG 17. The inspection of how health should be used as a ‘tool for peace’ and not as leverage during the war must be reiterated by international institutions.

Background: COVID-19 booster uptake remained poor among healthcare workers (HCW) despite evidence of improved immunity against Delta and Omicron variants. While most studies used a questionnaire to assess hesitancy, this study aimed to identify factors affecting true booster hesitancy by examining actual vaccine uptake across time. Method: COVID-19 vaccination database records among HCW working at 7 Singaporean public primary care clinics between January to December 2021 were extracted, with gender, profession, place of practice, vaccination type and dates. Time to booster was calculated from the date of vaccination minus date of eligibility. Chi-square test was used to compare relationship between 1st dose and booster hesitancy, Kaplan-Meier method and Log-rank test were adopted to evaluate differences in cumulative booster uptake. Multivariate cox regression was used to investigate predictors for timely booster vaccination. Vaccination rate was charted across time and corroborated with media releases pertaining to legislative changes. Results: 877 of 891 (98.9%) primary care HCW were fully vaccinated, 73.8% of eligible HCW had taken the booster. HCW were less booster hesitant [median 16 (5-31.3) days] compared to the 1st dose [median 39 (13-119.3) days]. 1st dose hesitant HCW were more likely to be booster hesitant (OR=3.66, 95%CI 2.61-5.14). Adjusting for sex, workplace and time to 1st dose, ancillary (HR=1.53, 95%CI 1.03-2.28), medical (HR=1.8, 95%CI 1.18-2.74) and nursing (HR=1.8, 95%CI 1.18-2.37) received boosters earlier compared with administrative staff. No temporal relationship was observed between booster uptake, legislative changes and COVID-19 infection numbers. Conclusion: Vaccine hesitancy among HCW had improved from booster to 1st dose, with timely booster vaccination among medical and nursing staff. Tailored education, risk messaging and strategic legislation might help to reduce delayed booster vaccination.

This study examines the psychological repercussions of the COVID-19 pandemic on a medical team in an Israeli general hospital. The research explores the professional quality of life, burnout symptoms, secondary traumatic stress, and mindfulness among team members across three distinct phases of the pandemic. Analysis was conducted for different subgroups based on job roles and seniority, allowing for an evaluation of the phase-specific effects on the ProQOL (Professional Quality of Life) and mindfulness. Results align with established crisis trajectories, encompassing honeymoon/heroic phases, inventory, disillusionment, and recovery. The findings demonstrate a negative correlation between compassion satisfaction and burnout, as well as between mindfulness and burnout/secondary traumatic stress. Remarkably, male doctors exhibit a more pronounced negative impact on mindfulness compared to their female counterparts, primarily comprising healthcare workers. Temporal analysis reveals significant differences in compassion satisfaction and mindfulness, with baseline measurements being notably higher. Furthermore, the study sheds light on the vulnerability of male doctors, emphasizing the crucial role of hospital management in conveying the message and implementing effective measures to support professional quality of life. These findings underscore the necessity of prioritizing the well-being of medical professionals, particularly male doctors who face unique challenges in seeking assistance.

Pediatric psychology action in the pediatric clinical-surgical high complexity unit has become increasingly necessary. It allows for the possibility of integrating complex pediatric clinical-surgical operations by reducing painful effects on the emotional world of children and adolescents. The pediatric psychologist in a pediatric unit continuously oversees children and adolescents (C/A) throughout the entire therapeutic process, extending activity to parents and healthcare personnel. In ill children and adolescents, physical pain and psychological suffering follow, with minimal temporary variations, the same clinical path with continuous changes which put a strain on the ability of children, adolescents, parents and healthcare staff to cope with stress.

In the dynamic field of mental health care, the nuanced application of Artificial Intelligence (AI) through Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) is a pivotal innovation in patient empowerment and service optimization. This study introduces a distinctive online therapy platform that capitalizes on the synergy of NLP and ASR to offer unprecedented levels of interactive and personalized therapeutic interventions. The architecture of our system is meticulously detailed, featuring an ASR component with an impressive Word Error Rate (WER) of 14% when trained on the diverse French subsets of the Mozilla Common Voice dataset, complemented by a high-precision NLP framework skilled in processing and responding to user inputs. The evaluation of our system highlights its efficacy in enhancing therapy sessions and user satisfaction, with an emphasis on the qualitative aspects of user feedback. The paper addresses challenges such as dataset representativeness and language model refinement and articulates the strategic solutions employed to overcome them. The paper concludes with forward-looking perspectives on AI's role in mental health services, advocating for the creation of sophisticated, language-specific datasets and models to satisfy the increasing demands of online therapy, reflecting a growing commitment of patients in the management of their therapy. This research underscores the transformative impact of AI in advancing mental health care into the digital age, representing a significant evolution over existing methodologies.

The Black population, often treated as ethnically homogenous, faces a constant challenge in accessing and utilizing healthcare services. This study examines the intragroup differences in health-seeking behavior among the multiple ethnic subgroups that comprise the Black community. A cross-sectional study was conducted among 239 adults ≥18 years who self-identified as Black in the United States and Canada. A multiple logistic regression model was fitted to evaluate the relationship between health-seeking behaviors and ethnic origin controlling for selected social and health related factors. The mean age of participants was 38.6 years, 31% were male, and 20% were unemployed. Sixty-one percent reported very good or excellent health status, and 59.7% reported not being treated for chronic disease at the time of the survey. Aging (OR=1.05, CI:1.01–1.09) and being female (OR= 0.30, CI: 0.14–0.65) were associated with favorable health-seeking behaviors. Compared with participants who had earned high school diplomas only, those who reported having earned graduate degrees were 75% less likely to have engaged in favorable health-seeking behavior (OR=0.25, 95% CI: 0.07–0.86). In the Black community of our sample, regardless of ethnic origins or country of birth, the factors associated with health-seeking behavior were age, gender, and educational attainment. Keywords: African American; African; Caribbean; Healthcare Utilization; Ethnic Disparities; Health-seeking behavior; intragroup difference, black populations, ethnic subgroups

This paper honours the importance of creative practice as a way of staying in touch with the affective dimensions of human situations and experience. Drawing on observations, field notes, and documentation of artistic practice, the author explores the transfer of ‘felt’ experience through an emergent ‘body’ of artwork made in response to experiences and observations in/of a neurorehabilitation day service in the context of an art-based doctoral enquiry (Michaels 2022).[i] Broadening the scope of response art to the performative nature of ‘making’, the ‘work’ of art amplifies the resonance of emotional and sensory affect. This brings an ethics of care to the fore, giving voice to aspects of organisational culture that might easily be lost, as well as the need for time and space to mourn lost parts.

The development of new medical monitoring applications requires precise modeling of the human body effects as well as simulating and emulating realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable human body 3D emulation platforms which could be used for evaluating emerging microwave-based medical monitoring/sensing applications such as the detection of brain tumors, strokes, breast cancers as well as for capsule endoscopy studies. The new phantom recipes are developed for microwave ranges for the realistic shaped phantom molds. The second aim is to validate the feasibility and reliability of the phantoms for practical scenarios with electromagnetic simulations using tissue layer models and biomedical antennas. The third aim is to investigate the impact of the water temperature in the phantom cooking phase on the dielectric properties of the stabilized phantom. The evaluations show that the dielectric properties of the developed phantoms correspond closely to those of real human tissue. The error in dielectric properties varies between 0.5-8%. In the practical scenario simulations, the differences obtained with phantoms-based simulations in S21 parameters are 0.1-13dB. However, the differences are smaller in the frequency ranges targeted for medical applications.

Abstract: Integration of Medical Cyber Physical Systems (MCPS) and Internet-of-Medical Devices (IoMT) with conventional hospital networks have facilitated easy and speedy data collection, vertical and horizontal connectedness and collaborations among healthcare providers. Federated identity management (FIM) provides a solution towards the identity management challenge arising from this integration of millions of MCPSs to get personalized care and frictionless experiences for the patients, doctors, and employees. FIM protocols such as OAuth, Security Assertion Markup Language (SAML) are highly susceptible to cyber attacks like theft of barrier token, replay attack, message insertion, and Man-in-Middle attacks. IoMT devices have vulnerabilities in their firmware, operating systems, data encryption, data at store and data transmission. Combined vulnerabilities of FIM framework and IoMT devices creates major cyber risks for the current digital healthcare system. Therefore, a comprehensive and evidenced based cyber risk assessment is an urgent need for a cyber-safe digital health care system that can avoid frequent life threatening situations. This paper proposes a comprehensive and evidenced based cyber risk assessment approach for FIM and IoMT based collaborative digital healthcare systems. The novelty of the proposed approach is that it considers three dimensional vulnerabilities arising from existing IT communication protocols and infrastructure, IoMT and MCPS medical devices, protocols of FIM and their combined impact on hospitals and provides corresponding recommendations of security controls. The proposed approaches combine two industry standards including Cyber Resilience Review (CRR) asset management, NIST SP 800-30 to take advantage of both approaches. We have used a large number of IoMT and MCPS devices from multiple providers for threat modelling and produced evidence based cyber-risks using attack trees and detailed attack sequence diagrams to validate proposed approaches. The corresponding recommendation of security controls will support healthcare professionals and providers significantly for improving both the patient and medical device safety management within the FIM enabled healthcare ecosystem.

Although the anti-COVID-19 vaccination has proved to be an effective preventive tool, "break-through infections" have been documented in patients with complete primary vaccination course. Most of the SARS-CoV-2 neutralizing antibodies pro-duced after SARS-CoV-2 infection target the spike protein receptor-binding domain which has an important role in facilitating viral entry and infection of the host cells. SARS-CoV-2 has demonstrated the ability to evolve by accumulating mutations in the spike protein to escape the humoral response of the host. The aim of this study is to compare the titers of neutralizing an-tibodies against the variants of SARS-CoV-2 by analyzing the sera of recovered and vaccinated healthcare workers (HCWs). 293 HCWs were enrolled and divided into three cohorts as follows: 91 recovered from SARS-CoV-2 infection (nVP); 102 vac-cinated who became positive after the primary cycle (VP); 100 vaccinated with complete primary cycle who concluded the follow-up pe-riod without becoming positive (VN). Higher neutraliza-tion titers were observed in the vaccinated subjects’ arms com-pared to the nVP arm. Differences in neutralization titers between arms for single variant were statistically significant (p<0.001) except for titers against the Alpha variant (p<0.05) between nVP and VP. Within the nVP group, the number of subjects with the absence of neutralizing antibodies was high. The presence of high-er titers in patients with a complete primary cycle compared to patients recovered from the infec-tion suggests a better efficacy of artificial immunization compared to natural im-munization, further encouraging the promotion of vaccination even in subjects with previous infection.

Cloud computing is a strong and efficient technology facing some efficiency challenges attributable to its decentralized nature. The emergence of fog computing has subdivided the expansive cloud into numerous smaller units known as "cloudlets." Fog computing is one of the most recent advancements in today’s cloud computing landscape. Each cloudlet is a novel and diminutive architectural element extending the contemporary cloud computing infrastructure. The term "cloudlets" has been integrated with fog computing due to its augmenting impact on the attributes of cloud computing. F fog computing is a significant foundation and an indispensable cloud component. Consequently, this technology holds the potential to address efficiency concerns effectively. As a result, this paper focuses on enhancing the efficiency of the cloud computing environment by fine-tuning the efficiency parameters derived from fog computing. These parameters encompass response time, latency, and network utilization. The proposed solution involves developing a simulation system to minimize task redundancy and increase performance levels in the healthcare environment. The outcomes manifest a substantial improvement in the performance of the suggested system, with a clear increase depending on the three key parameters (response time, network utilization, and latency). Therefore, these findings are encouraging and carry significant implications, promoting the application of the proposed system in the real healthcare environment.

