Search

Gülcan Gencer,1 Kerem Gencer2 1Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey; 2Department of Computer Engineering, Afyon Kocatepe University, Faculty of Engineering, Afyonkarahisar, TurkeyCorrespondence: Gülcan Gencer, Department of Biostatistics and Medical Informatics, Afyonkarahisar Health Sciences University, Faculty of Medicine, Afyonkarahisar, Turkey, Email gencergulcan@gmail.comBackground: The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency.Objective: This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main themes and future directions regarding applications in the healthcare sector.Methods: A systematic scan of publications until 08.05.2024 was carried out from an important database such as Web of Science.Using bibliometric tools such as VOSviewer and CiteSpace, we analyzed data covering publication counts, citation analysis, co-authorship, co- occurrence of keywords and thematic development to map the intellectual landscape and collaborative networks in this field.Results: The analysis included more than 500 articles published between 2021 and 2024. The United States, Germany and the United Kingdom were the top contributors to this field. The study highlights that neural network applications in diagnostic imaging, natural language processing for clinical documentation, and patient data in the field of general internal medicine, radiology, medical informatics, health care services, surgery, oncology, ophthalmology, neurology, orthopedics and psychiatry have seen significant growth in publications over the past two years. Keyword trend analysis revealed emerging sub-themes such as clinical research, artificial intelligence, ChatGPT, education, natural language processing, clinical management, virtual reality, chatbot, indicating a shift towards addressing the broader implications of LLM application in healthcare.Conclusion: The use of LLM in healthcare is an expanding field with significant academic and clinical interest. This bibliometric analysis not only maps the current state of the research, but also identifies important areas that require further research and development. Continued advances in this field are expected to significantly impact future healthcare applications, with a focus on increasing the accuracy and personalization of patient care through advanced data analytics.Keywords: large language models, chatbot, healthcare, artificial intelligence, clinical applications, diagnosis, treatment recommendations

The development of the social sphere in the country depends on the development of healthcare and medical industry, which serve as its basis, both at the municipal and regional levels, and show the level of its development. The standard of living and health of the population depend on this sphere. Its qualitative development directly conditioned by the effective model of the healthcare management system, which consists in optimising sources of financing. The study has assessed the most significant pressing problems related to the system and mechanism of financing the healthcare system and medical industry, new possible options for the sources of financing and directions for optimising the distribution of public financing have been explored as well. One of the most effective areas of financing medical institutions at the present stage is public private partnership (hereinafter referred to as PPP). With its help, issues related to modernisation of infrastructure, optimisation of the resource management process in the medical institutions as well as improvement of the level of services provided are addressed. The study also examines risks arising from the use of the PPP in terms of its application as financing for the medical industry and healthcare sector.

In response to the growing demand for healthcare and the increasing importance people place on medical services, efficiently meeting these needs within the constraints of limited healthcare resources is of great social and economic benefit. Therefore, research into applying Large Language Models (LLMs) in the healthcare sector holds significant importance. This paper provides a review of the research progress on the application of LLMs in the healthcare field. First, the basic framework of LLMs is summarized, and the training process of LLMs in healthcare is systematically reviewed. Next, six specific application areas of LLMs in healthcare are reviewed: disease diagnosis and decision support, dissemination of medical knowledge, medical assistance, medical image analysis, biomedicine, and medical education. Then, several representative healthcare-specific LLMs are discussed, along with their performance analysis. Following this, the challenges faced by LLMs in healthcare are summarized, and relevant suggestions are provided. The future development trends of LLMs in healthcare are also explored. Finally, a bibliometric analysis is performed. Through the literature review, we found: 1) After pretraining, LLMs are widely adaptable to downstream tasks, significantly enhancing processing performance and efficiency; 2) LLMs in healthcare possess multiple capabilities and can handle multimodal data; 3) Bibliometric analysis shows that researchers are paying increasing attention to the application of LLMs in healthcare; 4) Further research is needed in optimizing, improving reliability, and expanding practical applications of large healthcare models.

In medical research, the rapid proliferation of condition-specific studies has led to an information overload, making it challenging for researchers and practitioners to stay abreast of the latest findings. This paper presents a comprehensive survey on leveraging Generative Pretrained Transformers (GPTs) to summarize medical and biomedical artifacts systematically. We delve into the current applications of GPTs in this domain, discussing their role in understanding and summarizing research papers, medical dialogues, and medical records. Through a comparative analysis of recent studies and methodologies, we highlight the effectiveness of GPTs in distilling complex medical information into concise, understandable summaries. Our survey underscores the potential of GPTs as a tool for navigating the information overload in medical research and bringing clarity to healthcare professionals. This transformation will enhance patient care and outcomes, such as improving the accessibility and comprehensibility of medical research, assisting in rapid information retrieval, and facilitating the summarization of complex medical studies for broader audiences.

PURPOSE: The study aims to investigate the direct and indirect relationships among market orientation, service program innovativeness, and the financial performance of hospitals. Two types of market orientation – responsive and proactive – were considered, along with two dimensions of service program innovativeness: meaningfulness and novelty. METHODOLOGY: The study gathered data through a survey conducted on a random sample of 204 Polish hospitals. Structural equation modeling was used to analyze the data, test a conceptual model designed as a parallel two-mediator model, and validate the hypotheses. FINDINGS: The work revealed both direct and indirect effects. In terms of direct effects, the study found that implementing a responsive market orientation positively influences the meaningfulness of a hospital’s service program but has no impact on its novelty. Conversely, the implementation of a proactive market orientation has a positive influence on both the meaningfulness and novelty of the program. Furthermore, the meaningfulness and novelty of the program contribute positively to the hospital’s financial performance. In terms of indirect effects, the study identified mediation phenomena: a responsive market orientation positively affects a hospital’s financial performance through the meaningfulness of the service program, while a proactive market orientation enhances financial performance through the novelty of the program. IMPLICATIONS: The study contributes to the current understanding, confirming the positive impact of a proactive market orientation on innovations within the organization while contradicting the view that a responsive market orientation supports these innovations. Additionally, the results support the idea that innovations within the organization have a positive impact on its outcomes. The study also reveals specific mechanisms that influence market orientation on organizational outcomes, indicating that a responsive orientation affects a service provider’s outcomes through the meaningfulness of its service program, while a proactive orientation influences outcomes through the novelty of the program. The practical recommendations for hospital managers are as follows: (a) to achieve a high level of meaningfulness in the service program, it is advisable to implement both responsive and proactive market orientations; (b) to attain a high level of novelty in the program, a proactive market orientation is recommended; (c) to enhance the hospital’s financial performance, it is suggested to develop a service program that is both meaningful and novel, and to implement both responsive and proactive market orientations. ORIGINALITY AND VALUE: The study’s originality and value stem from its exploration of specific direct and indirect mechanisms through which market orientation affects hospital financial performance, filling a prior research gap. By investigating these mechanisms, the study enhances the overall understanding of hospital management.

Abstract This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data. This approach not only enhances the capabilities of FMs in medical diagnostics and personalized treatment but also addresses critical concerns about data privacy and security in healthcare. This survey reviews the current applications of FMs in federated settings, underscores the challenges, and identifies future research directions including scaling FMs, managing data diversity, and enhancing communication efficiency within FL frameworks. The objective is to encourage further research into the combined potential of FMs and FL, laying the groundwork for healthcare innovations.

