Health Care Delivery Research Articles

A 25-Year Retrospective of Health IT Infrastructure Building: The Example of the Catalonia Region

Over the past decades, health care systems have significantly evolved due to aging populations, chronic diseases, and higher-quality care expectations. Concurrently with the added health care needs, information and communications technology advancements have transformed health care delivery. Technologies such as telemedicine, electronic health records, and mobile health apps promise enhanced accessibility, efficiency, and patient outcomes, leading to more personalized, data-driven care. However, organizational, political, and cultural barriers and the fragmented approach to health information management are challenging the integration of these technologies to effectively support health care delivery. This fragmentation collides with the need for integrated care pathways that focus on holistic health and wellness. Catalonia (northeast Spain), a region of 8 million people with universal health care coverage and a single public health insurer but highly heterogeneous health care service providers, has experienced outstanding digitalization and integration of health information over the past 25 years, when the first transition from paper to digital support occurred. This Viewpoint describes the implementation of health ITs at a system level, discusses the hits and misses encountered in this journey, and frames this regional implementation within the global context. We present the architectures and use trends of the health information platforms over time. This provides insightful information that can be used by other systems worldwide in the never-ending transformation of health care structure and services.

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.

Task-Specific Transformer-Based Language Models in Health Care: Scoping Review

Background Transformer-based language models have shown great potential to revolutionize health care by advancing clinical decision support, patient interaction, and disease prediction. However, despite their rapid development, the implementation of transformer-based language models in health care settings remains limited. This is partly due to the lack of a comprehensive review, which hinders a systematic understanding of their applications and limitations. Without clear guidelines and consolidated information, both researchers and physicians face difficulties in using these models effectively, resulting in inefficient research efforts and slow integration into clinical workflows. Objective This scoping review addresses this gap by examining studies on medical transformer-based language models and categorizing them into 6 tasks: dialogue generation, question answering, summarization, text classification, sentiment analysis, and named entity recognition. Methods We conducted a scoping review following the Cochrane scoping review protocol. A comprehensive literature search was performed across databases, including Google Scholar and PubMed, covering publications from January 2017 to September 2024. Studies involving transformer-derived models in medical tasks were included. Data were categorized into 6 key tasks. Results Our key findings revealed both advancements and critical challenges in applying transformer-based models to health care tasks. For example, models like MedPIR involving dialogue generation show promise but face privacy and ethical concerns, while question-answering models like BioBERT improve accuracy but struggle with the complexity of medical terminology. The BioBERTSum summarization model aids clinicians by condensing medical texts but needs better handling of long sequences. Conclusions This review attempted to provide a consolidated understanding of the role of transformer-based language models in health care and to guide future research directions. By addressing current challenges and exploring the potential for real-world applications, we envision significant improvements in health care informatics. Addressing the identified challenges and implementing proposed solutions can enable transformer-based language models to significantly improve health care delivery and patient outcomes. Our review provides valuable insights for future research and practical applications, setting the stage for transformative advancements in medical informatics.

Benefits and Risks of AI in Health Care: Narrative Review

Background The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Objective This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Methods Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non–peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher’s Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. Results The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. Conclusions In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

Predicting individual patient and hospital-level discharge using machine learning

Abstract Background Accurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored. Methods We used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h. We fitted separate extreme gradient boosting models for elective and emergency admissions, trained on the first two years of data and tested on the final year of data. We examined individual-level and hospital-level model performance and evaluated the impact of training data size and recency, prediction time, and performance in subgroups. Results Our models achieve AUROCs of 0.87 and 0.86, AUPRCs of 0.66 and 0.64, and F1 scores of 0.61 and 0.59 for elective and emergency admissions, respectively. These models outperform a logistic regression model using the same features and are substantially better than a baseline logistic regression model with more limited features. Notably, the relative performance increase from adding additional features is greater than the increase from using a sophisticated model. Aggregating individual probabilities, daily total discharge estimates are accurate with mean absolute errors of 8.9% (elective) and 4.9% (emergency). The most informative predictors include antibiotic prescriptions, medications, and hospital capacity factors. Performance remains robust across patient subgroups and different training strategies, but is lower in patients with longer admissions and those who died in hospital. Conclusions Our findings highlight the potential of machine learning in optimising hospital patient flow and facilitating patient care and recovery.