Even if the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people who deal with serious long-term breathing problems, like triggering asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new Secure Machine Learning Monitoring System for a model for virus detection. Our proposed model makes use of 4 basic emerging technologies, Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses.

Chronic non-communicable diseases (NCDs) are major public health problems and a significant financial burden on public health systems. By 2030, mortality due to NCDs, such as cardiovascular diseases, cancer, respiratory diseases, and diabetes, is predicted to increase in Brazil. A peculiar aspect of NCDs involves their long-term and integrated care management. This paper proposes a chronic disease management platform based on artificial intelligence to deliver digital health services everywhere. The proposed platform is anchored and built with healthcare 4.0 technologies, such as wearable devices, the internet of medical things, and artificial intelligence cloud-based solutions that allow the deployment of a smart healthcare system. In addition, the paper presents the feasibility of the platform in a diabetes prediction study case. For the study case, an initial dataset was established with bio-impedance, oxygen concentration, pulse rate, skin impedance, and skin temperature attributes. A baseline was implemented with ten regression models to assess the prediction performance of the mean squared error, root mean squared error, and r-squared score to compare predictive findings with capillary blood glucose measurements. Results evidence that the decision tree regressor and three ensemble methods (bagging decision tree regressor, random forest regressor, and AdaBoost regressor) yielded improvement over the other models. Moreover, a comparison among those models revealed that the decision tree regressor outperforms them and presents promissory outcomes.

Objective: To describe the methodology used and the results found in the implementation of Lean in Emergencies in the Emergency sector of the Hospital de Clínicas of the Federal University of Uberlândia (HC-UFU), verifying the difficulties and particularities that involved the process, as well as the result obtained. Material and methods: For the comparison between the periods before (T1), contemporary (T2) and after (T3) the implementation of Lean, hospital data were used , and the indicators corresponding to 10 variables were analyzed: Male Hospitalization and Female Hospitalization; High Male and High Female; Male Transfer and Female Transfer, Male Death <24h and Female Death <24h; Male Death >24h and Female Death >24h and general occupancy rate. All raw data related to the 36 months of evaluation were provided by the HC-UFU Statistics sector in spreadsheets without identifying patients. The indicators were compared to each other in the three analyzed periods, with the study directed as follows: Previous stage - Pre -intervention (T1): considered between the months of April 2017 to March 2018, completing 12 months before the implementation of Lean; Intra-intervention stage - contemporary (T2): considered between the months of April 2018 to March 2019, in a total of 12 months which was the implementation period; Post-Intervention Stage (T3): later: considered between the months of April 2019 to March 2020, completing 12 months after the implementation of Lean. The Lean in Emergencies scores (NEDOCS and LOS) were only recorded during the period that the implementation team was able to record and were used only to demonstrate trend over time. Comparison of the indicators with those found in other hospital units that also implemented Lean was carried out based on bibliographical research. Results and Discussion: The analysis of the behavior of the indicators did not demonstrate effectiveness related to the implementation of the system in the emergency sector. The failure to obtain better indicators can be attributed to discontinuity in monitoring the project, the lack of involvement of the clinical staff and alignment with the needs of the staff training academy, the difficulty of understanding the project by local and regional public entities. geographical location where the hospital is located, and by users of the health system who were not properly informed about the development of the project. These aspects should be considered in future management projects that may be implemented in the hospital. Despite the result of the implementation of Lean in the emergency sector not having reached the expected goals, the attempt showed windows of opportunity for future action on points that can improve the hospital management process and that should be tested in the next experiences. Conclusion: The methodology followed for the implementation of Lean in Emergencies in the emergency sector of the hospital did not produce improvement in hospital indicators.

Background: Occupational accidents contribute 46-65% of hepatitis B virus (HBV) infections among healthcare workers (HCWs). Vaccine against HBV infection offers protection of more than 90%. Despite availability of the vaccines in Tanzania, it’s uptake among HCWs in primary health facilities remains understudied. This study explored HBV vaccine uptake and associated factors among HCWs in rural and urban settings of north western Tanzania. Methodology: A cross-sectional analytical study was conducted between June and July in 2022 among HCW in Misungwi and Ilemela districts. Data were collected using a self-administered questionnaire and analyzed using IBM SPSS® version 25. Results: A total of 402 HCWs were recruited, their mean age was 34.9±7.77 years. Approximately half (54.7% (220/402) of HCWs had received at least one shot of HBV vaccine with only (18% (76/402) being fully vaccinated. Ilemela showed significantly higher uptake (χ2=23.64, df=1, p=0.00) of HBV vaccine as compared to their counterparts in Misungwi. Being male (aOR=2.38, 95% CI 1.28-4.45, p=0.006), working in urban (aOR=5.75, 95% CI 2.91-11.35, p=0.00) and employment duration of more than two years (aOR=3.58, 95%CI 1.19-10.74, p=0.023) were significantly associated with higher odds of HBV vaccination. Moreover, high perceived susceptibility to HBV infection (aOR=2.20, 95% CI1.02-4.75, p=0.044) and history of needle prick injuries (aOR=6.87, 95%CI 3.55-13.26, p=0.00) were also significantly associated with higher odds of HBV vaccination. Conclusion: There was low uptake of HBV vaccine among HCW in primary health facilities with a noteworthy difference between rural and urban settings.. Therefore, advocacy campaigns as well as resource mobilization towards promotion of HBV vaccination in primary health facilities are pivotal.

Background: The currently advised follow-up scheme of PSA-testing after robot-assisted radical prostatectomy (RARP) is strict and might pose a burden to our healthcare system. We aimed to optimize the 1-year follow-up scheme for patients who undergo RARP. Methods: All patients with histologically-proven prostate cancer (PCa) who underwent RARP between 2018 and August 2022 in the Prostate Cancer Network the Netherlands were retrospectively evaluated. We excluded patients who underwent salvage RARP and patients who had &lt;1 year of PSA follow-up. Postoperative PSA-values were collected. Biochemical persistence (BCP) was defined as PSA-level &gt;0.10 ng/mL at 0-4 months after RARP, whereas biochemical recurrence (BCR) was defined as PSA-level &gt;0.2 ng/mL at any time point after RARP. We aimed to identify a group of patients who had a very low risk of BCR at different time-points after surgery. Results: Of all 1155 patients, BCP was observed in 151 (13%), of whom 79 (6.8%) had PSA ≥0.2 ng/mL. BCR further developed in 51 (4.7%), and 37 (3.4%) patients at 5-8 and 9-12 months after RARP, respectively. In 12 patients, BCR was found at 5-8 months after RARP in absence of BCP. These patients represented 1.2% (12/1004) of the entire group. In other words, 98.8% (992/1004) of patients who had an unmeasurable PSA-level at 0-4 months after RARP also had unmeasurable PSA-level 5-8 months after surgery. Limitations are the retrospective design and incomplete follow-up. Conclusions: Patients with an unmeasurable PSA-level at 3-4 months after RARP may not need to be retested until 12 months of follow-up as almost 100% of patients will not have biochemically recurrent disease at 5-8 months of follow-up. This will reduce PSA-testing substantially at the costs of hardly any missed patients with recurrent disease.

This study presents three forms of interdisciplinary expertise in the healthcare design context to approach a particular multifaceted problem around the current healthcare for older adult patients with chronic low-back pain (LBP). Using an interdisciplinary co-design framework, first, our design approach performs the role of an initiator to define the problem by exploring the current context of healthcare. Second, it facilitates the experiences of experts and patients to reach the roots of the problem by functioning as a mediator. Third, our approach fulfills the primary role of healthcare design in producing new meanings considering the principles of patient-centeredness. These roles significantly contributed to the design of healthcare innovations. Our framework transformed the distributed disciplinary knowledge developed while tackling the multifaceted problem into new forms of expertise for collaboration in healthcare innovation.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

Supported Wellbeing Centres have been set up in UK hospital trusts in effort to mitigate the psychological impact of COVID-19 on healthcare workers, although the extent to which these are utilised and the barriers and facilitators to access are not known. The aim of the study was to determine facility usage and gather insight into employee wellbeing and the views of employees towards this provision. The study included i) 17-week service use monitoring, ii) employee online survey with measures of wellbeing, job stressfulness, presenteeism, turnover intentions, job satisfaction, and work engagement as well as barriers and facilitators to accessing the Wellbeing Centres. Over 17 weeks, 14,934 facility visits were recorded across two sites (peak attendance in single week n= 2,605). Facilities were highly valued, but the service model was resource intensive with 134 wellbeing buddies supporting the centres in pairs. 819 hospital employees completed an online survey (88% female; 37.7% working in COVID-19 high risk areas; 52.4% frontline workers; 55.2% had accessed a wellbeing centre). There was moderate-to-high job stress (62.9%), low wellbeing (26.1%), presenteeism (68%) and intentions to leave (31.6%). Wellbeing was higher in those that accessed a wellbeing centre. Work engagement and job satisfaction were high. Healthcare organisations are urged to mobilise access to high-quality rest spaces and Psychological First Aid, but this should be localised and diversified. Strategies to address presenteeism and staff retention should be prioritised, and high dedication of healthcare workers should be recognised.

Objective: This study investigated the effect of the hierarchical medical system under the national health insurance program on resident’s healthcare-seeking behavior in Taiwan. Background: Healthcare authorities in Taiwan initiated the allowance reduction of outpatient visits at regional hospitals and higher hierarchical hospitals from 2018. The ultimate goal is to implement a hierarchical medical system and provide the residents accessible as well as consistent medical services. Methods: This research was conducted through the questionnaire survey method and data were collected between August and December 2018 from the records of subjects who had recently sought medical attention. A total of 1,340 valid questionnaires were returned. Results: Regarding the effect on healthcare-seeking behavior, the following factors were significant: being aged between 40 to 49 (p＜.1), subjects with an educational background of junior high school (p＜.05), those who were not aware of the policy (p＜.001), and an awareness about both the hierarchical medical system and the policy to reduce outpatient visits to large hospitals (p＜.001). Conclusion: The public should be made aware about the hierarchical medical system to improve healthcare.

Background: Several GLP-1 receptor agonists (GLP-1 RAs) are used to treat type 2 diabetes (T2DM). Their cardio- and renal protective effects and substantial weight loss have been evident and progressively expanded their role in the American Diabetes Association (ADA) guidelines, which are endorsed by the European Association for the Study of Diabetes (EASD). The increased demand leads to a global shortage. Methods: We utilized a repeated cross-sectional design, drawing data from national prescribing databases, to analyze six GLP-1 RAs: Dulaglutide, Enatide, Liraglutide, Lisenatide, Semaglutide, and Tirzepatide. AutoRegressive Integrated Moving Average (ARIMA) models with exogenous variables were applied to assess trends over time and regions. Results: The prescription rates significantly differ between regions. Wales shows the highest prescribing for most GLP-1 RAs. The ARIMA models indicated a significant increase in their prescribing after ADA guidelines 2022 (e.g., Dulaglutide: Post-ADA effect of 15.22, 95% CI: [12.97, 17.47]). Following GLP-1 RAs shortages in July 2023, the prescribing rates, particularly for Semaglutide, increased (Shortage effect: 74.36, 95% CI: [71.92, 76.80]). Model diagnostics, including the Akaike Information Criterion (AIC) and Durbin-Watson statistics, confirmed the robustness of these trends. Conclusion: Informed decisions should be made by considering prescribing trends before and after important events such as the issuing of new guidelines or safety alerts.