Steganography enhances data security by embedding information seamlessly within digital channels. While hospital systems focus on text and visuals in medical images, audio integration remains an overlooked area with untapped potential. This research addresses the security gap by exploring the embedding of audio data in medical images to enhance patient record protection and information integrity. The Proposed work introduced an innovative Steganography approach for medical image data by embedding Audio files aiming to addresses this research gap. This method integrate audio data into medical images, specifically DICOM-standard images without compromising their visual integrity. It also preserves the integrity of DICOM metadata, ensuring compliance with medical imaging standards. The Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) steganography techniques are used to embed audio file data into medical images. The study illustrates the effectiveness of LSB and DCT techniques on a computed tomography(CT) Images. The LSB method is renowned for its simplicity and its ability to extract data without introducing errors. Additionally, the method offers efficient insertion and extraction time during the watermark embedding and retrieval process, making it highly practical for real-time applications. An algorithm employs DCT technique for Audio integration within DICOM images are more robust and imperceptible. Both techniques have been proven to be highly precise and supported by empirical evidence such as PSNR, SSIM, and MSE analysis. Robustness analysis was conducted on various image quality variation scenarios that includes compression level, resolution and noise tolerance. The proposed DCT approach achieves a PSNR of 57.89 dB, SSIM of 0.9997, and MSE of 0.013, demonstrating minimal distortion and superior structural integrity. Robustness analysis shows that DCT outperforms the LSB method, consistently maintaining PSNR above 47 dB, MSE approximately zero, and SSIM at 0.99 across varying compression levels, image resolutions, and noise conditions. After conducting an experiment, the DCT technique is the most suitable approach for secure communication of medical data with capacity of 2.03 bits per pixel (bpp). The overall analysis of proposed work significantly improves result in terms of the security and robustness under various attacks.

Abstract Background Commissioning for health services has been implemented as one approach to improve the quality and access to healthcare for First Nations, regional and remote populations. This review systematically scoped the literature for studies that described or evaluated the governance, funding, implementation and outcomes from health service commissioning targeting these groups in Canada, Australia, Aotearoa/New Zealand and the United States (CANZUS nations). Methods Seventeen databases were searched for relevant peer reviewed and grey literature studies published in English from 2010 to 2023. Grounded theory methods were used to identify the enablers and strategies or processes that support commissioning and any challenges to implementation. Results Overall, 29 Peer reviewed and 18 grey literature studies remained after screening. The studies reported enabling conditions for effective commissioning including operating models that were responsive to beneficiary needs, workforce and technical capability, flexibility and duration of contracts, adequate funding, and achievable health outcomes and indicators. Supporting strategies focussed on multi-actor collaboration, relationship building, and service innovation. Reported impacts included improved access to care, and self-determination and wellbeing for First Nations populations. Challenges related to inflexible funding, high transaction costs, overcompliance, and poor relationships. Most studies were process evaluations or descriptions of the application of commissioning to various health areas, with comparatively limited assessment of the impacts across the health system, or on health status. Conclusion Findings suggests that a relational model drives success in commissioning for health and wellbeing services for First Nations, regional and remote populations. The relational model presented in this review is supported by the following attributes: responsive, resourced, collaborative, equitable, innovative and self-determined: and when applied by multiple actors in the commissioning process can address the complex health and wellbeing needs of end users.

Many chronic diseases have become severe public health problems with the development of society. A safe and efficient healthcare method is to utilize physical stimulus-responsive polymer patches, which may respond to physical stimuli, including light, electric current, temperature, magnetic field, mechanical force, and ultrasound. Under certain physical stimuli, these patches have been widely used in therapy for diabetes, cancer, wounds, hair loss, obesity, and heart diseases since they could realize controllable treatment and reduce the risks of side effects. This review sketches the design principles of polymer patches, including composition, properties, and performances. Besides, control methods of using different kinds of physical stimuli were introduced. Then, the fabrication methods and characterization of patches were explored. Furthermore, recent applications of these patches in the biomedical field were demonstrated. Finally, we discussed the challenges and prospects for its clinical translation. We anticipate that physical stimulus-responsive polymer patches will open up new avenues for healthcare by acting as a platform with multiple functions.

Abstract Background Maternal mortality is among the main indicators of health in nations. Identifying the causes of maternal mortality is crucial in designing preventive interventions. Therefore, this study aimed to evaluate the causes of maternal mortality during 8 years. Methods This descriptive-analytical study was conducted on the archived cases of maternal mortality in the Forensic Medicine Department of the Khorasan Razavi Province between March 2013 and March 2022, but as no data was available for the first two years, maternal mortality data for 8 years was evaluated. Demographic characteristics, midwifery data, pregnancy care method, pregnancy care and delivery agent, insurance type, and confirmed cause of death were compared between the first and last five-year periods of the study. Results Overall, 250 cases of maternal mortality were identified in the study period. The prevalence of indirect and unrelated causes of maternal mortality was significantly higher in the last four-year period compared to the first four-year period (p

Abstract Background After release from incarceration, individuals are rarely connected to primary care or to social services despite bearing a disproportionate burden of poor health (e.g., chronic illness) and structural determinants of health (e.g., housing instability). The Rhode Island Transitions Clinic (RITC) works to fill this gap, particularly for patients with complex needs, by linking individuals to primary care and social services. However, prior work has not formally assessed how successful the TCN is at connecting patients to social services. Our objective was to assess the relationship between RITC and connections to healthcare use and services addressing structural determinants of health. Methods This retrospective cohort study utilizes data among those released from the Rhode Island Department of Corrections (RIDOC) from 2018–2020. These data were linked with state agency data (e.g., unemployment, Medicaid medical claims, housing and homelessness services). We estimated 6-month risk differences (RDs) of each outcome, using stabilized inverse probability weights to account for censoring and confounding. Results Of 8,694 individuals, the 68 enrolled in RITC are, on average, older, more likely to be Black, and have had more incarcerations. The RITC was associated with higher likelihood of connection to housing and homelessness services (RD: 0.29; 95% CI: 0.17, 0.41), (re)instatement of Supplemental Security Income (RD: 0.17, 95% CI: 0.06, 0.28), Emergency Department (RD: 0.29; 95% CI: 0.17, 0.41), and primary care visits (RD: 0.32; 95% CI: 0.21, 0.43), and lower likelihood of receiving taxable wages (RD: -0.05; 95% CI: -0.14, 0.05) compared to not being in RITC within six-months of release. Conclusions RITC patients have complex healthcare and social service needs. The RITC is an innovative approach that is successful at connecting its patients to primary care and social services.

Abstract Healthcare supply chain management’s (HSCM) significance to economic and societal development is huge. In today’s very competitive market, supply chains have seen significant changes in the last several years. There is a need for technology that can handle the increasing complexity of today’s dynamic supply chain activities. Both machine learning (ML) and the quick dissemination of information have the potential to revolutionize the supply chain. ML has spawned a slew of useful supply chain applications in recent years, HSCM has received comparatively less attention. In this study, we applied three ML algorithms such as gradient boosting (GB), histogram gradient boosting (HGB), and cat boosting (CB) with data preprocessing tools to predict whether the medicines are delivered on time or not in the HSCM. The data preprocessing tools are used to manage datasets and increase the performance of ML algorithms. There are three methods of feature selection that are applied in this study such as Pearson correlation, chi-square test, and principal component analysis to select the best features to push in the ML algorithms. The main results show the CB is the best algorithm with the highest accuracy, precision, recall, and f1 score with values respectively. The three ML algorithms are compared with other ML algorithms to show the robustness of the applied ML algorithms. We made a sensitivity analysis to show the chaining in learning rate (LR) and compute the accuracy of the ML algorithms. We show the CB is not sensitive to values between 0.1 and 1.

