Learning systems have been very focused on creating models that are capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in order to interpret and explain their results. The need for interpretation is greater when these models are used to support decision making. In some areas this becomes an indispensable requirement, such as in medicine. This paper focuses on the prediction of cardiovascular disease by analyzing the well-known Statlog (Heart) Data Set from the UCI’s Automated Learning Repository. This study will analyze the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. It will be analyzed on a large set of classification techniques and performance metrics. Demonstrating that it is possible to make explainable and reliable models that have a good commitment to predictive performance.

Objectives: The aim of this study was to assess the knowledge, attitude and practice of healthcare professionals regarding adverse drug reaction [ADR] monitoring and pharmacovigilance [PV] in India. Materials and Methods: It was a questionnaire based cross sectional observational study. Data was collected with the help of data collection Google form that included the demographics and twenty two survey based questions. Data were analysed by using Microsoft Excel sheet, further analysed for results, including frequency, percentage, mean and standard deviation. Result: The questionnaire was filled by two hundred ten healthcare professionals in which 52.9 % were male and 47.10% of female. Most of the respondents were pharm d students (50.47%). Out of the total 91.4% responded to the definition of pharmacovigilance correctly. 87.6% participants said all ADR should be reported. 86.20% participants think Pharmacovigilance should be taught in detail to healthcare professionals. Most of the respondents (43.8%) always informed the patients about ADR while prescribing the medicines. Conclusion: Study revealed most of the participants have good knowledge about ADR and pharmacovigilance. Difficult to decide whether ADR occur or not and extra work load being major factors responsible for under reporting.

Background: The healthcare workers are exposed to dangerous pathogen agents during the outbreak of the new coronavirus COVID-19. To minimize the risk of becoming infected by this virus, healthcare workers need to wear the most appropriate personal protective equipment (PPE). Objective: The objective of this study was to analyze the guidelines that the World Health Organization (WHO) and the Centers for the Disease Control and Prevention (CDC) recommend for the rational use of EPP to protect healthcare workers against the novel coronavirus COVID-19.Methodology: To learn how to effectively protect healthcare workers against the COVID-19, a detailed analysis and comparison of the WHO and CDC guidelines related to the proper use of personal protection equipment (PPE) in different healthcare settings was carried out. Results: The results of this study based on an analysis of PPE recommended by the World Health Organization (WHO) and the Centers for the Disease Control and Prevention (CDC) indicated that the safety and protection of healthcare workers can be maximized if the guidelines suggested by these institutions are followed. In general, the WHO and CDC recommendations based on medical practices are similar, and depending on the healthcare activities and settings where the healthcare workers perform their work, suggest wearing medical/surgical facemasks, respirators, googles and face shields (eye protection), gloves, gowns and aprons. Conclusions: The protection and safety of the healthcare workers can be maximized during the outbreak of COVID-19 by following the WHO and CDC recommendations described in this study. The general guidelines offered by these institutions are similar and based on medical practices.

In the wake of the novel coronavirus, SARS-CoV-19, the world has undergone a critical situation in which grave threats to global public health emerged. Among human populations across the planet, travel restraints, border enforcement measures, quarantine, and isolation provisions were implemented in an attempt to control and limit the spread of the contagion. Decisions on how to implement and enforce various control policies should be determined based on available real-world evidence and theoretical prediction. In this study, we propose a novel exportation- importation epidemic model associated with the quarantine and hospitalized-isolation policies by considering the two-body system: a source country of a contagious disease and a neighboring country that is initially disease-free. The model is first applied to the original COVID-19 data in China, Italy, and the Republic of Korea (ROK) and observed through consistent fitting results with equivalent goodness-of-fit. Then, the data are estimated per the fitting parameters. Driven by these parametric settings and considering the normalized population, the numerical analysis, and epidemiological exploration, this work further elucidates the substantial impact of quarantine policies, healthcare facilities, and the public counter-compliance effect.

In the area of dental healthcare services, where patients and professionals deal with multiple auditory stimuli, this cross-sectional study reports on the intricate dynamics of sound preferences, noise obstacles, and the profound impact of music on anxiety reduction within dental office set-tings. The sample comprises of 134 dental patients, primarily females (56.7%), with varying age groups, education levels, and therapy durations in the university clinics of the department of Dentistry, National and Kapodistrian University of Athens and four private dental offices at the metropolitan area of Athens, Greece. Notably, 92.5% of the participants report no hearing loss, and 56.7% exhibit some level of noise sensitivity. The study reveals a moderate to good perceived health status (M=3.84, SD=0.95) among participants, with low perceived noise disturbance in university dental clinics. Preferences for instrumental music, foreign pop, and classical music are prominent during waiting times. Potential irritations include impersonal treatment (44%) and staff behavior (41.8%). Music player usage within the settings of the dental clinic involves radios (49.3%) and headphones (41.0%). Correlations and predictors indicate associations between health status, music preferences, and noise disturbances. More specifically, visiting the university dental clinics (β = .300, p = .005) as well as younger age (β = .300, p = .034) were significant predictors of better perceived health status. Clearer perception of external sounds while using the music player (β = .190, p = .039) was a significant predictor of better perceived health status, while poorer health was predicted by the preference of Greek folk songs (β = -.200, p = .030). Also, the use of smartphones for playing music was related to poorer health (β = -.370, p < .001). Following, younger age (β = -.270, p = .015), lower education (β = -.210, p = .035), higher noise sensitivity (β = .190, p = .027), as well as feelings of anxiety and nervousness due to ambient noise from people and machines (β = .510, p = .027) were significant predictors of noise disturbance from machines. Also, visiting private clinics (β = .240, p = .018), preferences of specific playlist (β = .310, p = .012), no music (β = .280, p = .016) and use of smartphones to play music (β = .230, p = .029) were significant predictors of noise disturbance from patients or staff. Finally, the preference of listening to a specific playlist was related to better health status (r = .190, p < .05). Findings aim to optimize eco-soundscapes in dental offices, con-tributing to enhanced patient well-being. The research underscores the potential of music inter-ventions to alleviate dental anxiety and improve the overall dental experience and quality care.

Introduction: Rural areas face additional challenges in preventing, preparing for, responding to, and recovering from disasters. This study aimed to understand how rural General Practitioners PHCPs perceive their roles, involvement, and capacity in disaster health management. Methods: For this qualitative descriptive research, semi-structured interviews were carried out with convenience and purposive samples of rural PHCPs before and during the COVID pandemic. Open, axial, and selective coding were employed to analyse the data inductively. Results: five interviews were conducted pre-COVID and ten interviews during the second and third waves of COVID. Identified themes were similar between the two periods. Rural PHCPs were underutilised due to a lack of awareness of their capacity and a lack of infrastructure and support for greater involvement. Conclusion: Rural PHCPs can be better integrated and supported in readiness for a whole of system response to future disasters.

The recent growth of wearable medical device technology in the form of fitness trackers, smart watches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully “connected healthcare system.” Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified, but even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors, but most are unaware that data from these systems may be transmitted, stored, and even sold without the user’s specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber-villains. It is also important that efforts be taken to make PGHD more secure, since medical data may have great value to telehealth applications and AI-physician assistants. Clinicians should take an active role in informing patients about both risks and benefits of wearables and similar devices.

Delving into the transformative role of digital health in Nigeria's remote communities, aiming to bridge healthcare gaps and enhance overall well-being. As digital interventions gain prominence, their impact on healthcare accessibility, quality, and awareness becomes pivotal in the unique context of remote regions. We explore the historical overview of digital health initiatives in Nigeria, contextualize healthcare challenges, and analyse the global and regional influences shaping digital health adoption. Challenges such as digital literacy, infrastructural constraints, and cultural barriers are addressed alongside solutions, emphasizing the need for inclusive policies and community engagement. Through examining the quantitative and qualitative impacts of digital health interventions, including telehealth, mobile applications, and electronic health records, we unveil the strides made in healthcare delivery. Voices from communities, healthcare providers, and leaders provide first-hand perspectives, offering insights into the effectiveness and challenges faced. Additionally, policy implications, grassroots advocacy, and collaborative efforts between public and private sectors are discussed as integral components for sustainable change. As we measure the data-driven insights and envision future directions, this article advocates for a comprehensive and inclusive approach to digital health, envisioning a future where remote communities in Nigeria experience improved healthcare outcomes and resilience.