Patient safety is a top priority for all healthcare systems globally. Promoting the adoption of policies for reporting and learning from errors is an important strategy for improving care safety. Therefore, the aim of our study was to detect how much patient safety culture influences the reporting of adverse events and the use of the incident reporting tool. The study protocol was developed according to PRISMA guidelines. Articles were searched electronically in PubMed/MEDLINE, the COCHRANE library, and Google Scholar by two independent reviewers, and those that met the eligibility criteria were included. Synthesis of qualitative data from included studies was performed by graphical descriptive statistical analysis. The results of the systematic review showed that health care organizations' increasing focus on staff development of a safety culture has led to a significant increase in incident reporting rates over the years. Moreover, in situations where safety culture is placed at the top of the nations, there is a higher frequency of incident reporting. An efficient incident reporting system should be an essential foundation for healthcare organizations, as it allows them to collect experiences and data and provide feedback to healthcare providers and staff involved in care.

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

Background/Objectives: The aim of this study is the operationalization of the International Classification of Functioning, Disability and Health in the rehabilitation care performed at the Institute of Cancer of the State of São Paulo through structuring a goal-oriented tool with relevant categories of evaluation and follow-up for cancer outpatients to be applied by physiatrists, physiotherapists, physical educators, occupational therapists, and psychologists. Methods: A qualitative study based on a literature review and a modified Delphi exercise, Estimate-Talk-Estimate (ETE) technique, was performed with the service’s rehabilitation experts. Consensus was obtained after four rounds under the 80% agreement threshold. Results: Forty categories were identified for neuropsychological evaluation and thirty-three for psychoaffective support, with 12 goals. Thirty-five categories with 13 goals were accounted for physiotherapy. Physical educators identified 18 categories, with 7 goals. Occupational therapists selected 54 categories, with 10 goals. A referral for personal factors was established. Functional Assessment of Cancer Therapy-General (FACT-G) was the quality of life tool chosen to be applied. Conclusions: A set of relevant ICF categories was structured, tailored by a clinically meaningful routine of assessment and follow-up of cancer outpatients based on specialists’ clinical practice and scientific literature. It will contribute for analyzable and more specific and quantifiable institutional health data. Its implementation and future discussion may promote the instrument’s refinement for its use in this population.

Research question/problem: This comprehensive review and comparative analysis aims to investigate the significance of enhancing oxygen provision in the context of COVID-19, focusing on the implications for healthcare policies and services.Background/context: The study addresses the shift in the oxygen dissociation curve of blood in COVID-19 patients, emphasizing the impact on oxygen delivery to tissues and the challenges it poses for clinical management. It also explores the disruption of healthcare systems due to the pandemic and the integration of social determinants of health in clinical environments.Methodology: The research incorporates a thorough literature review, comparative analysis of diverse approaches to oxygen delivery, and examination of successful interventions through case studies and best practices. Data collection techniques include the review of scholarly articles, case studies, and policy documents.Results/Key findings: The study highlights the imperative for augmented oxygen supply and the significance of maintaining appropriate oxygen saturation levels, especially in the context of respiratory illnesses like COVID-19. It also emphasizes the positive correlation between increased oxygen saturation levels and life expectancy, indicating potential health benefits of enhanced oxygen availability.Conclusions/Implications: The findings underscore the need for a structured approach to oxygen therapy and the importance of equitable distribution of oxygen to underserved regions. The implications of the research extend to enhancing healthcare policies and services regarding oxygen provision during the COVID-19 pandemic, especially in rural and remote areas.

This study addresses the influence of working conditions on Work-Related Quality of Life (WRQOL) among Spanish healthcare professionals. The research seeks to elucidate the mediating roles of job engagement and burnout, and the moderating effects of resilience and self-efficacy, in this relationship. Methods: The study included 374 healthcare workers from Spain, encompassing physicians, nurses, and nursing assistants. It employed a methodological approach using validated instruments to assess psychosocial risks, work-life quality, job engagement, burnout, and resilience. Data were gathered through self-reported questionnaires. Results: Key findings indicate a negative association between working conditions and WRQOL. Job engagement and burnout significantly mediated this relationship, accounting for 19% to 33% of the variance in WRQOL. Notably, lack of support emerged as a strong negative factor for work-related quality of life. Additionally, resilience moderated the relationship between lack of support and burnout. Conclusions: The study concludes that improving working conditions could increase WRQOL. Interventions aimed at enhancing engagement and addressing burnout may be effective. The moderating role of resilience suggests that strategies to enhance resilience could also be beneficial. These findings hold significant implications for policy formulation and human resources planning in healthcare, emphasizing the need for enhanced workplace support and the development of resilience among health workers.

(1) Background: Factors reported in literature associated with inappropriate prescribing of antimicrobials, such as physicians with less experience, uncertain diagnosis, and patient caregiver influences on physicians' decisions. Monitoring antimicrobial resistance is critical for identifying emerging resistance patterns, developing, and assessing the effectiveness of mitigation strategies. Improvement in prescribing `antimicrobials would minimize the risk of resistance and, consequently, improve patients' clinical and health outcomes. The purpose of the study is to delineate factors associated with antimicrobial resistance, describe the factors influencing prescriber’s choice during prescribing of antimicrobial, and examine factors related to consequences of inappropriate prescribing of antimicrobial. (2) Methods: Cross-sectional study conducted among healthcare providers (190) in six tertiary hospitals in the Eastern province of Saudi Arabia. The research panel has developed validated and piloted survey specific with closed-ended questions. A value of P &lt;0.05 was considered for statistical significance. All data analysis was performed using Statistical Package for Social Sciences (IBM SPSS version 23.0). (3) Results: 72.7% of the respondents have agreed that poor skills and knowledge are key factors that contribute to the inappropriate prescribing of antimicrobials. All the respondents acknowledged effectiveness and previous experience with the antimicrobial, and reading scientific materials (such as books, articles, and the internet) were key factors influencing physicians’ choice during antimicrobial prescribing. (4) Conclusion: The current study has identified comprehensive education and training needs for healthcare providers about antimicrobial resistance. Using antimicrobials unnecessarily, insufficient duration of antimicrobial use, and using broad spectrum antimicrobials, were reported to be common practices. Further, poor skills and knowledge were a key factor that contributed to the inappropriate use and overuse of antimicrobials and using antimicrobials without physician prescription (self-medication) were the key factors which contribute to AMR from participants’ perspectives. Furthermore, internal policy and guidelines are needed to ensure that the antimicrobials are prescribed in accordance with standard protocols and clinical guidelines.

The infections of bacterial origin represent a significant problem to public healthcare worldwide both in clinical and community settings. Recent decade was marked by limiting treatment options for bacterial infections due to growing antimicrobial resistance (AMR) acquired and transferred by various bacterial species, especially the ones causing healthcare-associated infections, which has become a dangerous issue noticed by the World Health Organization. Numerous reports shown that the spread of AMR is often driven by several species-specific lineages usually called the ‘global clones of high risk’. Thus, it is essential to track the isolates belonging to such clones and investigate the mechanisms of their pathogenicity and AMR acquisition. Currently, the whole genome-based analysis is increasingly used for these purposes, including epidemiological surveillance and analysis of mobile elements involved in resistance transfer. However, in spite of the exponential growth of available bacterial genomes, their representation usually lack uniformity and availability of supporting metadata, which creates a bottleneck for such investigations. In this dataset, we provide the results of a comprehensive genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen Klebsiella pneumoniae. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), assignment of the isolates to known global clones, capsular (KL) and lipooligosaccharide (O) types, presence of CRISPR-Cas systems, and cgMLST profiles are given, and the presence of AMR and virulence genes within the genomes is also reported. This dataset will be useful for researchers in the field of K. pneumoniae genomic epidemiology, resistance analysis and prevention measure development.

AbstractThe escalating impact of artificial intelligence (AI) has birthed the fascinating technology of deepfakes, capable of meticulously crafting hyper-realistic audio-visual manipulations. Within the realm of healthcare, this technology presents a double-edged sword, brimming with potential for revolutionizing various facets of care delivery while simultaneously harboring significant ethical and practical challenges. This research delves into the complex narrative surrounding AI deepfakes in healthcare, meticulously analyzing both the exciting opportunities they offer, and the inherent risks associated with their implementation.Background:Traditional healthcare practices face limitations in patient engagement, accessibility, and training efficacy. Deepfakes, with their ability to simulate real-world scenarios and interactions, emerge as a potential solution, offering personalized patient education, immersive mental health interventions, remote consultations with virtual avatars, and realistic training simulations for medical professionals.Objective:This research aims to comprehensively understand the multifaceted impact of AI deepfakes in healthcare systems. It seeks to: Identify and analyze the potential benefits of deepfakes in specific healthcare applications. Assess the ethical and practical challenges associated with deepfake implementation in healthcare settings. Propose recommendations for safe, responsible, and ethical integration of deepfakes within the healthcare system.Results/Conclusion:The research findings illuminate a landscape replete with opportunities and challenges. Deepfakes offer the potential to enhance patient education, personalize mental health interventions, facilitate remote consultations, and revolutionize medical training. However, concerns regarding misinformation, ethical breaches, data privacy, and technical limitations pose significant challenges.The conclusion emphasizes the need for a balanced approach that capitalizes on the innovative potential of deepfakes while mitigating potential harm. Collaborative efforts involving healthcare professionals, technologists, and policymakers are crucial for establishing ethical frameworks, ensuring responsible development, and harnessing the positive power of deepfakes to build a future where healthcare benefits from cutting-edge technology while prioritizing patient well-being and ethical principles.

Problem:Palestinian demographic landscape is undergoing an increase in the elderly population like many regions across the world. The elderly Palestinians face a unique challenges and problems of limited access to traditional care due to restricted movement, checkpoints, and also for limited healthcare resources. Therefore, the need arose to develop a sensitive and adaptable smart health monitoring system for Palestinian elderly to provide accurate and timely health data to improve well-being and prevent complications. Method: The proposed system (SHMS) leverages the Internet of Things (IoT) with appropriate sensors to track vital signs like heart rate, blood oximeter, body temperature and signal from a patient heart. Advanced algorithms analyze the data, generating alerts for potential health risks, triggering timely interventions, and enabling remote consultations. Results: This system has the potential to contributes to SDG 3 to significantly improve the health and well-being of elderly Palestinians by providing an accurate, low-power, and low-cost system for remote health monitoring. The system, being wearable, reads vital signs in real-time, overcoming geographical barriers and facilitating remote monitoring, especially in areas with limited healthcare centers and suffered from barriers along the way. The Doctor can suggest the patient’s prescription needed based on the momentary condition of the elderly and on previous readings of the sensors, if necessary. Which will also be notified via messages. Moreover, the doctor can take immediate intervention in case of an emergency. By implementing the system, it will be unnecessary for more than 50\% of the elderly to visit health care, thus reducing the cost to 50\%.}

In Taiwan, remote healthcare was initially limited to telemedicine services, and the systematic development of remote care was relatively delayed. It was not until 2007 that the Department of Health of the Executive Yuan first initiated the 'Remote Care Pilot Program,' followed by the 'Remote Care Service Improvement and Quality Enhancement Plan' in the subsequent year. In 2010, Taiwan officially launched the 'Remote Health Care Services Development Plan' and established a dedicated project office, marking the comprehensive start-up of remote care services. This stage saw significant contributions from artificial intelligence aids and ICT remote transmission technologies, greatly promoting the application of smart healthcare in home care. This study aims to explore the strategic gaps in the application of smart healthcare in home remote care. By analyzing gaps in 'legal regulations,' 'economic factors,' 'user behavior habits,' 'policy and culture,' and 'environment and technology,' this study employs a mixed-methods approach. Particularly, it uses the Analytic Hierarchy Process (AHP) to construct a weighted ranking of these strategic gaps. Through in-depth interviews with doctors and smart healthcare experts and the collection of 16 valid AHP questionnaire samples, which cover both industry and academia with a balanced distribution, the survey results are ensured to be representative and reliable. The findings indicate that 'legal regulations' are considered the most critical strategic gap that needs to be addressed first, followed by 'user behavior habits.' This suggests that when designing smart healthcare applications, greater consideration needs to be given to user acceptance and habits. By systematically analyzing and addressing these strategic gaps, this study not only provides solutions to issues in the application of smart healthcare in home remote care but also offers valuable references for future research and practice, aiming to make home remote care services more comprehensive and efficient.

