With the availability of wearable health monitoring sensor modules like 3-Lead Electrocardiogram (ECG), Pulse Oximeter (SpO2), Galvanic Skin Response (GSR), Hall effect sensor (for measuring Respiratory Rate), Blood Pressure and Temperature measuring and sensing elements, it has now become possible to device a composite health status monitoring kit that can measure vital signs and other physiological parameters pertaining to human health in real time. Traditionally, the physiological parameters along with vital signs related examination was possible only in a hospitalized or ambulatory environment, however due to advances in sensing and embedded system technology and miniaturization of data acquisition and processing elements health monitoring has become possible even when individuals remain engaged in their day to day activities at the convenience of space and location. The patients or individuals subject to monitoring may suffer from a traumatic experience due to their medical condition and may need emergent incidence response and the critical care team may have to prepare for the treatment only after the patient arrives, which often is too late, as in case of cardiac arrests or severe injuries. The research focused on real-time health status monitoring and trauma scoring using standard physiological parameters along with standard telemetry protocols to make the critical care team aware of an emergent situation and prepare for a medical emergency. Vital signs and physiological parameters (heart rate, temperature, respiratory rate, and blood pressure, SpO2) were measured in real time from human subjects non-invasively. In order to enable monitoring of the patients engaged in day to day activities, errors due to the motion were removed using stationary wavelet transform correction (correlation coefficient of 0.9 after correction) and signals from various sensors were denoised, filtered and were encoded in a format suitable for further data analysis. A composite sensor kit capable of monitoring vital signs and physiological parameters can be very useful in incident response when an individual undergoes a traumatic experience related to stroke, cardiac arrest, fits or even injury, as along with monitoring information the kit can calculate scores related to trauma like the Injury Severity Score (ISS), National Early Warning Signs (NEWS), Revised Trauma Score (RTS). Trauma Injury Severity Score (TRISS), Probability of Survival (Ps) score. An open access database of vital signs and physiological parameters from Physionet, MIMIC 2 Numerics (mimicdb/numerics) database was used to calculate NEWS and RTS and to generate correlation and regression models using the vital signs/physiological parameters for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). NEWS and RTS scores showed no significant correlation (r = 0.25, p<0.001) amongst themselves, however together NEWS and RTS showed significant correlation with Ps (blunt) (r = 0.70, p<0.001). RTS and Ps (blunt) scores showed some correlation (r = 0.63, p<0.001) and NEWS score showed significant correlation (r = 0.79, p<0.001) with Ps (blunt) scores. Furthermore, since individuals have to be monitored regardless of location, these kits have to have a built-in capability to locate the individual so that the incident response team can locate the individual based on Global Positioning System coordinates (GPS). A Quantum GIS (Geographical Information System) application using real-time GPS coordinates (OpenStreetMap coordinates) was used to calculate the shortest path using QGIS Network Analysis tool to demonstrate the calculation of shortest path and direction to locate the nearest service provider in shortest time. Along with locating the nearest healthcare service provider, it would help if the critical care team could be made aware of the physiological parameters and trauma scores using standard protocols accepted across the globe. The physiological parameters from the sensors along with the calculated trauma scores were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system and International Code of Diseases (ICD) codes and the trauma information was logged to Electronic Health Records (EHR) using Fast Health Interoperability Resources (FHIR) servers. FHIR servers provided interoperable web services to log the event information in real time. It could be concluded that analytical models trained on existing datasets can help in analyzing a traumatic experience or an injury and the information can be logged using a standard telemetry protocol as a telemedicine initiative. These scores enable the healthcare service providers to estimate the extent of trauma and prepare for medical emergency procedures and find applications in general and military healthcare.

Telemedicine has emerged as a transformative tool in modern healthcare, particularly during the COVID-19 pandemic, which catalyzed its rapid expansion. Despite its demonstrated potential in enhancing remote patient monitoring, AI-driven diagnostics, and telepsychiatry, widespread adoption of telemedicine remains constrained by persistent barriers. This review examines the current state of telemedicine, identifying major advancements alongside key challenges that impede its full integration into global healthcare systems. These barriers include the digital divide, regulatory inconsistencies across regions, and patient concerns about data security and the quality of virtual care. Addressing these issues requires a coordinated multi-sectoral approach, emphasizing infrastructure development, regulatory harmonization, patient education, and the incorporation of emerging technologies such as AI, blockchain, and virtual reality. By overcoming these challenges, telemedicine can be established as a cornerstone of equitable, accessible, and patient-centered healthcare worldwide.

In the dynamic arena of healthcare research, where the complexities of data often rival the intricacies of biological systems, the ability to model and analyze such multifaceted datasets is crucial. This comprehensive review delves into the evolution and application of Soft Sets and their extensions, including HyperSoft Sets, SuperHyperSoft Sets, IndetermSoft Sets, IndetermHyperSoft Sets, and TreeSoft Sets, in healthcare claims data analysis. These extensions address intricate challenges in data analysis, offering versatile frameworks for managing uncertainty and indeterminacy inherent in healthcare claims data. By exploring their definitions and applications, this review elucidates how these mathematical tools have evolved and their significance in advancing healthcare research and enhancing data analysis methodologies. Real-world examples underscore the implications of these tools, emphasizing their pivotal role in facilitating informed decision-making and knowledge discovery in healthcare. The review systematically examines various case studies and research findings to illustrate the practical utility of Soft Set extensions. Detailed analyses of real-world scenarios highlight advancements in processing complex healthcare data. The conclusions drawn from this analysis indicate that the adoption of Soft Sets and their extensions can significantly improve the accuracy and efficiency of healthcare data analysis, ultimately contributing to better healthcare outcomes and more informed policy-making. Future research directions are also discussed, suggesting further potential applications and developments in this field.

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, trustworthy, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including balancing interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends and providing valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, promoting the development of transparent, trustworthy, and effective AI-driven solutions.

Cancer, notably breast cancer, is a significant health concern globally, with a substantial burden in Asia, particularly in Pakistan. Late diagnosis exacerbates mortality rates, emphasizing the importance of robust screening programs. This article examines the effectiveness of breast cancer screening initiatives and intervention four years after initial pilot study that spanned 2015-2019, using data from the Islamabad Federal Breast Screening Centre, to assess readiness for national guideline implementation in Pakistan and beyond. Methods: This retrospective cross-sectional study, conducted at the Federal Breast Screening Center (FBSC), involved 5,580 patients undergoing mammography screening over an four-year period from 2015 to 2023. The study examined patients' data, including Breast Imaging Reporting and Data System (BIRADS) categories through double reading mammography and complementary ultrasound (USG) findings. Demographic information, including age, ethnicity, education status, socio-economic factors, breastfeeding history, breast density, and family history, was recorded to analyze its correlation with breast cancer diagnosis and subsequent management. Data analysis was performed using SPSS 2021. Results: The study included a total of 5580 patients who visited the Federal Breast Screening Centre from 2020 to 2023. The mean age of patients attending screenings from 2020 to 2023 was 48.50 ± 9.55 years, with the most common age group being 41-45 years 1,364 (24.4%), followed by 46-50 years 1,008 (18.1%). The ethnicity with the highest frequency was Punjabi 4,647 (83.3%). The distribution of patients across BI-RADS categories was as follows: BI-RADS 0 (n=175, 3.10%), BI-RADS I (n=1,933, 34.60%), BI-RADS II (n=1,691, 30.30%), BI-RADS III (n=642, 11.50%), BI-RADS IV (n=612, 11.00%), BI-RADS V (n=389, 7.00%), and BI-RADS VI (n=138, 2.50%). The prevalence rate of invasive breast cancer during these four years was 10.4%. Conclusion:In conclusion, breast cancer poses an escalating challenge associated with increasing mortality rates. Pakistan, alongside its South Asian counterparts, is witnessing a surge in cases, emphasizing the urgency for comprehensive interventions. Research endeavors such as this provide valuable insights into demographic profiles, aiding in the formulation of tailored guidelines. Moreover, the establishment of additional federal centers for screening is imperative to enhance patient access and mitigate the issue of delayed diagnoses, aligning Pakistan's healthcare infrastructure with that of developed nations.

Understanding HCPs COVID-19 vaccine concerns can provide critical insights to optimize success of the vaccine rollout program as they are a trusted source of health information to patients and the general population. We sought to understand reasons for health care providers’ COVID-19 vaccine hesitancy in Kenyatta National teaching and referral Hospital. From April to July 2021, we interviewed 60 HCPs purposively sampled from the Infectious Disease Unit (IDU) (n=20) and other departments (n=40). We analyzed data using inductive and deductive approaches to understand reasons for COVID-19 vaccine hesitancy. The HCPs had a median age of 37 years, (Interquartile range: [IQR 20.0-58.0]), 57% were female, 30% nurses among other cadres. Most HCPs cited low confidence in the vaccine such as mistrust in healthcare system and government, vaccine safety and efficacy concerns. Constraints such as uncertainty about the second vaccine dose availability, and misinformation caused hesitancy. Further, few were reluctant receiving the vaccine because of perceived herd immunity from a recent COVID-19 infection, and the need for more information before deciding. The study highlights the need to address any misinformation about vaccines safety and efficacy, and ensure accurate information is easily accessible to HCPs which can translate to higher vaccine uptake.

A Digital Twin can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. As virtual models, they can integrate with all the latest technologies, such as the Internet of Things (IoT), Cloud Computing and Artificial Intelligence (AI), among others. Digital twins have applications in a wide range of sectors, from manufacturing and engineering to healthcare. They have been used in managing healthcare facilities , streamlining care processes, personalizing treatments , and enhancing patient recovery . By analyzing data from sensors and other sources, healthcare professionals can develop virtual models of patients, organs, and human systems, experimenting with various strategies to identify the most effective approach. This approach can lead to more targeted and efficient therapies while reducing the risk of collateral effects . Digital twin technology can also be used to generate a virtual replica of a hospital to review operational strategies, capabilities, personnel, and care models to identify areas for improvement, predict future challenges, and optimize organizational strategies. The potential impact of this tool on our society and its well-being is quite significant. This article explores how digital twins are being used in healthcare. The article also introduces some discussions on the impact of this use and future research and technology development projections for the use of digital twins in the healthcare sector.

Background: This study assessed the nature of healthcare services offered in the David Umahi Federal University of Health Sciences health clinic and students' disposition to access the facility. Objectives: To assess the knowledge, attitude and practice of students in accessing healthcare services in the David Umahi Federal University of Health Sciences health clinic.Methods: Used cross-sectional design, a structured questionnaire of a five-point Likert scale as a veritable instrument for collecting the data from 50 student respondents. The questionnaire is made up of four key sections comprising student’s demographic parameters, knowledge, attitude and practice of both students and the clinic staff. Data collected was analyzed in a table of descriptive statistics to determine the total, range, mean and median, which were used to ascertain students’ utilization/accessibility disposition, perception of barriers to accessing healthcare service and overall viability of the clinic.Results: The mean value outcome of knowledge of students falls between 3.1 and 3.9, the mean value for attitude of students falls between 2.5 to 3.4 and 4.0, while the practice of students falls between mean values 2.6 and 3.1. Most of these outcomes represent poor or weak weight factor which requires several levels of intervention.Conclusion: This study highlights the overriding importance of the university healthcare clinic in maintaining a healthy study season among students of David Umahi Federal University of Health Sciences and their health-seeking behaviour. This helps to promote health, attract higher intakes and retain them in the institution till graduation.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

Personal protective equipment (P PE) rules must be respected and followed to the letter of healthcare professionals in Cameroon to avoid the transmission of infectious diseases. This research proposes solutions to the problems of non-adherence and non-compliance among healthcare workers in Cameroon towards PPE rules. Inadequate access to suitable PPE, discomfort when wearing PPE during poor quality, and a lack of training on correct PPE use are just a few of these obstacles amongst HCWs in Cameroon. Hospitals and healthcare facilities should prioritize offering thorough instruction on PPE use and pinpoint the need to adhere consistently to PPE rules to remove these obstacles. They should also ensure that all healthcare personnel have access to and a sufficient supply of the proper PPE. Through the organization of impromptu visits by surveillance teams to identify areas of support and training, there is a need for assistance from government bodies and public health groups in creating awareness of the security value of PPE use among healthcare professionals. There is a lack of PPE in health care settings in rural areas in Cameroon, thus a need for an adequate supply. Then can we put appropriate PPE policies and practices for HCWs in Cameroon, as they will keep to it.Healthcare professionals in Cameroon must comprehend that following PPE standards protects not just their selves from diseases but also their patients, coworkers, and communities. We are accountable for ensuring everyone's safety and well-being, and adhering to PPE regulations is an essential first step.