Objective: This analysis investigates patient experiences regarding pain management during gynecological procedures through a comprehensive survey, aiming to address the lack of clear guidelines in the clinical landscape and emphasize the importance of effective pain management strategies in enhancing patient care and satisfaction. Design: A cross-sectional survey with 258 respondents assessed pain management experiences during gynecological procedures. The survey was distributed via Qualtrics, and 258 responses were obtained. Participants/Materials, Setting, Methods: Participants provided demographic information and responded to questions regarding contraceptive methods, procedures undergone, pain management discussions, anxiety levels, and willingness to accept pain management interventions. Confidentiality and informed consent protocols were strictly observed throughout the study. Results: Most female respondents reported diverse contraceptive methods and procedures. Pain management discussions and provisions varied, with a significant portion reporting never being offered pain management. Many experienced anxiety and pain during procedures, with varying intensities, that were not addressed or discussed by their treating provider. Most expressed willingness to accept pain management interventions. Limitations: This study relies on self-reported data, potentially introducing recall bias, and has limited generalizability due to its cross-sectional design. Conclusions: Findings reveal a significant gap in pain management provision and discussion during gynecological procedures. Improved guidelines and practices are necessary to address patient discomfort effectively. Standardized pain management protocols are crucial to enhance patient experiences and outcomes, with further research needed to tailor strategies to individual patient needs.

This paper reviews the ethical issues involved and argues that each of the usual policies for handling conscientious refusals faces serious challenges. Healthcare providers who refuse to provide medical services should be expected to explain their reasons, make prompt referrals, and bear some of the resulting costs or burdens.

Interoperability has become crucial in the world of electronic health records (EHR), allowing for seamless data exchange and integration across diverse settings. It facilitates the integration of disparate systems, ensures that patient records are accessible, and enhances the care delivery process. The current interoperability landscape for electronic health records is saddled with challenges hindering efficient interoperability. Existing interoperability frameworks for EHR have failed to properly address many of these challenges relating to data exchange, security and privacy. To address these challenges, the TASSIPS framework is proposed as a comprehensive approach to achieving efficient interoperability. TASSIPS integrates robust security and privacy measures, providing real-time access to electronic health records that enable precise diagnoses, timely treatment plans and improved patient outcomes. TASSIPS offers a holistic and effective solution to healthcare interoperability challenges. An evaluation of TASSIPS via comparison with exicting frameworks concluded that it is an advancement in framework design for interoperabil-ity of EHR.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

Introduction: Guardians play a pivotal role in the management of childhood illness. Their responsibilities include home managing childhood illnesses and supporting continuity of care following healthcare facility treatment. Evidence has shown that guardians' negative experience in health care facilities when accessing under-five childcare services impacts their utilization of such facilities. Exploration of guardians' experiences is important in providing the evidence base necessary for quality childcare and reducing under-five morbidity and mortality. The study aimed to explore the experiences of guardians of children under five regarding the care received in Vhembe healthcare facilities. Methods: A qualitative approach using an interpretative phenomenology analysis (IPA) was used for this study. The study was conducted in eight primary healthcare facilities in the Vhembe district, with the highest headcount for children under five. Sixteen guardians were purposefully sampled for the study. Data analysis was done following the IPA steps of data analysis. Trustworthiness criteria were followed: credibility, confirmability, dependability, authenticity, and transferability. Ethical approval was obtained from the university and the province, followed by permission from the district and the operational managers before data collection. Results: The three emerging themes are associated with participants' positive experiences as they interfaced within the healthcare system: quality care, healthcare environment, support, and empowerment. The ten sub-themes were: timely service provision, availability of medicines, perceived nursing competence and professionalism, person-centred care, welcoming environment, effective communication, positive attitude of healthcare providers, guardians’ satisfaction with care received, guardians' involvement in childcare and community support. Conclusion: It is indeed not always bad. The participants in this study had positive care experiences, which is crucial in the optimal utilization of health care services, resulting in improved health outcomes.

The healthcare sector has undergone a profound transformation, owing to the influential role played by IoMT (Internet of Medical Things) technology. However, there are substantial concerns over these devices' security and privacy-preserving nature. Current literature on IoMT tends to focus on specific security features like data confidentiality or data integrity or data availability or access control and most solutions are simulated and not tested in a real-world live network. This paper proposes a smart secure by design solution that safeguards user’s data during health and wellbeing monitoring of patients locally (home, care-home, and hospital) and remotely. The proposed innovative solution is known as Secure by Design Real Time IoMT Architecture for e-Health Population Monitoring (RTPM) for smart hospitals and any healthcare monitoring management. In this system, keys can also be generated by the patient monitoring system independent to maintain high privacy standard and trust during the monitoring process and to enable the IoMT devices run independently even if the server is compromised and are made safe from external exploits to the client nodes. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices overheads and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system: confidentiality, integrity, availability, conducts authentication, protect from DoS attacks and prevent from non-repudiation attack of patient medical data and the devices in a real time, sensory data communication environment for live e-health monitoring. A secure communication is tested by capturing the live network traffic and the system’s performance analysis of RTPM using different security algorithms with different key sizes of RSA, AES, and SHA shows that resource constraint low powered system can also successfully integrate with high-end state-of-the-art secure parameters and features and have the potential to support real time secure interaction. A stress test is also conducted to ensure that the system can withstand huge key sizes and different data types including multi-media information.

Healthcare crises occur occasionally are very disruptive and resource consuming. The Covid-19 pandemic provided an example of inefficient wasteful resource management that would be unsustainable in the long term. This paper presents a framework for sustainable crisis management that will be applicable for future crises. The methodology is also applicable to other pandemics, wars, multi-casualty natural disasters, industrial disasters, etc. Attention focuses on rapid crisis resolution such as lockdowns. Despite unprecedented investments in healthcare, lack of capacity and timeliness are still problems affecting every country, even the wealthier ones. During the Covid19 pandemic, some healthcare systems around the world have collapsed. For example, Brazil, Spain, Italy, and more. This is due to the lack of a clear methodology for handling such crises. This paper presents a resource-based methodology to cope with healthcare crises considering three scenarios: “Peace”, “War” and “Tsunami”. The paper outlines a methodology for identifying the system bottlenecks for coping with the crisis in each scenario by using established simple tools. These tools assure significant resource frugality that will enhance sustainability. The paper also analyses where the system constraint should be and prescribes how to prepare for such scenarios. A large medical center case study demonstrates our approach. During “peace” times, evolutionary strategies should be used exclusively. During “war” times, a hybrid – evolutionary/disruptive (revolutionary) strategy. During the “Tsunami” scenario, one should rely only on the disruptive (revolutionary) strategy. This methodology can be extended to other scenarios and other lines of research.

Background Community infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have increased rapidly since the emergence of the Omicron strain. During the eighth and ninth pandemic waves—when movement restrictions in the community were eased—the all-case registration system was changed, and the actual status of infection became uncertain. Methods We conducted regular rapid antigen tests (R-RAT) once or twice a week to examine the actual state of coronavirus disease (COVID-19) diagnosis among healthcare employees. Results Overall, 320 (1.42/day) and 299 (1.76/day) employees were infected in the eighth and ninth pandemic waves. During both periods, 59/263 doctors (22.4%), 335/806 nurses (41.6%), 92/194 administrative employees (47.4%), and 129/218 clinical laboratory technicians (59.2%) were infected. In the eighth wave, 56 of 195 employees were infected through close contact; in the ninth wave, 26 of 62 employees were infected. No significant difference was observed in the number of vaccinations between infected and non-infected employees. The positivity rate of R-RAT was 0.41% and 0.45% in the eighth and ninth waves. R-RAT detected infection in 212 and 229 employees during the eighth and ninth waves, respectively; the ratio of R-RAT-detected positive employees to those who reported infection was significantly higher during the ninth wave (odds ratio: 1.67, 95% confidence interval: 1.17–2.37, p

Any Hospital’s primary goal is to restore human health and save lives through health services provided to patients, but at the same time, hazardous wastes are produced. Inconsistent management of unsafe wastes might cause adverse effects and other issues to workers, the environment and public health. Segregation is considered the critical stage for a successful Healthcare Waste Management. Mixing hazardous waste with non-hazardous waste will be avoided by correctly applying practices at the segregation stage. This study aimed to assess personnel’s knowledge about Infectious Medical Waste and segregation practices used at six wards in Nicosia General Hospital. An analytical cross-sectional study was conducted, and data were collected through a structured self-administered questionnaire. The Statistical Package of Social Science (SPPS) version 25 was used with a minimum statistical significance α=0.05. The study population was nurses, nurse assistants, ward assistants and cleaners working at the study wards. Out of 191 questionnaires, 82 were received, with a response rate of 42.93%. Most participants were female (72%) and nurses (85,4%). Participants had medium to good knowledge about Infectious Medical Waste management and good knowledge regarding segregation practices applied in their ward. Segregation was not carried out as it should have since most participants stated that Infectious Medical Wastes were mixed with non-hazardous waste. Correct answers given by the participants regarding color-coding of different Healthcare Waste categories was 67.5%, and only four answered correctly to all questions. Although participants knew segregation practices and the colour-coding process applied for Healthcare Waste, they didn’t use them satisfactorily. They applied methods regarding segregation without specific training, knowledge and guidance. Due to the issue's importance, training programs must be implemented and performed.