The impact of COVID-19 pandemic on the cardiovascular healthcare services in Croatia has not been systematically examined. In this study, we compared utilization and provision of cardiovascular services in Croatia during the pre-pandemic period (2017 to end of 2019) and pandemic period (2020 to end of 2021) by using large nationwide data from the Croatian Health Insurance Fund, an institution covering 95% of all insurance claims in Croatia. We report that the use of advanced heart failure treatment modalities increased while the number of coronary angiographies decreased while the number of performed percutaneous coronary interventions remained similar, even increasing in the setting of acute myocardial infarction, comparing pandemic to pre-pandemic period. Similarly, the number of transcatheter aortic valve implantations increased significantly during the pandemic era. There was a significant increase in laboratory diagnostic testing with troponin and natriuretic peptide assays during the pandemic period while the use of most cardiovascular services remained stable or only slightly decreased, compared to pre-pandemic period. These data suggest that Croatian cardiovascular healthcare services during COVID-19 pandemic showed resilience and managed to maintain or even increase the number of various diagnostic and interventional procedures despite the widespread societal and logistical challenges encountered during pandemic.

Mahalanobis distance is a useful multivariate statistic for determining how far apart two points are from one another. It is a very helpful statistic with excellent uses in multivariate anomaly detection, one-class classification, and classification on severely unbalanced datasets.

The novel coronavirus pneumonia epidemic is still raging around the world. Therefore, during the period of a global pandemic, maintaining the normal circulation of medicine and health fields is the key to fight against the epidemic. This paper based on relevant data such as the export volume of cross-border e-commerce in Hangzhou, China from 2015 to 2019, the article uses system dynamics modeling to explore the impact of overseas warehouse services on cross-border e-commerce export transactions. Studies have shown that the introduction of overseas warehouses has significantly improved customer satisfaction and improved the quality of cross-border logistics services, which has a positive impact on cross-border e-commerce export transactions. Through sensitivity analysis, it can be seen that under the same conditions, the efficiency of customs clearance has the greatest impact on overseas warehouse services. Therefore, when allocating various investments in overseas warehouses, you can favor the customs clearance process to enhance the competitiveness of the cross-border e-commerce market and provide a feasible experience for healthcare supplies export from cross-border e-commerce.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

WHO was informed on 31 December 2019 of cases of unknown cause pneumonia in Wuhan City, China. On 7 January 2020 Chinese authorities reported a novel coronavirus as the cause and was temporarily labeled "2019-nCoV." Coronaviruses (CoV) are a wide family of viruses which cause diseases ranging from common cold to more serious illnesses. A novel coronavirus (nCoV) is a new strain not previously found in humans. Countries around the globe have stepped up their surveillance to quickly detect any new 2019-nCoV cases. Blockchain is developing into a safe and efficient network for secure data sharing in applications such as the financial industry, operations management, food industry, energy market, the Internet of Things and healthcare. In this paper, we are using blockchain technology as a mean to share authentic data, tracking of relevant information and help speed up the treatment process. At the same time it will preserve person’s identity. Timely deployment and suitable implementation of the proposed model have the opportunity to curb COVID-19 transmissions and associated mortality, especially in environments with inadequate access to testing facilities. This work will also facilitate in the treatment of other infectious diseases. Smart contract have been designed and implemented using the ethereum blockchain platform which has been presented in this paper. This work would facilitate multiple stakeholders who are involved within the medical system to curb the transmission of this disease.

Based on the demographic shift and the related challenges resulting from the growing number of elderly and persons with chronic diseases, the idea of smart home that supports its inhabitants in the daily life, gains importance. The purpose of this paper was to examine in a prototypic Ambient Assisted Living environment if users after interaction with different health-supporting applications intend to use such in the future. Two experimental studies exemplary show possible applications of home-integrated technology that can support, assist and accompany the target group in different contexts, and examine to what extent participants are willing to future use such sophisticated technology at home. The results show that people in general, but especially the old and chronically ill ones are quite fascinated of health-supporting ambient technology and the majority intends to use such ambient assistance in the future (study I). Moreover, serious games for healthcare are shown as a hedonic use of technology in smart homes that have a great potential to retain or improve the physical health, mobility and the overall well-being of the inhabitants (study II). The article provides two examples of ambient technology to leverage the demographic change and presents important user factors for facilitating high user acceptance.

Immune escape is observed with SARS-CoV-2 Omicron (Pango lineage B.1.1.529), the predominant circulating strain worldwide. Booster dose was shown to restore immunity against Omicron infection, however, real world data comparing mRNA (BNT162b2; Comirnaty) and inactivated vaccine (CoronaVac; Sinovac) homologous and heterologous boosting is lacking. A retrospective study was performed to compare the rate and outcome of COVID-19 in healthcare workers (HCWs) with various vaccination regime during a territory-wide Omicron outbreak in Hong Kong. During the study period 1 Feb – 31 Mar 2022, 3167 HCWs were recruited, 871 HCWs reported 746 and 183 episodes of significant household and non-household close contact. 737 HCWs acquired COVID-19 which were all clinically mild. Time dependent Cox regression showed that, comparing with 2-dose vaccination, 3-dose vaccination reduced infection risk by 31.7% and 89.3% in household contact and non-household close contact respectively. Using 2-dose BNT162b2 as reference, 2-dose CoronaVac recipient had significantly higher risk of being infected (HR 1.69 P<0.0001). Three-dose BNT162b2 (HR 0.4778 P<0.0001) and 2-dose CoronaVac + BNT162b2 booster (HR 0.4862 P=0.0157) were associated with lower risk of infection. Three-dose CoronaVac and 2-dose BNT162b2 + CoronaVac booster were not significantly different from 2-dose BNT162b2. The mean time to achieve negative RT-PCR or E gene cycle threshold 31 or above was not affected by age, number of vaccine dose taken, vaccine type and timing of the last dose. In summary, we have demonstrated lower risk of breakthrough SARS-CoV-2 infection in HCWs given BNT162b2 as booster after 2 doses of BNT162b2 or CoronaVac.

Nowadays, wearable devices for human health monitoring are increasingly become popular and widely used. Typically, the wearable device is small size and operates with batteries. Therefore, the wearable device acquires bio-signals and transfers to smartphones or personal computers (PC) via WiFi/ Bluetooth for processing data. To reduce power consumption is one of the most important challenges of designing wearable devices. To solve this problem, the proposed signal quality valuation (SQV) method can be select the high-quality signal and reduce the transfer time to other devices. In this paper, the proposed SQV and data compression method rely on real-time PPG signals analysis to retain important information of PPG signal, improve performance and power consumption of PPG devices. Besides, we also proposed Heuristic rules for heart rate (HR) estimation with compression data. The experimental results show that the highest compression ratio (CR) is 387.8 with BIDMC Physionet database (sampling frequencies of 125 Hz) and HR error as 1.43 bpm for averaging absolute error (avAE), the standard deviation absolute error (sdAE as 0.4) and relative error mean (avRE as 0.019). The proposed real-time PPG measurement system (sampling frequency as 100/ 200/ 400 Hz) reduce power consumption and open the new structure for healthcare application systems

Background: This practice guideline was developed by the Chronic Kidney Disease (CKD) Task Force, which was composed of clinical and methodological experts. The Saudi Arabian Ministry of Health and its Health Holding Company commissioned this guideline project to support the realization of the Vision 2030's healthcare transformation pillar. Methods: The guideline synthesis was guided by the GRADE-ADOLOPMENT methodology. Results: The final adoloped guideline addressed 12 clinical questions on blood pressure management in patients with CKD through a set of recommendations and performance measures. The recommendations included antihypertensive agents in children, non-renin angiotensin system inhibition (Non-RASi) vs RASi in adults, intensive vs standard blood pressure targets, early vs late assessment for kidney replacement therapy (KRT), late vs early preparation strategy for KRT, CKD symptoms during assessment for KRT or conservative management, initiation of KRT in patients with deteriorating CKD, choice of KRT modality or conservative management in certain CKD patient groups, changing or discontinuing KRT modalities, review frequency for KRT or conservative management, and Information, education and support. Conclusions: These conditional recommendations were based on low to very low certainty of evidence that highlights the need for high-quality randomized trials comparing different antihypertensive agents in patients with CKD.

Healthcare workers' stress, burnout, and job dissatisfaction were exacerbated by the Covid-19 pandemic, whose mid-term negative effects are still present. The aim of this study was to investigate in healthcare workers the effect of the dimensions of burnout (Psychophysical exhaustion, Relational deterioration, and Professional inefficacy) on Career satisfaction. These relationships were evaluated by considering the direct and mediated effects of Disillusion. For these purposes, the Link Burnout Questionnaire (LBQ) and the Career Satisfaction Scale (Occupational Stress Indicator - OSI) were administered to 295 healthcare professionals working at three Italian public hospitals in Cagliari, Italy. The analyses were conducted using the Partial Least Square Structural Equation Model (PLS-SEM) method. Findings highlight that healthcare workers were in a condition of work distress, high risk of burnout, and career dissatisfaction. Also recognized was the direct negative effect of burnout dimensions on career satisfaction. Finally, the mediated negative effect of Disillusion in the relationship between burnout and career satisfaction was identified. This study emphasizes the relevance of the dimension of Disillusion, and its “bright side” Fulfilment, in healthcare workers, specifically referring to the disregarded relationship between burnout and career satisfaction.

Burnout in healthcare affects the quality of patient care. In 2020, Mayo Clinic implemented Advanced Care at Home (ACH), a new hospital-at-home program. We aimed to assess burnout levels among ACH providers, analyzing data from quarterly surveys. Physicians and nurse practitioners registered in ACH completed a 16-question anonymous survey distributed via email. Ten questions from the original survey used the Maslach Burnout Inventory to measure the three domains associated with burnout: depersonalization, emotional exhaustion, and personal achievement. We conducted a descriptive analysis using frequency distribution and percentages. A total of 27 providers participated, with response rates of 38%, 23%, and 66% in the first, second, and third quarters. Over 75% of the participants responded satisfactorily to nine of the ten questions associated with the risk of burnout. Moreover, 77% of providers felt joyful at work "always or most of the time." Of the 74% of participants who responded to the question about feeling burned out from work, 48% of the participants "strongly” or “somewhat” disagreed, while only 26% "strongly” or “somewhat” agreed. Mayo Clinic's ACH has demonstrated low levels of burnout among providers, positively impacting the well-being of healthcare workers and contributing to a high standard of patient care.

The COVID-19 pandemic has led to a substantial revolution in the incorporation of digital solutions in healthcare. This systematic review investigates the enduring physical and psychological consequences individuals experience up to two years post-recovery. Additionally, it focuses on examining the influence of mHealth interventions on these effects. Significantly, 41.7% of survivors experience lingering symptoms that have not been addressed, while 14.1% encounter difficulties in returning to work. The presence of anxiety, compromised respiratory functioning, and persistent symptoms highlight the immediate requirement for specific therapies. Telehealth, particularly telerehabilitation, presents itself as a possible way to address these difficulties. The study thoroughly examines 10 studies encompassing 749 COVID-19 patients, investigating the efficacy of telerehabilitation therapies in addressing various health markers. Telerehabilitation-based breathing exercises yield substantial enhancements in functional performance, dyspnea, and overall well-being. The results emphasize the potential of telerehabilitation to have a favorable effect on patient outcomes, however more research is needed to strengthen the existing evidence basis as one of the most important limitation is the limited number of trials and the evaluation of varied therapies. The analysis highlights the significance of digital solutions in post-COVID care and calls for ongoing research to improve comprehension and implementation of telehealth interventions in a swiftly changing healthcare environment.