Pharmaceutical industry payments to healthcare professional organisations in the United Kingdom: a seven-year cross-sectional analysis of the Disclosure UK database from 2015 to 2021

Objectives This study aimed to examine the size and trends in payments from the pharmaceutical industry to healthcare professional organisations (HPOs) in the United Kingdom (UK), and to characterise the conflict of interest (COI) management plans by HPOs who received large payments. Design Cross-sectional analysis of non-research payments disclosed in the Disclosure UK database from 2015 to 2021. Setting United Kingdom. Participants 148 pharmaceutical companies disclosing payment data and HPOs receiving the largest payments. Main outcome measures Total value and median of payments per HPO; existence and content of COI policies among HPOs receiving the largest payments. Results A total of 898 HPOs received non-research payments worth £99.9 million (17.4% of non-research payments to all healthcare organisations). The median seven-year payment per HPO was £4509 (interquartile range: £943–£30,360). The annual payments nearly doubled from £9.3 million in 2015 to £17.6 million in 2021. Event payments constituted the largest share (£68.2 million, 68.2%). HPOs representing physicians received over 91.5% (£91.5 million) of all payments, while those related to endocrinology and diabetology – 16.3% (£16.3 million). Over 59.9% (£59.9 million) went to the 30 top-funded HPOs, of which only 17 (56.7%) had a publicly accessible COI policy. However, just 6 (35.3%) of these policies included provisions for payments from external organisations. Conclusions HPOs received nearly one-fifth of pharmaceutical company payments to the UK healthcare sector. These payments were concentrated among a small number of HPOs, which often lacked effective policies for management of COI and payments from the industry.

Examining the Effects of Health Work Shortages and Deployment in Integrated Programme in Bihar, India: AnInstitutional Ethnography.

The integration of health programms in low-resource settings is a common approach aimed at providing quality and cost-effective healthcare services. However, programme integration can unintentionally impact service provision and disrupt the overall organisation of service delivery. In this paper, I examine the integrated programme for non-communicable diseases (NCDs) and explore the challenges related to the deployment of health workers. Specifically, I investigate the institutional processes that shape the work of health workers and the working conditions in district hospitals. To explore the social organisation of the implementation of the integrated NCD programme, I employed institutional ethnography as a research method. Data collection involved interviews, participant observation, and document reviews. Data analysis was conducted using social mapping, analytical writing, and textual analysis methods. The findings of the study suggest that due to staff shortages and a lack of doctors, health workers are continuously subjected to deployment and often required to undertake multiple tasks and responsibilities without any incentives or non-monetary benefits. Administrators perceive health workers in integrated programs as additional human resources available for use in district hospitals. However, the removal of health workers from integrated programs can have negative implications for service delivery and patients' health outcomes. Finding of the study suggests that it is recommended that administrators improve the staff recruitment process and promptly address the grievances of health workers to enhance healthcare delivery.

Assessing Yemeni university students' public perceptions toward the use of artificial intelligence in healthcare.

Artificial intelligence (AI) integration in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates university students' perceptions of using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants' views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied to compare variables. Two hundred seventy-nine (279) students completed the questionnaire. More than half of the participants (52%, n = 145) expressed their belief in AI's potential to reduce treatment errors. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the "Benefits and Positivity Toward AI in Healthcare" and "Concerns and Fears" domains (p = 0.024) and (p = 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries.