Despite the importance of influenza vaccination for healthcare workers (HCWs), their uptake remains challenging. This study aimed to measure influenza vaccine hesitancy among HCWs in Nakhon Phanom province, Thailand. A representative cross-sectional survey was conducted at six hospitals during August – September 2020 using a self-administered questionnaire. HCWs delaying getting influenza vaccines, accepting but unsure, or refusing with doubts were categorized as hesitant. Those accepting without any doubts were classified as non-hesitant. Vaccine hesitancy determinants were identified by multivariable logistic regression analysis. A total of 338 participants (97%) completed the questionnaires; most were female nurses working at district hospitals without pre-existing chronic medical conditions. Nearly 60% of participants (197/338) exhibited influenza vaccine hesitancy, and significant factors included age above 50 years adjusted odds ratio [aOR] 3.2, 95% CI 1.3-8.5), fair knowledge of influenza/vaccination (aOR 0.4, 95% CI 0.2-0.8), and negative influence of other HCW (High level– aOR 2.3, 95% CI 1.1-4.8; Moderate level- aOR 2.1, 95% CI 1.1-4.4). Imparting updated information to the HCW combined with positive guidance from influential peers may help reduce this hesitancy.

This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

The study conducts a comparative life cycle assessment (LCA) analysis to assess the environmental impact of two different manufacturing processes used to produce transparent dental aligners. The former method consists of thermoforming a polymeric disc over 3D printed, customized models, while the second, more innovative approach involves the direct printing of aligners using additive manufacturing (AM), specifically applying digital light processing (DLP) technology. The analysis results highlight how adopting direct printing through AM brings significant advantages in terms of environmental sustainability, thanks to the substantial reduction in raw materials and electricity consumption. These drops translate into decreased potential environmental impacts across all impact categories considered within the EF 3.1 method. Furthermore, lowering the amount of raw materials needed in the direct printing process contributes to a notable decrease in the overall volume of waste generated, emphasising the environmental benefits of this innovative technique.

BACKGROUND: Body mass index (BMI) is calculated by dividing a person's height in meters by their weight in kilograms and is always expressed in kg/m². BMI is a reliable risk indicator for various diseases that can develop due to a higher percentage of body fat. There is a possibility of developing certain medical conditions including heart disease, high blood pressure, type 2 diabetes, gallstones, breathing problems and some malignancies that increase with BMI. OBJECTIVE: This study seeks to identify the trends in the Body Mass Index (BMI) of women visiting Better Life Primary Health Care Centre in Ondo City, Ondo State, Nigeria for various healthcare services. METHODOLOGY: Seventy (70) women who participated in this study were randomly selected using systematic random sampling. Their necessary information was obtained using a prepared record sheet to collect their data. Results were analysed using SPSS version 21. RESULTS: The results show mean age of the respondents is 30.50 + 6.52921 21 (30%) were between 31 – 35 years, 21 (30%) were traders, 36 (51%) had tertiary education, 39 (55.7%) were between 151 – 160 cm in height. 32 (45.7%) were between 60 – 80 kg in weight and 30 (42.9%) had normal weight, respectively. CONCLUSION: Body Mass Index (BMI) calculation is an essential tool that can help in identifying one of the significant public health issues that leads to obesity; a predisposing factor to various medical conditions like hypertension, musculoskeletal issues, heart diseases, cancers, and lots more. It is therefore important to incorporate BMI calculation into routine checks to prevent or reduce health burdens that may arise from obesity.

Questionnaires are useful instruments for gathering responses to specific factual questions. However, the problems of questionnaire responses impede the effective use of the questionnaire. Some of these problems including non-responses, non-completion, issues of judgement, and social desirability such as information that the respondent is not willing to disclose need to be dealt with. The situation is more compounded in a scenario where an information security practice study in a typical hospital consists of broad categories of respondents and the survey findings are to be relied on to actually address information security compliance issues. This study, therefore, shares "pitfalls" to watch when preparing a questionnaire to measure the information security practice level in a hospital which is characterized by different respondents with varying domain knowledge such as knowledge in information security, information communication technology, and that of the domain knowledge of healthcare. A synergy of a conventional pretesting method and behaviour coding were therefore used to pretest the questionnaire. Questionnaire problems including a lack of understanding of the healthcare information systems’ structure of all hospitals, unclear questions, the insignificant difference between questions, problematic questions, inadequate questions, and complex terms were among the identified pitfalls to watch. Out of a total of 118 questionnaire items that were used in the pretesting, a total of 50 questionnaire items (representing 42%) were identified to have problems after the pretesting was conducted with a total of 36 respondents in behavior coding and 21 respondents in conventional pretesting.

Introduction: Blood transfusion involves the transfer of blood from donors to patients. A blood transfusion is carried out every 2 seconds in the US. It is made up of about 29000 units of red blood cells and is transfused every day in the US. When blood transfusion is done correctly, it can result in the saving of lives and the improvement of healthcare. However, it may also lead to immediate, late, delayed, and chronic complications. No previous studies have been conducted in Qatar to address this issue. Methods: This is a cross-sectional study intended to determine the knowledge, attitude, and practice toward blood transfusion among healthcare providers at Hamad Medical Corporation (HMC), which is the principal healthcare provider in Qatar. Participants between 18 and 25 years of age were selected for the research study. A 10-item online questionnaire that people can fill out on their own will be used to get the data needed for the analysis and meet the study's goals. Results: the analysis has indicated that facing negative reactions after blood transfusion and being worried about getting affected by any infection have a small positive association, with the specific values coming in at r = 0.317, p = 0.000. Fever after blood transfusion and feeling like refusing blood transfusion have a significant and moderate positive correlation, with the specific values coming in at r = 0.630 and p = 0.000. Conclusion: The findings of this study have helped us figure out how healthcare providers feel, what they know, and what they do during a blood transfusion.

Smart primary healthcare building facility services capture a new level of process and operational data through advanced monitoring, enabling experts to use the building facilities to produce significant and efficient healthcare service delivery within the individual spheres of influence. This study assessed the impact of IoT services on achieving smart primary healthcare building facilities in the rural area of South Africa to enhance primary healthcare delivery. The study identified three (3) basic constructs of IoT services that comprised the application of IoT location recognition and tracking services, the application of the IoT high-speed communication network-based services, and the application of IoT-based services. The study is quantitative, and a questionnaire was used to collect data from the project managers and healthcare practitioners working with the primary healthcare agency in South Africa. The study found a variable degree of impact between the three (3) IoT constructs and the achievement of primary healthcare building facility services in South Africa. The study recommends adopting IoT essential services for achieving smart primary healthcare building facility services in the rural areas of South Africa and other developing countries facing similar primary healthcare delivery challenges.

Recent reports have it that over 85\% of data breaches are still caused by the human element, of which healthcare is one of the suitable organizations mostly targeted by cybercriminals. The work of healthcare staff is often associated with high workloads, high emergency cases, and a broad range of psychological, social, and cultural factors. The significance of these factors could undermine conscious care information security (IS) practice leading to serious violations. This study comprehensively examined the correlation between the psycho-social-cultural factors, work factors with IS and privacy behaviour in a hospital that has fully adopted electronic health records (EHR) management system. The findings are to facilitate the decision-making process towards improving the cyber-security practice in healthcare. A quantitative approach was adopted where we collected responses from 212 healthcare staff through an online questionnaire survey. A broad range of constructs was selected from psychological, social, cultural perception and work factors based on earlier review work. These were therefore related to some security practices, to assess the IS knowledge, attitude and behaviour gaps among healthcare staff in a comprehensive way. From the study, IS self-reported conscious care behaviour (ISCCB) risk was relatively higher as compared to information security knowledge (ISK) risks and information security attitude (ISA) risk. Furthermore, the study revealed that work emergency has a positive correlation with ISCCB (r=1.95, p-value =0.001) risk. Conscientiousness also had positive correlation with ISCCB risk (r=0.157, p-value=0.05) however agreeableness negatively correlated with ISK risk (r=-0.166, p-value =0.05), and ISA risk (r=-0.140, p-value =0.05). Based on these findings, intrinsic and extrinsic motivation methods combined with cutting-edge technologies can be explored to discourage IS risks behaviours while enhancing conscious care security practice.

Background: To determine vitamin D supplementation frequency among infants, factors that influence adherence, and reasons for discontinuation of initiated vitamin D. Methods: This cross-sectional study was conducted using a questionnaire administered to the mothers via a face-to-face interview on 560 infants aged from 1 to 24 months admitted to outpatient clinics from June to December 2017. Results: A total of 351 infants were administered vitamin D, and the rate of supplementation in the first year of life was 83%, while it was only 28% between 13 and 24 months. The rate of vitamin D supplementation was higher among infants who were exclusively formula-fed (p<.05). When the data were analyzed using logistic regression analysis, only visit family physicians seems to be a statistically significant independent variable in increasing supplementation (p<.05). Compared with family refusal, the rate of discontinuation of vitamin D by the healthcare providers was higher after the first year of life (p<.05). The rates of vitamin D discontinuation by healthcare providers, especially by nurses who considered the duration of supplementation adequate, were statistically significantly higher when compared with the fontanel closure and other reasons (p<0.05). Conclusions: The rate of vitamin D supplementation was higher among families who visited family physicians, which suggests the importance of well-baby visits. Since vitamin D supplementation was less common among exclusively breastfed infants, mothers should be educated. Healthcare professionals need further education about the importance of vitamin D supplementation and indications for discontinuation.

Health literacy is an indicator of a society’s ability to make better health judgement for themselves and the people around them. This study investigates the prevalence of health literacy among Malaysian adults and provides an overall picture of the current health literacy state of the society. The study also highlights socio-demographics markers of communities with limited health literacy which may warrant future intervention. A population based self-administered survey using the Health Literacy Survey Malaysian Questionnaire18 (HLS-M-Q18) instrument was conducted as part of the National Health Morbidity Survey 2019 in Malaysia. The nationwide survey utilized two-staged stratified random sampling method. A sample of 9478 individuals aged 18 and above participated in the study. The health literacy score was divided into three levels; limited, sufficient and excellent. Findings showed that majority Malaysian population has sufficient health literacy level, albeit leaning towards the lower end of the category with an average score of 35.5. The limited health literacy groups are associated with respondents with older age, lower education level and lower household income. Overall health literacy state for Malaysia is categorized at a lower sufficient level. Health literacy improvements should focus on communities with limited health literacy level.

Background: Disability and caste are two different forms of oppression, however Dalits and people with disabilities commonly face similar types of marginalities. Dalit women with disabilities may experience double discrimination because of the intersectionality of disability and caste. This study examines whether the disability and caste identity of women together affects and compounds the utilization of maternal healthcare services. Methods: A cross-sectional survey was conducted using a semi-structured questionnaire among a total of 354 Dalit and non-Dalit women, with and without a disability aged between 15 – 49 years. Maternal healthcare service utilization was assessed by Ante-Natal Care (ANC), health facility (HF) delivery, and Post-Natal Care (PNC) during the last pregnancy. Logistic regression was performed to detect the predictors of service utilization and identify whether disability and caste were associated with service utilization. First, disability and caste were fitted separately in models. Secondly, the intersectionality of disability and caste was tested by the inclusion of disability*caste interaction term. Finally, the confounding effect of socio-demographic factors was investigated. Results: Out of surveyed women, 73% had 4+ ANC visits, 65% had HF delivery and 29% had a PNC visit during their last pregnancy. Women with a disability had lower odds of HF delivery (OR 0.50, CI 0.30 – 0.84) and PNC (OR 0.47, CI 0.25 – 0.88) than women without a disability. Adjustment for women’s age and household wealth explained associations in HF delivery by women with disabilities. There was no association between caste and service utilization. Disability overrode caste and there was no other evidence of effect modification by women’s caste status in the utilization. However, a weak interaction effect in the utilization of ANC services was found in the caste group by their education (OR 0.19, 95% CI 0.05 – 0.74). Conclusions: Disabled women – whether Dalit or non-Dalit - had lower rates of utilizing all maternal healthcare services than non-disabled women. However, Dalit women with disabilities were more likely to receive PNC than non-Dalit women with disabilities. Increasing equity in maternal healthcare service utilization requires that traditional approaches to service development and program intervention to be re-examined and more nuanced interventions considered to ensure improved access and outcome among all vulnerable groups.

While it is not new that Nigeria is challenged by a huge infrastructure deficit, the COVID-19 pandemic has exposed the country’s comatose healthcare system. Given the country’s dwindling revenue, massive debt profile and the inability of the public-sector to efficiently manage public facilities in the country, this paper examines how the public-private partnership model of infrastructure procurement can be deployed as a solution for Nigeria’s healthcare crisis. In addition to the above, this paper takes a look at how a partnership with the private sector can aid Nigeria’s quest towards achieving healthcare-related Sustainable Development Goals. The paper also considers two healthcare-based projects as case studies to serve as lessons for future projects in the country. Among others, the paper recommends a holistic long-term solution for the country’s healthcare needs.