Accountable Care Organizations (ACOs) are healthcare delivery systems that aim to coordinate and improve the quality of care for patients while reducing costs. ACOs bring together doctors, hospitals, and other healthcare providers to work as a team to care for a defined population of patients. The idea behind ACOs is to shift the focus from paying for each individual service provided to patients, known as fee-for-service, to paying for the overall care and well-being of patients.ACOs typically operate under a shared savings model and also encourages providers to use data and technology to better coordinate care and improve patient outcomes.ACOs can be physician-led organizations, hospital-led organizations, and collaborations between healthcare providers. The success of ACOs depends on many factors, including the level of cooperation and collaboration among healthcare providers, the quality of care provided, and the ability to effectively manage costs.Despite their potential benefits, ACOs also face challenges, including the difficulty of coordinating care across multiple healthcare providers, the need to invest in technology and data management systems, and the challenge of changing provider behavior.This study assessed a number of systemic reviews and journals to assess the impact of ACO’s on population health. ACO’s have shifted away from costly care patterns and thus helped many countries.

Background: SARS-CoV-2 continues to impact human health globally, with airborne transmission being a significant mode for transmission. In addition to tools like vaccination and testing, counter-measures that reduce viral spread in indoor settings are critical. This study aims to assess the efficacy of UV-C light, utilizing the Violett sterilization device, as a countermeasure against airborne transmission of SARS-CoV-2 in the highly susceptible Golden Syrian hamster model. Methods: Two cohorts of naïve hamsters were subjected to airborne transmission from experimentally infected hamsters; one cohort was exposed to air treated with UV-C sterilization, while the other cohort was exposed to un-treated air. Results: Treatment of air with UV-C light prevented the airborne transmission of SARS-CoV-2 from experimentally exposed hamster to naïve hamsters. Notably, this protection was sustained over a muti-day exposure period during peak viral shedding from hamsters. Conclusion: These findings demonstrate the efficacy of the UV-C light to mitigate airborne SARS-CoV-2 transmission. As variants continue to emerge, UV-C light holds promise as a tool to reduce infections in diverse indoor settings, ranging from healthcare facilities to households. This study reinforces the urgency of implementing innovative methods to reduce airborne disease transmission and safeguard public health against emerging biological threats.

Background: During the COVID-19 pandemic some family physicians were overburdened and experienced difficulties reaching vulnerable patients and meeting the increased need for psycho-social support. This randomized controlled trial (RCT) tested whether a primary healthcare (PHC) based community health worker (CHW) intervention could tackle psychosocial suffering due to physical distancing measures in patients with limited social networks. Methods: CHWs provided 8 weeks of tailored psychosocial support to the intervention group. Control group patients received ‘care as usual’. The impact on feelings of emotional support, social isolation, social participation, anxiety and fear of COVID-19 were measured longitudinally using a face-to-face survey to determine their mean change from baseline. Self-rated change in psychosocial health at 8 weeks was determined. Results: We failed to find a significant effect of the intervention on the prespecified psychosocial health measures. However, the intervention did lead to significant improvement in self-rated change in psychosocial health. Conclusions: This study confirms partially the existing evidence on the effectiveness of CHW in-terventions as a strategy to address mental health in PHC in a COVID context. Further research is needed to elaborate the implementation of CHWs in PHC to reach vulnerable populations during and after health crises.

The report describes organizational and occupational strategies adopted by Lody Public Hospital (the first epicentre of Covid-19 outbreak in Italy) in order to face pandemic. Occupational Safety System (OSH) introduced infection prevention and control strategies (IPC) for the protection of healthcare workers (HCWs, while a reorganization of whole hospital based on the level of intensity of care for COVID-19 patients was estabilished. Screening on HCWs (N= 248) by nasopharyngeal swabs for SARS-CoV-2 and comparison between HCWs and local population (N=1123) are shown. These preliminary data, collected from 25^th February till 25^th March 2020, confirms that measures adopted mitigate the effects of virus transmission within the hospital and reduced the HCW component of the transmission of the infection. Additional studies in depth are currently underway.

OBJECTIVE A quality improvement study to assess catheter-associated urinary tract infection (CAUTI) rate post-implementation of a bladder catheter with integrated active drain line urine clearance and automated intra-abdominal pressure monitoring in a burn intensive care unit (ICU). DESIGN Eight-year retrospective before and after study (2015–2022). SETTING A single American Burn Association verified Burn Center with fourteen inpatient beds. PATIENTS Patients meeting criteria for admission to a Burn Center METHODS Retrospective cohort study following the implementation of a novel urine output monitoring system with integrated drain line and urine clearance. Data from a 48-month (from January 2015-December 2018) historical control (period 1) were compared to data from a 28-month (from January 2020 to April 2022) post-implementation period (period 2). Pre- and post-implementation CAUTI event incidences were compared. Charts were reviewed to characterize the patients. RESULTS A total of 42 CAUTIs in 2243 patients were identified using the National Health and Safety Network (NHSN) definition during the analyzed period. There were 40 CAUTI events in period 1 and two CAUTIs in period 2. The incidence of CAUTI events pre-implementation was 0.030 (mean of 10 CAUTI events per year) compared to 0.002 (mean of 1 CAUTI event per year) post-implementation of an automatic drain line clearing UO monitoring system showing a significant reduction in CAUTI events (P<0.01, risk ratio novel vs. gravity bladder catheter 0.071, 95% confidence interval: 0.017-0.294). CONCLUSIONS CAUTIs were reduced in the period following the implementation of a novel urinary catheter system with an integrated active drain line and urine clearance in burn patients.

In 2021, Japan's medical expenses will exceed 45 trillion yen, and the shortage of doctors, especially in remote and mountainous areas, is becoming serious, making it difficult to maintain the medical system. We have conducted a study of 800 health consultation text data. We developed an on-premise health counseling LLM system by constructing a dialogue flow based on 800 health counseling text data. We conducted a Turing test of this system using 200 test data and verified its effectiveness with three medical professionals. The Turing test was a comparison experiment between this system and a conventional LLM system. The health counseling LLM infrastructure focuses on exercise guidance and analyzes gender, height, weight, body fat percentage, and muscle mass. While the accuracy of the conventional LLM system was 87.5%, this system showed a higher accuracy of 93.1%. Although telemedicine has been slow to spread in Japan, the introduction of a health consultation system using Personal Health Record and Large Language Models has the potential to reduce the burden on physicians. In the future, we aim to improve the accuracy of the system by using Japanese language and medical-specific evaluation scales.

We investigated how Artificial Intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March-April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1,970 comments from VH participants and trained two Machine Learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three Health Belief Model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March-April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.

The COVID-19 pandemic has caused significant medical and psychological challenges worldwide, and not only exceeded the capacity of hospitals and intensive care units but also an individuals’ ability to cope with life. Health-care workers have continued to provide care for patients despite exhaustion, fear of transmission to themselves and their family, illness or death of friends and colleagues, and losing many patients. They have also faced additional stress and anxiety due to long shifts combined with unprecedented population restrictions, including personal isolation. In this study, we study the effect of an app-based Yoga of Immortals (YOI) intervention on mental health of healthcare workers. In this study, the health care workers were digitally recruited, and their psychological parameters were measured using validated questionaries. The participants were randomly grouped into control and test groups. The validated psychological measures were the Patient Health Questionnaire-8 (PHQ-8), Insomnia Severity Index (ISI) and generalized anxiety disorder (GAD-7) scales. The digital YOI intervention significantly reduced the anxiety, depression symptoms, and insomnia in healthcare workers of all age groups. In contrast, there was no improvement in the control group. This study details the effectiveness of an app-based YOI intervention in healthcare workers.

Growing evidence suggests that healthcare provider advice may increase tobacco cessation, reduce alcohol use, and improve the adoption of desirable lifestyle behaviors among patients. However, how brief interventions and other provider-patient interactions can shape cumulative adoption of multiple modifiable behaviors is less well studied for diabetes prevention and control. Using weighted internet panel survey data from a large socio-demographically diverse urban population in the United States (n=1,003), the present study describes differences in group characteristics among those who had been “ever diagnosed” with prediabetes/diabetes versus those who had not. It also examines the associations between the cumulative adoption of lifestyle behaviors and each of the following: a) lifetime prediabetes/diabetes diagnosis; b) brief lifestyle intervention exposure (i.e., received provider advice/encouragement); and c) recent provider-patient communication about diabetes. There were several group differences in “ever diagnosed” prediabetes/diabetes status by age, employment status, health status, nutrition knowledge, blood pressure/hypertension diagnosis, and diabetes-related health behaviors (p<0.05). Each of the three provider-patient interactions of interest were positively associated with a higher cumulative sum of adopted modifiable lifestyle behaviors for diabetes prevention and management. Results suggest that provider advice/provider-patient interactions of any type can have a salutary impact on whether individuals with prediabetes or type 2 diabetes will engage in recommended lifestyle behavior modifications.

Artificial intelligence (AI)-based conversational models, such as ChatGPT, Microsoft Bing, and Google Bard, emerged as valuable sources of health information for the lay individuals. However, the accuracy of information provided by these AI models remains a significant concern. This pilot study aimed to test a new tool referred to as “CLEAR”, designed to assess the quality of health information delivered by AI-based models. Tool development involved a literature review on health information quality, followed by initial establishment of the CLEAR tool comprising five items that aimed to assess the following: completeness of content in response to the prompt, lack of false information, evidence support, appropriateness, and relevance of the generated content. Each item was scored on a 5-point Likert scale from excellent to poor. Content validity was checked by expert review of the initial items. Pilot testing involved 32 healthcare professionals using the CLEAR tool to assess content on eight different health topics deliberately designed with varying qualities. The internal consistency was checked using the Cronbach α. Feedback through the pilot test resulted in language modifications to improve the clarity of the items. The final CLEAR tool was used to assess health information quality generated through four different AI-based models in five different, yet common health topics. The AI models were ChatGPT 3.5, ChatGPT 4, Bing, and Bard, and the content generated was scored by two independent raters with Cohen κ to assess the inter-rater agreement. The final five CLEAR items were: (1) Is the content sufficient? (2) Is the content accurate? (3) Is the content evidence-based? (4) Is the content clear, concise, and easy to understand? and (5) Is the content free from irrelevant information? Pilot testing using the eight different health topics revealed an acceptable internal consistency with a Cronbach α range of 0.669–0.981. The use of the final CLEAR tool yielded the following average scores: Bing (mean=24.4±0.42), ChatGPT-4 (mean=23.6±0.96), Bard (mean=21.2±1.79), and ChatGPT-3.5 (mean=20.6±5.20). The inter-rater agreement revealed the following Cohen κ values: for ChatGPT-3.5 (κ=0.875, P<.001), ChatGPT-4 (κ=0.780, P<.001), Bing (κ=0.348, P=.037), and Bard (κ=.749, P<.001). The CLEAR tool is a brief yet helpful tool that can aid to standardize testing of the quality of health information generated by the AI-based conversational models. Future studies are recommended to validate the utility of the CLEAR tool to assess the quality of the AI-generated health-related content using a larger sample across various complex health topics.