Abstract Background Menstrual health is a recognised important public health issue and is essential for the realisation of gender equality and the achievement of Sustainable Development Goals. This study aimed to explore the menstrual health experiences of young people in Sweden and how the menstrual cycle affects their health and lives. The study also aimed to identify the facilitators and barriers to achieving menstrual health. Methods We conducted a qualitative study in Sweden. Sixteen young people aged 18–28 who have experienced the menstrual cycle participated in individual interviews. Purposeful sampling combined with snowball sampling was applied to recruit the participants. The data were analysed using reflexive thematic analysis. Results Participants viewed menstruation as a sign of having a healthy and functioning body. Menstruation was linked to becoming a woman and fostered a sense of community and sisterhood, which was viewed as positive among cis women. Further, the results showed that physical and emotional symptoms related to the menstrual cycle limited the participants’ everyday lives and social relationships and had a negative effect on their sexual and mental health. While managing their emotional discomfort and other menstrual complaints, they also had to deal with the public stigma and norms about menstruation contributing to shame and worries. Barriers to menstrual health included stigma and norms related to menstruation, which led to the adoption of expected behaviours, such as avoiding participation in social activities. The normalisation of menstrual complaints also contributed to delays in seeking healthcare, despite having symptoms that had a negative effect on their health. An important factor promoting menstrual health and quality of life is access to prompt treatment to mitigate and decrease symptoms that limit everyday life. Conclusions The results indicate that menstrual stigma and related norms create challenging situations limiting menstruating people’s everyday lives and reluctance to seek healthcare despite needing to. To promote the menstrual health of menstruating young people in Sweden, organised and systematic screening of menstrual cycle-related symptoms should be provided within student health services at schools and universities, and primary healthcare. Policymakers should consider integrating stigma-reducing efforts into public health interventions to improve general awareness and promote gender equality.

ChatGPT, developed by OpenAI, is a large language model (LLM) that leverages artificial intelligence (AI) and deep learning (DL) to generate human-like responses. This paper provides a broad, systematic review of ChatGPT’s applications in healthcare, particularly in enhancing patient engagement through medical history collection, symptom assessment, and decision support for improved diagnostic accuracy. It assesses ChatGPT’s potential across multiple organ systems and specialties, highlighting its value in clinical, educational, and administrative contexts. This analysis reveals both the benefits and limitations of ChatGPT, including health literacy promotion and support for clinical decision-making, alongside challenges such as the risk of inaccuracies, ethical considerations around informed consent, and regulatory hurdles. A quantified summary of key findings shows ChatGPT’s promise in various applications while underscoring the risks associated with its integration in medical practice. Through this comprehensive approach, this review aims to provide healthcare professionals, researchers, and policymakers with a balanced view of ChatGPT’s potential and limitations, emphasizing the need for ongoing updates to keep pace with evolving medical knowledge.

Urologic healthcare in low- and middle-income countries is recognized to be underserved. The goal to improve urologic health outcomes for populations in these countries may be met through urologic humanitarian work, often brought about by aid workers with clinical expertise originating from high-income countries. This essay serves as a brief narrative review of the literature describing urologic outreach efforts brought to low- and middle-income countries and perspective on the purpose of these efforts. A range of urology-specific organizations are engaged in international volunteerism efforts. The foundation of this activity, to the greatest extent, can be characterized as international collaboration involving healthcare providers of the local region of service. Service activities include not just medical or surgical missions but span from clinical workshops to educational programming, faculty training programs, research enterprises and health care system initiatives. Whereas challenges confront aid workers primarily relating to difficult resources, there are definite rewards for humanitarian work. These rewards are not viewed only as a one-way proposition benefitting the local region receiving health care. Visiting aid workers also prosper by way of life lessons in service and humanity and an appreciation of health equity in a worldwide sense.

This study investigates compassion’s role in healthcare, examining its relationship between job demands, job social resources, and well-being outcomes (engagement and burnout) across organizational levels. Data from 714 healthcare professionals in 35 Spanish hospital teams were collected using the Brief Compassion Scale and HERO questionnaire. Hierarchical linear modeling showed team-level job social resources positively associated with individual-level compassion (β = .65, p< .01). Compassion positively associated with engagement (β = .88, p< .001) and negatively with burnout (β = -.89, p< .001). Individual-level job demands negatively affected compassion (β = -.60, p< .001). These findings highlight compassion's role in improving engagement and mitigating burnout across organizational levels. The results emphasize the importance of fostering compassion and enhancing team job social resources to improve healthcare professionals’ well-being, offering valuable insights for understanding workforce dynamics in healthcare organizations.

The new industrial era has brought new opportunities and chances for the entire business development. Smart machines, artificial intelligence, cloud computing, the Internet of Things, big data are taking over many jobs and roles, thus leaving room for the development of new skills and abilities. The rapid technological development in terms of automation and digitization has made machines replace human work. In this sense, it is a matter of time when technology will replace traditional accountants. (Management) accountants who want to adapt and survive in the digital world have to improve their offer and change the focus from data calculation to interpretation of results and business management. Thus, by applying new digital information technology tools, management accounting can provide quality information for determining the costs of products and services, performance measurement, planning and control, strategic and operational decision-making and the like. The general objective of this paper is to review the potential impact of digital information technologies on the usefulness of cost accounting systems and organizational performance in healthcare institutions in the Republic of Serbia with the help of statistical analysis of the relationship between the selected variables. The results of the analysis show that digital technologies have a great impact on the usefulness of the cost accounting system. Also, the largest number of respondents pointed out that improved IT systems have a positive effect on increasing organizational performance.

Abstract Background Burnout is among the greatest challenges facing healthcare today. Healthcare providers have been found to experience burnout at significant rates, with COVID-19 exacerbating the challenge. Burnout in the healthcare setting has been associated with decreases in job satisfaction, productivity, professionalism, quality of care, and patient satisfaction, as well as increases in career choice regret, intent to leave, and patient safety incidents. In this context, there is a growing need to reduce provider burnout through targeted interventions, yet little is known about what types of interventions may be most effective. The present study aims to contribute to and extend prior literature by using rigorous randomized controlled trial (RCT) methodology with a parallel group design to examine the effectiveness of different interventions in decreasing mental distress, increasing self-efficacy and attenuating inefficiencies and dissatisfiers in the work environment to achieve sustainable improvement.’ Methods The present study is an ongoing randomized controlled trial (RCT) that examines the effectiveness of three different types of interventions to reduce provider burnout: an intervention targeting emotional wellbeing and resilience, Electronic Health Record (EHR) skills training, and performance improvement training, relative to a no-treatment control group. This study aims to enroll a total of 400 healthcare providers in a large urban hospital system. Outcomes will be assessed at post-treatment and 6-month follow-up. Key outcomes include burnout, emotional health, intent to leave, EHR mastery, and confidence in performance improvement. Changes in outcome measurements from baseline to post-intervention across the intervention and control groups will be conducted using linear mixed-effects models (LMM). Discussion This study is novel in that it compares several interventions addressing both personal as well as system-level drivers of provider burnout that have been theorized to operate among healthcare providers. In addition, post-treatment and longer-term follow-up assessments will provide insight into the maintenance of effects. Another innovation is the inclusion of different types of patient-facing providers in the study population (doctors, nurses, and therapists). Trial registration This study was registered at ClinicalTrials.gov (NCT05780892) on March 10th, 2023.

Abstract Background This research aims to assess the level of awareness and readiness for the utilization of Electronic Prescription System (EPS) among medical professionals and patients, as well as to identify their opinions regarding the advantages and disadvantages of this system. Methods The study was conducted in Russia among 423 respondents, including medical professionals and patients, with an average age of 40 years. Results The awareness of EPS among medical professionals (20%) and patients (45.7%) is notably low. The majority of physicians possess only basic computer skills (62.5%). In contrast, pharmaceutical workers (60%) and medication consumers (45.7%) exhibit a confident level of computer proficiency. EPS is evaluated as more reliable (100% of physicians) and convenient (93.3% of pharmacists) compared to paper-based prescriptions. The results highlight the necessity of educating medical professionals and patients on the use of EPS. The study confirms the low awareness and readiness for the utilization of EPS among medical professionals and patients. However, the EPS is perceived as more reliable and convenient compared to paper prescriptions. Conclusions This research holds practical significance for the development of strategies for implementing EPS and enhancing the preparedness of medical professionals and patients for their use.