Nowadays, healthcare-associated infections (HAIs) represent a major global public health problem and the burden of these infections is high. In order to reduce the incidence of HAIs and to prevent the spread antibi-otics resistant organisms is mandatory to develop surveillance systems. We undertook a retrospective case-control study of all patients presenting HAIs admitted in Intensive Care Unit (ICU) in order to assess risk factors associated with death among patients with HAIs. Patients admitted to Intensive Care Unit who died were more likely to present an infection with a multidrug resistant bacteria, an infection with Acineto-bacter baumannii, and to suffer from acute myocardial infarction. Among the patients enrolled in the study, a higher probability of death was also observed in association with certain gram-negative pathogens: Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa. The longer the patient remains admitted in ICU, the higher the risk of getting an infection that can often become fatal. Acquiring a noso-comial infection also increases the length of hospitalization which will lead to increased financial damage.

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

Despite the Australian Government’s attempts to reduce domestic violence (DV) incidences, impediments that exist within the social and health systems and interventions designed to identify DV victims may contribute to female victims’ reluctance to disclose DV experiences to their primary healthcare providers. This scoping review aimed to provide the state of evidence about reasons for such reluctance to reveal DV, symptoms and comorbidities patients present to healthcare providers, detection and interventions in the clinical setting, and recommendations to generate more effective responses to DV. Female victims are reluctant to reveal DV because they do not trust or believe that general practitioners can help them to solve the issue, they do not acknowledge they are in an abusive relationship or are unaware that they are, or had been, victims of DV. The most common symptoms and comorbidities victims present with are sleep difficulties and fear. DV screening programs are the most prominent intervention type within Australian primary health services. This scoping review provides formative evidence that further research and interventions are required to increase professional skills, knowledge, sensitivity and referral processes among primary healthcare professionals treating patients experiencing DV in Australia.

A medical entity (hospital, nursing home, rest home, revalidation center, etc.) usually includes a multitude of information systems that allow for quick decision-making close to the medical sensors. The Internet of Medical Things (IoMT) is an area of IoT that generates a lot of data of different natures (radio, CT scan, medical reports, medical sensor data). However, these systems need to share and exchange medical information in a seamless, timely, and efficient manner with systems that are either located within the same entity or located in other healthcare entities. The lack of inter and intra entity interoperability causes major problems in the analysis of patient records and leads to additional financial costs (e.g., redone examinations). In order to develop a medical data interoperability architecture model that will allow providers and different actors in the medical community to exchange patient summary information with other caregivers and partners in order to improve the quality of care, the level of data security, and the efficiency of care, we take stock of the state of knowledge.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

No-show appointments in healthcare is a problem faced by medical centers around the world, and understand the factors associated with the no-show behavior is essential. In the last decades, artificial intelligence took place in the medical field and machine learning algorithms can work as a efficient tool to understand the patients behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on a SLR following the Kitchenham methodology, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each studies were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patients age, whether the patient missed a previous appointment, and the distance between the appointment and the patients scheduling.

Background: Using the COM-B model as a framework, an EU-wide survey aimed to ascertain multidisciplinary healthcare workers’ (HCWs) knowledge, attitudes and behaviours on antibiotics, antibiotic use and antibiotic resistance. The UK findings are presented. Methods: A 43-item questionnaire was developed through a two-round modified Delphi consensus process. The UK target quota was 1,315 respondents. Results: 2,404 participants responded. The highest proportion were nursing and midwifery professionals (42%), pharmacists (23%) and medical doctors (18%). HCWs correctly answered that antibiotics are not effective against viruses (97%), they have associated side effects (97%), unnecessary use makes antibiotics ineffective (97%) and healthy people can carry antibiotic resistant bacteria (90%). However, fewer than 80% correctly answered that using antibiotics increases a patient’s risk of antimicrobial resistant infection or that resistant bacteria can spread from person to person. Whilst the majority of HCWs (81%) agreed there is a connection between their antibiotic prescribing behaviour and the spread of antibiotic resistant bacteria, only 64% felt that they have a key role in controlling antibiotic resistance. The top three barriers to providing advice or resources were lack of resources (19%), insufficient time (11%) and the patient being uninterested in the information (7%). Approximately 35% of UK respondents who were prescribers prescribed an antibiotic at least once in the last week due to fear of patient deterioration or complications. Conclusion: These findings highlight that a multifaceted approach to tackling the barriers to prudent antibiotic use in the UK is required and provides evidence for guiding targeted policy, intervention development and future research. Education and training should focus on patient communication, information on spreading resistant bacteria and increased risk for individuals.

Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

The world is currently, subjected to the worst health crisis documented in modern history; an epidemic led by the novel coronavirus disease 2019 (COVID-19). At the epicenter of this crisis, healthcare professionals continue working to safeguard our well-being. To the regular high levels of stress, COVID new heights even more to healthcare professionals so depending on the area, specialty, and type of work. Here we investigated what are the tendencies, or areas most affected. Through an adaptation of the original COVID-stress scale, we developed a remote, fast test designed for healthcare professionals of the Northeastern part of Mexico, an important part of the country with economic and cultural ties to the US. Our results showed 4 key correlations as highly dependent: Work area – Xenophobia (p &lt; 0.045), Work with COVID patients - Traumatic stress (p &lt; 0.001) and Total number of COVID patients per day – Traumatic stress (p &lt; 0.027), and Total number of COVID patients - Compulsive checking and reassurance. Overall concluding that normal levels of stress have increased (mild – moderate). Additionally, we further determine that the fear of being an asymptomatic patient (potential to spread without knowing) continues being a concern.

Statistical data on officially registered cases of health care-associated infections (HCAIs) in Ukraine in the period 2009-2019 have been analysed. On average, 5089±756 cases of HCAIs were registered annually. Odessa region of Ukraine is the leading country in the number of reported cases. The majority of HCAIs cases involve surgical and therapeutic invasive interventions and perinatal HCAIs. On average, 78.0±5.8 % of HCAIs cases involved adults. The estimated minimum number of HCAIs in Ukraine was expected to be about 1 million per year. Official statistics on registered cases of HCAIs in Ukraine do not reflect reality, so the system of registration and investigation of HCAIs in Ukraine needs to be reformed.

The tribal belt of Pakistan-the Pak-Afghan border region is famous for its unique culture, ethnography and wild food plants and traditional knowledge. People of these regions gather wild plants for number of purposes including plants or plant parts for direct use, use it in the traditional cuisines and selling in local markets. However, there is huge lack of documentation of food system particularly the Wild Food Plants (WFPs). In current study we have focused on the uses and contributions of the WFPs in the tribal traditional food system. The ethnobotanical data were gathered through questionnaire surveys with Eighty-four informants 69 men and 15 women belonging to 21 different villages. We documented Sixty-three WFP species belonging to 34 botanical families, of which 27 were used as vegetables, 24 as fruits, 6 in different kinds of chutneys (starters) formation and six as fresh food species. Fruits were the mostly used part (40%) followed by leaves (24%), aerial parts (24%), seeds (7%), stem (3%), legume (2%) and young inflorescence (1%). Use of Carthamus oxycanthus & Pinus roxburghii seeds and Marsillea quadrifolia leaves were the novel reports for the gastronomy of Pakistan. The results elucidate that WFPs have a significant contribution in the Tribal Food Systems. Tribal people use WFPs not only due to their nutritional importance but also as a cultural practice - an inseparable component of the tribal communities. This important traditional Knowledge about the consumption of WFPs has been eroding with an alarming speed among the younger generations due to introduction of fast food chains, modernization, and globalization. Therefore, appropriates strategies are imperative not only to safeguard traditional knowledge but also the cultural heritage, food security and hence public healthcare via food supplement in the region.

Contact tracing has become an indispensable tool of various extensive measures to control the spread of COVID-19 pandemic due to novel coronavirus. This essential tool helps to identify, isolate and quarantine the contacted persons of a COVID-19 patient. However, the existing contact tracing applications developed by various countries, health organizations to trace down the contacts after identifying a COVID-19 patient suffers from several security and privacy concerns. In this work, we have identified those security and privacy issues of several leading contact tracing applications and proposed a blockchain-based framework to overcome the major security and privacy challenges imposed by the applications. We have discussed the security and privacy measures that are achieved by the proposed framework to show the effectiveness against the security and privacy issues raised by the existing mobile contact tracing applications.