Implications of the novel usage adoption of the internet of things in various sectors of works and life are researched and documented at pace. This is related to the overall high rate at which new technologies are adopted in modern society. Healthcare is a vital aspect of everyday activities and as such overlaps with the increasingly important role played by use of the internet and associated technologies. The purpose of this review article is to draw attention to the potential social, ethical, legal and professional limitations to using IoT in the context of healthcare. The social and ethical aspects in particular, focus on IoT usage in care of the elderly with relevant case studies as reference.

With an increasing demand for quality of care and lower costs, hospitals are looking for industry-based methods to improve efficiency in their processes. This study aims to reduce waste in a public hospital in Mexico by improving the medical supply process for the operating room. To this end, a lean healthcare (LH) implementation following the DMAIC approach (Define-Measure-Analyze-Improve-Control) is carried out. We analyze the value stream of the supply process, including main surgical procedures and their related medical supplies, and identify different causes of inefficiency, which are evaluated and controlled through different tools, including a value stream map, Kanban, and the 5S program. As a result, five types of waste were reduced. Over-processing requests were reduced by 15.3%, defective identification numbers were reduced by 46.5%, redundant processing was improved by 94.8%, near 2.8% of the unnecessary inventory was reduced, and transportation waste was reduced by up to 16.7%. Finally, the lead-time for the main supplies was reduced by 33 days. This work demonstrates that LH and DMAIC are effective in reducing waste and are highly conducive to improving sustainability in healthcare processes. Moreover, it provides practical insights for practitioners regarding the implementation of LH in public hospitals in developing countries.

The burnout syndrome, which affects so many healthcare workers, has recently awakened wide interest due to the severe repercussions related to its appearance. Even though job factors are determinant to its development, not all individuals exposed to the same work conditions show burnout, which demonstrates the importance of individual variables such as personality. The purpose of this study was to determine personality characteristics of a sample of nursing professionals based on the Big Five model, and then, having determined the personality profiles, analyze the differences in burnout and engagement based on those profiles. The sample was made up of 1236 nurses. An ad hoc questionnaire was prepared to collect the sociodemographic data, and the Brief Burnout Questionnaire, the Utrecht Work Engagement Scale and the Big Five Inventory-10 were used. The results showed that the existence of burnout in this group of workers, is associated negatively with extraversion, agreeableness, conscientiousness and openness to experience, and positively with the neuroticism personality trait. These personality factors showed the opposite pattern with regard to engagement. Three different personality profiles were also found in nursing personnel, in which professionals who had a profile marked by strong neuroticism and low scores on the rest of the personality traits where those who were most affected by burnout.

Blockchain technology enables a decentralized and distributed environment with no need for a central authority. Transactions are simultaneously secure and trustworthy due to the use of cryptographic principles. In recent years, blockchain technology has become very trendy and penetrated different domains, mostly due to the popularity of cryptocurrencies. One field where blockchain technology has tremendous potential is healthcare, due to the need for a more patient-centric approach to healthcare systems and to connect disparate systems and increase the accuracy of electronic healthcare records (EHRs). In this systematic review, an analysis of state-of-the-art blockchain research in the field of healthcare is conducted. The aim is to reveal the potential applications of the technology and to highlight the challenges and possible directions of blockchain research in healthcare. First, background information is discussed, followed by a description of the exact methodology used in this paper. Next, an analysis of the results is given, which includes a bibliometric overview, an analysis of gathered data and its properties, and the results of a literature quality assessment. Lastly, there is a discussion of the results from the analysis. The findings indicate that blockchain technology research in healthcare is increasing and it is mostly used for data sharing, managing health records and access control. Other scenarios are very rare. Most research is aimed at presenting novel structural designs in the form of frameworks, architectures or models. Findings also show that technical details about the used blockchain elements are not given in most of the analyzed publications and that most research does not present any prototype implementation or implementation details. Often even with a prototype implementation, no details about blockchain elements are given.

Interdisciplinary conferences play a vital role in advancing explainable Artificial Intelligence (AI) within increasingly intersecting healthcare and computational biology arenas. Challenges result-ing from algorithmic bias and the necessity for domain-specific Explainable AI (XAI) techniques underscore the increasing significance of transparent and interpretable AI systems used in patient care. Collaborative efforts between clinicians, data scientists, and various stakeholders are nec-essary to innovate solutions tailored to healthcare needs. Specialized conferences may foster these interdisciplinary interactions, providing platforms for knowledge exchange, networking, and collaborative partnerships. The legal, ethical, and societal dimensions of medical AI advocate for an integrative approach that aligns technological advancements with patient-centered care val-ues. The role of specialized conferences is therefore essential for shaping future directions in ex-plainable AI and computational biology that contribute to improved patient outcomes and healthcare innovations.

The COVID-19 pandemic had a significant impact on the mental health of individuals globally, and primary healthcare (PHC) nurses play a critical role in providing mental healthcare services. However, limited research has explored the experiences of PHC nurses in providing mental healthcare services during the COVID-19 pandemic. This study explored the experiences of PHC nurses in providing mental healthcare services during the pandemic in Durban, South Africa. The aim was to identify the challenges faced by healthcare providers and the potential for innovative approaches to improve access to care. A qualitative, exploratory design guided the study, and data were collected through in-depth interviews with twelve PHC nurses purposively selected. Thematic analysis was used to analyse the data. Findings from interviews with primary healthcare nurses reveal that the pandemic exacerbated existing challenges, including medication adherence issues, fear and uncertainty among patients, vaccine hesitancy, decreased clinic visits, and the mental and emotional toll on both patients and healthcare workers. PHC nurses adapted their services by increasing outreach efforts, prioritising patient care, and utilising technology and non-governmental organisations’ (NGO) support. Challenges included reduced patient visits, complexities in healthcare provision, and lack of adequate support. Positive changes observed include increased mental health awareness among healthcare professionals and younger generations. Recommendations include implementing outreach and awareness campaigns, providing accurate information about COVID-19 and vaccinations, and promoting cultural sensitivity in mental healthcare provision.

Healthcare is undergoing a technological transformation, particularly in robotic surgery and arti-ficial intelligence (AI) applied to cytology. These innovations are improving the precision and effi-ciency of both surgical procedures and laboratory diagnostics. Robotic systems, such as the Da Vinci, enable surgeons to perform complex procedures with enhanced control and precision, leading to minimally invasive surgeries with reduced recovery times. This technology has signifi-cantly impacted urological, cardiac, and gastrointestinal surgeries, and its use is expected to grow. In cytology, AI automates the analysis of cell samples, leading to faster and more accurate diag-noses, especially in detecting diseases like cervical cancer. AI reduces errors and enhances diag-nostic accuracy by identifying complex patterns in cellular images. Predictive analytics, driven by big data, also allows for personalized treatments based on patient-specific data. However, these technologies face challenges, including high costs, ethical concerns, data privacy issues, and resistance from healthcare professionals. Continuous training is essential for medical personnel to effectively adopt these tools. Furthermore, the rise of telemedicine and telesurgery, supported by advancements in 5G, offers new opportunities for remote care but also introduces risks, such as depersonalization of medical care and technological barriers. Ultimately, while robotic surgery and AI hold great promise for improving clinical outcomes, their successful integration will depend on addressing these challenges and ensuring that healthcare professionals are adequately prepared for the digital future.

Introduction and Objectives: Multimorbidity, defined as the coexistence of two or more chronic conditions, poses significant challenges in healthcare by affecting patient outcomes and increasing costs. This study aimed to evaluate multimorbidity's impact on patients with genitourinary cancer (GUC) in Chile, focusing on prevalent comorbidities, their combinations, and their association with hospitalization severity. Materials and Methods: A retrospective, population-based study was conducted using data from the Fondo Nacional de Salud (FONASA) in Chile, including patients with bladder, prostate, kidney, and testicular cancer between 2019 and 2021. Diagnosis-related group (DRG) data were used to analyze comorbidity prevalence, hospitalization type (elective vs. emergency), severity (moderate/major vs. minor/none), length of stay, and associated costs. Results: Among 4,028,597 hospital events, 11.6% were related to GUC, involving 18,792 patients. Multimorbidity was present in 67.3% of patients, with hypertension and diabetes being the most common comorbidities. These patients accounted for 69.1% of total GUC care costs. Hospital mortality was higher in multimorbid patients (7.5% vs. 3.7%; P<0.001), who also had longer stays (mean 8 vs. 5 days). Most patients were admitted electively (60.3%), while 39.7% were admitted through the emergency room. Patients with multimorbidity had higher rates of moderate/major severity hospitalizations compared to those without (56.1% vs. 32.5%; P<0.001). Conclusions: In Chile, multimorbidity in GUC patients is linked to increased costs, longer hospital stays, higher mortality, and greater hospitalization severity. Comprehensive care strategies are needed to improve outcomes and reduce healthcare system burdens.

A recent publication regarding a March 2023 Google Scholar search found Japan unique in healthcare providers’ reaction to COVID-19 vaccines. According to one article, negative sentiment by healthcare providers toward vaccines defines the research in this area, with concerns about side effects outweighing worries regarding infection. This current study investigates the anomaly through a scoping review of “COVID-19, Japan, vaccine hesitancy” in six relevant databases: Cochrane COVID-19 Study Register, OVID, ProQuest, PubMed, Scopus, and Web of Science. By following PRISMA guidelines, the intent is a more thorough examination of this unusual evalua-tion of COVID-19 vaccines by Japanese healthcare providers than offered by the March 2023 search. The finding is that of the 997 returns, only four were relevant for assessment inclusion. Of these four, three, published in Vaccines, support vaccine hesitancy in Japanese healthcare providers and their becoming more so regarding a subsequent dose of the vaccine. One article published in BMJ Open did not find this. Yet, the design of none of the studies was specific to investigating vaccine hesitancy in Japanese healthcare providers, making the conclusion questionable. Suggest-ed future research directions include investigating the primacy of those databases searched and the need for timeliness in examining COVID-19 anomalies.

Background: In 2023, ChatGPT-4 has been impactful across several sectors including healthcare, where it aids in medical information analysis and education. Electronic Patient-Reported Outcomes (ePROs) play a crucial role in monitoring cancer patients’ post-treatment symptoms, enabling early in-terventions. However, managing the voluminous ePRO data presents significant challenges. This study assesses the feasibility of utilizing ChatGPT-4 for analyzing side effect data from ePROs. Methods: Thirty cancer patients were consecutively collected via a web-based ePRO platform, reporting side effects over 4 weeks. ChatGPT-4, simulating oncologists, dietitians, and nurses, analyzed this data and offered improvement suggestions, which were then reviewed by professionals in those fields. Results: Two oncologists, two dieticians and two nurses evaluated the AI’s performance across roles with 540 reviews. ChatGPT-4 excelled in data accuracy and completeness, and was noted for its empathy and support, enhancing communication and reducing caregiver stress. It was particularly effective as a dietician. Discussion: ChatGPT-4 shows promise in processing ePRO data with high accuracy, suggesting that AI can enhance clinical decision-making. Nevertheless, its limitations in emotional engagement highlight the need for AI to supplement, not replace, human care. Emphasizing the role of dietitians underscores the importance of nutritional advice in cancer care. Future studies will further explore the feasible application of AI and ePRO in cancer care clinical processes.

Robotic systems are increasingly being used in healthcare to improve patient care and support healthcare professionals. These robots are especially effective at performing repetitive tasks. In complex cancer cases, robots can enhance surgical precision and efficiency, resulting in reduced pain for patients and a lower risk of complications. Robot-assisted surgery offers high-quality 3D imaging, improved clarity, and increased efficiency. This review paper examines the application of Artificial Intelligence (AI) and robotic systems in the healthcare domain, specifically focusing on the interaction among operators, patients, and robots. The goal is to promote collaboration and improve healthcare provision through human-robot interaction. We considered already published articles by relevance, to identify the primary safety issues that need addressing for the integration of AI and robots in hospitals. Concluding with insights into understanding the focal points of social errors in Human-Robot Interaction (HRI) is crucial for mitigating these errors. While current technologies have reached a certain level of maturity, there are still numerous challenges in utilizing robot-assisted clinical practice, including technological, safety, clinical, financial, insurance, psychological, social, ethical, and legal issues. In addition, there is an urgent issue that needs to be addressed: the alignment of values in artificial intelligence, ensuring that AI systems reflect ethical principles and human values is crucial to avoid negative consequences such as bias, discrimination, and prejudicial decisions. Resolving this complex issue requires collaboration among clinical staff, researchers, programmers, and political decision-makers from various fields.

