Improve Health Care Outcomes Research Articles

Chemistry as a Catalyst for Transforming the Health and Wealth of South Africa.

South Africa's rich natural resources remain a key driver of its chemical industrialisation but are tainted and constrained by its complex colonial history. Thirty years since the advent of democracy in South Africa, deep sociopolitical inequalities are amplified by an intolerable infectious disease burden amongst disadvantaged communities. In that respect, South Africa shares challenges with many other nations in the Global South; there are limited opportunities for the economic development and growth needed to uplift all strata of society. This Viewpoint examines the role of the chemical sciences in South Africa's unique history and the current state of its academic and industrial sectors, with a focus on the intersection of chemistry, healthcare and biomedical research. We argue that the opportunities offered through chemistry research and development, including local manufacturing, should be exploited and that scientific advancements should be tailored to and integrated with the socioeconomic realities of South Africa for an effective and multidisciplinary approach to improving healthcare outcomes. This Viewpoint aims to inspire a renewed focus on the pivotal role of chemical scientists and their broader societal contributions to combating the country's infectious disease burden and shaping a healthier future for South Africa.

Effectiveness of Decentralisation by Devolution Approaches in Public healthcare services Delivery in Ilala Municipal Council and Geita Town Council, Tanzania

This study investigates the effectiveness of Decentralisation by Devolution (D-by-D) in public healthcare service delivery within Ilala Municipal Council and Geita Town Council, Tanzania. Employing a mixed-methods approach, including quantitative surveys and qualitative interviews with local key informants, the research reveals critical insights into how D-by-D frameworks impact healthcare quality and accessibility. Findings indicate that while some approaches, such as project monitoring and budget allocation, are perceived as effective, others—particularly multi-stakeholder involvement, participation, and strategic planning—are regarded as ineffective. The average effectiveness score across both councils suggests a moderate perception of D-by-D functioning, yet significant gaps exist regarding community engagement, resource allocation, and local governance capabilities. Challenges include inadequate funding, personnel shortages, and a lack of transparency, which hinder the realization of Decentralisation’s intended benefits. The study underscores a prevalent sense of skepticism regarding healthcare quality in both councils and highlights the necessity for reforms to better align local health priorities with community needs. To enhance the effectiveness of D-by-D, the research recommends strengthening financial support to Local Government Authorities, enhancing human resource capacity, fostering community participation, and promoting accountability and transparency in healthcare governance. This research contributes to the understanding of local governance in Tanzania and offers essential insights for policymakers aiming to improve healthcare outcomes through decentralized frameworks.

Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.

The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics. This study offers a thorough review of various 3D CNN algorithms, evaluating their efficacy in segmenting and classifying COVID-19 across a range of medical imaging modalities. This review systematically examines recent advancements in 3D CNN methodologies. The process involved a comprehensive screening of abstracts and titles to ensure relevance, followed by a meticulous selection and analysis of research papers from academic repositories. The study evaluates these papers based on specific criteria and provides detailed insights into the network architectures and algorithms used for COVID-19 detection. The review reveals significant trends in the use of 3D CNNs for COVID-19 segmentation and classification. It highlights key findings, including the diverse range of networks employed for COVID-19 detection compared to other diseases, which predominantly utilize encoder/decoder frameworks. The study provides an in-depth analysis of these methods, discussing their strengths, limitations, and potential areas for future research. The study reviewed a total of 60 papers published across various repositories, including Springer and Elsevier. The insights from this study have implications for clinical diagnosis and treatment strategies. Despite some limitations, the accuracy and efficiency of 3D CNN algorithms underscore their potential for advancing medical image segmentation and classification. The findings suggest that 3D CNNs could significantly enhance the detection and management of COVID-19, contributing to improved healthcare outcomes.