This study was conducted to determine whether overcrowding in the emergency department (ED) affects the occurrence of a return visit (RV) within 72 hours. The crowding indicator of index visit was the average number of total patients, patients under observation, and boarding patients during the first 1 and 4 hours from ED arrival time and the last 1 hour before ED departure. Logistic regression analysis was conducted to determine whether each indicator affects the occurrence of RV and post-RV admission. Of the 87,360 discharged patients, 3,743 (4.3%) returned to the ED within 72 hours. Of the crowding indicators pertaining to total patients, the last 1 hour significantly affected decrease in RV (p=0.0046). Boarding patients were found to increase RV occurrence during the first 1 hour (p=0.0146) and 4 hours (p=0.0326). Crowding indicators that increased the likelihood of admission post-RV were total number of patients during the first 1 hour (p=0.0166) and 4 hours (p=0.0335) and evaluationg patients during the first 1 hour (p=0.0059). Overcrowding in the ED increased the incidence of RV and likelihood of post-RV admission. However, overcrowding at the time of ED departure was related to reduced RV.

Background: The evolution of names, from “medical informatics” to “connected health”, implies that the evolvement of technology in health care has been shifted from technology-oriented to healthcare-oriented implementation. Connected healthcare, a healthcare platform of remote monitoring and self-management through technological measures, is suggested to contribute to the efficiency, cost effectiveness, and satisfaction of healthcare recipient enhancement. However, limited understanding of related connected health (CH) terminology may constrain its implementation. Whether CH is a Buzzword only or a practice that can contribute to an ageing society is controversial. Objective: This study aims to distinguish CH-related terminology and to identify the trend of CH through reviewing its definition, initiation, development, and evolvement, in order to offer management insights and implications. The objective is to understand what is connected and who is cared about in the connected health model, so that better applications can be addressed for the benefit of society. Method: This study reviews the evolution of names, from “medical informatics” in the 1970s to “connected health” after 2000, as well as relevant literature of CH, including e-health, telemedicine, telehealth, telecare, and m-health, to discover the trend of technology-related healthcare implementation. The current status and issues facing CH will be presented and its future trends will be explored through reviewing how changes in healthcare are managed, in addition to its operation and practice. Issues of accessibility, quality, and cost will be discussed, as well as its market status. Results: Preconditions and requirements for implementing CH are identified to select a typical case to study. Areas with a complete business ecosystem, including advanced technology and medical services, a payment system, an ageing population, geographic isolation, integrated health, and social care, are prevalent. These findings may be beneficial to designing and establishing comprehensive CH implantation and environments. Conclusion: The evidence and tendency of technological convergence create a demand for innovation and partnering with start-up companies that offer a competitive advantage in innovation. Specifically, it is necessary to innovate both the public and private operation model of the CH ecosystem. This focus will be further explored in future work.

This study explored the effects of a comprehensive health care program (CHCP) on blood pressure, blood glucose, body composition, and depression in older adults living alone. We used a quasi-experimental, two-group, pre-posttest design. The CHCP consisted of open lectures, health counseling, exercise classes, nutrition counseling, and self-help group meetings at a local senior welfare center. Fifty-eight subjects participated in this study, with thirty subjects in the experimental group and twenty-eight subjects in the control group. Data were analyzed using the descriptive statistics, χ²-test, and t-test. Comparisons of the pretest and posttest systolic blood pressure (t = - 2.530, p < .016) and blood glucose (t = 3.089, p < .004) between the experimental and control groups showed significant differences. In both the experimental (t = 3.949, p < .001) and control groups (t = 3.816, p < .002), depression symptoms showed a significant decrease post-test, compared with pre-test. Our findings infer that older adults require physical and psychosocial healthcare and that more efforts must be made to improve the general health and wellbeing of this population group.

After conducting a detailed survey among the villagers of Pallissery and Karukutty, it was observed that in most of the villages the native people have to travel long distances for their basic health needs. Also most of the villagers lack knowledge regarding live health updates. At times, these problems have even resulted in death of many people including pregnant women and children. The objective of our research is to propose an integrated and easy to use Medical Kiosk that can be installed at various locations in rural areas. The Kiosk will provide an integrated environment for all medical related activities and would perform numerous functions like sending notifications regarding medical camps, mobile medical help, important dates for vaccinations, child care, insurance policies and provide other live medical updates to the villagers. It would also support the basic facilities for measurement of body parameters like height, weight, BMI, blood pressure, and heartbeat and also facilitate live consultation facilities with specialized doctors through video and voice chats.

Organ transplantation centers set criteria for candidate qualification, which has led to disparate healthcare resource allocation practices affecting those with a substance use history. These individuals are denied organ transplants by committees and healthcare providers who assign them lower priority status. The lower priority argument claims that healthcare resources should not be provided equally to individuals failing to share responsibility for not doing enough in addressing the diseases associated with substance use. The purpose of this work is to explore the interrelatedness between the ethics of a merit-based system of moral responsibility and lower priority setting involved in healthcare resource allocation pertaining to those with substance use histories. An integral approach to the argument against the lower prioritists with a focus on the relationship between different organ allocation practices affecting substance users and the justification for resource allocation practices in healthcare and transplant committees. Lower priority setting is challenged, and an argument offered in which substance users are assigned higher priority when relying on “doing enough” in a merit-based system of moral responsibility. It is determined that one cannot substantiate assigning a lower priority status since a lack of success in rehab does not imply a lack of effort. Additionally, neither to confirmatory behavior, nor to non-conforming behavior may freedom be justifiably ascribed in a merit-based system of responsibility because freedom to choose can neither be established a priori, nor a posteriori with respect to meritorious behavior.

Background: Health insurance (HI) run by government is providing health care service to large population. Due to poor accountability, participation and sustainability, cooperative health insurance is becoming more popular and effective in low and middle income and some high-income countries too. In Nepal, there are public and cooperative HI is in practice. The aim of this study is to compare the effectiveness of public (government) and cooperative HI in relation to benefit packages, population coverage, inclusiveness, health care utilization, and promptness for treatment in these two health insurance models in Nepal. Method: This is an institution based concurrent mixed study consists of qualitative and quantitative variables from public and cooperative groups. We included all public HI operated by government hospitals and cooperatives groups those purchased hospital service in contract. Two separate study tools were applied to access the effectiveness of insurance models. The key questions were asked for the representatives of government and private health insurance. The numeric information consisted of in quantitative data and subjective response was included in qualitative approach. Descriptive statistics and Mean Whitney U test was applied in numeric data and qualitative information were analyzed by inductive approach Results: The study revealed that new enrolment was not increased, health care utilization rate was increased and the benefit package was almost same in both groups. The overall inclusiveness was higher for the government HI, but enrolment from the religious minority, proportion of negotiated amount during treatment were significantly higher (p<0.05). During illness, the response time to reach hospital was significantly faster in cooperative health insurance than government health insurance. Qualitative findings showed that level of participation, accountability, transparency and recording system was better in cooperative health insurance than public. Conclusion: Cooperative HI could be more sustainable and accountable to the community for all; low, middle and high-income countries.

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.

Understanding and addressing dehumanization in healthcare is crucial due to its pro-found impact on patient care, ethical implications on patient dignity and autonomy, and its potential to affect the psychological well-being of healthcare professionals. The primary aim of this study was to establish reliable and valid instruments measuring two different types of dehumanization, namely animalistic dehumanization (i.e., stripping one of their uniquely human characteristics) and mechanistic dehumanization (i.e., stripping one of their human nature characteristics) among healthcare professionals. In a cross-sectional validation study among healthcare professionals, we tested measures of both animalistic and mechanistic dehumanization, focusing on dehumanization of patients (hetero-dehumanization) and oneself (self-dehumanization), respectively. All measures were developed and validated based on a concept analysis, a literature review, and an appraisal of pre-existing scales. The research was conducted among 400 nurses and medical doctors employed in Greek public hospitals. Coefficient Validity Ratio re-sults showed that 100% of items were acceptable for both measures. The newly estab-lished and validated hetero-dehumanization scale encompassed two-factors (factor 1: animalistic dehumanization, factor 2: mechanistic dehumanization, Cronbach’s alpha was equal to 0.86 for each measure). The self-dehumanization scale was a mono-factorial measure mechanistic dehumanization (Cronbach’s alpha = 0.97). Two validated measures of (self and hetero-) animalistic and mechanistic dehumanization measures were developed for the assessment of dehumanization among health profes-sionals, which will form the basis for future research in this important scientific field.