Mariam I Shaqfeh,1 Ahmad R Alsayed,2 Luai Z Hasoun,2 Heba A Khader,3 Malek A Zihlif4 1Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan; 2Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, Jordan; 3Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmaceutical Sciences, The Hashemite University, Zarqa, Jordan; 4Department of Pharmacology, School of Medicine, The University of Jordan, Amman, JordanCorrespondence: Ahmad R Alsayed, Department of Clinical Pharmacy and Therapeutics, Applied Science Private University, Amman, 11937, Jordan, Tel +962786770778, Email a_alsayed@asu.edu.jo, a.alsayed.phd@gmail.comPurpose: This study aimed to assess the knowledge and attitudes of the community toward the brand names of the most commonly used over-the-counter (OTC) analgesics in Alkarak, Jordan, as well as to assess community’s self-medication behaviors that may lead to misuse of OTC drugs.Patients and Methods: This is a questionnaire-based cross-sectional study performed between 7 September and 1 October 2023. The total number of recruited participants was 730 adults from Alkarak, Jordan. One-way ANOVA was used to determine the statistical differences among the means of independent groups. The reliability of each question was evaluated using Cronbach’s alpha. The Cronbach’s alpha was within the accepted range for all questionnaire items (0.7– 0.9).Results: Most participants were male (60.3%), with an average age equal to 38 ± 11.06 years, having a bachelor’s degree (61.0%) from humanity colleges (43.6%). The highest proportion of the participants were reported to be without chronic diseases and not under chronic medications (57.1%). This study revealed a low knowledge score regarding OTC drug brand names (2.86 ± 0.99 out of five) and unfavorable attitudes toward OTC medicines (2.68 ± 1.04 out of five). However, the study finds a significant relationship between the level of education and knowledge and attitude toward OTC drug brand names (p-value < 0.001). Bachelor’s holder participants had the lowest knowledge of OTC drug brand names, whereas the worst attitude was reported among below diploma participants. Many participants held misconceptions about OTC drug brand names that lead to consumption of the same drugs under different brand names at the same time; especially OTC analgesics.Conclusion: Low levels of knowledge and unfavorable attitudes regarding OTC drug use from different brand names were reported. Increasing the awareness of the community and enhancing the role of physicians and pharmacists in OTC drug consumption may lead to decrease the misuse of these drugs.Keywords: public health, healthcare, non-prescription drugs, brand name, analgesics

The healthcare supply chain encounters difficulties with transparency, efficiency, and security, which have an impact on patient safety and the quality of treatment concerning the items involved. The use of blockchain technology, which has intrinsic characteristics such as confidentiality, transparency, and traceability, offers a possible resolution to tackle these problems. This paper aims to comprehensively review the adoption of blockchain technology in healthcare supply chain management, particularly in response to the challenges posed by the COVID-19 pandemic. It investigates the significance of efficient and transparent healthcare supply chains, focusing on blockchain's application in vaccine distribution, Personal Protective Equipment (PPE), drugs, medical devices and blood products. The analysis critically evaluates research papers proposing innovative blockchain-powered solutions, discussing their benefits, challenges, and the need for further research. Findings highlight blockchain's potential in enhancing vaccine traceability, preventing counterfeit vaccines, and ensuring equitable access to immunization. It also outlines blockchain's role in real-time tracking of PPE shipments, secure distribution of medical devices, managing blood products, and combating counterfeit drugs. The paper also emphasizes the prevalence of consortium-based and public blockchain implementations and the importance of smart contracts while advocating for addressing scalability and technological challenges. This review offers a critical assessment of blockchain's potential in fortifying healthcare supply chains during crises, underscoring the need for ongoing research and development to overcome implementation limitations. Doi: 10.28991/HIJ-2024-05-04-019 Full Text: PDF

This contribution examines the concept of circular economy (CE) within the healthcare sector, introducing the 9R paradigm as a novel framework for the implementation of sustainable strategies. Given the significant CO2 emissions from the medical device sector and the heterogeneous digitalization across the industry, CE offers an avenue to minimize environmental impact while simultaneously enhancing patient care. Leveraging advanced data platforms and digital technology can help reduce resource waste and CO2 emissions, while also optimizing patient care by avoiding redundant diagnostics and treatments. The contribution provides a brief introduction for the application of the 9R paradigm in the healthcare sector and highlights the opportunities associated with the implementation of CE strategies. It outlines innovative data-driven business models based on CE and digital transformation principles. In conclusion, the strategic application of R-strategies can be described as a multi-layered approach requiring close collaboration among all stakeholders to shape a more sustainable and efficient future in healthcare and provide new digital business models.

The integration of Artificial Intelligence (AI) into healthcare represents a transformative shift, offering opportunities for enhancing patient care, diagnostic accuracy, process optimization and treatment pathways. This research sets out to forge a strategic management decision support framework for leveraging AI within the healthcare sector, aimed at systematically exploring and integrating AI innovations to bolster the patient health outcomes. By creating a comprehensive categorization system, we attempt to navigate the complex array of possible AI applications within the field of healthcare, hence enabling the identification, selection, and advancement of AIdriven initiatives. Through a blend of systematic literature review and expert insights, this study maps possible AI applications across dimensions like ‘medical disciplines’, ‘healthcare processes’, ‘AI research areas’, and ‘user groups’. By reflecting the diverse perspectives, this system transcends mere classification and stands as a cornerstone for identifying, selecting, and developing AI-driven medical use cases to guide strategic implementations of AI within clinical settings. This multidimensional system offers a blueprint for healthcare entities to strategically navigate the AI landscape, enabling them to make informed decisions about technology adoption and change management processes, ultimately leading to improved patient care and operational efficiency.

Introduction: Proper biomedical waste (BMW) disposal and its management are essential among health-care workers as it causes serious health and environmental hazards. Objectives of the study were to assess the knowledge, attitude, and practice regarding BMW management among health-care providers and to determine the factors associated with, at a tertiary care hospital in Kolkata, India. Material and Methods: A study was done among 256 health-care providers at a tertiary care hospital who were actively involved in handling BMWs. A pretested, self-administered questionnaire on knowledge, attitude, and practice regarding BMW management was used. Structured, nonparticipant observation was carried out using the Kayakalp assessment tool in the 20 BMW handling checkpoints. Statistical analysis was done using the SPSS version 20.0 (SPSS Inc., Chicago, Illinois, USA). Results: Among 256 health-care workers, 44.9% had adequate knowledge regarding BMW management and the doctors had a higher median score. 37.5% had positive attitudes and the laboratory technologists had higher median scores. 45.3% had satisfactory practice regarding BMW management and both the doctors and nurses had higher median scores. The housekeeping staff had the poorest knowledge, attitude, and practice of BMW management. Conclusion: Regular certified training among health-care staff and intense quality assessment by the waste management committee can improve BMW management overall.

Çalışmanın amacı, finansal tabloları bağımsız denetimden geçen özel hastanelerin finansal durumu ve performansının finansal açıdan analiz edilmesidir. Çalışmada nitel yöntem kullanılmıştır. Kamu Gözetimi Muhasebe ve Denetim Standartları Kurumu (KGK) tarafından yayımlanan 2018-2022 yıllarına ait Hastane hizmetleri sektörünün Finansal Durum Tablosu ile Kar veya Zarar Tablosu verileri analiz edilmiştir. Elde edilen verilerin analizinde hastanelerin likidite, varlık kullanımı, finansal yapı ve karlılık durumları karşılaştırılarak incelenmiştir. Çalışma hem Türkiye Finansal Raporlama Standartlarını (TFRS) hem de Büyük ve Orta Boy İşletmeler için Finansal Raporlama Standardını (BOBİ FRS) uygulayan ve bağımsız denetime tabi olan hastane işletmeleri kapsamaktadır. Bu kapsamda 2018’de 102, 2019’da 100, 2020’de 106, 2021’de 87 ve 2022’de 117 hastane işletmesinin analizi yapılmıştır. Elde edilen bulgulara göre, TFRS’ye tabi işletmelerin likidite, varlık kullanımı, finansal yapı ve karlılık analizi açısından BOBİ FRS’ye tabi işletmelere göre daha iyi oldukları görülmüştür. Buna karşın her iki Standarda tabi olan hastanelerin finansal durumlarının 2021-2022 yıllarında istenilen seviyede olmadığı tespit edilmiştir. Bu çalışma sağlık işletmelerinin pandemi ve sonrası dönemler açısından finansal analizi, sağlık hizmetleri sektörünün karlılıklarının etkilenme düzeyinin tespiti bakımından literatüre katkı sağlamaktadır.