Enhancing Fraud Detection Systems through Advanced Data Engineering Techniques

Aims: Fraud remains a persistent issue in various industries, particularly in finance, e-commerce, and healthcare, where traditional rule-based systems have struggled to keep pace with the evolving complexity of fraudulent activities. This study aims to develop an enhanced fraud detection framework by addressing the limitations of traditional rule-based systems, particularly in industries where sophisticated fraud schemes prevail. Study Design: The research utilizes advanced data engineering techniques, including big data analytics, machine learning, and real-time processing, to improve the accuracy and efficiency of fraud detection systems. Place and Duration of Study: The study was conducted over two years across industries with high fraud susceptibility, including financial services, e-commerce platforms, and healthcare organizations. Methodology: The framework integrates various data sources, including transaction logs, user behavior, and external fraud indicators. These datasets were pre-processed through data cleaning, feature engineering, and integration. Supervised and unsupervised machine learning models, such as Random Forest and Gradient Boosting, were applied to detect fraud patterns. Real-time data processing enabled immediate detection and response. The system continuously learned from historical data, adapting to new fraud tactics and improving detection over time. Results: The proposed framework demonstrated a significant improvement in fraud detection accuracy, with machine learning models achieving over 90% accuracy rates. There was also a 30% reduction in false positives compared to traditional methods, and detection times were shortened by 40%, enabling faster identification and mitigation of emerging fraud schemes. Conclusion: This study concludes that integrating advanced data engineering techniques with machine learning significantly enhances fraud detection systems' accuracy, scalability, and adaptability. While promising, further improvements are needed, particularly in addressing the evolving nature of fraud schemes and ensuring the scalability of real-time data processing. These areas present opportunities for future research and development.

Assessing the interdependency among effectiveness, satisfaction and efficient use of the Lightwave Health Information Management System (LHIMS) by health professionals in Ghana.

The success of an electronic health records (EHR) system is dependent on the effectiveness, satisfaction, and efficient use of these systems by health professionals. This paper explores the interdependency among effectiveness, satisfaction, and efficient use of Lightwave Health Information Management Systems (LHIMS) which is an EHR by health professionals. A non-interventional descriptive cross-sectional study design was employed for this research. The study used stratified random sampling for the selection of participants. The population for the study was all the health professionals in the Central Region of Ghana. The number of health professionals who responded to the questions was 1126. The findings suggest that understanding the interdependency of efficiency, effectiveness and satisfaction is crucial for the successful implementation and adoption of LHIMS in healthcare organisations. The study investigated the interdependence among effectiveness, satisfaction, and efficient use of the LHIMS by health professionals, and found a positive but weak significant relationship between these factors. Inconclusion, this study aimed to assess the interdependency among effectiveness, satisfaction, and efficient use of LHIMS by health professionals. The. results support a positive but weak significant relationship between satisfaction, effectiveness, and efficient use of the LHIMS. These findings have implications for policymakers and hospital management in their efforts to improve the use of LHIMS. Policymakers can focus on any one of the three usability areas to enhance LHIMS use, with visible effects across the other two domains.

Evaluating the usability of Iran's national comprehensive health information system: a think-aloud study to uncover usability problems in the recording of childcare data.

Health information systems play a crucial role in the delivery of efficient and effective healthcare. Poor usability is one of the reasons for their lack of acceptance and low usage by users. The aim of this study was to identify the usability problems of a national comprehensive health information system using the concurrent think-aloud method in the recording of childcare data. A descriptive cross-sectional study was conducted in the health centers of Kashan University of Medical Sciences, Iran, in 2020. Ten healthcare providers as system's users were purposively selected to evaluate the system. To identify problems, a concurrent think-aloud evaluation was conducted. Two administrators of the system designed scenarios for ten childcare data recording tasks. By analysing the recorded files, usability problems were identified. The severity of the problems was then determined with the help of the users and problems were assigned to usability attributes based on their impact on the user. A total of 68 unique problems were identified in the system, of which 47.1% were rated as catastrophic problems. The participants assigned 47 problems (69%) to the user satisfaction attribute and 45 problems (66%) to the efficiency attribute; they also did not assign any problems to the effectiveness attribute. The problems identified in the national comprehensive health information system using the think-aloud method were rated as major and catastrophic, which indicates poor usability of this system. Therefore, resolving the system problems will help increase user satisfaction and system efficiency, allowing more time to be spent on patient care and parent's education as well as improving overall quality of care.