Introduction: Rural Americans face significant health disparities due to limited access to healthcare services. Telehealth has emerged as a promising solution to mitigate these disparities by facilitating remote access to medical care. This paper comprehensively reviews the advantages, challenges, and implementation strategies of telehealth in rural areas.Method: A narrative review methodology was employed to synthesize existing literature and case studies on telehealth in rural settings. Databases such as PubMed, Google Scholar, and JSTOR were searched for relevant studies published in the last three decades. Keywords such as "telehealth," "rural health," and "healthcare disparities" guided the search process. The review focused on studies assessing the impact of telehealth on healthcare access, cost-effectiveness, and quality of care in rural areas.Results: The review findings indicate that telehealth significantly enhances healthcare access by reducing travel distances, increasing availability of specialist consultations, and promoting patient engagement. It improves chronic disease management and patient satisfaction by enabling convenient access to healthcare services at home. Despite initial setup costs and challenges such as broadband access and regulatory issues, telehealth demonstrates long-term cost savings through reduced travel expenses and improved healthcare efficiency. Quality of care is enhanced through improved communication between healthcare providers and patients, leading to better treatment adherence and health outcomes.Conclusion: Telehealth represents a transformative approach to address healthcare disparities in rural areas. It offers a cost-effective solution to improve access to healthcare services, enhance patient outcomes, and reduce healthcare costs in the long run. However, successful implementation requires addressing infrastructure challenges, refining reimbursement policies, and ensuring regulatory support. Future efforts should focus on expanding broadband infrastructure, refining policy frameworks, and conducting further research to optimize telehealth's impact on rural healthcare delivery.

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.

Preschoolers with disabilities and their family caregivers are missed out in many public health initiatives especially in low-income countries. Yet they can benefit from early intervention to promote their development from soon after birth and to provide a better quality of life for their families. In this paper, we describe how a community-based approach has been implemented with minimal funding in two areas in Zimbabwe: a township in Harare and in rural areas of Manicaland Province. Our aim in sharing this information – allied with references to research studies recently undertaken in Africa – is that it will enable similar supports to be replicated in other communities by local personnel. A logic model is used to describe the situation in which the two projects work, the various inputs they have provided to their community and the different forms of support they have offered to the children and their caregivers. The project outputs are listed in terms of the number of beneficiaries helped and activities undertaken. The outcomes achieved for the children, the family and communities are reported. The sustainability and extensions of community-based projects to address unmet needs are discussed. The main conclusion is that disadvantaged communities can be energized to address the needs of their most marginalized residents.

Understanding and addressing dehumanization in healthcare is crucial due to its profound impact on patient care, ethical implications on patient dignity and autonomy, and its potential to affect the psychological well-being of healthcare professionals. The primary aim of this study was to develop reliable and valid instruments measuring two different types of dehumanization, namely animalistic dehumanization (i.e., stripping one of their uniquely human characteristics) and mechanistic dehumanization (i.e., striping one of their human nature characteristics) among healthcare professionals. In a cross-sectional validation study among healthcare professionals, we developed and tested measures of both animalistic and mechanistic dehumanization, focusing on dehumanization of patients (hetero-dehumanization) and oneself (self-dehumanization), respectively. All measures were developed and validated based on a concept analysis, a literature review, and an appraisal of pre-existing scales. The research was conducted among 400 nurses and medical doctors employed in Greek public hospitals. Coefficient Validity Ratio results showed that 100% of items were acceptable for both measures. The newly developed hetero-dehumanization scale encompassed two-factors (factor 1: animalistic dehumanization, factor 2: mechanistic dehumanization, Cronbach’s alpha was equal to 0.86 for each measure). The self-dehumanization scale was a mono-factorial measure mechanistic dehumanization (Cronbach’s alpha = 0.97). Two validated measures of (self and hetero-) animalistic and mechanistic dehumanization measures were developed for the assessment of dehumanization among health professionals, which will form the basis for future research in this important scientific field.

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.

Preschoolers with disabilities and their caregivers have been neglected in health and social service provision in most low-income countries and arguably too in low resourced areas of more affluent nations. Yet as this rapid review of published literature identifies, there are low-cost, evidence-based strategies to address their needs that can be implemented in communities by local people. Five key features of the necessary supports are examined. First the leadership functions required to create and implement the support services. Second, the family-centred, home-based support provided to caregivers and the personnel undertaking this form of support. Third, providing opportunities for peer support to flourish and encouraging the formation of advocacy groups across families. Fourth, mobilizing the support of significant groups within the community; notably traditional healers and leaders; health services and poverty alleviation initiatives. Fifth, devising ways in which preschool educational opportunities can be offered to children as a prelude to their inclusion in primary schools. The review serves a further purpose. It provides an example of how public health researchers and academics could achieve more rapid implementation of evidence-based knowledge into existing and new support services through dissemination to community practitioners.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

Understanding the dynamics of teamwork and communication among healthcare professionals is crucial in the face of evolving healthcare challenges. This study assessed relational coordination among healthcare professionals in the South Tyrolean healthcare system in Italy, focusing on communication and teamwork dynamics in a cross-sectional survey. Using the validated Relational Coordination Survey (RCS) instrument and 525 completed online responses, the questionnaire aimed to understand the implications of different levels of relational coordination ratings by general practitioners, hospital physicians, nurses, and administrative personnel (response rate 26%). The demographics of the participants revealed a predominance of female professionals (64%), with an average age of 50 and 18 years of service. The resulting RCS scores varied significantly across professional groups, with nurses reporting the highest within-group scores, indicating moderate coordination, and administrators reporting the lowest scores, reflecting areas of weak coordination. Between-group relational coordination was generally perceived as weak across professional groups, with the least weakness observed between general practitioners and nurses. German or Italian language and health district affiliation emerged as significant factors influencing relational coordination ratings, highlighting the need for differentiated understanding and strategies in multilingual and diverse regional settings. Assessments of interdisciplinary feedback and referral practices highlight the variation in teamwork and communication weaknesses and underscore the need for targeted interventions to improve relational coordination. This study provides insights into the complexity of relational dynamics in health care settings. It suggests that improving relational coordination through tailored strategies could significantly improve team effectiveness, quality of patient care, and overall system efficiency.

(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.

Until recently there was a paucity of published information on the demographics of themedical and surgical workforce for the subspecialty of liver transplantation. This is relevant as it relates to whether gender equity is being achieved across the workforce. This manuscript focuses on what eventually led to the recognition that more data were required along with what is now actually known. This involves exploring the genders of the liver transplant workforce along with their related academic activities. Potential solutions to address any ongoing imbalances are also examined. The extent and range of gender disparities previously reported for other cohorts of physicians and surgeons, are also apparent amongst the liver transplant workforce in most regions of the world. This also pertains to the higher leadership positions within liver transplant centers as well as for the relevant editorial and scientific congress roles. Common themes/recommendations are now emerging as to how best to address the lack of progress towards gender equity. These include the development and implementation of policies, the removal of barriers to career progression, plus adequate governance. Hence ongoing actions are going to be required to achieve gender equity across the relevant workforce in liver transplantation around the world.

This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of 'Artificial Intelligence in Healthcare'. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman's population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model's performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model's capability in diabetes detection. The implementation of this LSTM model in Oman's healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges globally.st diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

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.

Fiber-based flexible sensors have promising applications potential in human motion and healthcare monitoring, owing to their merits of lightweight, flexibility, and easy processing. Now, high-performance elastic fiber-based strain sensors with high sensitivity, large working range and excellent durability are in great demand. Herein, we have easily and quickly prepared a highly-sensitive, durable fiber-based strain sensor by dip-coating of highly-stretchable polyurethane (PU) elastic fiber in MXene/ waterborne polyurethane (WPU) dispersion solution. Benefiting from the electrostatic repulsion force between the negatively charged WPU and MXene sheets in the mixed solution, very homogeneous and stable MXene/WPU dispersion was successfully obtained and the interconnected conducting networks were correspondingly formed in coated MXene/WPU shell layer, which makes as-prepared strain sensor exhibit a gauge factor of over 960, a large sensing range of over 90% and detection limit as low as 0.5% strain. As elastic fiber and mixed solution have the same polymer constitute, tight bonding of MXene/WPU conductive composite on PU fibers was achieved, enabling the as-prepared strain sensor endure over 2500 stretching-releasing cycles and thus show good durability. Full-scale human motion detection was also performed by the strain sensor, and a body posture monitoring, analysis, and correction prototype system was developed via embedding the fiber-based strain sensors into sweater, strongly indicating the great application prospect in exercise, sports and healthcare.

Introduction The climate crisis is essentially a public health crisis. Response is imperative to prevent economic and social crises associated with the growing burden of climate impacts on human health and the health care sector. Green Podiatry Pillars By adopting the three pillars of Exercise, Evidence, and Everyday changes, podiatrists can contribute to more sustainable health and health care. Discussion Educating our patients to use their feet for low carbon active transport, eliminating interventions not supported by evidence, and reducing fossil fuel driven supply chains and energy use, are three impactful measures that all podiatrists, and indeed, all health and medical personnel, need to adopt. The avoidable suffering, enormous and costly use of resources for diabetic foot disease, is a scourge that must realise its preventable potential. It is too late, too costly, too polluting, and too sad, to continue to direct health care and research efforts and budgets, to an expensive modifiable diabetes disease process, potentiated by poor food and physical inactivity. Affected patients require skilled support, to avert such chronic disease processes, often not of their making. Conclusions Healthcare contributes 5 to 8% of green house gases (GHG), and non-communicable diseases (NCD) are increasing, so healthcare must lead on primary health. As citizens and health professionals, we must advocate for better community health, and educate our patients. Future Directions All health and medical personnel need to consider planetary health and sustainable healthcare within their daily work. Healthcare must act to address climate change, and realise benefits for people and planet.