Amelie Koch, Larissa Brust, Matthias Weigl Institute for Patient Safety (IfPS), University Hospital Bonn, Bonn, GermanyCorrespondence: Larissa Brust, Institute for Patient Safety (IfPS), University Hospital Bonn, Venusberg-Campus 1, Bonn, 53127, Germany, Email larissa.brust@ukbonn.dePurpose: In order to obtain sustainable healthcare, engagement of patients in patient safety improvement is vital. Drawing upon a multi-perspective approach, this study aimed to investigate perspectives of patients and healthcare professionals on key implementation factors (ie, barriers and facilitators) for effective patient engagement (PE) in healthcare organizations to improve patient safety.Patients and Methods: A two-round Delphi technique comprising semi-structured interviews and an online survey was applied to consolidate the individual perspectives of stakeholders and establish consensus on factors (expected, potential or experienced) that facilitate or mitigate successful implementation of PE in healthcare organizations (ie, all types, including hospital and outpatient medical practices). Adult, German-speaking experts in patient safety or PE (ie, with professional background or personal experience) were eligible to participate. Purposive and convenience sampling for inclusion of different healthcare professionals and patient representatives was established. Thirty-four panelists participated in data collection.Results: We identified eight key barriers and seven facilitators for effective patient engagement in healthcare organizations. Time constraints and perceived low effectiveness of patient engagement activities were deemed as most critical barriers. Supportive organizational culture, education and training opportunities, and clearly nominated responsibilities for patient feedback and engagement were rated as the most important facilitators. There were no statistically significant differences in the ratings between patient representatives and healthcare professionals.Conclusion: Our findings contribute to a deeper understanding of real-world implementation factors for effective patient engagement in healthcare organizations in Germany to improve patient safety. Our insights may further inform recommendations for future development and implementation of effective patient involvement in healthcare organizations, especially for similar countries with low levels of PE.Trial Registration: German Clinical Trials Register (ID: DRKS00031837).Keywords: patient involvement, patient participation, patient safety culture, healthcare, Delphi technique

The Metaverse is like another world functioning parallel to the actual physical world. It merges physical reality with digital virtuality by the convergence of different technologies that enable interactions with virtual objects and environment. The complete immersive experience with metaverse generates huge data which comes with its own set of challenges, a major one being the security and storage of user centric data. Considerably, blockchain offers a significant solution due to its exceptional features of decentralization, immutability, and transparency. For a detailed insight into the role of blockchain in the metaverse, the paper explores its usage in the healthcare industry. The paper then proposes a healthcare system named MetaBlock that is a fusion of both the metaverse and blockchain techniques. The paper discusses the components of the system from a detailed technical interpretation, such as data acquisition, data storage, virtual object and space simulation, data sharing and data privacy protection. Finally, an important research direction in terms of distributed consensus mechanism is discussed.

Abstract This study compared a purposive sample of secular and religious female social work students during four years of the pandemic. The objective of this study was to assess the association between COVID-19 fear, religiosity, well-being, and substance use in Israel. The Fear of COVID-19 Scale (FCV-19S) was used for study purposes. A total of 1790 students completed an online survey from May 2020 to April 2023. Findings evidence deterioration of student well-being, as well as increased substance use and alcohol binge drinking over 4 years of the pandemic. Regression analysis shows that the predictors associated with substance use are most evident during the first 2 years of COVID-19. The last 2 years of COVID-19 evidence less fear and substance use due to possible student adjustment to health conditions. Present findings do not evidence religiosity as a protective factor. Additional research is needed over time, academic study profession and location to further understand the possible impact of fear on healthcare students during and after disaster conditions including those related to war and violence.

Hospital financial failure means bankrupt in economic point-of-view. While from the patient care perspective, it can compromise healthcare delivery and deliver possible harm due to substandard services. Leveraging digitalization and information technology can significantly enhance financial management, improve efficiency, and ultimately support sustainable healthcare services. This bibliometric analysis through Scopus database. Retrieved articles were examined based on annual publication numbers, contributing countries, subject areas, authors, and cited documents. Visual analysis was done using VOSviewer to provided insights into word co-occurrence in clusters and R Studio's Bibliometrix package was employed to identify the most contributing countries and the most cited publications. The analysis on total 429 articles revealed that healthcare and activity-based costing is an emerging trend, with a growing number of publications each year. The United States emerged as the leading country in this research area, though other nations also contributed significantly. Medicine was identified as the most common subject area. VOSviewer analysis identified seven clusters related to the topic. Activity-based costing has evolved to accurately assess product costs in changing business landscapes, particularly in healthcare, where it helps optimize profits. The findings indicate a growing awareness of the importance of financial management in sustaining healthcare services globally. However, the study's reliance on a single database and lack of a comprehensive systematic review limit its scope, potentially excluding relevant research from other sources.

Abstract Introduction Medical imaging's critical role in diagnosis requires prompt and precise image interpretation. Numerous radiology departments, especially in low‐resourced settings, encounter challenges such as a shortage of radiologists in their operational setup. This study explored the perceptions of radiographers and radiologists from low‐resourced departments in a single country regarding operational challenges and potential solutions in image interpretation. Methods A qualitative approach was utilised, involving heads of departments, senior radiographers, and radiologists with a minimum of 5 years of experience, from three major state referral hospitals. Face‐to‐face, semi‐structured interviews were conducted in November 2022, using an interview guide that included questions on the challenges encountered during image interpretation and the proposed solutions. Data analysis was conducted using Atlas.ti version 9.0, following the four‐step content analysis method. All participants willingly provided consent to participate in the study. Results Ten participants, comprising two radiologists and eight radiographers participated in the study. The research identified three main themes: image interpretation pathways, image interpretation operational challenges and proposed solutions for image interpretation. In addition, a total of 10 subthemes were generated from the three main themes. Conclusion The study revealed critical challenges and the need to explore the formal inclusion of radiographers in image interpretation, as a way to improve efficiency. However, a comprehensive assessment of the image interpretation system, encompassing radiographers' knowledge and competence, is recommended for context‐specific, empirical‐based modifications to enhance service provision.