Feasibility and Impact of Community Pharmacy and Novel Pick-up Points for Antiretroviral Therapy Pre-exposure Prophylaxis Initiation and Continuation in Low and Middle-income Countries.

This review assesses recent developments in community access to pre-exposure prophylaxis (PrEP) for HIV prevention in low-and middle-income countries (LMICs). It examines literature on differentiated service delivery (DSD) and alternative delivery modes for PrEP, focusing on the role of community pharmacies and novel pick-up points. Key considerations include barriers to access, potential benefits, and strategies for implementation. Challenges to optimal HIV healthcare delivery persist globally, with LMICs facing greater barriers due to resource constraints and structural obstacles. Community pharmacies and novel pick-up points offer promising avenues to expand access to HIV medication, especially in hard-to-reach populations. However, operational complexities and regulatory frameworks present significant challenges. Recent initiatives, such as collaborative practice agreements and programmes by global health agencies, highlight efforts to integrate community pharmacies into HIV prevention and care delivery. Mobile health clinics and home delivery services have also shown promise in improving treatment coverage. Community pharmacies and novel pick-up points play a crucial role in enhancing access to HIV PrEP in LMICs. Despite challenges related to infrastructure, funding, and regulatory oversight, innovative strategies like DSD and mobile outreach offer opportunities to reach marginalized populations. Real-life examples from LMICs demonstrate the feasibility and effectiveness of leveraging community pharmacies for HIV treatment. However, addressing policy gaps, strengthening pharmacist training, and promoting patient-centred approaches are essential for scaling up access to PrEP. Collaboration between governments, health agencies, and local communities is key to realizing the full potential of community pharmacies in HIV prevention and care.

Racial and Ethnic Disparities in Epidural Blood Patch Utilization Among Obstetric Patients in the United States: A Nationwide Analysis, 2016–2020

BACKGROUND: Racial and ethnic disparities in health care delivery can lead to inadequate peripartum pain management and associated adverse maternal outcomes. An epidural blood patch (EBP) is the definitive treatment for moderate to severe postdural puncture headache (PDPH), a potentially debilitating neuraxial anesthesia complication associated with significant maternal morbidity if undertreated. In this nationwide study, we examine the racial and ethnic disparities in the inpatient utilization of EBP after obstetric PDPH in the United States. METHODS: In this retrospective observational study, we used the National Inpatient Sample, a nationally representative database of discharge records for inpatient admissions in the United States, from 2016 to 2020. We analyzed delivery hospitalizations of women of childbearing age (15–49 years) diagnosed with PDPH. Adjusting for maternal and hospitalization characteristics as confounders, we used a multilevel mixed-effects logistic regression model to compare the rates of EBP utilization by race and ethnicity. Secondarily, among hospitalizations with an EBP, we examined the association between race and ethnicity and the timing of the EBP procedure. RESULTS: We analyzed 49,300 delivery hospitalizations with a diagnosis of PDPH. An EBP was performed in 24,075 (48.8%; 95% confidence interval [CI], 47.8%–49.9%) of these hospitalizations. EBP was performed in 52.7% (95% CI, 51.3%–54.1%) of White non-Hispanic patients with PDPH. Compared to White non-Hispanic patients, Black non-Hispanic (adjusted odds ratio [aOR] = 0.69; 99% CI, 0.56–0.84), Hispanic (aOR = 0.80, 99% CI, 0.68–0.95), and Asian or Pacific Islander patients (aOR = 0.74, 99% CI, 0.58–0.96) were less likely to receive an EBP. The median (interquartile range [IQR]) time to perform an EBP was 2 (1–3) days after admission, with 90% of EBP procedures completed within 4 days of admission. There was no significant association between race and ethnicity and the timing of EBP placement. CONCLUSIONS: In this nationwide analysis of delivery hospitalizations from 2016 to 2020 in the United States with a diagnosis of PDPH, we identified racial and ethnic disparities in the utilization of EBP. Minoritized patients identified as Black non-Hispanic, Hispanic, or Asian or Pacific Islander were less likely to receive an EBP for the treatment of PDPH compared to White non-Hispanic patients. Suboptimal treatment of PDPH may be associated with adverse long-term outcomes such as postpartum depression, posttraumatic stress disorder, and chronic headaches. Racial and ethnic disparities in EBP utilization should be further investigated to ensure equitable health care delivery.