In bioinformatics research, traditional machine learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompasses non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-dimensional, sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research direction. It embeds graphs into a low-dimensional space while preserving the structural and attribute information of the graph, enabling better feature extraction for downstream tasks. This study extensively reviews representation learning advancements, particularly in the research of representation methods since the emergence of COVID-19. We begin with an analysis and classification of neural network-based language model representation learning techniques as well as graph representation learning methods. Subsequently, we explore their methodological innovations in the context of COVID-19, with a focus on the domains of drugs, public health, and healthcare. Furthermore, we discuss the challenges and opportunities associated with graph representation learning. This comprehensive review presents invaluable insights for researchers as it documents the development of COVID-19 and offers experiential lessons to preempt future infectious diseases. Moreover, this study guides future bioinformatics and biomedicine research methodologies.

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.

Lifestyle physical activity (PA) and nutrition are known to be effective interventions in preventing and managing obesity related comorbidities is adult populations, but less so in children and adolescents. We examined the effectiveness of lifestyle interventions in children from minority ethnic populations in western high-income countries. Our systematic review included 53 studies involving 26045 children from minority ethnic populations within western countries, who followed lifestyle intervention programs lasting between 8 weeks and 5 years with an aim to prevent and/or manage childhood obesity and associated comorbidities, including adiposity and cardiometabolic risks. Studies were heterogenous in terms of lifestyle intervention components (nutrition, PA, behavioural counselling) and settings (community vs. schools and after schools). Our meta-analysis included 31 eligible studies and showed no significant effects of lifestyle interventions when they focused on body weight and body mass index (BMI) outcomes [pooled BMI mean change = -0.09 (95% CI -0.19, 0.01), p=0.09]. This was irrespective of the intervention program duration (< 6 months vs. ≥ 6 months), type (PA vs nutrition/combined intervention) and weight status (overweight or obese vs no normal weight), all showed no significant effects in the sensitivity analysis. Nonetheless, 19 of the 53 studies reported reduction in BMI, BMI z scores and body fat percentage. However, majority of lifestyle interventions adopting a quasi-design with a combined primary and secondary obesity measures (11 out of 15 studies), were effective in reducing obesity comorbidities of cardiometabolic risks including metabolic syndrome, insulin sensitivity, and blood pressure in children with overweight and obesity. Preventing childhood obesity and associated comorbidities in children from high-risk ethnic minority groups is best achieved using a combined PA and nutrition intervention approach, which jointly target preventing obesity and cardiometabolic disease primary and secondary outcomes, especially measures of diabetes, hypertension, and cardiovascular disease.

Healthcare workers (HCWs) in long-term care (LTC) faced and continue to experience significant emotional and psychological distress throughout the pandemic. Despite this, little is known about the unique experiences of LTC workers. This scoping review synthesizes existing research on the experiences of HCWs in LTC during the COVID-19 pandemic. Following Arksey and O’Malley’s framework, data were extracted from six databases from inception of the pandemic to June 2022. Among 3,808 articles screened, 40 articles were included in the final analysis. Analyses revealed three interrelated themes: carrying the load (moral distress); building pressure and burning out (emotional exhaustion); and working through it (a sense of duty to care). Given the impacts of the pandemic on both HCW wellbeing and patient care, every effort must be made to address the LTC workforce crisis and evaluate best practices for supporting HCWs experiencing mental health concerns during and post-COVID-19.

With the advent of modern technologies, the healthcare industry is moving towards a more Personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence. These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five publicly available datasets from Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

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.

Type 2 diabetes (T2D) is associated with increased cardiovascular morbidity, mortality, and hospital admissions. There is variability in clinical practice. The objectives are to analyze the variability in the control of Blood Pressure (BP), HbA1c, and LDL-C in T2D patients and its influence on admissions due to cardiovascular events (CVE) Methods: We analyzed the electronic records in Primary Care Health centers in Navarra (Spain) and hospital admission for CVE. We follow 480637 people from 2012 to 2016. We calculated indicators of control of patients with T2D for each year, percentage with: HbA1c &lt; 7%; HbA1c &gt;= 9%; BP &lt;140/90 mmHg; LDL-C &lt;100 mg/dl. We used logistic and Cox regression. Results: Patients in the best control GP practices cluster are 2.5 times more likely to have HbA1c &lt;7% [OR: 2.46 (95% CI: 2.29-3.64)]. Poor HbA1c control ≥ 9% is more likely in the worst control cluster [OR: 1.73 (95% CI:1.63-1.83)]. The probability of admission for CVE increases with age, being male, low income, obesity, history of CVE, having HbA1c ≥ 9%, and belonging to a GP practice in the cluster of HbA1C ≥ 9% worst control. In contrast, it decreases in patients with HbA1c &lt;7%, BP&lt;140/90 mmHg and LDL &lt;100 mg/dl.

Tinnitus is a complex and heterogeneous psycho-physiological disorder responsible for causing a phantom ringing or buzzing sound albeit the absence of an external sound source. It has a direct influence on affecting the quality of life of its sufferers. Despite being around for a while, there hasn’t been a cure for tinnitus, and the usual course of action for its treatment involves use of tinnitus retaining and sound therapy, or Cognitive Behavioral Therapy (CBT). One positive aspect about these therapies is that they can be administered face-to-face as well as delivered via internet or smartphone. Smartphones are especially helpful as they are highly personalized devices, and offer a well-established ecosystem of apps, accessible via respective marketplaces of differing mobile platforms. Note that current therapeutic treatments such as CBT have shown to be effective in suppressing the tinnitus symptoms when administered face-to-face, their effectiveness when being delivered using smartphones is not known so far. A quick search on the prominent market places of popular mobile platforms (Android and iOS) yielded roughly 250 smartphone apps offering tinnitus-related therapies and tinnitus management. As this number is expected to steadily increase due to high interest in smartphone app development, a contemporary review of such apps is crucial. In this paper, we aim to review scientific studies validating the smartphone apps, particularly to test their effectiveness in tinnitus management and treatment. We use the PRISMA guidelines for systematic identification of studies on major scientific literature sources and delineate the outcomes of identified studies.

In the context of the COVID-19 pandemic, countries around the world varied in the strength and timeliness of their responses. In Romania, specific challenges were faced with regards to managing the spread and limiting the impact of the disease, ranging from healthcare infrastructure to demographic and sociocultural aspects. As the country has a sizeable diaspora, major difficulties were faced when large numbers of individuals from highly affected areas returned to Romania. However, the fast implementation of control measures successfully averted a surge in the number of COVID-19 cases. This delayed the overburdening of an already challenged healthcare system during the initial phases of the epidemic. Furthermore, early control was facilitated by the exploitation of communication channels that penetrated all layers of society, from ordinary citizens to governmental authorities and high-ranking religious figures. The management of the COVID-19 crisis in Romania illustrates the importance of a fast initial response which takes into account the role played by sociocultural aspects in the context of an epidemic. As the challenges faced by Romania are not unique, these results could inform future public health strategies worldwide.

Background: Malnutrition is a widespread issue among old people, significantly impacting health outcomes. Nutritional interventions can improve health, but their success often depends on the attitudes and knowledge of healthcare workers. This study examines the influence of continuing education on healthcare workers' attitudes and practices toward old people's nutrition. Aim: The study assesses healthcare workers' attitudes toward old people's nutrition using the validated Italian version of the SANN-G questionnaire, focusing on staff in nursing homes in Northern Italy. Methods: A cross-sectional study was conducted with 1,789 healthcare workers from 41 facilities. The SANN-G questionnaire measured attitudes across five dimensions: nutritional norms, habits, assessment, intervention, and individualization. Data were collected online and on paper, with descriptive and inferential statistical analyses (chi-square and ANOVA) performed using R software. Results: Most respondents were female (68.59%) and aged 41–50 (33.31%), with nursing assistants comprising 35.83%. Only 23.48% scored positively on the SANN-G scale, with most displaying neutral or negative attitudes. Education on malnutrition significantly improved attitudes, particularly in assessment, intervention, and individualization. Younger respondents and nurses were more likely to have positive attitudes after educational interventions, while older respondents and physicians tended to show neutral or negative attitudes. Conclusion: Continuing education on malnutrition is crucial for fostering positive attitudes among healthcare workers. Specialized education leads to more proactive approaches, highlighting the need for targeted educational programs to improve old people's nutritional care.