Women of color (WoC) in the health professions encounter challenges in advancement to higher positions, disparities in wages, discrimination, lack of expectation to achieve leadership positions, and absence of extensive support networks. Articles in the literature have addressed race and/or gender in the context of professional development. However, applying an intersectionality lens or framework to better understand the contextual issues of professional development for WoC remains to be addressed. Thus, this scoping review aimed to (i) identify health professions literature that addresses disparities affecting WoC, and (ii) describe strategies and approaches to support WoC in the health professions. Methods: Literature searches were conducted in multiple databases. Three independent reviewers reviewed and screened articles for inclusion. Results: 31 articles were included - medicine(17), nursing(1), pharmacy(7), other(4), and multiple health professions(2). Key findings included underrepresentation of women and minority groups, inequities in professional advancement and leadership positions for WoC, and greater dissatisfaction and attrition among minority and women professionals. Conclusion: WoC face unique and distinct challenges and barriers in their professional careers resulting from the intersectionality of not only race and gender, but also lived experiences and opportunities. Strategies to improve diversity and representation should include an intersectionality framework or lens and be critically evaluated.

To ensure high-quality care, operationalize resilience and fill the knowledge gap regarding how to improve the prerequisites for resilient performance, it is necessary to understand how adaptive capacity unfolds in practice. Coronavirus 2019 stress-tested complex healthcare systems, and intensive care was driven to an unprecedented escalation. The main aim of this research was to explain the escalation process of intensive care from a microlevel perspective, including expressions of resilient performance, intervening conditions at the micro-meso-macrolevels and short- and long-term consequences. A secondary aim was to provide clinical recommendations regarding how to optimize the prerequisites for resilient performance in intensive care. A grounded theory methodology was used. First-person stories from different healthcare professionals (n70) in two Swedish regions were analyzed using the constant comparative method. A novel conceptual model of the escalation process of intensive care emerged, and 41 clinical recommendations concerning the optimal prerequisites for resilient performance were identified. Escalation was conceptualized as a transition from threatening chaos to temporary order through a complex process of adaptation. To prepare for the future, the components space, stuff, staff, system and science with associated continuity plans must be implemented, anchored and communicated to actors at all levels of the system.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and GL, DPF, and BMI are more significantly important features in the dataset for prediction of diabetes disease. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease and can easily be deployed in IOT wireless sensor technologies based e-healthcare environment.

The healthcare sector in Kuwait has been nurtured for many decades by the government where the Ministry of Health (MoH) controls up to 80 percent of the country’s health services. General and specialized public hospitals represent a significant proportion of health expenditures in Kuwait; amidst dwindling health resources, a declining global oil market in an oil-dependent welfare state, and a heavy reliance on a non-national clinical workforce. Although healthcare services in public sector hospitals are at highly subsidized rates, causing private sector involvement in healthcare to be considerably low, the growing demands for private delivery of care burgeoned participation of private hospitals in Kuwait, and improving hospital efficiency and productivity is more critical and timelier than ever. This paper aims to analyze public health system efficiency and hospital performance in the State of Kuwait, where we begin by evaluating the input-oriented technical efficiency (TE) of MoH hospitals in 2015-2019 and identifying potential areas for efficiency improvement by exploring influencing institutional and environmental factors. Over the five years between 2015 and 2019, TE in MoH hospitals has decreased by an average of 2.98% solely based on technical regress, where the six MoH general hospitals reported a pooled mean efficiency of 86.58%, and the nine sampled MoH specialized hospitals had a five-year pooled average of 65.47% efficiency. MoH policymakers should focus on improving allocative efficiency in the public health system, and healthcare policy reforms should focus on strengthening management structures in Kuwait’s public hospitals to improve production efficiency and financial sustainability.

COVID-19 has affected 212 countries around the world, killing nearly 300,000 and infecting more than 4.4 million by May 14, 2020. Bangladesh, a South Asian low-middle-income economy, has experienced a demographic and epidemiological transition with rapid urbanization and a gradual increase in life expectancy. It is the seventh most populous country in the world and population of the country is expected to be nearly double by 2050. The increasing burden of communicable diseases in Bangladesh can be attributable to rapid urbanization and nearly 50% of all slum dwellers of the country live in Dhaka division. In 2017, National Rapid Response Team of IEDCR investigated 26 incidents of disease outbreak. The joint survey of the Power and Participation Research Centre and BRAC Institute of Governance and Development reveals that per capita daily income of urban slum and rural poor drops by 80% due to present countrywide shutdown enforced by the government to halt the spread of Covid-19. 40%-50% of these population took loans to meet the daily expenses. However, the country has just 127,000 hospital beds, 91,000 of them in government-run hospitals. Researchers say, the country’s economy is economy is losing BDT 33 billion every day from its service and agriculture sectors during the nationwide shutdown.

Explainable AI (XAI) has emerged as a pivotal tool in healthcare diagnostics, offering much-needed transparency and interpretability in complex AI models. XAI techniques, such as SHAP, Grad-CAM, and LIME, enable clinicians to understand AI-driven decisions, fostering greater trust and collaboration between human and machine in clinical settings. This review explores the key benefits of XAI in enhancing diagnostic accuracy, personalizing patient care, and ensuring compliance with regulatory standards. However, despite its advantages, XAI faces significant challenges, including balancing model accuracy with interpretability, scaling for real-time clinical use, and mitigating biases inherent in medical data. Ethical concerns, particularly surrounding fairness and accountability, are also discussed in relation to AI's growing role in healthcare. The review emphasizes the importance of developing hybrid models that combine high accuracy with improved interpretability and suggests that future research should focus on explainable-by-design systems, reducing computational costs, and addressing ethical issues. As AI continues to integrate into healthcare, XAI will play an essential role in ensuring that AI systems are transparent, accountable, and aligned with the ethical standards required in clinical practice.

Spatial Data Infrastructures (SDI) support the harvesting, curating, storage, and sharing of data along with providing access to development, analytic, and visualization tools that enable the building of innovative applications to address broad or specific challenges. SDIs can be especially powerful in bringing together data and tools supporting a particular theme – and this paper discusses and demonstrates the value of an SDI focused on Health. Many potential benefits of a Health SDI are proposed, and the case of supporting emergency response efforts is developed in detail. Leveraging a Health SDI, a Health Risk Index was created that provides emergency response personnel (both Emergency Operations Managers and Emergency Medical Responders) key insights into the unique health risks the impacted population faces due to the disaster. In order to establish the Health Risk Index, datasets from multiple national and global sources representing health data and social data that influences health outcomes – typically called social determinants of health – are harvested, merged, and republished to support further efforts at advancing the Health Risk Index. Visualizations of the Health Risk Index at the global, national, and sub-national levels down to the address level are presented along with demonstrations of its use.

Background: The present work aimed to estimate the economic impact of PTSD following COVID-19 in a population of patients affected by systemic autoimmune disease (SAD) using a cost-of-illness approach and accounting for the perspective of society. Methods: Considering data collected from SAD patients enrolled in a specialized outpatient clinic in the Tuscany region, Italy, generalized-linear-models and LASSO logistic regression were used to evaluate the impact of PTSD on costs and its relevance as possible predictors of being a high-cost patient, respectively. Results: Considering 301 SAD patients, 161 (51.2%) of whom diagnosed with PTSD, the overall costs were 3,670 [890;40,529] Euro per patient/year among patients with PTSD and 2,736.7 [283;21,078] Euro per patient/year among those without PTSD (P-value<0.001), with differences mainly attributable to significantly greater direct non-healthcare and indirect costs. PTSD was estimated to increase overall costs (β=0.296 (0.140), P-value=0.035), direct non-healthcare (β =1.193 (0.392), P-value =0.002) and indirect costs (β =3.741 (1.136), P-value =0.001). PTSD diagnosis was also significantly associated with the likelihood of being a high-cost patient. Conclusions: Findings from the present study offer a novel perspective on the economic impact of COVID-19 and provide valuable data for policymakers to better understand demand for healthcare services and associated costs.

Advanced air treatment systems have the potential to reduce airborne infection risk, improve indoor air quality (IAQ) and reduce energy consumption, but few studies reported practical implementation and performance. PlasmaShield®, an advanced multi-modal HVAC-integrated system was directly compared with a standard MERV-13 system in a post-surgical pediatric healthcare setting. The evaluation entailed monitoring of multi-size airborne particles, bioaerosols and key IAQ parameters. Measurements were made of outside air, recovery room supply air and air in the occupied space for 3 days prior to, and after, installation of the PlasmaShield system. Compared with the existing arrangement, very significant reductions in particle number concentrations were observed in the occupied space, especially with virus-like submicron particles. Significant reductions in airborne culturable bacteria and fungi were observed in the supply air, with more modest reductions in the occupied space. In the case of virus-like particles, there was an 8-fold improvement in equivalent clean air, suggesting a 5-fold infection risk reduction for long range exposure. The data suggest multiple benefits of airborne particle and biaerosol reduction, with applications beyond healthcare. Long term studies are recommended to confirm the combined IAQ, health and energy benefits.

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 to August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, a brief analysis of specific features of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis.

The home is becoming a key location for healthcare delivery, including the use of technology driven by autonomous systems (AS) to monitor and support healthcare plans. Using the example of a smart mirror, this paper describes the outcomes of focus groups with people with multiple sclerosis (MS; n=6) and people who have had a stroke (n=15) to understand their attitudes towards the use of AS for healthcare in the home. We thematic analysis to analyse the data. The results indicate that the use of such technology depends on the level of adaptability and responsiveness to the users’ specific circumstances, including their relationships with the healthcare system. A smart mirror would need to support manual entry, responsive goal setting, effective aggregation of data sources and integration with other technology, have a range of input methods, be supportive rather than prescriptive in messaging, and give the user full control of their data. Barriers to adoption include a perceived lack of portability and practicality, lack of accessibility and inclusivity, a sense of redundancy, being overwhelmed by multiple technological devices, and a lack of trust in data sharing. These results inform the development and deployment of future health technologies based on the lived experiences of people with health conditions who require ongoing care.

Healthcare workers (HCW) have been the professional category most exposed to SARS-CoV-2. The pandemic’s impact on HCW was analyzed in terms of COVID-19-related temporary disability (TD) between February 15 2020 and May 1 2021. TDs in HCW for COVID-19 infection or quarantine were described. TD quarantine/infection ratios and TDs per 100,000 affiliated HCW were compared with the cumulative incidence (CI) of COVID-19 cases notified to the National Network of Epidemiological Surveillance. TDs rates by economic activity and occupation were computed. A total of 429,127 TDs were recorded, 36,6% for infection. Three-quarters (76%) were women. The median TD quarantine/infection ratio was 2.5 (Interquartile range [IQR] 1.5-3.9). TDs rates in HCW were always above the CI except for the last two months of the fourth wave. Hospital activities accounted for 84% of TDs and showed the highest TD rate for infection (8,279/100,000). The highest TDs rates were registered among Nursing assistants, Nursing professionals and Physicians: 7,426, 6,925 and 5,508/100,000, respectively. The results demonstrate the high impact of COVID-19 on HCW in Spain and it’s inequalities. They also confirm that TDs represent a complementary source of information for epidemiological and public health surveillance and could provide an early warning of new emerging infections.

The 2019-2020 Australian bushfires followed by the COVID-19 pandemic brought the significant mental health implications of working in healthcare to the fore. The importance of appropriate support services to ensure the resilience and recovery of healthcare workers has been highlighted. In response to healthcare staff experiences during the bushfires, the SEED Wellness Program was created in 2020 in the Illawarra Shoalhaven Local Health District, in NSW Australia. SEED used a participatory action methodology to engage and collaborate with healthcare staff teams in workplace-based restorative activities. Guided by Practice Theory, this study aimed to identify and describe SEED wellness practices that supported healthcare staff. Thirty-three healthcare workers participated in focus groups or individual interviews between June 2021 and March 2022. The analysis involved inductive thematic individual and collective exploration of SEED practices, including co-analysis with participants. Eight core practices that supported participants’ wellbeing were identified including responsive and compassionate leading, engaging staff at every stage of the recovery process, creating a sense of connection with others, and collective caring. The study found that workplace wellness initiatives are optimised when place-based and grounded in local knowledge, needs, and resources incorporating a collective and supportive team approach. Moreover, to ensure engagement in, and sustainability of these initiatives, both bottom-up and top-down commitment is required.