Hassan Ali Abdi,1 Mohamed Omar Warsame,2 Mohamud Abdinor Adan,3 Mohamed Ahmed Hassan4 1Quality Assurance and Patient Safety, Federal Ministry of Health and Human Services, Mogadishu, Somalia; 2Department of Public Health, Federal Ministry of Health and Human Services, Mogadishu, Somalia; 3Contract Management, Federal Ministry of Health and Human Services, Mogadishu, Somalia; 4MEAL, IRC, Mogadishu, SomaliaCorrespondence: Mohamed Omar Warsame, Email diirshe143@gmail.comBackground: Patient satisfaction in healthcare a crucial aspect of quality assessment, especially in resource-limited settings like Mogadishu, Somalia, where challenges in service quality persist due to socioeconomic and infrastructural constraints. It plays a significant role in evaluating the overall healthcare experience and provides valuable insights into healthcare system strengths and weaknesses.Objective: This study aimed to assess patient satisfaction in primary health care facilities in Mogadishu to identify areas for improvement.Methodology: A facility-based cross-sectional survey of 358 patients was conducted from August 2023 to November 2023; Data was collected through questionnaires and analyzed using SPSS software. Quantitative data was analyzed using appropriate statistical methods, such as descriptive statistics and chi-square tests, to examine the relationships between patient satisfaction and various factors the statistical significance was declared at p-value < 0.05. Ethical approval was obtained from the Ministry of health Somalia before data collection permission was also obtained from the health facility in charges.Results: While a majority of respondents, 71.5% express overall satisfaction with the healthcare services provided, there were significant concerns regarding empathy, privacy, facility cleanliness, and waiting times. There is a statistically significant association between patients’ visit frequency and their perceptions of various healthcare aspects. Patients who visited the healthcare facility 2– 4 times expressed significantly more positive perceptions of healthcare providers’ competence (p < 0.020), empathy (p < 0.009), time spent (p < 0.001), pharmacy services (p < 0.001), and facility cleanliness (p < 0.001) when compared to those with first-time visits or more frequent visits.Conclusion: The study highlights the importance of addressing communication gaps, enhancing facility hygiene, and reducing waiting times to improve patient satisfaction. To enhance patient satisfaction and overall healthcare quality, recommendations include healthcare provider training, facility hygiene upgrades, and the implementation of strengthened privacy protocols in Mogadishu’s primary healthcare facilities.Keywords: patient satisfaction, primary, healthcare, services and health centers

Machine Learning centers on applications that gain for a fact and further develop their dynamic or prescient exactness over the long run. Behavioral Modification is the use of basic learning techniques such as conditioning, biofeedback, assertiveness training, positive or negative reinforcement, aversion therapy to change unwanted individual or group behavior. Behavior change is vital to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. This paper provides a solution about the utilization of machine learning in behavioral modification by giving some real-time examples. The device based on machine learning is used to develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesizes, and interprets findings from Brain Computer Interface (BCI) evaluation reports to generate new insights to conduct change and further develop forecast of intervention viability and permits clients to effectively and productively examine the framework to find solutions. Organizations engaged in healthcare are charged with the complex task of keeping expenses down without compromising healthcare quality. The key prerequisite is to focus instead of fix, with the greatest test being the need to follow up on enormous volumes of totaled medical care driven Big Data.

India’s healthcare system is a complex structure divided into public and private sectors. The public sector, funded and managed by the government, aims to offer accessible and affordable healthcare to the citizens. It includes a network of primary health centres, district hospitals, and specialised medical institutions like the All India Institute of Medical Sciences (AIIMS). In contrast, the private sector, comprising of the small clinics and large hospitals offering a wide range of services often at a higher cost. This dual structure reflects the challenges of ensuring equitable access to quality healthcare across the country, highlighting the need for integrated strategies to bridge the gap between these sectors. This review discusses a patient-centric model, rather than a pathy-centric one, which can offer a more effective healthcare solution. Patient-centric models offer various health services at a single point of contact, allowing patients to benefit from the strengths of different systems of medicine in one place. Achieving this model requires a multilevel approach, with systems of medicine functioning alongside each other as complementary services. Key areas for integration include clinical practice, medical education, and research, all aimed at fostering a patient-centric approach. The benefits of such an integrated system include holistic health approaches, improved accessibility, and enhanced research opportunities. However, significant hurdles must be addressed, including cultural biases, regulatory fragmentation, and the need for evidence-based practices. Strategies for overcoming these challenges include the development of supportive national health policies, educational reforms, and community engagement initiatives. This review article explores the concept of “One Nation, One Health System”, which proposes the integration of traditional and modern health practices into a unified framework to enhance healthcare delivery.

Abstract Background Health information technology has developed into a cornerstone of modern healthcare. It has changed workflows and enhanced communication, efficiency, and patient safety. However, technological development has progressed faster than research on its potential effects on care quality and the healthcare work environment. Using the Job Demand-Resources theory, this study investigated the associations between "frustration with technology" and three outcomes: stress, emotional exhaustion, and staff satisfaction with care, holding job resources and the demand workload constant. Method A cross-sectional correlational study was conducted between January and April 2022. Healthcare staff from different professions (e.g., physicians, registered nurses, physiotherapists, licensed practical nurses) and workplaces (n = 417, response rate 31%) answered a survey regarding job demands and resources in the workplace, frustration with technology, stress, emotional exhaustion, and satisfaction with care. Data were analyzed with Spearman’s rank correlation coefficient, the Mann–Whitney U test, and the Kruskal–Wallis test, and multiple variables, one for each outcome, were tested with Generalized Estimated Equations models in SPSS. Results The bivariate correlation analyses confirmed statistically significant associations between all the independent variables and the outcomes, except for the independent variable high workload. A high workload was associated with stress and emotional exhaustion but not with staff satisfaction with care. In the three GEE models, one for each outcome, higher stress was statistically significantly associated with more frustration with technology and lower scores for the variables participation in decision-making, sense of community at work, and higher workload. Higher emotional exhaustion was associated with more frustration with technology, higher workload, a lower teamwork climate, and lower growth opportunities. Lower staff satisfaction with care was associated with lower scores for the variable participation in decision-making. Conclusions Taking other variables into account, technology frustration matters in staff ratings of stress and emotional exhaustion, but not with the satisfaction of given care. Future studies should aim to further investigate what causes technology frustration and how to mitigate it.

Abstract Background Disparities in access to healthcare has been implied before in Finland, a country with universal healthcare but de facto tiered primary care. Less is however known about the content of care provided in different settings. Previous studies indicate potential disparities in prescribing newer medicines between healthcare sectors. We compared the preceding prescribing patterns of patients who initiated a sodium-glucose co-transporter 2 (SGLT2) inhibitor or a glucagon-like peptide-1 (GLP-1) analogue in public, occupational, and private healthcare. Methods We used logistic models and patient-level register data from the city of Oulu, Finland, during 2014–2018. Among patients who initiated SGLT2 inhibitors or GLP-1 analogues, we studied whether it was a first-line treatment or if other antidiabetic medicines preceded the use. In addition, prior use of statins (a lipid-lowering medicine) and insulins were studied. Clinical guidelines for type 2 diabetes recommend in most cases metformin in first-line, and insulin only at later stages or in case of severe hyperglycaemia. Using a lipid-lowering medicine is typically recommended for all. Results The examined novel antidiabetic medicines were seldom initiated in first-line, and no significant differences were observed for preceding statin use across sectors, net of patient characteristics. However, patients in the public sector were more likely to have used insulin previously compared to patients in occupational sector. Conclusions Before the initiation of the examined novel antidiabetic medicines, no marked differences across sectors in the use of other antidiabetic medicines or statins were observed. The higher likelihood of prior insulin use in the public sector might reflect initiation at a later stage and/or unobserved differences in clinical characteristics across patient populations.

Abstract Background Digital health innovations are considered a promising avenue in transforming the resourcing, organization and delivery of healthcare services and are often nurtured in organization-based innovation labs. One of their biggest challenges is developing effective processes for sifting through ideas. To improve the existing work process, innovation management is often rooted in lean thinking principles. To illustrate the optimization of the efficiency of innovation processes, an example of a lean innovation process concept will be evaluated in this study. Methods The setting of this study was the Health Innovation Lab (HIL) of the Maastricht University Medical Center + (MUMC +) in the Netherlands. The aim was to evaluate its lean “speed dating” concept to improve the implementation of Digital Health innovations in secondary health care. A qualitative study was performed based on a focus group and semi-structured interviews. 36 stakeholders were selected by purposive sampling. Data was collected from 11 respondents. The transcripts of the focus group and interviews were coded according to the open and axial coding phases of the methods of Grounded Theory. Results Five major themes emerged during the thematic analysis of the transcriptions: (1) Preparation before the Sign in, (2) Mapping out the process between Sign in and Sign out, (3) Defining the roles and deliverables of experts, (4) Guiding the idea owner, and (5) Supporting capacity of resources. Conclusions Through multidisciplinary interaction, by encouraging creative thinking and by fostering innovative behavior, innovation labs cultivate the development of innovative solutions. While the creative phase of innovation commonly exhibits non-linearity, most innovation pilots and implementations follow a linear path. This linearity seems to contradict the importance of flexible approaches to prevent waste in the context of innovation. However, our results suggest that improving cooperation between stakeholders may be a strategy to mitigate this effect. This paper broadens the knowledge on how multidisciplinary interaction during a lean innovation process can be improved, thereby enhancing the implementation of DH innovations.