The role of Physician Assistant emerged in 1960s America and their number has increased to become a significant component of the health care workforce in that country. On the eve of a new policy regulating Physician Associates in the United Kingdom, the role of key actors in shaping the development of regulatory policies in the two countries is critically compared against a backdrop of very different political contexts and health care systems, to reveal the factors that might help or hinder the deployment of Physician Associates/Assistants and ameliorate workforce shortages.

Objectives: To analyse and detail surgical process improvement activities that achieve the highest economic impact. Methods: Over 4 years, a team of technicians and healthcare professionals implemented a set of Lean surgical process improvement projects at Vall d’Hebron University Hospital (VHUH), Barcelona, Spain. These projects now form part of the daily routine in the surgical process. The economic impact on the hospital's surgical activity budget was analysed. Results: Process improvements have led to annual operational savings of over € 8.5 million. These improvements include better patient flow, better management of information between healthcare professionals, and improved logistic circuits. Conclusions: The current cultural shift towards process management in large hospitals implies shifting towards results-based healthcare, patient-perceived value (VBHC) and value-added payment. A Lean project implementation process requires long-term stability. The reason a considerable number of projects fail to complete process improvement projects is the difficulty involved in establishing the project and improving management routines. Few studies in the literature have investigated the economic impact of implementing Lean management a posteriori, and even fewer have examined actual cases. In this real case study, changes to surgical block management were initiated from zero. After being carefully thought through and designed, changes were carried out and subsequently analysed.

Solid waste management is challenging in many countries, especially developing economies. The impacts of the pandemic continue to be felt and have indicated secondary impacts on waste management. This work draws on data on household solid waste (HSW), healthcare waste (HCW), and recyclables collected in the Rio de Janeiro municipality, analyzed from January 2018 to December 2022. As expected, the collection of HCW in Rio de Janeiro municipality experienced a noticeable increase in 2020 and 2021, with an average of 46.9 tons and 54.3 tons collected, respectively. Notably, 2021 saw a significant 30% increase compared to 2019 (p-value<0.001). No significant difference was observed in recyclables collected from 2018 to 2021. The average recyclable amount was 3,638 and 3,747 tons per month in 2020 and 2021. In contrast, the monthly amount was equal to 4,819.5 tons in 2022, representing an increase higher than 32% compared to previous years (p-value<0.05). Our findings can be used to adapt and improve waste systems to cope with similar emergency scenarios in the future, serving as a template for municipalities with similar challenges.

Background Healthcare workers (HCWs) have suffered considerable morbidity and mortality during the COVID-19 pandemic. Few data on COVID-19 vaccine effectiveness (VE) are available from middle-income countries in Europe. We evaluated primary series COVID-19 VE against laboratory-confirmed COVID-19 among HCWs in Georgia. Methods HCWs in six hospitals in Georgia were invited to enroll in a prospective cohort study conducted during March 19–December 5, 2021. Participants completed weekly symptom questionnaires. Symptomatic HCWs were tested by RT-PCR and/or rapid antigen test (RAT), and participants were routinely tested for SARS-CoV-2 by RT-PCR or RAT, regardless of symptoms. Serology was collected at enrolment, and quarterly thereafter, and tested by electrochemiluminescence immunoassay for SARS-CoV-2 antibodies. We defined primary series vaccination as two doses of COVID-19 vaccine received ≥14 days before symptom onset. We estimated VE as (1-hazard ratio)*100 using a Cox proportional hazards model with vaccination status as a time-varying covariate. Estimates were adjusted by potential confounders that changed the VE estimate by more than 5%, according to the change-in-estimate approach. Results Overall, 1561/3849 (41%) eligible HCWs enrolled and were included in the analysis. The median age was 40 (IQR: 30-53), 1318 (84%) were female, and 1003 (64%) had laboratory evidence of prior SARS-Cov-2 infection. At enrolment, 1300 (83%) were unvaccinated; By study end, 1082 (62%) had completed a primary vaccine series (69% BNT162b2 (Pfizer-BioNTech); 22% BBIBP-CorV (Sinopharm); 9% other). During the study period, 191(12%) participants had a new PCR- or RAT-confirmed symptomatic SARS-CoV-2 infection. VEa gainst PCR- or RAT- confirmed symptomatic SARS-CoV-2 infection was 58 (95%CI: 41; 70) for all primary series vaccinations, 68% (95%CI: 51; 79) for BNT162b2, and 40% (95%CI: 1; 64) for BBIBP-CorV vaccines. Among previously infected HCWs, VE was 58% (95%CI: 11; 80). VE against medically attended COVID-19 was 52% (95%CI: 28; 68), and VE against hospitalization was 69% (95% CI: 36; 85). During the period of predominant Delta variant circulation (July-December 2021), VE against symptomatic COVID-19 was 52% (95%CI: 30; 66). Conclusions Primary series vaccination with BNT162b2 and BBIBP-CorV was effective at preventing COVID-19 among HCWs, most of whom had previous infection, during a period of mainly Delta circulation. Our results support the utility of COVID-19 primary vaccine series, and the importance of increasing coverage, even among previously infected individuals.

Healthcare workers have atypical working schedule and are exposed to experienced stress that led to poor sleep health and frequent mental complaints, particularly since the Covid-19 crisis. We conducted a cross-sectional study to determine the prevalence of poor sleep hygiene and mental complaints and their associations among healthcare workers. Usual sleep-wake timings were ex-plored during workdays and free days and used to compute sleep duration, sleep efficiency and social jetlag. Insomnia, sleepiness, anxiety and depressive symptoms, and fatigue were calculated using validated scales. A total of 1,562 participants were included (80.5% of women and mean age of 40.0). Among them, 25.9% slept less than 6 hours, 24.3% had a poor sleep efficiency, and 11.5% reported a social jetlag. A total of 33.9% of participants reported insomnia, 45.1% reported EDS, 13.1% reported fatigue, 16.5% reported depression and 35.7% reported anxiety. After ad-justment, sleep duration and sleep efficiency were associated with mental complaints. Social jetlag was associated with significant insomnia but not with anxiety or depression symptoms. Healthcare workers have a high prevalence of poor sleep hygiene and mental complaints. The promotion of sleep health through behavioral sleep strategies should be encouraged to ensure good health for these professionals.

This comprehensive review examines the critical issue of antimicrobial resistance (AMR), a growing global concern with profound societal implications. This article presents an overview of the causes of AMR, including the misuse and overuse of antibiotics in healthcare, agriculture, and veterinary practices. Various conventional and advanced detection methodologies have been explored, highlighting the importance of the accurate and timely identification of resistant strains. The challenges associated with tackling AMR, encompassing scientific, economic, and regulatory aspects have been discussed in depth. In addition, this article sheds light on the far-reaching societal implications of AMR, including increased healthcare costs, treatment complexities, and potential disruptions to healthcare systems. This emphasizes the urgency of adopting proactive strategies to combat AMR and underscores the need for global cooperation, policy interventions and public awareness campaigns. By unraveling the multifaceted dimensions of AMR, this review aims to enhance our understanding, stimulate further research, and promote concerted efforts to mitigate the threat of antimicrobial resistance.

The need for systemic healthcare policies to systematically eliminate NTDs globally and in India has been stressed for more than two decades. Yet, the present policies and the research on them do not meet the need. We present an ontological framework, a research roadmap, and a policy brief to address the gap. The ontology clearly, concisely, and comprehensively represents the combinations of diseases, the objectives regarding the diseases, the entities to address them, the outcomes sought, and the potential policy instruments to invoke. The paper explicates the state-of-the-policies and state-of-the-research on policies to eliminate NTDs in India. It highlights the significant gaps in both. Last, it presents a set of systemic policies congruent with the ontology to systematically address the gaps. The recommendations are aligned with the present research, policies, practices, and recommendations in India and of WHO, UN agencies, and other similar bodies. The approach can be generalized to provide roadmaps for other countries facing a similar challenge and for other diseases of similar complexity. The roadmaps, with continuous feedback and learning, can help navigate the challenge efficiently and effectively.

Introduction Prostate Cancer (PCa) exhibits one of the widest racial and socioeconomic disparities. PCa disparities have also been widely linked to location as living in more deprived regions was associated with lower healthcare access and worse outcomes. This study aims to examine PCa survival across various US counties in function of different socioeconomic profiles and discuss the role of potential intermediary factors. Methods The SEER database linked to county-level SES was utilized. Five-year PCa-specific survival using the Kaplan Meier method was performed for 5 racial/ethnic categories in function of SES quintiles. Multilevel Cox proportional hazards regression was performed to assess the relationship between county-level SES and PCa survival. Multivariate regression analysis was performed to examine the role of healthcare utilization and severity. Results 279,000 PCa records were extracted, 5-year PCa-specific survival was 94%. Overall, living in counties with worst poverty/income quintile and highest proportions of foreign-born/language-isolated increased PCa mortality by 23% each. No association was observed with county-level High-School education, while Bachelor’s-level education decreased mortality risk by 23%. Associations varied considerably upon racial/ethnic stratification. Multilevel analyses showed varying contributions of individual and area-level factors to survival within minorities. The relationship between SES and PCa survival appeared to be influenced by healthcare utilization and disease stage/grade. Discussion Racial/ethnic categories responded differently under similar county-level SES and individual-level factors to the point where disparities reversed in Hispanics. The inclusion of Healthcare utilization and severity factors may provide partial early support for their role as intermediaries. Healthcare access (insurance) might not necessarily be associated with better PCa survival, through performing biopsy and or/surgery. County-level education plays an important role in PCa decision-making as it might elucidate discussions of other non-invasive management options. Conclusion Findings of this study demonstrate that interventions need to be tailored according to each group's needs. This potentially informs the focus of public health efforts in terms of planning and prioritizing. This study could also direct further research delving into pathways between area-level characteristics with PCa survival.

Innovation development in healthcare has paved the path towards the improvement in system efficiency, patient care and cost-effective healthcare services, increasing the overall efficiency of the healthcare system in a significant manner. Electronic medical record system has fostered interoperability and collaboration in healthcare departments by enabling different systems to exchange and use patient data. This trend has been accelerated dominantly by the pandemic, leading to increased use of telemedicine, referring specifically to the use of electronic communication to provide healthcare services remotely. Moreover, an increased prevalence towards the use of artificial intelligence and machine learning in healthcare has contributed effectively towards the rigorous and informed analysis of large amounts of data as well as to identify patterns or trends that may be useful for improving care delivery or identifying potential issues. A unified electronic medical record System that will generate competitive opportunities and success prospects with reference to enhanced interoperability in US healthcare departments, can contribute towards the improvement in the quality and efficiency of care by making it easier for healthcare providers to access and share patient information as well as reducing the risk of errors in effective patient care.

An artificial intelligence (AI)-based conversational large language model (LLM) was launched in November 2022 namely, “ChatGPT”. Despite the wide array of potential applications of LLMs in healthcare education, research and practice, several valid concerns were raised. The current systematic review aimed to investigate the possible utility of ChatGPT and to highlight its limitations in healthcare education, research and practice. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar under the term “ChatGPT”. Eligibility criteria included the published research or preprints of any type that discussed ChatGPT in the context of healthcare education, research and practice. A total of 280 records were identified, and following full screening, a total of 60 records were eligible for inclusion. Benefits/applications of ChatGPT were cited in 51/60 (85.0%) records with the most common being the utility in scientific writing followed by benefits in healthcare research (efficient analysis of massive datasets, code generation and rapid concise literature reviews besides utility in drug discovery and development). Benefits in healthcare practice included cost saving, documentation, personalized medicine and improved health literacy. Concerns/possible risks of ChatGPT use were expressed in 58/60 (96.7%) records with the most common being the ethical issues including the risk of bias, plagiarism, copyright issues, transparency issues, legal issues, lack of originality, incorrect responses, limited knowledge, and inaccurate citations. Despite the promising applications of ChatGPT which can result in paradigm shifts in healthcare education, research and practice, the embrace of this application should be done with extreme caution. Specific applications of ChatGPT in health education include the promising utility in personalized learning tools and shift towards more focus on critical thinking and problem-based learning. In healthcare practice, ChatGPT can be valuable for streamlining the workflow and refining personalized medicine. Saving time for the focus on experimental design and enhancing research equity and versatility are the benefits in scientific research. Regarding authorship in scientific articles, as it currently stands, ChatGPT does not qualify to be listed as an author unless the ICJME/COPE guidelines are revised and amended. An initiative involving all stakeholders involved in healthcare education, research and practice is urgently needed to set a code of ethics and conduct on the responsible practices involving ChatGPT among other LLMs.