Why are organisational approvals needed for low-risk staff studies in the UK? Procedures, barriers, and burdens.

Health care staff should be given the opportunity to participate in research, but recruiting clinicians via their employing organisation is not always straightforward or quick in the UK. Unlike many countries outside the UK, very low-risk survey, interview or focus group studies can be subject to some of the same governance approval procedures as interventional studies. An exemplar study carried out by the NIHR funded Palliative Care Research Partnership North West Coast is used to highlight the challenges still faced by researchers and health care organisations when setting up a low-risk staff study across multiple NHS and non-NHS sites. A study database was created and information was collected on the first point of contact with the clinical site, Health Research Authority (HRA) and local organisational approval times, time from trust or hospice agreement to the first survey participant recruited and overall site survey recruitment numbers. Descriptive statistics (median, range) were used to analyse these data. Across participating NHS trusts, it took a median of 147.5days (range 99-195) from initial contact with the local collaborator to recruitment of the first survey participant and hospice sites mirrored these lengthy timescales (median 142days, range 110-202). The lengthiest delays in the HRA approval process were the period between asking NHS trusts to assess whether they had capacity and capability to support the research and them granting local agreement. Local approval times varied between trusts and settings which may indicate organisations are applying national complex guidance differently. There is the potential for HRA processes to use more NHS resources than the research study itself when recruiting to a low-risk staff study across multiple organisations. There is a need to reduce unnecessary administrative burden and bureaucracy to give clinicians and research staff more opportunities to participate in research, and to free up NHS R&D departments, research nurses and clinicians to focus on more demanding and patient focused research studies. Hospices need standardised guidance on how to assess the risk of being involved in low-risk research without adopting the unnecessarily complex systems that are currently used within the NHS.

Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

Background: Patient engagement refers to the actively involvement of individuals in their own treatment, decision-making, and partnering with healthcare providers. Due to the progress of Artificial Intelligence (AI), there is a groundbreaking shift happening in healthcare that is bringing numerous benefits to patients and health systems. AI technologies are not only used as instruments in healthcare but also evolving into collaborators for diagnosing and treating patients, as well as enhancing the quality of services, offering a personalized, timely, and interactive healthcare experience. This article delves into how AI is changing patient engagement by increasing effectiveness and catering to individual patient requirements. Methods: The research made use of previously published materials on artificial intelligence, data, and robotic technologies in healthcare settings to explore ways in which patients can be effectively involved in the advancement of these technologies in healthcare settings. Results: AI-powered decision support systems improve healthcare operations by giving instant access to data analysis and medical advice, ultimately aiding in making decisions based on real-time evidence. AI enables proactive healthcare interventions by detecting potential health issues in real time, allowing for remote patient monitoring. Motivating patients to actively participate can result in improved adherence to the treatment plan. Overall, patient engagement appears to be the most well-developed and progressive concept for enabling patients to participate actively in their healthcare. Conclusions: AI technologies are more than just tools in healthcare; they are evolving into collaborators in patient involvement, providing a personalized, proactive and engaging healthcare journey. Further evidence is required to comprehend how patients engage in the process and if this leads to better quality of care. In the dynamic health sector, change management is essential for continuously updating and adjusting healthcare facilities to address the evolving needs of patients. Health systems that do not adjust to changes in a timely manner are unsuccessful.