Abstract Limited longitudinal studies can be found to describe the psychological effects of COVID-19 on healthcare workers (HCW); Therefore, the present study aimed to investigating the anxiety caused by COVID-19 (COV-anxiety) and job burnout (JB) among a sample of Iranian HCW during this pandemic longitudinally. This longitudinal study performed on 310 Iranian HCW. The data collection is done twice during 2020 and 2022. The Corona Diseases Anxiety Scale (CDAS) and the Maslach Burnout Inventory (MBI) have been used through online platform (Google Form). Using frequency tables, Paired-samples T tests, Pearson correlations as well as linear regression modeling data were analyzed through SPSS 16. While reductions observed in prevalence of COV-anxiety in time 2, the prevalence of severe levels of JB dimensions increased in time 2 compared to time (1) Negative correlations existed between JB and COV-anxiety. Moreover, using multivariate linear regressions, we found that COV-anxiety and JB were the final predictors of each other both in time 1 and (2) Despite the positive association between COV-anxiety and JB, increasing in JB levels with respect to decreasing in COV-anxiety levels highlights the existence of other risk factors that should be considered in future research.

Abstract Background People experiencing homelessness access specialist palliative care late in their illness trajectory, if at all. There is also little evidence they receive generalist palliative care or are given opportunities to engage in Advance Care Planning. This qualitative study describes the central role of key workers in supporting access to healthcare in homeless communities and identifies implications for improving palliative care provision. Methods Qualitative data were collected via focus groups and individual interviews with staff working for a key provider of support and housing/accommodation for people experiencing homelessness in an urban area of Aotearoa New Zealand. Results The ability to provide palliative care for people experiencing homelessness is dependent upon supporting engagement with mainstream health services. It is here that we identified the key worker role as central due to the complex and expert work they undertake to facilitate healthcare access for their clients. As a result of the high burden of chronic conditions this community experiences, most of this work related to support managing serious conditions, as well as death and dying. Key workers often went ‘above and beyond’ to support their clients in engaging with mainstream health services, during outpatient appointments, hospital admissions and in emergency department settings. They felt clinicians in these settings did not recognise the knowledge they held about the person, or their skills in terms of providing trauma informed care. The inflexibility of current care provision, as well as people experiencing homelessness feeling stigmatised, and neither valued nor respected in these settings, also created barriers to receiving care. Conclusions New models of palliative care are required which recognise the central role of non-health care key support staff and engage them more actively in supporting people experiencing homelessness when they interact with mainstream health services. Such models will need to be responsive to the nature and complexity of palliative care need in this population and facilitate support for people who typically do not see healthcare spaces as safe. The trusted relationships key workers have developed over time are crucial resources for identifying palliative care need and supporting access to palliative care for people experiencing homelessness.

Uncrewed Aerial Vehicles (UAVs; commonly known as drones) have been gaining interest as a potential transport mode for logistics (i.e., payload delivery), bringing suggested benefits such as reduced transit times and improved access in hard-to-reach locations. However, drones have yet to become widely established in routine logistics systems, with a postulated reason being that the higher costs associated with operating drones relative to traditional surface transport modes (e.g., vans, bicycle couriers) present a barrier to uptake. Based on case studies of two real-world logistics networks transporting patient pathology samples in a healthcare setting in the United Kingdom, this study investigated the effects of the relative costs of drones on mode choice in integrated, multi-modal logistics systems. Results suggested that drones could be a financially viable option if their costs reduced below ∼19% of current values, although such a reduction may not be feasible, even in a future involving increased drone automation. Drones reduced sample transit times by up to ∼70% compared to vans but benefits to the wider healthcare system were negligible because level of service requirements for transit times could be achieved by all modes.

The ageing global population puts heavy pressure on healthcare systems everywhere. Addressing ageing-related chronic conditions requires employment of novel innovative solutions. Telehealth technologies, including telepresence robots (TPRs), are being rapidly developed to provide healthcare services efficiently wherever needed. This article explores the role of TPRs in addressing the challenges of providing healthcare to an ageing population, emphasizing their potential advantages and drawbacks. Employing an exploratory research approach with qualitative data collection techniques, we tested three TPR usage scenarios in simulated healthcare settings: anamnesis, measurements, and falls and frailty. The study employed a non-random purposive sample comprising 25 participants, and was conducted at a medical facility in June 2023. The findings suggest that TPRs offer promising solutions for healthcare professionals and patients, especially in scenarios when physical presence is impossible or physical isolation is required to prevent contagion. However, the technology is not yet ready to substitute fully human medical workers, potentially causing patient reluctance and emphasizing the need for patient-centered approaches to technology adoption. In addition, more studies are needed to address ethical, privacy, and scalability concerns.

Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term “eXplainable and Interpretable Artificial Intelligence” (XIAI) is introduced to distinguish XAI from IAI. Different models are further categorized based on their functionality (model-, input-, output-based) and scope (local, global). Our analysis shows that attention mechanisms are the most prevalent emerging IAI technique. The use of IAI is growing, distinguishing it from XAI. The major challenges identified are that most XIAI does not explore “global” modelling processes, the lack of best practices, and the lack of systematic evaluation and benchmarks. One important opportunity is to use attention mechanisms to enhance multi-modal XIAI for personalized medicine. Additionally, combining DL with causal logic holds promise. Our discussion encourages the integration of XIAI in Large Language Models (LLMs) and domain-specific smaller models. In conclusion, XIAI adoption in healthcare requires dedicated in-house expertise. Collaboration with domain experts, end-users, and policymakers can lead to ready-to-use XIAI methods across NLP and medical tasks. While challenges exist, XIAI techniques offer a valuable foundation for interpretable NLP algorithms in healthcare.

Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving multi-modal synthetic data generation were also explored. The type of method used for the synthetic data generation process was identified in each study and was categorized into statistical, probabilistic, machine learning, and deep learning. Emphasis was given to the programming languages used for the implementation of each method. Our evaluation revealed that the majority of the studies utilize synthetic data generators to: (i) reduce the cost and time required for clinical trials for rare diseases and conditions, (ii) enhance the predictive power of AI models in personalized medicine, (iii) ensure the delivery of fair treatment recommendations across diverse patient populations, and (iv) enable researchers to access high-quality, representative multimodal datasets without exposing sensitive patient information, among others. We underline the wide use of deep learning based synthetic data generators in 72.6 % of the included studies, with 75.3 % of the generators being implemented in Python. A thorough documentation of open-source repositories is finally provided to accelerate research in the field.

Abstract: Introduction: Healthcare decision-making is a critical yet understudied aspect of medical education. It holds significant importance among healthcare professionals and influences their overall behavior. We adapted and validated the Decision-Making Questionnaire, which measures factors affecting decision-making among obstetricians and gynecologists. Methods: We used a cross-sectional survey design to establish the construct validity of the DMQ. We established content validity using the Lawshe content validity ratio and conducted a survey to establish the construct validity. Results: Exploratory factor analysis (n = 150) revealed three factors, task-related, context-related, and individual-related, having eigenvalues greater than 1 and adequate alpha reliability. Confirmatory factor analysis (n = 197) found adequate model fit indices. We assessed the reliability coefficient of the Decision-Making Questionnaire using Cronbach’s alpha and McDonald’s Omega coefficient, which showed an acceptable range (0.86–0.90). Hence, the scale appears to be psychometrically sound and a practical measure for obstetricians and gynecologists. Discussion: The Decision-Making Questionnaire demonstrated convergent validity by positively correlating with the Team Decision-Making Questionnaire and divergent validity by showing correlations with the measure of professional quality of life. The scale is useful for practitioners and researchers to measure factors affecting decision-making in naturalistic settings.