N26 Factors associated with Inflammatory Bowel Disease (IBD) helpline (HL) utilisation: Crohn’s Colitis Cure Data Insight’s Program

Abstract Background Specialised IBD nurse-led HLs are essential to improve healthcare outcomes through the provision of timely support, proactive symptom, and disease management as well as remote medication management. This study explored patient, disease, and treatment characteristics to understand whether there are specific drivers of HL use. Methods Crohn’s Colitis Care (CCCare) is a cloud-based IBD-specific electronic medical record used in Australia and New Zealand. It includes a ‘HL’ tab to enable specific HL encounter documentation. De-identified data feeds into a clinical quality registry (CQR). HL-associated data prospectively entered during clinical practice from September 2023 to September 2024 were retrospectively analysed. Multivariate analysis was performed with a negative binomial regression model to investigate significant predictors influencing HL utilisation. Results During the 12-month period, there were a total of 10,705 contacts in a cohort of 7506 eligible people (1.4 per person (pp)), of which 8082 (75.5%) were emails and 2623 (24.5%) telephone calls. People with Crohn’s disease on average had more HL contacts than ulcerative colitis and IBDU (1.6pp vs 1.2pp vs 1.2pp respectively). See Table 1 for details. People currently on an advanced therapy were more likely to use the HL (OR 2.3, [95% CI 0.51 to 1.13], P<0.001) compared to those who were not receiving an advanced therapy. By contrast, those residing in New Zealand were less likely to use the HL than those residing in Australia (OR 0.2, 95% CI [-2.3 to -1.0], P<0.001), as were people with composite endoscopic and radiologic remission compared to those not in remission (OR 0.8, 95% CI [-0.55 to -0.0002], P<0.05). Conclusion As expected, individual’s requiring complex care (on advanced therapy or not in endoscopic and radiologic remission) warrant more frequent contact with healthcare providers. A helpline bridges the gap between IBD services and the community and likely enhances positive outcomes for this population.

MANAGEMENT ASPECTS OF THE INTEGRATION OF MEDICAL STATISTICS AND INFORMATION SYSTEMS FOR EFFECTIVE RESOURCE PLANNING IN THE HEALTHCARE SYSTEM

This article delves into the administrative aspects of integrating medical statistics and information systems, focusing on their role in enhancing the efficiency of resource planning within the healthcare system. It emphasizes the critical importance of medical statistics in the development and implementation of effective healthcare programs. These statistics provide essential data on the health status of the population, the distribution of diseases, the utilization of medical resources, and the effectiveness of healthcare services, all of which are crucial for making informed decisions at both the local and national levels. The article also identifies several barriers and challenges in the current medical data management processes, such as the lack of unified standards for data collection and the insufficient integration of information systems. These issues result in fragmented data and complicate the process of data analysis and interpretation, thereby hindering efficient decision-making and resource optimization. The study stresses the need to overcome these obstacles to ensure that medical statistics can be used more effectively to improve healthcare outcomes. In addition, the article explores the integration of various medical information systems, including the Ministry of Health's central database (CBD) and the medical institutions’ internal data management systems. It underlines the necessity for seamless data exchange between different levels of healthcare management to promote transparency, coordination, and accountability in healthcare services. Such integration is crucial for improving the overall quality of healthcare delivery and for making timely, evidence-based decisions. Furthermore, the paper addresses the need for modernizing reporting systems to ensure they align with the latest legal frameworks and to adapt to the evolving healthcare landscape. This is particularly important in the context of the ongoing war in Ukraine, where new challenges are emerging in healthcare management. The article highlights that the current systems of data reporting and collection often fail to meet the specific needs arising from the current socio-political situation, such as the monitoring of displaced persons and the need for rehabilitation services. By investigating these issues, the article aims to provide recommendations for enhancing the management of medical statistics and improving the overall functionality of healthcare systems. The ultimate goal is to create a more integrated and efficient healthcare system that can better respond to the challenges posed by both the ongoing conflict and the long-term health needs of the population.

Exploring the dielectric properties of herbal medicine and modern pharmaceuticals: an integrative review.

The integration of herbal medicine with modern pharmaceuticals offers a novel approach to addressing complex healthcare challenges. This study investigates the role of dielectric spectroscopy in analysing key physicochemical properties such as solubility, stability, and molecular interactions. The findings reveal that combining herbal extracts with pharmaceutical agents enhances solubility and stability. It also reduces adverse effects, improving therapeutic efficacy. Dielectric spectroscopy is highlighted as a powerful analytical tool in this process. The study demonstrates how traditional herbal knowledge can be effectively linked with modern scientific methods. This approach enables the development of innovative therapeutic solutions that address safety and efficacy challenges. The results underline the potential of combining advanced analytical techniques with ethnopharmacological practices. This integration paves the way for the creation of safe, effective, and scientifically validated formulations for improved healthcare outcomes.

Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach

Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2) metrics. Our findings highlighted that Random Forest models excelled in department-specific applications, achieving an MAE of 16.32, an RMSE of 31.19, and an R2 of 0.92, significantly outperforming general models and conventional estimates. This improvement emphasizes the advantage of customizing models to fit the distinct characteristics and data patterns of each department. Additionally, our SHAP-based feature importance analysis identified morning operation timing, ICU ward assignments, operation codes, and surgeon IDs as key factors influencing surgical duration. This suggests that a detailed and nuanced approach to model development can substantially increase prediction accuracy. By providing a more accurate, reliable tool for predicting surgical case durations, our department-specific Random Forest models promise to enhance surgical scheduling, leading to more effective OR management. This approach underscores the importance of leveraging tailored, data-driven models to improve healthcare outcomes and operational efficiency.

Enhancing organizational performance through fintech innovation: a multi-dimensional analysis of healthcare projects in Africa

This communication explores the factors driving the continuous adoption of digital technologies and fintech innovations in healthcare services in Africa, with a particular emphasis on task-technology fit. By analyzing task-technology alignment within healthcare projects, the study seeks to provide insights into how fintech can enhance organizational performance, supported by effective training programs and government involvement. To assess the scope of fintech in healthcare services, particularly in World Bank-financed projects in Africa, the paper employs a multi-dimensional approach that examines key indicators across four critical dimensions: technology, economy, and environment. Using Principal Component Analysis, the research evaluates fintech development at two key stages of digital transformation investment and development phases allowing for a nuanced examination of the interactions and synergies that shape fintech evolution. Data analysis is conducted using R software to ensure robust and accurate insights. The findings reveal that fintech enhances organizational performance through cost savings, improved transparency, innovative business model creation, and optimized service supply chains. Moreover, fintech facilitates operational adaptation, boosts connectivity, and increases agility in a competitive and complex environment, enabling organizations to operate more efficiently and maintain a competitive edge. This study contributes to the existing literature by providing a comprehensive assessment of fintech's impact on healthcare services within the context of World Bank-financed projects in Africa, highlighting the significance of task-technology fit in enhancing organizational performance and offering valuable insights for practitioners and policymakers looking to leverage fintech for improved healthcare outcomes.

We See Ourselves as a Part of the Healthcare Team: Involving Patients and Their Caregivers in the Prevention of Hospital-acquired Pressure Injuries

Hospital-acquired pressure injuries are among the preventable complications in healthcare settings. Patients and their caregivers should be encouraged to actively participate in the prevention and treatment of these ulcers and follow evidence-based guidelines to improve healthcare outcomes and the safety of the patient. This requires that healthcare professionals support the education of preventive measures.

Overcoming barriers and enabling artificial intelligence adoption in allied health clinical practice: A qualitative study.

Artificial intelligence (AI) has the potential to revolutionise healthcare. If the implementation is successful it has the potential to improve healthcare outcomes for patients and organisations. Little is known about the perceptions of allied health professionals (AHPs) towards AI in healthcare. This study investigated barriers and enablers to AI implementation in the delivery of healthcare from the AHPs perspective. Qualitative methodology informed by behaviour change theory using focus groups with AHPs at a health service in Queensland, Australia. Twenty-four barriers and 24 enablers were identified by 25 participants across four focus groups. Barriers included: lack of AI knowledge, explainability challenges, risk to professional practice, negative impact on professional practice, and role replacement. Enablers include AI training and education, regulation, reputation, understanding the healthcare benefits of AI and engaging clinical champions. AHPs have concerns about the impact and trustworthiness of AI and the readiness of organisations to support its use. Organisations must take a proactive approach and adopt targeted and multifaceted strategies to address barriers. This may include workforce upskilling, clear communication of the benefits of AI use of local champions and ongoing research.