Viral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) pose a significant global health challenge, with an increasing number of deaths and a substantial burden on healthcare systems, among prisoners in particular. Nigeria ranks fifth in Africa for prison population, with 98.2% being males and are more likely than females to have higher rates of viral hepatitis and other risky behaviours. This literature review explores the impact of social determinants of health (SDoH) on the prevalence and outcomes of HBV and HCV infections among male prison inmates in Nigeria, by examining factors such as overcrowding, inadequate healthcare access, and food insecurity. This review reports the updated data on HBV and HCV infection rates among inmates in different regions in Nigeria, with a special focus on related risk factors, highlighting how these determinants contribute to the high rates of hepatitis infections in this category. The review underscores the urgent need for a multifaceted approach involving policy changes, improved healthcare access, and enhanced preventive measures to address these health disparities. The findings aim to inform public health strategies and interventions to reduce the transmission and impact of viral hepatitis in prison settings, aligning with global health goals and promoting health equity among this population group.

The Internet of Things (IoT) has a growing presence in society's daily lives. These lightweight devices can be easily deployed and maintained, enabling extensive adoption in different environments. Furthermore, one of the most promising areas for using IoT devices is healthcare, comprising devices referred to as the Internet of Medical Things (IoMT). Several examples of healthcare services are supported by IoMT devices, e.g., continuous health monitoring. Conversely, there is an increasing concern with the cybersecurity aspects of these devices, and several attacks against IoT infrastructures have been launched in the past few years. These cybersecurity concerns also apply to healthcare applications, where the tradeoff between the benefits and security of IoMT devices must be observed. Given the complexity and amount of data IoMT network traffic generates, advanced methods become especially useful in these environments. Although Machine Learning (ML) brings various techniques and solutions to improve cyberattack detection, prevention, and mitigation, essential features are not addressed in the current state-of-the-art benchmark dataset contributions. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of IoMT security solutions. In order to accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT, and Bluetooth). These attacks are categorized into five classes: DDoS, DoS, Recon, MQTT, and spoofing. This effort aims to establish a baseline complementary to the state-of-the-art contributions. The outcome supports researchers in investigating and developing new solutions to make healthcare systems more secure using different mechanisms (e.g., machine learning - ML). This research goes beyond merely conducting attacks on IoMT devices. We also attempt to capture the lifecycle of these devices in different vital phases, from the moment they join the network until they leave, which is called profiling. Profiling allows the different classifiers to identify anomalies of each device individually in the healthcare network. The CICIoMT2024 dataset has been published on CIC's dataset page, making it available for other researchers to use.

Transportation tasks in nursing are common and shorten the time for direct patient care in times of nursing shortage and an aging society. There is a lack of research on transportation needs, with most studies focusing on overall nursing task analysis without a deeper analysis of transportation tasks. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N=1,830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05 % (n=495) of all transports, followed by medical supplies at 17.32 % (n=317), pharmacotherapy at 14.10 % (n=258) and other other categories like meals or drinks contributing 12.68 % (n=232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15 % of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5 % of all transports. Our economic evaluation spotlighted meals as the most costly transport, with 9,596.16€ p.a. in the observed clinics. Budget-friendly robots would amortize over one year by transporting meals. We support understanding nurses’ transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

(1) Background: Human-generated greenhouse gas (GHG) emissions are the largest contributor to climate change worldwide. Climate change negatively impacts human and planetary health threatening the existence of life on earth. The healthcare industry is responsible for approximately 8.5% of carbon emissions in the United States. Measuring baseline GHG emissions is the first step in emissions reduction. However, very few models of measurement exist for health care organizations. This project aimed to develop and implement a program to measure and track GHG emissions at a Midwestern Academic Medical Center (MAMC) and to educate staff to manage the process. (2) Methods: A Plan, Do, Study, Act (PDSA) cycle and Quality Improvement methodology was used to develop, implement, and assess a standardized GHG emission inventory process to measure Scope 1 and Scope 2 baseline emissions, and provide virtual training and education to the accountable staff. A pre- and post-survey design was used to measure the knowledge and readiness of the staff after the implementation of the GHG inventory training. (3) Results: The GHG inventory process was validated through an external verification process and the measurement of Scope 1 and Scope 2 baseline GHG emissions was completed and verified for accuracy through a data comparison review. The pre-post-training survey showed an increase in the knowledge and readiness of staff in maintaining a GHG inventory. (4) Conclusions: This work shows the feasibility of obtaining baseline GHG emissions data at large medical centers. It represents the initial phase of the overarching goal to develop site-wide and system-wide carbon reduction strategies and a climate action plan within this health system.

Through a partnership with three Federally Qualified Health Centers (FQHCs), the local health department in Los Angeles County implemented a produce prescription program (PPR) to increase fresh fruit and vegetable purchases among patients with diabetes or prediabetes. The PPR, which began in 2020, provided eligible patients with a $40 voucher every month for 6 months to promote the purchase of fresh produce at large chain grocery stores. This qualitative assessment describes staff experiences with the PPR, documenting the facilitators and barriers they encountered while implementing the program. Fifteen clinic staff (i.e., PPR implementers) were interviewed for this assessment. Thematic analysis and coding were conducted using the ATLAS.ti software; the coding was carried out by two separate coders. Interviewees discussed the importance of having preexisting partnerships and programs to support the PPR at their clinic site. Hidden costs related to implementing the program included a large and unexpected amount of staff time devoted to enrolling patients into the program. Collecting quality data and having limited expertise to rigorously evaluate the program were other challenges. Because patients often share their food with their family, the monthly $40 incentive was generally not enough to support their needs; interviewees suggested giving a higher inventive amount to those with a larger household. Future PPR efforts and similar food incentive programs should consider these and other facilitators and barriers to implementation and sustainability, especially when making adjustments to these programs to improve services and access to food resources.

The history of health records (later also called medical records), including ones regarding individual patients, is thousands of years old. It finds it roots in the first ancient civilisations. Up until the 19^th century the records’ purpose was mainly an educational one. In the 19^th and 20^th century they started becoming significant in other roles as well, including those not strictly limited to medicine. In particular, to account for medical procedures, insurance proceeds or legal action. Currently we are living in a revolutionary era when it comes to health records, in which their character has changed from a “paper-based” to an electronic one. This paper presents the development of health records from the ancient to modern times, mainly in Europe and North America. Other cultures and civilisations, including China and India, are not discussed. An analysis of available sources was conducted, inter alia digital versions of manuscripts up to hundreds of years old. The analysis was based on PubMed and Google Scholar (several key words, all the available sources). Sources published in non-international languages (e.g. Dutch) were also investigated. Overall, approximately 600 articles were analysed, 158 of which were used and cited in this paper. The conclusions drawn from the analysis are as follows: (1) Health records, priorly used mostly for educational purposes, for about 100 years now have acquired a fully formal status. (2) We are currently facing the most revolutionary changes regarding the transformation of paper-based records into electronic ones. (3) The consequences of this process include systematic applications of solutions within the area of e-health, which allow us to make medical services more flexible, improve the health of individual patients and entire populations and potentially limit expenditure. (4) In the light of the ongoing COVID-19 pandemic, introducing electronic health records could be beneficial in terms of limiting the potential sources of contamination (physical copies of health records), saving time and resources, and improving the network of communication between medical centres.

The assessment of Health-Related Quality of Life (HRQoL) in Thalassemia offers a holistic approach to the disease and facilitates better communication between physicians and patients. This study aimed to evaluate the HRQoL of Transfusion-Dependent Thalassemia (TDT) patients in Greece. This was a multicentre, cross-sectional study conducted in 2017, involving 283 adult TDT patients. All participants completed a set of two QoL questionnaires, the generic SF-36v2 and the disease-specific TranQol. Demographic and clinical characteristics were used to predefine patient subgroups. Significant factors identified in univariate analysis were entered into a multivariate analysis to assess their effect on HRQoL. The SF-36 scores of TDT patients were consistently lower, compared to the general population in Greece. The mean summary score of TranQol was relatively high (71±14%), exceeding levels observed in national surveys in other countries. Employment emerged as the most significant independent factor associated with better HRQoL, whereas age had the most significant negative effect. This study represents the first comprehensive QoL assessment in a representative sample of the TDT population in Greece. The implementation of TranQol allowed for the quantification of HRQoL in Greece, establishing a baseline for future follow-up, and identifying more vulnerable patient subgroups.

In modern times, sustainability in supply chain management is one of the keys to boosting the performance of the supply chain, and it enables the accomplishment of SSCM. It has been found in recent years that SSCM with assist of industry 4.0 applications, can benefit splendid productivity and efficiency in the supply chain. The numerous industry 4.0 applications that are involved are Artificial intelligence (AI), Machine Learning (ML), Internet of Things (IoT) Big Data Analytics (BDA), etc. Industry 4.0 with SSCM has gained a lot of interest from various authors and scholars across the world. Healthcare is one of the most critical sectors due to its importance in handling public health. More recently, the COVID-19 pandemic and other viruses (SARS and Ebola) have highlighted supply chain vulnerabilities that are further mitigated by their lean and globalized structures. Covid-19 underlined the need for supply chain visibility problems within the healthcare sector. When the need for medical supplies became urgent and global distribution was disrupted, companies faced the consequences of needing the technology, systems, and plans in place to correctly predict the onslaught. One report shows that about 94 percent of Fortune 1000 companies reported supply chain disruptions due to the COVID-19 global outbreak. Existing studies have discussed supply chain issues and the importance of AI in addressing these challenges. AI, like Industry 4.0 in the healthcare supply chain, is proving to be a game-changer. This paper aims to review and present the various aspects of SSCM and industry 4.0 applications to get a comprehensive literature review, which contains the analysis of multiple articles published from 2010-2020. In addition, the various challenges SSCM and industry 4.0 applications face are identified and discussed. Also, the impact of Industry 4.0 and sustainable practices brings together the healthcare supply chain. This study will help in getting a good understanding of the different industry 4.0 technologies that are important for setting up SSCM.

Background: The use of peripherally inserted central catheters and midline catheters is growing due to their potential benefits. These devices can increase patient safety and satisfaction while reducing the use of resources. As a result, many hospitals are establishing vascular access specialist teams staffed by nurses who are trained in the insertion and maintenance of these catheters. Objective: Evaluate ex-ante whether the benefits of replacing peripheral venous catheters, peripherally inserted central catheters and midline catheters with ultrasound-guided peripherally inserted central catheters and midline catheters by a vascular access specialist team outweigh their costs. Methods: Cost-benefit analysis from the perspective of the healthcare provider based on administrative data. The study estimates the reduction in resources used when replacing peripherally inserted central catheters, midline catheters and peripheral venous catheters with ultrasound-guided peripherally inserted central catheters and midlines, as well as the additional resources required for their use. Results: Each ultrasound-guided peripherally inserted central catheter replacing a peripherally inserted central catheter, results in a measurable resource reduction of approximately €31. When 3 peripheral venous catheters are replaced by an ultrasound-guided peripherally inserted central catheter, the saving is €63. Similarly, each ultrasound-guided midline catheter replacing a midline, results in a reduction of €16, while each ultrasound-guided midline catheter replacing 3 peripheral venous catheters results in a reduction of €96. Conclusion: The benefits of replacing peripheral venous catheters, peripherally inserted central catheters and midline catheters by introducing a vascular access specialist team trained in the implantation of ultrasound-guided peripherally inserted central catheters and midline catheters, outweigh its cost mainly because of the decrease in hospital stay due to the lowered risk of healthcare-associated infections. These results motivate the implementation of the service, adding to previous experience suggesting that it is also preferable from the point of view of patient safety and satisfaction.