Scaling the operation is the single greatest challenge in every enterprise. The more complex the enterprise, the greater the challenge. Scaling in healthcare has been a consistent challenge, with the need to meet clinical endpoints with less staff, increased costs of resources, regulatory oversight, and the goal of improving access to care. The article explores the emerging paradigm of multi-agent AI systems in healthcare, representing a significant leap beyond traditional Large Language Models. We comprehensively analyze the potential of these systems to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. The paper describes a hypothetical sepsis management system comprising seven specialized AI agents, each handling specific aspects of patient care, from data collection and diagnosis to treatment recommendations and resource management. We also examine applications in chronic disease management and hospital patient flow optimization. The technical implementation of these systems is discussed, including the use of advanced Large Language Models, inter-agent quality control measures, implementing guardrails, self-reflection, integration with Electronic Health Records, and the importance of explainable AI for decision transparency. In addition to describing the promising potential benefits, such as enhanced diagnostic accuracy and personalized treatment plans, we address significant challenges, including data quality assurance, workflow integration, and ethical considerations. We conclude the article by highlighting future directions, such as integrating IoT devices and developing more sophisticated natural language interfaces. Our work underscores the transformative potential of multi-agent AI systems in healthcare while emphasizing the need for rigorous validation, ethical oversight, and a patient-centered approach in their development and implementation.

The development of intelligent medical service robots for patient care presents significant chal-lenges, particularly in integrating diverse knowledge sources and enabling robots to autono-mously perform tasks in dynamic and unpredictable healthcare environments. This study in-troduces a novel framework that combines large language models (LLMs) with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant (RHA). Utilizing OpenAI’s ChatGPT, the RHA processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the RHA successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the LLM and predefined task specifications. The results demonstrate the potential of LLMs to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural lan-guage.

Background: The hub-and-spoke model (HSM), offers a framework for efficient healthcare service delivery. This scoping review seeks to establish the implementation and effectiveness of the HSM in Antimicrobial Stewardship (AMS) programs. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guideline was followed. A systematic search was conducted in four electronic databases (PubMed, Medline, Cochrane library and Google scholar) from inception until January 2024. Studies where the HSM was used for delivering any AMS activity, intervention or action, were included. No study specific filters were applied and all populations, study designs and health settings were included. Data screening and selection was achieved using Rayyan. Three authors independently screened studies, with conflicts resolved by a fourth author. Data were narratively synthesized. Standard appraisal tools were impractical; however, critically evaluation of data collection and outcome reporting was ensured. Results: Out of 1,438 articles, three were included in the scoping review. The primary interventions utilising the HSM in AMS involved reducing antibiotic misuse, training healthcare professionals, case-based learning, establishing AMS programs, developing antibiograms, and formulating policies or guidelines pertinent to AMS. The studies demonstrated significant clinical improvements in AMS. Clinical outcomes from the studies include a significant reduction in antimicrobial usage and improved antibiotic management, with a notable decrease in days on antimicrobial therapy and increased antibiotic de-escalation. Key facilitators for AMS program success: tailored education, collaborative learning, strong leadership, strategic practices, and data-driven decisions. Key barriers: leadership challenges, change resistance, knowledge gaps, inadequate data systems, resource limitations, and technological constraints. Conclusion: The review identified a literature gap in HSM use for AMS programs. Further studies are needed to assess HSM's effectiveness, feasibility, and cost-effectiveness in AMS contexts.

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.

Improving the quality and access to healthcare services in rural areas is fundamental to develop sustainable healthcare systems. This research aims to explore the motivations of healthcare professionals to work and settle in rural island areas of Greece with limited access to secondary and tertiary care. The study suggests practical ways to encourage self-motivation and attract more health workers in rural areas. An exploratory qualitative research approach was employed, involving semi-structured interviews with 16 healthcare professionals working in primary care units that lack direct hospital or hospital-health center access. The research was conducted specifically in the rural islands of Cyclades. Thematic analysis was conducted to identify common themes and unique insights from the participants. The analysis revealed three thematic categories. Τhe «attraction» thematic was influenced by personal factors, random selection, origin, accommodation factors, professional factors and obligatoriness. The «recruitment» thematic was associated with understaffing, special care issues, unstable working environment, educational and organizational aspects, and insular lifestyle. The thematic of «retention» highlighted personal issues, accommodation difficulties, economic and work-related issues, and unique challenges posed by an insular lifestyle. This research provides valuable insights into the motivations that drive healthcare professionals to settle, work, and remain in remote island units, as well as the challenges they encounter in making this decision. The study proposes strategies to motivate and attract more healthcare professionals to rural areas. These findings should be considered when formulating or reviewing primary healthcare empowerment policies to ensure equitable healthcare access for all individuals.

Positive Health (PH) has been described as a promising transformative innovation to address the challenges of promoting well-being and reducing the burden of disease. The aim of this study is to review the current state of knowledge about PH as introduced by Huber et al. (2016) within the available scientific literature. A rapid review of the literature following the Cochrane Rapid Review recommendations. Three databases were searched (PubMed, Google Scholar, and CINAHL). Data were extracted and synthesized using a narrative approach. A total of 55 articles were included. Initial evaluation studies reveal promising results both at individual and collective levels. However, a need is expressed in several articles for further refinement of the conceptualization of PH and ways to make the effects of the PH approach more measurable. Professionals also expressed a desire for a more informed application and elaboration of the PH method across various settings and populations to enhance its effectiveness in practice. This rapid review highlights the transformative potential of PH in shifting from a disease-focused to a health-oriented paradigm of healthcare. It underscores the need for continued research regarding further development of the concept and its practical method, along with the necessity for methodological innovation.

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.

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.

(1) Background: Mpox was declared an international public health emergency by WHO. Initially, Smallpox vaccine in Italy was offered only to laboratory personnel at direct risk of infection. Currently, Healthcare workers (HCWs) are all considered by European Centre for Disease Prevention and Control (ECDC) to be at low or moderate risk. (2) Methods: To assess smallpox vaccination coverage among HCWs in an Italian hospital with different levels of risk of exposure to Mpox infection and to assess their attitudes toward vaccination, a self-report was administered to HCWs stratified at high and low/moderate risk. (3) Results: Of the 338 HCWs evaluated, 60.36% provided the requested data; among them the percentage of previously vaccinated staff was 38.7%, which corresponds to 23.4% of the total HCWs in the departments considered. Considering those born before 1979 as vaccinated, vaccination coverage increases from 23.4% to 41.4%. The department with the highest percentage of vaccinated is infectious diseases (60.5%); The percentage of HCWs who adhered to vaccination is 23%; only 8.8% of laboratory staff want to vaccinate, compared with 100% of midwives. The department with the highest percentage of vaccination adherence is proctology (85.7%). (4) Conclusions: Given the vaccination adherence rate and the vaccination coverage of the departments analyzed, it would be useful to extend vaccination against Smallpox to frontline HCWs involved in patient care in Italy too.

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.

Despite the research conducted worldwide, there is no treatment specific for SARS-CoV-2 infection with efficacy proven by randomized controlled trials. A chance for a breakthrough is vaccinating the majority of the global population. The public opinion surveys on vaccine hesitancy prompted our team to investigate the Polish medical community's attitude towards the SARS-CoV-2 and influenza vaccinations. In-person and online surveys of Healthcare Workers (HCWs): doctors, nurses, medical students, and other allied health professionals (n=419) took place between 14.09.2020 and 5.11.2020. In our study, 68.7% of respondents would like to be vaccinated with the COVID-19 vaccine. The safety and efficacy of vaccination against COVID-19 would persuade 86.3% of hesitant and those who would refuse to be vaccinated. 3.1% of all respondents claimed that no argument would convince them to get vaccinated. 61.6% of respondents declared a willingness to receive an influenza vaccination, of which 83.3% were also inclined to receive the planned COVID-19 vaccination. Although a significant part of respondents - 62.5% (262/419) indicated, they trusted the influenza vaccine more than the COVID-19 vaccine in direct comparison, more respondents intended to get the COVID-19 vaccination than the influenza vaccine in the 2020/2021 season.

The term dehumanisation refers to the ways in which a person is perceived to be less than human or treated as if they are less than human. It involves treating a person as if they are a child or infant, like an animal or non-living object, or as if they are dead, or any other way in which someone fails to be treated like a human being with an adult identity. Dehumanisation is widespread in dementia care; it is a major obstacle to person-centred care and has poor outcomes for people with dementia. This article proposes a new model of dehumanisation. The New Model presents a new way of describing dehumanisation that has been synthesised from current understanding about the phenomenon and reflects what is presently known about the subject. The model aims to summarise existing theories and models of dehumanisation and make dehumanisation theory accessible to students and trainees in healthcare. This article describes the new model, discusses existing models and theories in the literature, and explores the potential role of the new model in solutions for reducing dehumanisation in dementia care.