Participation in the digital transformation of healthcare: a review of qualitative studies.

The purpose of this review is to identify how health professionals perceive participation in implementation of new technology in healthcare organizations. A qualitative systematic review based on the PRISMA diagram, was conducted using qualitative synthesis. NVivo software was used for thematic analysis. The searches were performed in PubMed, CINAHL and Scopus. A total of 15 articles were included in the review, four themes describing how participation of health professionals in digital transformation affects the outcomes were identified, and three themes describing the factors that are necessary to promote participation. The underlying latent theme of an unmet desire to participate in the digital transformation was also identified in the analysis. The digital transformation of healthcare is complex and faces many obstacles if not managed correctly. Professional participation in the implementation seems to be essential for success. Focus on increased resources and planning during early stages, as well as teamwork and ethical reflection is important addressing the challenges that professionals face in digital transformation of healthcare.

Understanding community resilience during the drinking water contamination event on Oahu, Hawaii, 2021-2022: a mixed mode approach.

A petroleum leak into the Joint Base Pearl Harbor-Hickam water system on Oahu, Hawaii in November 2021 contaminated the drinking water of approximately 93,000 users, causing many to relocate for months. Perceptions of health and wellbeing were captured using the Centers for Disease Control/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Assessment of Chemical Exposures (ACE) cross-sectional survey in collaboration with the Hawaii Department of Health (HDOH). Responses from the ACE online survey of community members, businesses, schools, health care and veterinary care organizations during the contamination event, containing quantitative questions and qualitative information from an open text field, were analyzed. Separately, a qualitative key informant questionnaire was administered to community establishments. Thematic content analysis was used to analyze and identify prominent themes from the ACE open text field and the key informant responses that were triangulated by the quantitative data when the themes aligned. Six major themes of disruption, communication, trust, stress, support, and ongoing needs were identified. Burdensome logistics from obtaining alternate water, negative financial impacts from relocation or losing business, distrust of information, perceived lack of support from response entities and uncertainty of long-term health impact caused significant disruption, stress and mental health. Individuals reported needing water, shelter, and mental health care while establishments wanted financial reimbursement and a resolution. The findings show that environmental disasters have significant disruptive and mental health impacts from stress. Identified themes can inform and improve emergency response and communication strategies and increase trust with community members during and after large chemical exposure events.

Considering patient-centred care and patient empowerment as essential to quality health care delivery in Ghana

Background: Rising cases of non-communicable diseases (NCDs), poor health choices, mistrust of the health system, self-medication, resistance of diseases to medication and dissatisfaction with the service experience serve as red flags on the path to Universal Health Coverage (UHC).Aim: This study explored the importance of patient-centred care (PCC) and patient empowerment (PE) within the context of healthcare service delivery in Ghana.Setting: The study considered PCC and PE from the perspective of three public healthcare facilities within the Greater Accra region.Methods: The researcher adopted a qualitative exploratory research approach. The study employed purposive sampling for the selection of healthcare facilities and healthcare providers. Convenience sampling was applied to select patient participants. A total of 33 in-depth interviews and 4 focus group discussions (FGDs) were conducted across the three sampled facilities as part of the data collection process.Results: Healthcare providers and patients unanimously view quality as essential for effective healthcare delivery. The application of the capacitating role of PE and PCC is inexact and largely left to subjective interpretation.Conclusion: The Ministry of Health (MOH) has recognised that patient experiences, a key indicator of care quality, have often been suboptimal and is actively working to address these weaknesses.Contribution: This study supports the call for health systems to remain committed to efforts to achieve UHC, with a focus on PCC and PE.