The research community and digital sector are currently engaged in the development of diverse services within the metaverse ecosystems. Conversely, as concerns about safeguarding personal privacy mount, individuals anticipate exercising greater authority over the way third parties make use of sensitive information, such as medical data. This study aims to develop tools that can facilitate the provision of healthcare services within the metaverse ecosystem in a safe and trustworthy way. This project proposes a trust framework for digital healthcare applications to run in a metaverse environment. The framework is based on the use of self-sovereign identity (SSI) and has been tested on the design principles outlined for the SSI-based architecture. This study outlines two use-case scenarios that can serve as foundations for implementing a trust framework.

Coaching is a dynamic, evolving field that seeks to unlock individuals’ potential to achieve their personal and professional best. It has gained widespread recognition across various sectors as an effective means of leadership skills development, including in the vital healthcare sector, which requires innovative strategies to build leadership skills that can sustain high-quality services and maintain patient safety. Many healthcare organizations have thus embraced coaching as a valuable tool for leadership development. This review presents the definitions, concepts, methodology, and applications of coaching, and identifies its salient characteristics in relation to other professions. The second section of this study focuses on a literature review, specifically examining coaching in the healthcare industry and its role as a method for leadership development. The outcome from the review offers insights and results from a review of related research, specifically identifying the potential for coaching within healthcare and its effectiveness in leadership development. The information gathered from this study provides valuable guidance to healthcare leaders and organizations seeking to implement coaching practices in their leadership development programs.

This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses the inherent uncertainty and variability in healthcare data. This study demonstrates the real-world applicability of BNNs through three key case studies: personalized diabetes treatment, early Alzheimer’s disease detection, and predictive modeling for HbA1c levels. By leveraging the Bayesian approach, these models provide not only enhanced predictive accuracy but also uncertainty quantification, a critical factor in clinical decision making. While the findings are promising, future research should focus on optimizing scalability and integration for real-world applications. This work lays a foundation for future studies, including the development of rating scales based on BNN predictions to improve clinical outcomes.

Objective: This study investigates the perceptions of Long COVID patients in Spain regarding the healthcare they receive to identify demands and areas for improvement. Methods: Using a qualitative descriptive phenomenological approach, the study included 27 participants selected through non-probabilistic convenience sampling. Data were collected via online semi-structured interviews and analyzed using thematic analysis. Results: The findings reveal three key themes: (i) health status and challenges in healthcare during the initial COVID-19 infection; (ii) perceptions about healthcare as Long COVID patients; and (iii) demand for and aspects of improving quality of healthcare. The participants, predominantly women (66.67%) with a median age of 51 years, experienced symptoms that they generally perceived as severe, although only 14.81% required hospitalization. The participants reported initial self-management of symptoms at home, which was influenced by familial responsibilities and hospital overcrowding, and the persistence of a wide range of Long COVID symptoms that significantly impacted their daily lives. Satisfaction with healthcare services varied, with frustrations over systemic inefficiencies and long waiting times. Conclusions: The study highlights the need for timely access to medical care, comprehensive and empathetic healthcare services, and specialized Long COVID units. The results emphasize the importance of patient-centered approaches and multidisciplinary care to address the complex nature of Long COVID effectively. These findings provide crucial insights for improving healthcare protocols and systems to better support Long COVID patients. This study was prospectively registered with the Ethics Committee for Research on Medicines of the Albacete Integrated Health Care Management System (registry) on 22 February 2022 with registration number 2022/001.

The use of AI in the healthcare sector is facing some formidable concerns raised by the practitioners themselves. This study aimed to establish the concerns that surround the adoption of AI among Saudi Arabian healthcare professionals. Materials and methods: This was a cross-sectional study using stratified convenience sampling from September to November 2024 across health facilities. This study included all licensed healthcare professionals practicing for at least one year, whereas interns and administrative staff were excluded from the research. Data collection was conducted through a 33-item validated questionnaire that was provided in paper form and online. The questionnaire measured AI awareness with eight items, past experience with five items, and concerns in four domains represented by 20 items. Four hundred questionnaires were distributed, and the response rate was 78.5% (n = 314). The majority of the participants were females (52.5%), Saudis (89.2%), and employees of MOH (77.1%). The mean age for the participants was 35.6 ± 7.8 years. Quantitative analysis revealed high AI awareness scores with a mean of 3.96 ± 0.167, p < 0.001, and low previous experience scores with a mean of 2.65 ± 0.292. Data management-related worries came out as the top worry, with a mean of 3.78 ± 0.259, while the poor data entry impact topped with a mean of 4.15 ± 0.801; healthcare provider-related worries with a mean of 3.71 ± 0.182; and regulation/ethics-related worries with a mean of 3.67 ± 0.145. Health professionals’ main concerns about AI adoption were related to data reliability and impacts on clinical decision-making, which significantly hindered successful AI integration in healthcare. These are the particular concerns that, if addressed through robust data management protocols and enhanced processes for clinical validation, will afford the best implementation of AI technology in an optimized way to bring better quality and safety to healthcare. Quantitative validation of AI outcomes and the development of standardized integration frameworks are subjects for future research.

Abstract In the face of ongoing conflicts and economic instability, researchers in low- and middle-income countries, particularly in regions like Syria and Palestine, grapple with significant financial barriers to publishing their work. These challenges not only hinder the dissemination of critical medical research but also impede the advancement of medical education. The exorbitant costs associated with publishing in prestigious journals, coupled with limited financial support, create an environment where groundbreaking research often remains inaccessible. This comment utilizes a review of the literature and researcher observations to explore the profound impact of these financial obstacles on the progress of medical research and education, advocating for more equitable publishing practices that support researchers in conflict-stricken regions.

Abstract Background Health is a key driver of the United Nations Sustainable Development Goals. This study examined the relationships between economic indicators, demographic metrics, and health system factors and morbidity due to non-communicable diseases, such as diseases of the circulatory system, in Kazakhstan. Methods This retrospective ecological study utilized regionally aggregated data from 2010 to 2020 for all 14 regions in Kazakhstan. Data on incidence rates of diseases of the circulatory system (DCS) and socioeconomic, demographic, and healthcare variables were sourced from the Bureau of National Statistics and the Ministry of Health. Variables included gross regional product per capita, population density, living wage, unemployment rates, average monthly salary, Gini coefficient, income below subsistence level, housing space per capita, average monthly wage in healthcare, doctor and nurse densities per 10,000 population, and number of hospital beds. A correlation analysis was performed followed by stepwise regression to identify significant predictors. Results The analysis identified that higher living wages (β = 0.7), population density (β = 0.275), nurse density (β = 0.212), and average monthly salary (β = 0.502) were positively associated with higher DCS incidence rates. Conversely, gross regional product per capita (β = -0.68), housing space per capita (β = -0.441), and income below the subsistence level (β = -0.161) were negatively associated with DCS incidence. The model explained approximately 63.7% of variance in DCS incidence. Conclusions Socioeconomic and healthcare factors significantly influence the incidence of circulatory diseases in Kazakhstan. Policies aimed at improving economic conditions such as increasing living wages and reducing unemployment may help lower DCS morbidity. Additionally, equitable distribution of healthcare resources like nurses could enhance early detection and management of circulatory diseases, contributing to better public health outcomes in a middle-income country setting.