Background: Over 900,000 cases of COVID-19 and 23,000 deaths have been reported till 13 July in India. Preserving the limited healthcare workforce is part of the strategy against the pandemic. Mortality and morbidity data have a role in customising this strategy. At this time, there is no published study on COVID-related mortality among doctors or other healthcare workers in India. Methods: A multi-pronged search was made for all reported deaths linked with COVID-19 among doctors in India. Details of COVID-linked deaths reported by mainstream media and by multiple professional social media sources were collected, screened, verified and analysed. Violent deaths occurring in the setting of pandemic-related work were separately listed. Deaths from other diseases were excluded. Results: Among 108 COVID-linked deaths among doctors, there were four pandemic-related violent deaths including three road accidents and a suicide. Of the 104 non-violent deaths, 55.5% were below 60 years of age, while 29.6% and 21% were below the age of 50 and 40 respectively. The average age at death was 56.3 (range 22 - 96). Over half of the deaths occurred among general practitioners, while surgical specialties accounted for 27% of the mortality. The geographic distribution of deaths of doctors correlated with the reported number of COVID-19 patients in each state. The total number of COVID-related healthcare worker deaths was 136, out of which eight (5.8%) were violent, and occurred in young individuals with an average age of 27.8 years. Conclusions: The majority of the 104 COVID-related non-violent deaths among doctors (55.5%) occurred below the age of 60. The average at death was 56.6 years. The states with highest number of COVID-19 cases had greater number of doctor deaths. Violent deaths among young healthcare workers in the setting of the pandemic requires special attention.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called ‘the clones of high risk’ often drive the spread of resistance within particular species. Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. In this dataset, we present the results of a comprehensive genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen Acinetobacter baumannii. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic blaOXA-51-like gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. These data will be useful for researchers in the field of A. baumannii genomic epidemiology, resistance analysis and prevention measure development.

Background and purpose: Chronic kidney disease (CKD) is a common chronic disease usually associated with cardiovascular disease (CVD), and premature death and millions of people are at increased risk for CKD. As this disease is extremely complex, multidisciplinary care (MDC) is needed to provide complete and continuous care. Usually, the nephrology units need coopera-tion from several other Healthcare Professionals (HCPs) such as cardiologists, nutritionists, ob-stetricians, and so forth. All modalities of treatments, peritoneal dialysis (PD) and Hemodialysis (HD) require HCPs and patients to develop a variety of skills to effectively deliver and manage dialysis tasks in the center or at home. Information and Communication Technology (ICT) in-terventions can offer tools that provide HCPs and patients with tools that facilitate the manage-ment of kidney patients in centers or at home. ICT interventions can allow to automate registra-tions, videoconferencing, minimize the geographic burden and resource cost savings. ICT can be the best way to promote cost saving on health spending and ensure the sustainability of the health system and, the environment as well. In the Iberian Peninsula, kidney patients consume about 3% of the total cost of healthcare. The cost of kidney transplants and dialysis is around 1.8 billion euros annually, and 75 % of this amount goes to dialysis treatment. Objective: This study aimed to evaluate and analyze the opinion of Healthcare Professionals (HCP) in Nephrology units about the Intervention of ICT in nephrology care and the Clinical benefits results from the use of ICT in Nephrology Services and dialysis Units in Portugal and Spain. Thus we collected the opinion of Health care professionals concerning the role of ICT intervention in nephrology. Understanding HCPs' feelings and attitudes is essential to understand if such technologies are correctly adjusted to the workflow and really bring huge benefits for the clinical practice of nephrology and also what features need improvement and what should be implemented in the future. ICT intervention has been implemented to promote better healthcare in nephrology units and improve patient outcomes. In the Survey, we intended to evaluate nephrology HCPs' opinions concerning the ICT interventions, in nephrology, represented by several tools that al-low the HCPs to implement solutions such as Remote Patient Monitoring(RPM); Remote Moni-toring of Treatment (RMT); Tele homecare; Telehealth (Tele-nephrology); Home Dialysis; mHealth; mobile App; Web Portal, Telemedicine, and so forth. Key Points: In many countries particularly in the Iberian Peninsula (Portugal and Spain) the incidence of CKD is substantially high. The “treatments” for kidney disease have not improved substantially over the past 50 years, leaving too many kidney patients with a poor quality of life and reduced life expectancy. This situation is associated with staggering aggregate annual costs amounting to €140 billion per year in Europe, more than the annual healthcare costs of cancer or diabetes [1]. The increased daily use of information and communication technologies may lead to the need for healthcare professionals to incorporate technology use competencies into practice.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

1) Background: Genomic medicine harbors the real potential to improve the health and healthcare jour-ney of patients, care provider experiences, and improve health system efficiency – even reducing health care costs. There is expected to be an exponential growth in medically necessary new genome- based tests and test approaches in coming years. Testing can also create scientific research and commercial opportu-nities beyond healthcare decision-making. The purpose of this research is to generate a better under-standing of Canada’s state of readiness for genomic medicine, and to provide some insights for other healthcare systems; (2) Methods: a mixed-methodsapproach of literature review and key informant in-terviews with a purposive sample of experts was used. Health system readiness was assessed using a pre-viously published set of conditions. (3) Results: Canada has created some of the established conditions but more needs to be done to improve the state of readiness for genome-based medicine. Important gaps are the need for linked information systems and data integration; evaluative processes that are timely, and transparent; navigational tools for care providers; dedicated funding to facilitate rapid onboarding and supports test development and proficiency testing; and broader engagement with a broader set of inno-vation stakeholders. These findings highlight the known role of organizational context, social influence, and other factors that are known to affect the diffusion of innovation within health systems

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

Based upon 30-years of research by the author a new approach to hospital bed planning and international benchmarking is proposed. The number of hospital beds per 1000 population is commonly used to compare international bed numbers. This method is flawed because it does not consider population age structure or the effect of nearness-to-death on hospital utilization. Deaths are also serving as a proxy for wider bed demand arising from undetected outbreaks of 3000 species of human pathogens. To remedy this problem a new approach to bed modelling has been developed which plots beds per 1000 deaths against deaths per 1000 population. Lines of equivalence can be drawn on the plot to delineate countries with higher or lower bed supply. This method is extended to attempt to define the optimum region for bed supply in an effective health care system. England is used as an example of a health system descending into operational chaos due to too few beds and manpower. The former Soviet bloc countries represent a health system overly dependent on hospital beds. Several countries also show evidence for over-utilization of hospital beds. The new method is used to define a potential range for bed supply and manpower where the current most effective health systems currently reside. The method is applied to total curative beds, medical beds, psychiatric beds, critical care, geriatric care, etc., and can also be used to compare different types of healthcare staff, i.e., nurses, physicians, surgeons. Issues surrounding the optimum hospital size and the optimum average occupancy will also be discussed. The role of poor policy in the English NHS is used to show how the NHS has been led into a bed crisis. The method is also extended beyond international benchmarking to illustrate how it can be applied at a local or regional level in the process of long-term bed planning. Issues regarding the volatility in hospital admissions are also addressed to explain the need for surge capacity and why an adequate average bed occupancy margin is required for an optimally functioning hospital.

People must have particular personality traits and social resources, also known as health literacy, in order to access, comprehend, and use information to make decisions about their health. Patients' ability to engage in complex disease management and self-care is strongly related to their level of health literacy. It can help us stay healthy by preventing illness and effectively managing existing illnesses. People with low health literacy (LHL) may find it difficult to manage their condition and prevent illness, which may lead to increased use of healthcare services. Furthermore, LHL is associated with increased hospitalizations, increased use of emergency care, decreased use of preventative services, and a worsened ability to understand labels and health messages, a worsened state of health, higher mortality, and more expensive medical care.

Nowadays, according to spectacular improvement in health care and biomedical level, a tremendous amount of data is recorded by hospitals. In addition, the most effective approach to reduce disease mortality is to diagnose it as soon as possible. As a result, data mining by applying machine learning in the field of diseases provides good opportunities to examine the hidden patterns of this collection. An exact forecast of the mortality after heart surgery will cause Successful medical treatment and fewer costs. This research wants to recommend a new stacking predictive model after utilizing the random forest feature importance method to foresee the mortality after heart surgery on a highly unbalanced dataset by using the most practical features. To solve the unbalanced data problem, a combination of the SVM-SMOTE over-sampling algorithm and the Edited-Nearest-Neighbor under-sampling algorithm is used. This research compares the introduced model with some other machine learning classifiers to ensure efficiency through shuffle hold-out and 10-fold cross-validation strategies. In order to validate the performance of the implemented machine learning methods in this research, both shuffle hold-out, and 10-fold cross-validation results indicated that our model had the highest efficiency compared to the other models. Furthermore, the Friedman statistical test is applied to survey the differences between models. The result demonstrates that the introduced stacking model reached the most accurate predicting performance after Logistic Regression.

The SWOT analysis, pivotal in the competitive healthcare sector, aids dentists in identifying and analyzing internal strengths and weaknesses, as well as external opportunities and threats. This study focuses on senior dental students, aiming to scrutinize their use of SWOT analysis and evaluate its efficacy as an educational tool for career decision-making in dental entrepreneurship. The research sample comprises 116 senior dental students in the final stages of their dental education, with data collection accomplished through the administration of an e-questionnaire. The data extracted from the SWOT analysis encompass internal and external factors, gender distinctions, and outcomes derived from Stepwise Binary Logistic Regression concerning predictor markers. The results from the SWOT analysis of 114 questionnaires, revealed that participants identified communication skills (50%) and organization skills (49.10%) as their primary strengths, followed by favorable personal traits contributing to goal success (36%). Weaknesses predominantly centered around emotional and personal traits like anxiety (41.20%) and other characteristics, alongside practical challenges such as lack of initial capital (24.60%). Main opportunities included collaboration with experienced dentists (33.30%) and access to training programs (27.20%), while economic instability in Greece (77.20%) and the saturated dentist profession (26.30%) were perceived as significant threats. Gender differences were notable, with female dentists more likely to report organization skills as a strength and anxiety as a weakness. Values such as industriousness, persistence, and ethics were commonly shared, with actions focusing on training programs (57.9%) and gaining experience with experienced dentists (29.8%). Cluster analysis identified two subgroups, with one emphasizing on utilizing all available options (n=49) and the other prioritizing on gaining professional skills and experience (n=65). Logistic regression indicated that participants valuing industriousness were less likely to explore all available options, while those recognizing personal traits were more likely to do so. The study's outcomes highlight key predictor factors linked to a proactive orientation in career decision-making among senior dental students. These insights offer valuable implications for educational institutions and career counselors.

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future "Orwellian" ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics and comprising 12 items, with three items for each construct. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% somewhat anxious (n = 36), and 2.4% extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35±2.78), followed by Ethics construct (mean: 10.86±2.90), Fear (mean: 9.49±3.53), and Anxiety (mean: 8.91±3.68). Sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores in the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores of the FAME scale. These findings highlighted the critical need for refined educational strategies to address the integration of genAI in medical training. The results demonstrated a pervasive anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity for curriculum modifi-cations that focus specifically on these areas. Interventions should be tailored to increase genAI familiarity and competency, which would alleviate apprehension and equip future physicians to engage with this inevitable technology effectively. The study also highlighted the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. Conclusively, the study calls for a proactive evolution of medical education to prepare students for AI-driven healthcare practices shortly to ensure that physicians are well-prepared, confident, and ethically informed in their professional interactions with genAI technologies.

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.