Neurological Surgery Manpower Training and Density in Islamic Republic of Iran: A Population Study

BACKGROUND AND OBJECTIVES: Significant disparities in worldwide neurosurgical training and workforce distribution are prominent primarily in low-income and middle-income countries. Although Iran is considered a lower middle–income country, neurosurgical density and distribution in Iran has surpassed the recommended ratio of 1 neurosurgeon for every 100 000 population. The objective was to determine neurological surgery density and distribution in Iran and the factors significant in the relative success in training and allocation of neurosurgeons in Iran. METHODS: Review of PubMed and administration of site surveys of multiple data sources including Neurosurgical Society of Iran, Iranian Board of Neurological Surgery, Medical Council of Islamic Republic of Iran, Universities of Medical Sciences in Iran, and Ministry of Health and Higher Education of Iran. RESULTS: Over the 72-year period from 1952 to 2024, 1200 neurosurgeons have been trained and distributed in 31 provinces in Iran, attaining a ratio of 1.4/100 000 population. All but 40 neurosurgeons were trained after 1981, which coincided with the Iran-Iraq War. Decentralization of medical and neurosurgical residency training programs, resolving the immediate need for neurosurgeons managing penetrating traumatic brain and spinal cord injuries during the 1980 to 1988 Iran-Iraq War, and active participation of legislative and executive branches of government in solving health care disparities were major factors in meeting the needs of the country. At the present time, more than 555 neurosurgeons are practicing in Tehran Province, a proportion of 3.8 neurosurgeons for every 100 000 population, which indicates an element of disparity in density distribution across Iranian land. CONCLUSION: Legislative initiatives and government support of public health care delivery and decentralization of medical and residency training programs after the Iran-Iraq War and introduction of the Ministry of Health and Medical Education are considered the main reasons for the relative success in meeting the neurosurgical demand and manpower density. Still, further adjustment of distribution of manpower is needed.

Exploring the attitudes and experiences of Hungarian primary care physicians on the utilisation of digital health solutions.

During the COVID-19 pandemic, digital health solutions ensured the continuity of care especially in primary healthcare practices. COVID-19 accelerated the adoption of digital health solutions. The aim of this study is to describe the digital health-related attitudes and experiences of Hungarian Primary Care Physicians (PCPs) and also analyse the socio-demographic effects on digital health attitudes among PCPs. This study used a quantitative and qualitative mixed methodological approach to examine the digital health-related attitudes and experience of Hungarian PCPs. As part of the "E-physicians and E-patients in Hungary" survey, we conducted an online survey among medical doctors working in Hungary between July 2021 to May 2022. A total of 1,774 questionnaires were received, consisting of 1,576 medical doctors and 198 dentists. Among the medical doctors there were 415 primary care physicians (PCPs). In addition to the online questionnaire survey, qualitative research in the form of semi-structured interviews with doctors was also conducted. These interviews took place between October 2021 and June 2022. A total of 62 interviews were conducted,19 with PCPs. Primary care physicians are more open to technologies that facilitate communication and collaboration with patients, while showing less interest in technologies that support clinical work. Of the demographic variables (age, gender, type of settlement), age was found to have the most significant effect on digital health knowledge, use and intended use. Both the interviews and the multivariate analysis indicate that individuals with greater knowledge, expertise, and experience are more likely to perceive the advantages of digital solutions. This highlights the importance of training, especially given the significant aging population among Hungarian primary care physicians, who may not be accustomed to using these tools naturally. The way PCPs perceive patient expectations regarding the use of digital health tools has a significant impact on the PCPs' use and intended use of digital tools. When looking at perceived patient needs among PCPs the effect of age and municipality type of PCPs is significant. As age increases, the perception of patient needs decreases (from 5.02 to 4.47), and by municipality type, the average number of perceived needs decreases as one moves from larger cities to smaller municipalities (from 4.85 to 4.14). Digital health solutions have the potential to enhance the work of PCPs, but successful implementation requires addressing specific needs, demographic differences, and challenges faced by PCPs. Development of infrastructure, education, and institutional support is necessary to ensure more efficient and higher-quality healthcare delivery through the use of digital technologies.