Over the years, researchers have been developing different ventilation strategies as a potential solution for controlling infectious airborne transmissions in healthcare facilities. The effects of ventilation systems on airflow characteristics have been extensively discussed among researchers since SARS-CoV-2 outbreak in 2019. Based on a bibliometric study of 154 publications from 1970 to 2021 retrieved from the Web of Science (WoS) database, this paper examines the research landscape on the effects of ventilation strategies on airflow analysis in healthcare facilities. Results show that China is the most productive country due to its significant contributions to the top prolific authors and funding agencies, which have promoted the research development of the topic. Several research hotspots were identified through keyword co-occurrence analysis. Cluster 1 highlights the ventilation studies involving ventilation layouts and ventilation rates in healthcare facilities. Cluster 2 underlines the computational analysis of ventilation performance for reducing the exposure risk of occupants, which promotes the development of infection control measures. Cluster 3 strengthens the knowledge of the emission and dispersion characteristics of respiratory droplets in healthcare facilities. Overall, this paper highlights the future direction of the current research field other than identifying strategic collaborators and suitable publication outlets for interested researchers.

Patients satisfaction with fast and high-quality services is the most important in the healthcare settings and specially in outpatient departments and private clinics. High quality services on health sector are indicated by continuous quality improvement (CQI). CQI is a progressive incremental process focused on safety of all participants, outcomes, systematic process, regulated and improved working environment at the later than earlier stages. Among the various suggested strategies, we adopted the patents feedback to improve the quality of services our clinic. All of the patient visited hospital were sent a massage on mobile and requested to provide their feedback on our services. Roughly around 5% of all visitors responded and filled up questionnaire. Majority of them were satisfied with the services in various department. However, they provided us some suggestions to identify the further gaps and improve the services in improving patients experience at our clinic. We considered their feedback, identified problems, redesigned the policies and implemented. After implementation of new strategies, we preliminary again evaluated the patients’ feedback on our services. Patients feedback explores that optimized methods of services for the has considerably increased the patients’ satisfaction. Taken together, our this shows that patients’ feedback is very important factor to improve patients’ experience at outpatient clinics using continuous quality improvement tools. For that reason, this study would serve as reference for public health stack holders, administrators and researchers.

Background: Fluoride toothpaste (FT) has recently been included in the World Health Organization’s (WHO) Model List of Essential Medicines. Whereas it is essential for preventing dental caries, its current affordability around the globe remains unclear. This study aimed to analyse the affordability of FT in as many as possible countries worldwide. Methods: A standardized protocol was developed to collect country-specific information about the characteristics of the cheapest FT at a common point of purchase. 82 members of the WHO Global Oral Health Network of Chief Dental Officers (CDOs), directors of WHO Collaborative Centres and other oral health experts collected data using mobile phone technology. The Fluoride Toothpaste Affordability Ratio (FTAR) was calculated as the price associated with the recommended annual consumption of FT, relative to the daily wage of the lowest-paid unskilled government worker (FTAR >1 = unaffordable spending on fluoride toothpaste). The daily expenditure per capita of the poorest 15% of the income distrubition was used as the main proxy of the daily wage, using World Bank data. Alternatively, we used the daily minimum wage as a proxy of the daily wage. Stratified descriptive statistics were undertaken with respect to different World Bank Income Groups and WHO Regions. Results: There are significant discrepancies in the affordability of FT across 78 countries. FT was strongly affordable in high-income countries, relatively affordable in upper middle-income countries, and strongly unaffordable in lower middle-income and low-income countries. The affordability of FT across WHO Regions was dependent upon the economic mix of WHO Regions’ member states. Conclusion: FT is still unaffordable for many people, particularly in low-resource settings. Health policy members should develop better strategies to address and improve the universal affordability of FTs, for the incidence of dental caries to be reduced at a global level.

The need for miniaturized shear force sensors is expanding, particularly for biomedical applications. Examples include measuring interfacial shear stresses between a human and an external device (e.g., footwear or a prosthesis). However, there are considerable challenges in designing a shear sensor for these applications due to the need for a small package, low power requirements, and resistance to interference from motion artifact and electromagnetic fields. This paper presents the design, fabrication, and characterization sensor that measures two-axis shear force by detecting displacement between a color panel and a red, green, and blue light-sensing photodiode. The sensor response to applied displacements and forces was characterized under benchtop testing conditions. We also present the design of a prototype wireless version of the sensor for integration into footwear. The sensor exhibited strong agreement with gold standard measurements for two axis shear displacements (R2>0.99, RMSE≤5.0 µm) and forces (R2>0.99, RMSE≤0.94 N). This performance, along with the sensor’s scalability, miniaturized form, and low power requirements make it well-suited a variety of biomedical applications.

Pneumonia is the leading cause of hospitalization in pediatric patients. Disease severity greatly influences pneumonia progression and adverse health outcomes such as hospital readmission. Hospital readmissions have become a measure of healthcare quality to reduce excess expenditures. The aim of this study was to examine 30-day all-cause readmission rates and evaluate the association between pneumonia severity and readmission among pediatric pneumonia hospitalizations. Using 2018 Nationwide Readmissions Database (NRD), we conducted a cross-sectional study of pediatric hospitalizations for pneumonia. Pneumonia severity was defined by the presence of respiratory failure, sepsis, mechanical ventilation, dependence on long-term supplemental oxygen, and/or respiratory intubation. Outcomes of interest were 30-day all-cause readmission, length of stay, and cost. The rate of 30-day readmission for the total sample was 5.9%, 4.7% for non-severe pneumonia, and 8.7% for severe pneumonia (p&lt;0.01). Among those who were readmitted, hospitalizations for severe pneumonia had a longer length of stay (6.5 vs. 5.4 days, p&lt;0.01) and higher daily cost ($3,246 vs. $2,679, p&lt;0.01) than admissions for non-severe pneumonia. Factors associated with 30-day readmission were pneumonia severity, immunosuppressive conditions, length of stay, and hospital case volume. To reduce potentially preventable readmissions, clinical interventions to improve the disease course and hospital system interventions are necessary.

COVID-19 has been a major issue in most countries throughout the world with 213 countries being affected till date due to the disease. The pandemic has raised concerns over the healthcare facilities available in various countries and question the government decisions made during this period of outbreak. Despite having the best healthcare facilities several countries across Europe and America have found it difficult to contain the disease outbreak questioning the available solutions to contain an area. This paper focuses on presenting information on solutions available to control outbreaks in order to prevent another pandemic occurring in the future. The paper also highlights the strategies and plans implemented by various governments who have been successful in combatting the disease with minimum damage. By using available resources such as technology, scientific innovation and digitalized healthcare this paper focuses on providing solutions which are already available to be utilized in the right manner.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the heath care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization.Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

It is not new in medical history to propose a global concern to be classified as a disease. Defining a concern into disease allows to assign ethical responsibilities to develop powerful and effective interventions. It also allow to appropriate distribute the resources uniformly economically and morally. In 2003, World Health Organization report stated that 30-50 % of patients do not take their medications as prescribed associated with morbidity, mortality and health cost. It was considered a global concern, however, irrespective of decades of researches conducted on medication adherence, we are still unable to state that medication adherence issues are being resolved. In this review, we have described few apprehensions in current understandings of medication adherence that have limited its research. We have also proposed medication adherence as disorder and provided its’ definition and classification

Background: Healthcare attention is sometimes considered purely technical, but communication has proven to be closely related to clinical results and patient satisfaction. Therefore, evaluation of communication in the scope of healthcare is a priority. The purpose of this study was to validate and adapt, if necessary, the Spanish version of the Communication Styles Inventory (CSI) in a sample of nursing professionals. (2) Methods: The sample was made up of 2313 nursing professionals selected at random from various medical centers in Spain, and is therefore a sample actively employed at the time data were acquired. We started out from the Communication Style Inventory, a questionnaire for evaluating the predominance of certain individual communication behaviors on six scales (expressiveness, preciseness, verbal aggressiveness, questioningness, emotionality and impression manipulativeness). (3) Results: Confirmatory Factor Analysis of the model proposed showed god fit indices. The reliability of the model shown by the Cronbach’s alpha of α=.81 was adequate, and so was single-level and aggregate consistency. Finally, in the analysis of variance by type of contract, configural, metric and scalar invariance was acceptable, but not strict invariance. (4) Conclusions: This instrument progresses in measuring non-technical attributes, such as communication styles, in nursing personnel.

In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real-time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 seconds, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.

In service economy, scholars and practitioners focus on the development and the appliance of innovative services. The importance of service innovation is rising in many sectors and among different organizations. Several disciplines (e.g. marketing, management, operations research, etc.) focus on this innovation, a concept widely used, but with different definitions. In this paper, service innovation has been analyzed according to SD Logic and a service ecosystem perspective. Literature still call for a deeper understanding of how new or renewed resources’ combination affect the shaping of service ecosystems. To contribute to fill this gap, the study explores the practices that different actors, internal and external to a healthcare service ecosystem, enact to co-create value in novel ways that is service innovation. The paper is structured as follows. In the next section, the main academic contributions on service research have been reviewed, focusing on healthcare service innovation. Follows, the research method and the discussion of research findings. Finally, theoretical and managerial implications have been detailed and an agenda for future research suggested. The paper offers interesting insights to develop new or renewed practices that foster the reshaping and maintaining of a healthcare service ecosystem. Some recommendations are included to support managers in the development of service innovation strategies.

Approximately 10–20% of individuals suffering from COVID-19 develop prolonged symptoms known as long COVID or post-COVID condition (LC). This review aimed to assess healthcare resource use (HCRU) and healthcare costs associated with LC. Because LC is not clearly defined and often remains undiagnosed, studies reporting on long-term follow-up of individuals with a COVID-19 diagnosis were also included. Among the 41 publications included, 36 reported on HCRU and 16 on costs. Individuals with LC had significantly elevated HCRU and healthcare cost vs controls without a COVID-19 diagnosis over ≥15 months, with a 7.6%–13.1% increase in total healthcare costs per person per month as assessed by difference-in-difference analysis. Among studies that did not specifically refer to LC, having a COVID-19 diagnosis was associated with a significant 4–10% increase in long-term total HCRU over 6–8 months and a 1.3- to 2.9-fold relative increase in total healthcare costs over 6 months. Due to the heterogeneity of included studies, high-quality evidence is needed to better understand the economic burden of LC. In the absence of effective treatments, prioritizing prevention of acute COVID-19, e.g., through vaccination, may be crucial for preventing LC and the associated long-term HCRU and medical spending.

Populations with healthcare needs often reside in post-conflict settings where basic services needed to maintain good health may be non-existent or hard to access. Therefore, there is a need for better identification and reallocation of resources as part of the post-conflict health rehabilitation effort. This study applies an asset-based approach to explore the more optimal design of health services and to identify the resource constraints for basic health service delivery to the most vulnerable communities in eastern Congo. We implemented the asset mapping in two phases. Firstly, we combined a qualitative survey with community walks to identify the assets already present in the communities. Secondly, we conducted group discussions to map out assets that are the core of Asset-Based Community Development (ABCD) practice. We finally documented all assets in a Community Asset Spreadsheet. Overall, 209 assets were identified as available and potentially valuable resources for the communities in eastern Congo. Among them, 60 were local associations, 24 were land and physical environment, 43 were local institutions, 46 were individuals, 32 to economy and exchange, and only 6 were related to culture, history, and stories. Drawing upon the findings from the qualitative survey, community walks, and group discussions, we conclude that an important number of resources were in place for basic health service delivery. By activating the existing and potential resources, the most vulnerable populations in eastern Congo might have the required resources for basic health service delivery. Our findings support the use of an asset-mapping research method as appropriate to identify existing and potential resources for basic health services in a post-conflict setting.

With increased life expectancy and improved modern lifestyles, there has been a growing concern for health, including the prevention of disease and the improvement of physical appearance. Consumers are increasingly aware of the benefits of using natural ingredients in healthcare products and at the same time are concerned about the challenges of sustainability. In this sense, the use of marine bioactive compounds as cosmetic ingredients and in food supplements has become increasingly popular due to the benefits derived from their various properties. However, some of these compounds have limited use, mainly due to their low stability and poor aqueous solubility, and solutions need to be developed to overcome these limitations. In this sense, the use of lipid nanoparticles, such as solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC), has been explored as these carriers show ability to protect and enhance the absorption of molecules. Several studies have demonstrated the efficacy of encapsulating these compounds and incorporating them into cosmetics and food supplements. However, there is still no global legislation and safety concerns have been raised. Further clinical studies in animals and humans are needed to clarify safety issues.

This study aims to comprehensively understand the provision of skincare solutions for individuals living with HIV, focusing on the efficacy of existing treatments, challenges in accessing dermatological care, and the preferences of individuals living with HIV for personalized skincare solutions. The study triangulated data from structured surveys and in-depth interviews with a purposive sample of individuals living with HIV, healthcare professionals, and experts in public health and social work, using a mixed-methods approach. The structured surveys provided specific data points, while the in-depth interviews and participant observations yielded rich, contextual insights. The results revealed a need for more personalized skincare solutions for individuals living with HIV, challenges in accessing dermatological care, and concerns regarding the efficacy of existing treatments. The integrated analysis of quantitative and qualitative data provided a nuanced understanding of the factors influencing skincare solutions for this population. Conclusions drawn from the study emphasize the importance of tailored skincare interventions, improved access to dermatological care, and the need for collaborative efforts between healthcare providers and individuals living with HIV. Future research directions include longitudinal studies to assess the long-term effectiveness of personalized skincare solutions and the exploration of innovative interventions integrating dermatological care and social support for individuals living with HIV.