Bioinformatics-driven deep learning for nail disease diagnosis: A novel approach to improve healthcare outcomes

Bioinformatics-driven deep learning for nail disease diagnosis: A novel approach to improve healthcare outcomes

Body Positions and Physical Activity Levels Modulate the Ratio of Abdominal to Thoracic Breathing and Respiratory Rate in Young Individuals.

Background/Objectives: The COVID-19 pandemic highlighted that body positions substantially affected the mortality rate. We hypothesized that body position modulates the contribution of abdominal (AB) and thoracic breathing (TB) to the breathing cycle (BC), as well as respiratory rate (RR). In addition, we hypothesized that physical activity level can increase the contribution of abdominal breathing. Methods: Thus, we used plethysmography respiratory belts to measure changes in abdominal (AB) and thoracic (TB) circumferences, their ratio (AB/TB), and respiratory rate (RR) under resting conditions. Measurements were taken in four body positions-standing (St), sitting (Si), supine (Su), and prone (Pr)-for two groups of young adults (aged 21 ± 2 years) with different physical activity levels (low and high PA). Results: The AB/TB ratios significantly differed between the body positions (Si: 45.5/54.5%, St: 40.5/59.5%, Su: 56.8/43.2%, Pr: 52.2/47.8% (p < 0.001)). AB was significantly the highest in Su and lowest in Si and St (p < 0.001). There was a significant difference in respiratory rate (RR) between the four body positions (p = 0.005). RR in the four body positions was the following: Si: 15.1, St: 15.0, Su: 13.7, and Pr: 14.4. RR was the lowest in Su (13.7), where AB was the highest (56.8%), and RR was the highest in Si (15.1) and St (15.0), where TB was higher compared to Su (p < 0.001). PA significantly affected the various body positions' AB/TB ratio and RR. The high PA group showed a significant difference in the AB/TB ratio between the body positions (p < 0.001). The low PA group showed a significant difference in RR between the body positions (p = 0.025). Conclusions: In young, healthy adults, we found that body position significantly influenced the abdominal/thoracic breathing ratio during the breathing cycle. The supine position showed the highest contribution of abdominal breathing, which correlated with the lowest respiratory rate. Additionally, a higher level of physical activity increased the contribution of abdominal breathing in the Si, Su, and St positions, suggesting a more energy-efficient breathing pattern. These findings suggest the potentials for breathing pattern monitoring and position-based interventions to improve healthcare outcomes and enhance sports performance and recovery.

The Relationship Between Happiness and Caring Behaviors in Nurses: A Descriptive-Analytical Study

Background: Caring behaviors encompass the supportive actions undertaken by nurses to assist patients, with the primary aim of reducing the duration of illness. Enhanced happiness may positively influence these behaviors, which are essential for nurses in maintaining and promoting patient health. Objectives: This study investigates the factors affecting the happiness of Iranian nurses and examines whether increased happiness correlates with improved caring behaviors. Methods: This descriptive-correlational study employed random sampling to survey 220 nurses in Isfahan, Iran, in 2024. Data were collected through questionnaires evaluating demographics, happiness (measured by the Oxford Happiness Questionnaire-Short Form), and caring behaviors. Data analysis was performed using descriptive statistics, independent t-tests, ANOVA, and Pearson correlation coefficients in SPSS V.23. Results: The Iranian nurses in this study demonstrated above-average happiness (mean score: 34.48 ± 5.82). Happiness showed a significant correlation with caring behaviors (P &lt; 0.05) and was also significantly associated with gender, work experience, and income level. No significant associations were found between happiness and age, marital status, education, employment type, or department (P &gt; 0.05). Conclusions: Higher levels of nurse happiness are associated with improved caring behaviors, suggesting that initiatives aimed at enhancing nurse happiness can lead to better quality of patient care. It is imperative for government and healthcare organizations to prioritize programs and policies that focus on increasing nurse satisfaction to improve healthcare outcomes.

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.

Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics. This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework is designed to effectively process nonregistered ultrasound image pairs, a common challenge in medical imaging, across various clinical settings and device types. By addressing these challenges, the research seeks to provide a more generalized and adaptable solution that can be widely applied across diverse medical scenarios, improving the accessibility and quality of diagnostic imaging. A retrospective analysis was conducted by using a cycle-consistent generative adversarial network (CycleGAN) framework enhanced with perceptual loss to improve the quality of ultrasound images, focusing on nonregistered image pairs from various organ systems. The perceptual loss was integrated to preserve anatomical integrity by comparing deep features extracted from pretrained neural networks. The model's performance was evaluated against corresponding high-resolution images, ensuring that the enhanced outputs closely mimic those from high-end ultrasound devices. The model was trained and validated using a publicly available, diverse dataset to ensure robustness and generalizability across different imaging scenarios. The advanced CycleGAN framework, enhanced with perceptual loss, significantly outperformed the previous state-of-the-art, stable CycleGAN, in multiple evaluation metrics. Specifically, our method achieved a structural similarity index of 0.2889 versus 0.2502 (P<.001), a peak signal-to-noise ratio of 15.8935 versus 14.9430 (P<.001), and a learned perceptual image patch similarity score of 0.4490 versus 0.5005 (P<.001). These results demonstrate the model's superior ability to enhance image quality while preserving critical anatomical details, thereby improving diagnostic usefulness. This study presents a significant advancement in ultrasound imaging by leveraging a CycleGAN model enhanced with perceptual loss to bridge the quality gap between images from different devices. By processing nonregistered image pairs, the model not only enhances visual quality but also ensures the preservation of essential anatomical structures, crucial for accurate diagnosis. This approach holds the potential to democratize high-quality ultrasound imaging, making it accessible through low-cost portable devices, thereby improving health care outcomes, particularly in resource-limited settings. Future research will focus on further validation and optimization for clinical use.

Recommendations on Rapid Diagnostic Point-of-care Molecular Tests for Respiratory Infections in the United Arab Emirates.

Traditional testing methods in the Middle East Region, including the United Arab Emirates (UAE), particularly the testing of Respiratory Syncytial Virus (RSV), influenza, group A streptococcus (GAS), and COVID-19 have the potential to be upgraded to new and advanced diagnostics methods that improve lead time to diagnosis, consumption of healthcare resources and patient experience. In addition, based on the research, it was reported that there is an underreporting of respiratory cases, overuse of antibiotics, and prolonged hospitalizations which is posing pressure on UAE healthcare stakeholders. A literature review was done exploring UAE's current diagnostic practices, recommended guidelines, diagnostic gaps, and challenges in RSV, GAS, Influenza, and COVID-19. This was followed by stakeholder discussions focusing on assessing current diagnostic practices, usage of rapid molecular point-of-care (POC) diagnostic tests, current gaps in diagnosis, targeted profiles for POC testing, and potential impact on patient management for targeted respiratory infections. A round table discussion with healthcare experts, insurance experts, key opinion leaders, and pulmonologists discussed challenges and opportunities in treating respiratory diseases. UAE healthcare stakeholders suggest that introducing alternative and up-to-date diagnostic methods such as POC molecular testing is expected to improve healthcare outcomes, optimize resources, and develop a robust case management of respiratory tract infections. It is essential to emphasize that by introducing POC testing, precision medicine is reinforced, efficiency is achieved, and the overall management of population health is enhanced.

The rise of artificial intelligence in teledentistry: A comprehensive review

The integration of Artificial Intelligence (AI) into teledentistry represents an unprecedented step towards increased accessibility in dental care, given India's diversity and vast population. This comprehensive review attempts to outline the practical applications of AI in teledentistry, from patient education, remote diagnosis, treatment planning, to follow-up care. It critically analyses works relating to AI's effectiveness on the detection of caries, monitoring orthodontic, and the preliminary screening of OPMDs in relation to improving health care outcomes, especially in underserved regions. It also highlights challenges such as data privacy, accuracy, acceptance, and need for regulatory clarity among dental professionals and patients. This review primarily proposes the recommendations that we should be attempting to fuse AI based solutions with a human touch as a fixture of provider of dental rendering, based on peer-reviewed study evidence. The practice will change as collaboration is required with AI to pursue a teledental practice that is ethical, effective and accessible to everyone.

Voices of Care: Nurses' Perspectives on End-of-Life Care for Black/African American Patients.