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.

Stakeholder input into the decision-making process when developing public health programs and policies is crucial. This article presents an innovative approach, involving online participation with a wide group of stakeholders located in different geographic locations, for policy consensus by research methodology. The results of the project have been used to propose assumptions regarding a strategy for preventing blood-borne diseases in Poland. The research was conducted iteratively using a multi-stage qualitative methodology to explore risk assessment involving blood-borne infections. The final output of the entire project is a list of key problems/challenges and solutions associated with medical and nonmedical services that are connected to the breakage of tissue continuity. Qualitative research is rare in risk assessment as priority is usually given to statistical data and end-points (quantitative studies ). Statistical data on health risk allows to assess the health implications, while the methodology applied also allowed diagnosis of the processes leading to these effects. In addition to policy preparation for blood-borne illnesses, the methodology employed in the study can also be used to successfully explore other areas of public health policy decisions.

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.

Technology advancements are evident in the healthcare field; numerous new opportunities and applications have emerged during the last years based on embedded intelligence and related to real-time diagnosis of medical issues, tele-care and telemedicine, remote monitoring of patients, computer-assisted “smart” transportation in case of emergencies, as well as new types of remotely controlled surgical operations. This paper aims to provide an overview of the application of embedded intelligence in the field of healthcare; it gathers the main critical requirements, related technologies and research advancements up to date, and it presents the most important challenges that are yet to be faced. The main focus is given in the development and optimization of Body Area Networks (BANs) based on advanced embedded sensing devices, the optimization of smart gateways in such networks, and the provision of holistic scalable and secure solutions in the healthcare domain. In addition, the paper presents two principal use cases that stem from the combination of novel information and communication technologies with classic healthcare practices, explaining the functional and non-functional requirements, as well as the mixed criticality characteristics of the associated systems.

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.

Objectives: To develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aimed at aiding community healthcare workers in the early identification of individuals at high risk of Mild Cognitive Impairment (MCI); Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively; Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). Calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801); Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

This brief report investigates the potential correlation between manual labor and opioid addiction, focusing on contributing factors such as chronic pain, regular exercise, healthcare access, and housing stability. Although manual labor is associated with higher rates of work-related injuries and chronic pain, the review finds no strong, direct correlation between manual labor and increased opioid addiction rates. The relationship is complex and influenced by a range of socio-economic factors. The findings highlight the need for further research, particularly longitudinal studies, to explore these dynamics in depth and develop targeted interventions for at-risk populations.

Emergency Medical Response Systems (EMRS) play a vital role in delivering medical aid during disasters. In Serbia, these systems encounter several obstacles due to economic limitations, scarce resources, and ongoing healthcare reforms, all of which affect their performance. This study focuses on examining the risk factors and effectiveness of Serbia's EMRS, identifying systemic issues, and proposing enhancements to improve emergency preparedness for disasters. A survey was conducted across healthcare institutions in Serbia to gather comprehensive insights. The survey explored various aspects, including organizational structures, resource distribution, communication systems, and the speed of emergency responses. Statistical methods such as Pearson's correlation, multivariate regression analysis and chi-square tests were utilized to interpret data collected from 172 healthcare institutions. The multivariate regression analysis of EMS organization identified that factors such as the structuring of working hours and shift patterns are significant predictors, contributing to small but meaningful variations in various EMS outcomes. In contrast, other factors, such as financial resources and the availability of ambulance vehicles, showed more targeted significance depending on the specific EMS aspect being analyzed. Institutions with written mass casualty plans and procedures demonstrate superior effectiveness in EMS activities, excelling in resource allocation, operational continuity, and emergency preparedness, particularly through enhanced coordination and specialized systems for managing large-scale emergencies. The findings highlighted notable disparities in how EMRS are organized, with only 46% of institutions having specific emergency departments. Resource limitations were evident, as many facilities operated with minimal staff and outdated equipment. The communication systems revealed weaknesses, with just 54% having established protocols for handling emergency calls. Response times frequently surpassed international standards, which negatively affected patient outcomes. Additionally, the study found training deficiencies, with 68% of participants expressing a need for further education. Serbia’s EMRS are significantly impacted by systemic issues such as insufficient funding, disjointed service delivery, and inadequate training. Overcoming these challenges necessitates a comprehensive approach that includes boosting financial support, implementing standardized protocols, and fostering enhanced collaboration across different sectors.

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus (DM), often leading to hospitalization and non-traumatic amputations in the United States. It is particularly prevalent among individuals of Hispanic descent, with a prevalence rate exceeding 10% [1, 2]. The target of this study is to identify markers of severity of DFU. This work follows a strategy that uses first Electronic Health Records and machine learning to identify risk factors reported as measurements of blood tests, such as cholesterol, blood sugar, and specific proteins in a cohort of thousands of patients. After this RNA risk factors are predicted using samples of bulk and single-cell gene expression across patients with different levels of DFU severity. We found the Albu-min/Creatinine Ratio (ACR) test as a key factor in severe DFUs. We also found a cluster of cells that harbor high expression of Apolipoprotein E protein (APOE) and it is significant in non-healing DFU cases. Overall, the study shows how the use of hundreds of thousands of EHR can reduce and inform the search space of molecules in a few samples of bulk-single cell transcriptomics and identify molecular markers of severity of DFU.

Background: Amidst escalating climate change concerns, sustainable practices in healthcare logistics are critical. This study evaluates the ecological impact and efficiency of medical sample transportation using drones, combustion cars, and electric cars across diverse terrains and weather in Liechtenstein and Switzerland. Methods: We conducted a comparative analysis of CO2 emissions, transport distances, and delivery times for each transportation modality. Data collection involved multiple runs under various conditions, with CO2 emissions measured using onboard systems, and transport efficiency assessed via GPS tracking and time monitoring. Results: Combustion cars exhibited the highest average CO2 emissions at 159.5 grams per kilometer (g/km). Electric cars emitted significantly less, averaging 3.43 g/km, which is 2.15% of combustion car emissions. Drones were the most environmentally friendly, with an average emission of 0.09 g/km, representing 0.07% of combustion car emissions and 2.6% of electric car emissions. Drones also showed substantial transport efficiency, covering 17% shorter distances on flat terrain and 24% shorter in mountainous areas, with time savings ranging from 13% to 80%. Conclusion: This study highlights the potential of drone technology to revolutionize healthcare logistics by reducing environmental impact and optimizing operational efficiency. Integrating drones can significantly lower the carbon footprint, improve sample integrity, and enhance patient care, promoting a greener, more resilient healthcare system.

This paper presents a co-designed solution aimed at improving community healthcare assistance management. To enhance patients’ self-awareness of their health status, encouraging active participation in care process, and to support nurses in delivering patient-centered care, we have developed an innovative platform with a highly customized app. This platform was designed using a multi-disciplinary bottom-up approach. Patient data collection and processing facilitate the automation of care timeline planning, based on real time patients’ needs and the available resources. To achieve this goal different components have been considered: real-time health data collection and processing, patient care planning, decision support for nurses, secure communication for data transmission, and user-friendly interfaces to ensure easy access to platform functionalities.

This study presents a strategic approach to developing applications focused on implementing Large Language Models using the Langchain framework in Python. Three language models are highlighted: GPT-3.5 (turbo mode), LLaMA 2, and Vicuna 7B, each with their distinctive features and capabilities. The methodology used is described in detail, including data extraction from medical reports using zero-shot prompting data extraction techniques, interaction with language models, and structured storage of results. The performance of the models in data extraction is evaluated, presenting metrics such as precision, recall, and F1 score. The results demonstrate high model capability in extracting information, although areas for improvement are identified, particularly in data extraction precision. In conclusion, the efficacy of the models in extracting information from medical histories is not considerably acknowledged, with an emphasis on the importance of improving precision and increasing the volume of trained data for future research in healthcare digitalization.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

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.

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

C. auris is a newly emerging yeast, raising public health concerns due to its outbreak potential, lack of protocols for decontamination and isolation of patients or contacts, increased resistance to common antifungals and associated high mortality. This research aimed to describe the challenges related to identifying the outbreak, limiting further contamination, and treating affected individuals. We retrospectively analysed all cases of C. auris detected between October 2022 and August 2023, but our investigation focused on a three-month-long outbreak in the department of cardio-vascular surgery and the related intensive care unit. Along with isolated cases in different wards, we identified 13 patients who became infected or colonized in the same area and time, even though the epidemiological link could be traced only in 10 patients, according to the epidemiological investigation. In conclusion, our study emphasizes the substantial challenge encountered in clinical practice when attempting to diagnose and limit the spread of an outbreak. Therefore, it is crucial to promptly apply contact precaution measures and appropriate environmental cleaning, from the first case detected positive.