Preventing COVID-19 in Gaza is crucial due to the devastation of advanced health services infrastructure by war. Despite the high protection offered by COVID-19 vaccines against severe disease, a 2021 survey in Gaza found only half of the population vaccinated and one-third vaccine hesitant. This follow-up study conducted in March 2023 aimed to re-evaluate vaccination levels, hesitancy, exposure to vaccine promotion efforts, and other risk factors in Gaza. A community-based cross-sectional survey with multistage stratified sampling was used. Associations of primary exposures and other determinants with vaccine status and hesitancy were quantified using bivariate and multivariate logistic regression. In 2023, 63.5% of adults received at least one vaccine dose compared to 49.1% in 2021. Vaccine hesitancy prevalence was 31.7% in 2023 versus 34.1% in 2021 (p=0.395). Adjusted odds of vaccination were 4.2 times higher among those referred by health workers compared to those not referred. Adjusted odds of vaccine hesitancy among those who received information on the vaccine from health workers were 0.3 times that of people who did not receive information. Results suggest health workers could play a crucial role in future vaccination strategies, as their vaccine promotion efforts were linked to better vaccine outcomes. Investing in the skills development of community health workers to contribute to these efforts is recommended.

Background: IoT and Big Data are newer technologies that can provide substantial support for healthcare systems and help overcome their shortcomings. The aim of this paper was to analyze literature production descriptively, thematically, and chronologically from an interdisciplinary perspective in a holistic way to identify the most prolific research entities and themes. Methods: The synthetic knowledge synthesis qualitatively and quantitatively analyzes the production of literature through a combination of descriptive bibliometrics, bibliometric mapping and content analysis. For this analysis the Scopus bibliometric database was used. Results: In the Scopus database, 2272 publications were found, which were published between 1985 and June 10, 2024. The first article in this field was published in 1985. Until 2012 the production of literature was steady increasing, after that exponential growth began, which reached its peak in 2023. The most productive countries were the USA, India, China, the United Kingdom, South Korea, Germany and Italy. The content analysis resulted in 8 themes (four from the perspective of computer science and four from the perspective of medicine) and 21 thematic concepts (eight from the perspective of computer science and 13 from the perspective of medicine). Conclusions: The results show that IoT and Big Data have become key technologies in preventive health. The study outcomes might represent a starting point for the further development of research that combines multidisciplinary aspects of healthcare.

Additive Manufacturing (AM), commonly known as 3D printing, has revolutionized various industries, including the medical field. This technology enables the creation of complex structures layer by layer, offering unprecedented customization and precision. In medical applications, AM has proven transformative in producing patient-specific implants, prosthetics, and anatomical models, facilitating better surgical planning and outcomes. Additionally, it has shown promise in bioprinting, where cells and biomaterials are used to create tissue-like structures for regenerative medicine. The ability to rapidly prototype and manufacture medical devices reduces time and costs, and enhances personalized patient care. This abstract explores the diverse applications, benefits, and challenges of AM in the medical sector, highlighting its potential to revolutionize healthcare and improve patient outcomes.

The fulfillment of rights to maternal healthcare is a key factor for wellbeing of woman. However, there is a lack of instrument to know the experience of women during maternity to bind the monitoring. The aim of this study was to validate an instrument to measure the woman’s perception of the fulfillment of rights during healthcare in pregnancy, childbirth and immediate postpartum. The instrument consist in 50 items was screening by an exploratory factor analysis. Additionally, the final version of the instrument consists in 29 items which was validate by confirmatory factor analysis and know-group validity. The instrument was applied in 185 Spanish women. The global Aiken’s V of initial instrument proposal was 0.89. Process resolves an instrument with 5 factors (information, privacy, consent, support, and participation) that explained the 60% of the total variance. The score of the instrument was correlated with resilience, maternity beliefs, and positive and negative affect. External validation showed relations with age, gravida, and number of labors. Additionally, the reliability Cronbach´s was 0.93 [0.91; 0.94]. In conclusion, the instrument developed is consistent and has appropriate psychometric properties for assess the fulfillment rights maternity healthcare.

This study addresses a gap in literature by comprehensively analyzing the emotional well-being of healthcare workers. Methods: The study employed an analytical cross-sectional design utilizing both qualitative and quantitative methodologies among 84 healthcare workers. Quantitative data was analyzed using STATA version 17 while qualitative data was analyzed using thematic analysis. Ethical approval was sought from the University of Livingstonia Research Ethics Committee (UNILIA-REC). Results: 88% of the participants had experienced verbal abuse; stress and anger, 87% emotional tiredness, 22% depression, and 4% physical abuse. Emotional tiredness was associated with department (p=0.004) and work demand (p=0.0047), stress and anger were associated with experience (p=0.033), department (p=0.002) and work demand (p=0.002), depression was associated with age (p=0.035), experience (p=0.025), cadre (p=0.046), staffing (p=0.005) and work demand(p=0.044), verbal abuse was associated with department (p=0.035) and physical abuse was associated with knowledge of policies (p=0.002). Being aware of emotions and controlling them was the common coping strategy used by most (26%) of the respondents. The study established that most (78%) of healthcare workers did not receive formal training on coping skills for emotional disturbances. Conclusions: Healthcare workers experience some emotional disturbances within their professional roles. There is a need for healthcare workers to be trained on how to cope with emotional disturbances.

Antimicrobial resistance (AMR) is a major public health threat linked to increased morbidity and mortality. It has the potential to return us to the pre-antibiotic era. Antimicrobial stewardship (AMS) programs are recognized as a key intervention to improve antimicrobial use and combat AMR. However, implementation of AMS programs remains limited in Africa, particularly in Rwanda. This qualitative study, conducted at King Faisal Hospital (KFH) in Rwanda, aimed at assessing current prescription practices to identify areas for improvement and promote adherence to AMS principles. The interviews were recorded, transcribed, and thematically analyzed, revealing four emerging themes. First, emerged on AMS activities that were working well which includes availability of microbiology laboratory results, and prescription guidelines as factors influencing antibiotic prescription adjustments. Second, Implementations challenges to AMS which identified the prescription of broad-spectrum antibiotics, limited local data on AMR patterns, stock-outs of essential antibiotics, Thirdly, the importance of adhering to AMR management guidelines at KFH was underscored. Lastly, participant recommendations centered on regular training for healthcare workers, widespread dissemination of AMR findings across departments, and the enforcement of antibiotic restriction policies. These actions can improve prescription behaviors, upholding the highest standards of patient care, and strengthening the nascent AMS program.

In areas like commercial and policy decisions—it's important to know how different treatments affect things and how well they work. However, treatments affect things on average—but now—with fancy computer methods and big sets of data, interest lies in figuring out how treatments might work differently for different people. For instance, let's say instead of just looking at what works for most people, researchers want to know which treatment is best for each person. This would help doctors to decide the right treatment for each patient. This idea isn't just for healthcare—it can help in other areas too, like deciding how to govern employees or making government policies. There are some ways people have been trying to do this already, but they struggle when there are lots of different factors to consider. New computer methods, though, are good at handling lots of factors, but they need a ton of data to work well. Therefore, this study looks at three computer methods that are—Virtual Twin Random Forest (VTRF), Causal Forest (CF), and Causal Net (CN) using real-world data about encouraging people to do manual labour. One problem this study is trying to solve is something called the "winner's curse", where a treatment might seem better than it really is because of how it's predicted by the computer. We are also testing some ideas to see if we can fix this problem and make the predictions more accurate.

Background Praziquantel is the commonly used conventional treatment recommended for schistosomiasis in the mainstream healthcare system. Although Praziquantel may be free in healthcare facilities, many individuals may resort to the use of traditional medicine. This study aimed to assess perceptions of participants on the availability of Praziquantel and to ascertain whether individuals infected with schistosomiasis used concurrently African traditional medicines with this treatment. Methods A cross–sectional descriptive study, using face-to-face interviews, was conducted from January to April 2023 among healthcare workers (HCWs) and patients. A review of medical chart records of patients was further carried out to triangulate the information from interviews. Ddescriptive statistics including frequency and percentage were used; categorical data were represented as tables. Chi-squared tests were performed for the association between continuous variables. A p value ≤ 0.05 was estimated as statistically significant. Results Most of HCWs (114/124; 91.9%) reported that Praziquantel was available in healthcare facilities. Majority of HCWs (76/124; 61.3%) did not know whether patients seen by them used concurrently traditional medicine and Praziquantel for schistosomiasis. There was no significant relationship between the availability of Praziquantel and the concurrent use of TM by patients for schistosomiasis (X2 = 3.042, p = 0.551). Patients seen by THPs (8/20, 40%) did not use Praziquantel for schistosomiasis. Those patients (8/15, 53.3%) who crossed from TM to the mainstream healthcare system did not disclose their use of TM to HCWs. Their main reason was that concurrent use of TM and Praziquantel could destroy organs or make Praziquantel not to work properly. None of the medical chart records analysed documented the concurrent use of TM and Praziquantel. Conclusion Most of HCWs were not aware of the concurrent use of TM and Praziquantel by patients for the treatment of schistosomiasis. Patients seen by THPs relied solely on TM for schistosomiasis. Although patients indicated crossing from TM to the mainstream healthcare system, there was no evidence of documented use of TM in medical chart records of patients. Further studies are needed to contribute to the knowledge of medicinal plants used by THPs for schistosomiasis.

To study the spatial accessibility of healthcare services in cities on the Tibetan Plateau, this paper proposes an improved 3SFCA method in the main urban area of Xining City. The improved 3SFCA method incorporates both selection weights from the Huff model and the M2SFCA approach to addressing suboptimal allocation of supply resources. Results show that the accessibility of healthcare for the main urban area of Xining City decreases in a circle to four axes, with good accessibility in the east and center but poor accessibility in the north. And there is a positive spatial relationship between population and accessibility. Besides, this paper proposes a framework for studying the spatial accessibility of healthcare on the Tibetan Plateau.

Background: The COVID-19 pandemic has affected Madagascar, Cameroon, and the Central African Republic (CAR), with each experiencing multiple waves by mid-2022. This study aimed to evaluate immunity against SARS-CoV-2 strains Wuhan (W) and BA.2 (BA.2) among healthcare workers (HCWs) in these countries, focusing on vaccination and natural infection effects. Methods: HCWs' serum samples were analyzed for neutralizing antibodies (nAbs) against W and BA.2 variants, with statistical analyses comparing responses between countries and vaccination statuses. Results: Madagascar showed significantly higher nAb titers against both strains compared to CAR and Cameroon. Vaccination notably increased nAb levels against W by 2.6-fold in CAR and 1.8-fold in Madagascar, and against BA.2 by 1.6-fold in Madagascar and 1.5-fold in CAR. However, in Cameroon, there was no significant difference in nAb levels between vaccinated and unvaccinated groups. Conclusion: The study highlights the complex relationship between natural and vaccine-induced immunity, emphasizing the importance of assessing immunity in regions with varied epidemic experiences and low vaccination rates.

Fall detection systems play a crucial role in addressing the significant health concern of elderly falls, a leading cause of health deterioration and mortality. As the aging population grows and life expectancy increases, the development of accessible tools becomes vital for predicting and preventing falls, offering a practical and widely applicable solution in contrast to costly and expertise-dependent assessment tools. In contrast, due to the formidable challenges encountered, a comprehensive investigation into the comparative performance of standard ML models within this field still needs to be explored. This paper proposes a standard pipeline for pre-processing, training, and evaluating ML models for fall detection on the SisFall dataset. We conducted extensive experiments to evaluate the performance of various ML models for fall detection. The results validate the efficiency of the deep model in identifying the time windows in which a fall occurred. Among the deep models, the architecture, including a combination of convolutional neural networks and fully connected layers, outperforms the others by macro-averaged Precision, macro-averaged Recall, and macro-averaged F1-Score of 87.03\%, 86.83\%, and 86.93\%, respectively.