This qualitative study examines the distinct experiences of Black/African American nurses in providing end-of-life care to patients of the same racial background and their families. It fills a significant gap in existing research by focusing on the lived experiences and perceptions of Black/African American nurses, gathered through semistructured interviews. Using phenomenological analysis with Colaizzi's method, the study identified principal themes such as racial and ethnic disparities in treatment, challenges in pain management, the need for cultural sensitivity, and the significant influence of historical Black/African American patients' mistrust in health care interactions. The findings illustrate pervasive health care disparities, which are intensified by socioeconomic variables, cultural incompetence, and systemic bias. The nurses highlighted the urgent need for enhanced cultural competence in health care practices to ensure equitable treatment and patient-centric care. This research underscores the necessity for systemic reforms to improve cultural sensitivity and trust within health care services, advocating for comprehensive cultural competency training, greater diversity in health care representation, and effective mechanisms for reporting and addressing disparities. These measures are critical for improving health care outcomes and trust among Black/African American communities at the end of life.

Strategic Policy Framework for Ensuring the Sustainability of Essential Healthcare in Korea

South Korea’s rapid economic growth over the past 50 years has significantly improved healthcare outcomes, with life expectancy rising from 62.3 years in the 1970s to 82.7 years in 2022. However, the sustainability of its high-performing healthcare system is increasingly uncertain. Healthcare expenditure as a percentage of GDP reached 9.7% in 2022, surpassing the OECD average, while the fertility rate dropped to 0.72, undermining the system’s demographic foundation. Challenges include freezing health insurance premiums despite rising costs, short-term fiscal injections without thorough evaluation, and expanding the physician workforce without accounting for demographic changes. These policies risk overburdening future generations and escalating systemic financial pressures. To ensure sustainability, a shift in healthcare policy is necessary; from expanding services to managing limited resources and controlling demand. South Korea must adopt a strategic, long-term framework that balances current needs with future sustainability, ensuring its healthcare system remains equitable and resilient.

A Quantitative Analysis of Human and Material Resources for Endoscopy Services in Pacific Island Countries.

This study aims to evaluate the current state of endoscopy services in Pacific Island Countries (PICs) by quantifying human and material resources, including the number of trained endoscopists and nurses, the types of procedures performed, and the availability and maintenance of endoscopic equipment. A mixed methods survey was conducted in 2023, targeting doctors and nurses who participated in the WGO-FNU-ANZGITA endoscopy training program as well as non-participants. Survey invitations were sent through email, WhatsApp, and Facebook Messenger. Data were collected from 16 sites across 12 PICs, achieving an 85% response rate. Survey results indicated a total of 58 endoscopists (2.1/100000 population) and 52 nurses (1.9/100000 population), with a majority having received training through international partnerships. Basic endoscopy services, such as gastroscopy and colonoscopy, were widely available, but advanced procedures were limited to a few sites. Most sites reported using donated equipment, with significant challenges in equipment maintenance and repair. The availability of functional endoscopes averaged four per site. Common issues included outdated equipment, lack of qualified personnel, and insufficient funding for new equipment. Despite efforts to enhance endoscopy services in PICs through international collaborations, significant gaps remain, particularly in terms of advanced procedural capacity and equipment maintenance. Recommendations include expanding training programs, improving equipment maintenance infrastructure, securing funding for new equipment, and fostering stronger partnerships to support the sustainability of endoscopy services. Addressing these areas can enhance the quality and availability of endoscopy services, ultimately improving healthcare outcomes for populations in PICs.

Leveraging regulatory pharmacist expertise to develop a framework for drug repurposing in underserved areas of the USA

The United States continues to grapple with healthcare access disparities, particularly in underserved regions. Regulatory pharmacists, with their unique expertise in pharmaceutical laws, drug development, and safety regulations, can play a vital role in drug repurposing efforts. This paper proposes a framework for utilising regulatory pharmacist expertise to develop drug repurposing strategies to meet unmet medical needs in underserved areas. We explore the regulatory hurdles, the necessity for collaborative efforts between healthcare professionals, and the potential for drug repurposing to improve healthcare outcomes in rural and socioeconomically disadvantaged communities.