The coronavirus (COVID-19) pandemic limitations may negatively affect children and youth in terms of health behaviors, and it might be especially challenging for children who are diagnosed with neurodevelopmental disorders such as children and youth diagnosed with Autism Spectrum Disorder (ASD). The present study assessed alterations in physical activity, screen time, and sleep duration of ASD children and the prevalence of meeting the 24-hour movement guidelines during the COVID-19 outbreak. Forty-six Arab Israeli mothers of children with ASD were surveyed by an online cross-sectional survey. As reported by the mothers, the results show a significant decrease in physical activity, a significant increase in screen time, and a significant increase in sleep duration during the COVID-19 pandemic. Moreover, the proportion of the sample who gained the physical activity and screen time recommendations lessened while the percentage of children who met the sleep duration guidelines increased. The prevalence of ASD children who achieved the overall 24-hour movement guidelines was very small during the COVID-19 outbreak. The outcomes extend the body of knowledge regarding the negative effects of the COVID-19 pandemic on children diagnosed with neurodevelopmental disorders and highlight the need for instant healthcare and interventions and programs for children with ASD.

In the present study, we aimed to assess the prevalence and the relationships between alexithymia, burnout and hopelessness in a large sample of healthcare workers (HCWs) during the third wave of Covid-19 in Italy. Alexithymia was evaluated by the Italian version of the 20-item Toronto Alexithymia Scale (TAS-20), hopelessness was measured using the Beck Hopelessness Scale (BHS) and irritability (IRR), depression (DEP) and anxiety (ANX) were evaluated with the Italian version of Irritability‚ Depression‚ Anxiety Scale (IDA). This cross-sectional study recruited a sample of 1445 HCWs from a large urban healthcare facility in Italy from 1 June—31 May 2021. Comparison between individuals positive (n=214, 14.8%) or not for alexithymia (n=1231, 85.2%) controlling for age, gender and working seniority revealed that positive subjects showed higher scores on BHS, MBI, IRR, DEP and ANX than not positive ones (p<0.001). In the linear regression model, higher working seniority and higher MBI, DEP, ANX and TAS-20 scores were associated with higher hopelessness. In conclusion, increased hopelessness was associated with higher burnout and alexithymia. Comprehensive strategies should be implemented to support HCWs mental health and mitigate the negative consequences of alexithymia, burnout, and hopelessness

University students, particularly those in the healthcare disciplines, constitute a category of particular interest in regard to COVID-19 vaccines and the attitudes to vaccination, as their future professional role will enable them to inform and educate citizens regarding vaccination. The objectives of the study were to investigate the vaccination coverage with a COVID-19 vaccine among students from different degree programs at the Medical universities in Bulgaria behaviors and attitudes toward vaccination with a COVID-19 vaccine. A prospective cross-sectional study in the period September 2021- March 2022 was conducted. Information on demographics, university program, year of study, general attitudes and behavior towards vaccines and attitudes and personal experience with COVID-19 and COVID-19 vaccines were collected. The chi-square test was used to test for associations and binominal logistic regression was used to identify possible predictors for vaccination. A total of 3050 students with a median age of 22 years, predominantly female took part in the study. Three-thirds of the students (73.5%) have been vaccinated against COVID-19. The main reasons for vaccine hesitancy in both group of students were the fear of side effects and the doubts about the safety of the vaccines although non-vaccinated students significantly more frequently express those fears. Respondents who considered to have: a) limited access to sufficient information to inform COVID-19 vaccine uptake; b) lack of public awareness and education campaigns about effectiveness and safety of COVID-19 vaccines; c) insufficient information about COVID-19 vaccines during the University education; and d) had a positive COVID-19 diagnosis were less likely to be vaccinated. Students who have not completed the mandatory immunization cycle according to country schedule were also less likely to have completed COVID-19 vaccination. The students showed an overall positive attitude towards the COVID-19 vaccines. Promoting informational campaigns that emphasize the vaccine's safety will be more effective to further increase the vaccination coverage with COVID-19 vaccines.

Background: Fungi are ubiquitous microorganisms that are easily dispersed through the air. In healthcare environments, indoor air can favor the spread of healthcare-associated fungal infections, compromising mainly immunocompromised hospitalized individuals. Thus, this study aimed to evaluate the indoor air contamination in healthcare environments, investigating mainly the presence of potentially pathogenic yeasts. Methods: Indoor air samples were collected from twelve healthcare environments (hospital and medical clinics). After the growth, isolation, and purification of the yeast colonies, the isolates were identified by polymerase chain reaction using species-specific primers for yeasts of the genus Candida and sequencing of D1/D2 domains of the large ribosomal subunit (LSU rRNA). Results: Fourteen yeast species were identified, including emerging pathogens. Species of clinical importance such as Candida parapsilosis, Candida orthopsilosis, Candida glabrata, Rhodotorula mucilaginosa, and Trichosporon mucoides were present. C. Parapsilosis was the most prevalent species, followed by Rodothorula mucilaginosa. Conclusions: The present study shows that potentially fungal pathogens were present in air samples from healthcare environments, proving the role of indoor air in spreading infections. Thus, monitoring air quality in healthcare environments is a fundamental approach in developing infection control measures, especially those related to invasive fungal infections.

High-fidelity patient simulation (HFPS) is widely used in professional training to enhance students’ competence in clinical management. Problem-solving (PS) and clinical reasoning (CR) skills are essential to developing students in professional competence in safe and effective care. These two skills should be initiated at the early training. However, little is known about the effects of HFPS on developing PS and CR skills in first year undergraduate students. Therefore, this pre- and post-experimental study aimed to investigate the effects of HFPS on development of PS and CR skills in first-year nursing students. The students were required to go through four sessions, preparation, pre-briefing and orientation, simulation role-playing, and debriefing, for the HFPS. The study utilized the Problem-Solving Inventory (PSI) and the Nurses Clinical Reasoning Scale (NCRS) to measure problem-solving and clinical reasoning abilities before and after HFPS. Bivariate analysis, one-sample t-test, and independent t-test were performed to evaluate the performance of the PS and CR skills at the two study periods. One hundred eighty-nine students were recruited, with a mean age of 20.56 years, and 73% were female students. The analytic results showed that the PSI, particularly in domains of Problem-Solving Confidence (PSC) (p&lt;0.001) and overall PS (p&lt;0.001), and the CR (p&lt;0.001) had significant improvement after HFPS. The study concluded that HFPS is an effective innovative method to significantly improve students’ problem-solving and clinical reasoning abilities. Nurse educators play an important role in providing explicit learning instructions in a simulation guideline that directs and guides students to learn at each stage of HFPS. Students should take more initiative and engage in their learning through HFPS to enhance their competence in knowledge and higher intellectual skills for personal and professional developments.

The aim of this study was to investigate the work engagement and burnout in healthcare professionals in a private health unit in Greece. A cross-sectional study was conducted with a sample of 151 professionals (doctors, nurses, administrative staff and professionals of other specialties). The questionnaire included demographic and work-related information and the Utrecht Work Engagement Scale and Maslach Burnout Inventory. Analysis was performed by SPSS v.26. With regard to the work engagement, the participants presented a medium score in absorption and a medium to high score in vigor and dedication. In addition, they presented low score in depersonalization, a medium score in emotional exhaustion and a high score in personal accomplishment. Those who had a working contract for an indefinite period had higher score in all the dimensions of burnout. Vigor, dedication and absorption were negatively correlated with emotional exhaustion and depersonalization and positively with personal accomplishment. Healthcare professionals in private health sector in Greece present moderate work engagement and experience moderate levels of burnout. Work engagement is associated with burnout and plays an important role in its prevention. There are some significant differences in work engagement and burnout based on several demographic and work-related characteristics.

Background: The diagnosis of phenylketonuria (PKU) in an infant is a devastating and overwhelming event for their parents. Providing appropriate information and support is paramount, especially at the beginning of a child’s life. Investigating if parents are receiving the right support is important for their continued care. Methodology: An online survey was distributed to explore parents’ perceptions of current support and information provided by their healthcare provider and to rate sources of other support (n=169 participants). Results: Dietitians received the highest (85%) rate of “very helpful” support. Overall, parents found Facebook to be helpful for support, but had mixed reactions when asked if healthcare professionals (HCPs) should provide advice as part of the groups. When rating the most effective learning methods, the top three were: 1:1 sessions (n=109, 70%), picture books (n=73, 50%) and written handouts (n=70, 46%). Conclusion: Most parents were happy with the support and information they received from their dietitian, but required more support from other HCPs. Facebook groups provide parents with the social support that HCPs and their family may be unable to offer, suggesting a place for social media in future PKU care.

Access to healthcare services is an essential component of promoting public health and sustainable development. Our aim was to assess socio-demographic correlates of barriers to access healthcare services among children in Albania, a post-communist country in Europe. An online survey was conducted in September 2022, including a nationwide representative sample of 7831 schoolchildren (≈54% girls) pertinent to grades 6-9 from all regions of Albania. A structured and anonymous questionnaire was administered to all children inquiring about a range of potential barriers to access healthcare services. Overall, about 42% of the children reported that they had barriers to access healthcare services. There were no gender differences, but significant: ethnic differences (51% among Roma/Egyptian children vs. 42% among the general sample of the children); urban/rural differences (46% rural vs. 39% urban); and socioeconomic differences (52% among children with a lower maternal education vs. 31% among children with a higher maternal education; 66% among children from poor families vs. 35% among children with a higher family income). In transitional Albania, children residing in rural areas, children from Roma and/or Egyptian communities and especially those pertinent to low socioeconomic families report considerably more barriers to access healthcare services, which is a cause of concern.

Vaccination attitudes among healthcare workers (HCWs) is a vital factor for measuring their level of vaccination uptake and intention to recommend vaccinations to their patients. To our knowledge, no study has been conducted in South Africa to assess hesitancy to influenza vaccines among HCWs. We used questionnaire adapted from Betsch and colleagues to conduct an online and face-to-face cross-sectional study among HCWs at the start of COVID-19 vaccine roll-out prior to the flu season. Main outcome was influenza vaccine hesitancy. We used multivariate logistic regression to assess predictors of influenza vaccine hesitancy. Of 401 participants, 64.5% were women, 49.2% nurses, and 12.5% physicians. A total of 54.9% were willing to accept vaccination, 20.4% were undecided, and 24.7% intended to refuse. Older participants above 17-25 years and physicians were likely to receive the vaccine. Key predictors of vaccine acceptance were confidence in the effectiveness, consideration of benefits and risks, and willingness to be vaccinated to protect others. Influenza vaccine hesitancy was highest in those who did not trust that influenza vaccines are safe. For future flu seasons, tailored education programs targeting younger HCWs and more information about the composition of flu vaccines would be vital to improve vaccine uptake.

Objectives: As institutions modified medical practices to mitigate the spread of the SARS-CoV-2 virus, providers were forced to modify care through the implementation of telemedicine. The objective of this article is to review the current practices of otolaryngology during the COVID-19 pandemic and the limitations of telemedicine in current practice. Methods: A thorough review of the PubMed and MEDLINE databases was performed for publications between 2020 and 2021. Studies discussing telemedicine in otolaryngology during COVID-19 pandemic were selected. Results: As worldwide cases of COVID-19 continue to oscillate, we must continue to be vigilant in our clinical practice, implementing safe techniques to mitigate the spread of the SARS-CoV-2 virus through the continual use of telemedicine in all specialties of otolaryngology. Trends of increased patient acceptance of the use of telemedicine have been seen across subspecialities, including otolaryngology. Conclusion: Currently there are challenges associated with the implementation of telemedicine in the field of otolaryngology that rely heavily on adjunct physical examination, imaging studies, and specialty procedures. However, telemedicine has tremendous potential and can be adapted to include in the otolaryngologist’s toolbelt during public health emergencies such as the COVID-19 pandemic.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

Medical Trust-Network is one of the most promising fields of study in network science. Establishment of trust within medical entities ensures better treatment and increases better medical facilities. The word ‘Trust’ signifies a very important behavioral aspect between any human entities, especially among doctors and patients. To represent such relationships Trust Network Models are built to express the interactions between human entities within such networks. Though the idea of a Trust-Network has traditionally been one of the major areas of research, yet the concept of a medical trust network model is relatively a new domain. In this paper, we introduce an overall multilayered Trust Network to represent the entire healthcare architecture. More specifically our model is based on an evolutionary graph system with a discrete relationship between the three most important entities of any healthcare system, namely – Doctors, Departments, and Hospitals. Observations indicate that based on our model, the medical healthcare system is a multilayered model unlike a feed-forward model as indicated by previous studies.

This study investigated co-constructed research poetry as a way to understand the lived experiences of people affected by rarer dementia and as a means to use poetry to convey those experiences to healthcare professionals. Using mixed-methods, 71 people living with rarer dementia and care-partners (stakeholders) contributed to co-constructing 27 poems with professional poets; stakeholders’ verbatim words were analysed with descriptive qualitative analysis. Stakeholders were also surveyed and interviewed about their participation. Healthcare professionals (n = 93) were surveyed to elicit their responses to learning through poetry and its acceptability as a learning tool. Poems conveyed a shared narrative of different aspects of lived experience, often owing to atypical symptoms, misunderstandings by professionals, lack of support pathways, and a continuous struggle to adapt. Stakeholder surveys indicated it was a valuable experience to both co-create and respond to the poems, whilst group interviews revealed people’s experiences of the research poetry were characterised by reflection on lived experience, curiosity and exploration. Healthcare professionals’ responses reinforced the poetry’s capacity to stimulate cognitive and affective learning specific to rare dementia support and prompt both empathy and critical thinking in practice. As the largest poetry-based study that we are aware of, this novel accessible approach of creating group poems yielded substantial information about the experiences and needs of those affected by rarer dementia and how poetry can contribute to healthcare education and training.

The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.

Leveraging on the theory of social exchange theory, the current study investigated the influential role that perceived organizational support plays in understanding the relationship between organizational cynicism and organizational deviant work behaviour. Scant research has addressed contextual factors in determining organizational cynicism and its relationship to negative work outcomes such as deviant work behaviour. An interactive effect of perceived organizational support was investigated to determine if deviant individuals are more or less prone to become organizational cynics based on the presence or absence of organizational support. The study was conducted in South-Eastern Nigeria, involving 575 nurses using two-wave design. Validated self-reported measures were used to collect data for perceived organizational support, organizational cynicism and organizational workplace deviance, which were analyzed using the Hayes PROCESS Macro for SPSS model 1 version 3.00. The results revealed that organizational cynicism positively correlated with organizational workplace deviant behaviour, whereas perceived organizational support negatively correlated with workplace deviant behaviour among nurses. In addition, perceived organizational support moderated the relationship between organizational cynicism and organizational workplace deviant behaviour. The study's findings revealed the imperativeness of a supportive work environment by underscoring the importance of contextual factors like perceived organizational support in mitigating workplace deviant behaviour. By focusing on fostering a supportive environment, organizations can better manage employee attitudes and reduce deviant behaviours, which enhances overall workplace performance and culture.

Background/Objectives: The introduction of robotic surgery has revolutionized surgical procedures bringing significant improvements in procedures such as radical prostatectomy. In Poland, robotic technology was first used in public healthcare in 2010 for colorectal surgery in Wrocław. Public funding for robotic urologic surgery began in 2022. The aim of this study is to evaluate the impact of integrating a stable source of funding on the development and utilization of robotic surgery. Methods: Using data from the National Health Fund, a retrospective analysis of these procedures was conducted, focusing on radical prostatectomies from the beginning of the funding period to November 2023. Results: The study showed that in the first year of public funding (2022), 2,149 radical prostatectomies were performed using robotic systems, and this number almost doubled to 4,331 by October 2023. Patients aged 60 years and older accounted for more than 80% of those undergoing robotic surgery. The rise in the popularity of robotic surgery is attributed to its advantages, including high precision, reduced invasiveness in the surgical field, minimized bleeding and pain, and reduced postoperative wound size. The use of robotic surgery also brings environmental benefits. Conclusions: The growing number of robot-assisted surgeries in Poland reflects the increasing role of robotics in medicine. These advances meet the growing demand for healthcare services and demonstrate the precision and effectiveness of robotic technology, potentially improving healthcare access and outcomes. Further implementation and refinement of robotic solutions are necessary to meet the changing needs of medical specialties.

The escalating antibiotic crisis in Nigeria presents a critical challenge, violating fundamental human rights and ethical obligations. Skyrocketing antibiotic costs severely restrict access to essential medications, particularly for vulnerable populations. This crisis not only exacerbates health disparities but also fuels the emergence of antibiotic resistance, posing grave public health risks. Rooted in regulatory gaps, lack of transparency in drug pricing, and inadequate healthcare infrastructure, the crisis demands a comprehensive, collaborative response. Policy interventions targeting antibiotic affordability, regulatory reforms, and healthcare infrastructure investments are imperative. Upholding ethical standards and accountability mechanisms is essential to ensuring equitable access to antibiotics and safeguarding public health in Nigeria.

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’ or ‘severe’ based on neurological impairment. Yet stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment among stroke survivors classified as mild. We used mild stroke survivors’ data from the START longitudinal stroke cohort (n=73) with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work and social adjustment over 12 months. Given the multi-source, multi-granular and unlabelled nature of data, we utilised a structure-adapting, unsupervised machine learning approach, the Growing Self-organising Map (GSOM) algorithm to generate distinct clinical profiles. These diverse impairment profiles revealed that mild stroke survivors experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasises the necessity of creating a holistic and comprehensive representation of mild stroke survivors’ needs over the first-year post-stroke to improve rehabilitation and post-stroke care.

The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients' hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.

This manuscript offers a concise overview of paper microfluidics, emphasizing its sustainable sensing applications in healthcare, environmental monitoring, and food safety. Researchers have developed innovative sensing platforms for detecting pathogens, pollutants, and contaminants by leveraging the paper’s unique properties, such as biodegradability and affordability. These portable, low-cost sensors facilitate rapid diagnostics and on-site analysis, making them invaluable tools for resource-limited settings. The review discusses fabrication techniques, principles, and applications of paper microfluidics, showcasing its potential to address pressing challenges and enhance human health and environmental sustainability.

The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

In the context of sustainable development, integrating the work aspect into health promotion systems is essential, ensuring that employees maintain a healthy balance between their professional and personal lives, which respects and amplifies the potential of every individual. Lately, there has been a noticeable shift of medical professionals from the public to the private sector. This trend paints a bleak picture, highlighting the vulnerabilities of a system teetering on the brink of failure, with significant impacts on patient care. Among the myriad reasons behind this shift, burnout stands out prominently. Medical professionals face increasing pressures due to understaffing, limited organizational support, and insufficient resources, leading to feelings of cynicism, exhaustion, and detachment from their roles. Utilizing the Maslach et al. questionnaire, we assessed the risk of burnout among physicians in public hospitals to identify and underscore preventive measures and improvements, correlating our findings with key aspects of organizational empowerment. Our research indicates that enhancing interpersonal relationships and resources—both within teams and between the organization and its physicians—is crucial for the revival of the healthcare system. This approach aims at improving the delivery of healthcare services and offering appropriate recognition to healthcare professionals, aligning with the principles of sustainable employment.

Addressing the increasing nursing demand in the face of a limited nursing workforce, understanding job satisfaction and retention becomes critical. Our cross-sectional study, conducted among 2,572 nurses in Bavaria, Germany, uncovers a dichotomy where nurses have strong empathy for their profession yet yearn for significant improvements. The top areas identified for enhancement include (i) ensuring sufficient time for patient care, (ii) providing a reliable service organization with minimal unplanned shift changes, and (iii) streamlining nursing documentation. Moreover, the largest disparity between expectations and job satisfaction was measured in patient care time. Multivariable regression analysis further investigates factors influencing intentions to stay in the profession, including (i) demographic aspects (age, living conditions), (ii) working area, (iii) working hours, (iv) career choice satisfaction, (v) career opportunities, (vi) payment, (vii) working and rest times, and (viii) satisfaction derived from aiding individuals in challenging life situations. The findings uncover a need to change nursing conditions, and measures for younger nurses and elderly care nursing. Although there are limitations, and a potential bias due to convenience sampling, our study provides key insights into nursing conditions. Future research should broaden its scope to investigate nurse job satisfaction and retention, emphasizing potential relief tools such as digitization.

The prevalence of depression and anxiety among healthcare workers (HCWs) during the Covid-19 pandemics is high. The aim of the study is to identify the importance of self-esteem and occupational factors in association with the incidence of depression and anxiety among HCWs through a longitudinal cohort study during the Covid-19 pandemics in Latvia. Participants were interviewed repeatedly seven times during the Covid-19 pandemics. 322 participants were included in the data analysis for depression and 352 for anxiety. Low self-esteem and working in the general practitioner's office is associated with the incidence of depression and anxiety. Undergraduate education and direct contact with Covid-19 patients are associated with the incidence of depression. The risk of anxiety decreases by 1% each year among HCWs. Our study shows the importance of personality factors of HCWs during the Covid-19 pandemics.

One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. The state-of-the-art envisages various methods for estimating the claims reserve but without being applied to systems that do not present huge variability and heterogeneity, such as the healthcare sector. The methodology is based on generalized linear models using the Overdispersed Poisson distribution as shown in the state-of-the-art. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss-Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss-Newton method, where the choice of the initial guess is fundamental. The proposed methodology has been entirely developed using MATLAB and has been applied to estimate the claims reserve in the healthcare system of the Tuscany Region in Italy, as case study. The results obtained were validated by comparing them with the state-of-the-art by measuring the confidence intervals of the Overdispersed Poisson distribution parameters. Therefore local healthcare authorities could use the proposed and improved methodology to allocate the resources dedicated to healthcare and global management.

The global SARS-Cov-2 which caused the coronavirus disease 2019 (COVID-19) pandemic has had a significant impact over the last 3.5 years, especially among healthcare workers (HCWs) who were constantly in the forefront of the pandemic. Due to the nature of their work, HCWs faced a potential risk of exposure. Therefore, this study investigated the individual-level factors associated with perceived COVID-19 risk among South African HCWs. We used an analytical cross-sectional study design to analyse data from the South African Health Care workers’ response to the Coronavirus (COVID-19) pandemic online survey conducted by the Human Sciences Research Council across the nine provinces in South Africa, from 11th April to 7th May 2020. In this study, only data (n= 5 579) with non-missing values for COVID-19 risk and individual-level factors (age, gender, educational level, occupational category, and race) was analysed. Univariate and Multivariate binomial logistic regression analyses were conducted. The crude and adjusted odds ratio and p-value ≤ 0.05, were used to describe the association between the individual-level factors and perceived COVID-19 risk, from low-medium to high. The binomial logistic regression was used to assess the factors associated with high perceived risk. Perceived COVID-19 risk among HCWs in South Africa was significantly associated with age, race, educational level and occupational category. Being 60 years and older, being “White”, having a Master’s and Doctorate degrees and being a healthcare worker other than a nurse practitioner and medical practitioner, all had higher odds of having high perceived COVID-19 risk. The findings may be used to guide policies and programs aimed at mitigating the impacts of COVID-19 and other similar pandemics.

Climate Change is the most serious planetary emergency of our time. Carbon emissions secondary to the healthcare industry account for about ten percent of all emissions in the United States. Health professionals, therefore, need to understand how they can make a difference in their profession, by understanding the health-related impacts of climate change and the importance of healthcare sustainability. An 8-week telementoring Climate Change Healthcare Sustainability ECHO series was developed to educate healthcare professionals in these topics such as the health-related effects of climate change, healthcare sustainability, quality healthcare and carbon accounting. A total of 376 participants from throughout the US and 16 other countries- completed this 8-week series and received no-cost continuing medical education credits. The evaluation consisted of pre and post Zoom polls, weekly post-session surveys and the registration demographics. Participants were primarily physicians and public health professionals who significantly improved their knowledge and communication skills after the course as compared to starting the training.

The prevalence of chronic diseases is dramatically increasing demand for emergency healthcare. Existing systems rely on patients self-identifying symptoms, causing dangerous delays. This study develops an AI and IoT-powered “digital twin” solution to enable continuous real-time monitoring and timely prediction of diverse medical emergencies. A digital twin is a virtual representation of an individual, modeled using multidimensional physiological data from wearable sensors. Machine learning techniques analyze patterns in this data to identify anomalies and predict emergencies like heart attacks or falls. A key contribution is an optimized ensemble algorithm combining gradient boosted trees, neural networks, and other techniques to accurately detect emergency events. Evaluation on a dataset of 9158 samples shows the digital twin identifies key emergencies with over 90% recall, enabling prevention and rapid response. It allows risk stratification and personalized interventions based on early warnings, circumventing over 2 million avoidable emergency room visits annually. This study demonstrates the feasibility of an integrated, predictive, patient-centric emergency response system enabled by digital twin technology.

The purpose of this study was to evaluate the financial performances of the publicly held healthcare companies in crisis periods in Türkiye. The 2018 Economic Crisis and the COVID-19 pandemic crisis were included in the study as the crisis periods. We collected the financial data of the publicly held healthcare companies and calculated three liquidity, three turnover, three leverage and three profitability ratios by ratio analysis to use as financial performance indicators. We then conducted Wilcoxon signed-rank tests and we did separate analyses for the 2018 Economic Crisis and the COVID-19 pandemic crisis. The results of the analyses showed that there were no statistically significant differences between the publicly held healthcare companies’ liquidity, turnover, leverage, profitability ratios and thus their financial performances before the crises and after the crises. While the results are reassuring and give valuable insights to managers and policy makers to determine the areas that needs to be strengthened to be better prepared for possible future crises, our sample was limited. Therefore, this study presents an exploratory foundation for future studies which are needed to make a case of financial stability for the publicly held healthcare companies before and after the crisis periods.