Healthcare Efficiency Research Articles

Collaboration of Pharmacy, Laboratory, Nursing, Radiology, Healthcare Administration, Health Security, Physiotherapy, and Administration Departments to Enhance Healthcare Quality and patient safety in Saudi Arabia

The healthcare system in Saudi Arabia has undergone a significant transformation toward an integrated, collaborative approach to healthcare delivery, driven by the ambitious goals of Saudi Vision 2030 and the National Transformation Program in Healthcare. This review investigates and analyzes practical collaborations between various departments, including Pharmacy, Laboratory, Nursing, Radiology, Healthcare Administration, Health Security, Physiotherapy, and Administration in Saudi healthcare institutions to enhance quality and patient safety. By examining organizational structures, department-specific roles and interdepartmental interactions, this study highlights how coordinated efforts between these departments contribute to improved patient outcomes and healthcare delivery efficiency. The analysis revealed successful collaboration depends on clear communication channels, standardized operating procedures, and integrated information systems that facilitate real-time data sharing across departments. While challenges exist, including organizational hierarchies, technical integration issues, and communication barriers, opportunities for improvement through technological advancement, professional development initiatives, and refined policy frameworks show promise for future development. The findings emphasize that interdepartmental collaboration is essential for achieving the quality healthcare goals outlined in Saudi Vision 2030, particularly emphasizing patient safety, resource optimization, and service excellence. This review provides valuable insights into the current state of healthcare collaboration in Saudi Arabia and offers recommendations for enhancing interdepartmental coordination to advance healthcare delivery in the country.

Usefulness of Point-of-Care Testing for Respiratory Viruses in a Pediatric Emergency Department Setting

Background: Respiratory tract infections (RTIs) are a leading cause of pediatric emergency department (PED) visits, especially in children under five. These infections are primarily viral, complicating diagnosis and management. This study assesses the impact of point-of-care (POC) rapid diagnostic tests for respiratory viruses on clinical and economic outcomes in a PED setting. Materials and Methods: A retrospective analysis of 1396 POC tests for RSV, adenovirus, and influenza A/B was conducted in the PED of the Giannina Gaslini Institute, Genoa, Italy, from December 2022 to April 2024. Demographics, blood tests, admissions, and readmission rates were evaluated. Statistical analyses were performed using appropriate tests for categorical and continuous variables. Results: Of the tests, 31.5% were positive for at least one virus. Positive patients were younger and had higher hospitalization rates (50.7% vs. 39.9%) but fewer blood tests (38.2% vs. 51.7%). Economic analysis indicated lower costs for virus-positive patients. RSV-positive patients showed a significant association with higher hospitalization rates (67.8%) and readmission within 72 h. Conclusions: POC testing significantly improves patient management in PEDs by enabling rapid diagnoses, reducing unnecessary tests and hospitalizations, and guiding appropriate treatment. This approach supports better resource allocation, crucial during peak seasons, and has implications for reducing antibiotic use and resistance. Further research is warranted to explore long-term impacts on patient outcomes and healthcare efficiency.

Assessing Social Sustainability Efficiency in European Countries: Focusing on Health and Well‐Being

ABSTRACTThis study investigates the challenges of achieving social sustainability efficiency in European countries, especially in health and well‐being, under sustainable development goal 3 amid demographic aging pressures. Using an innovative modified circular dynamic three‐stage systematic network data envelopment analysis model, this research evaluates the influence of social protection investments on healthcare and well‐being systems. The findings reveal that Denmark, Estonia, France, and Germany demonstrate optimal efficiency, while Lithuania, Luxembourg, the Netherlands, and Slovenia require strategic improvements, particularly in healthcare efficiency. Policy recommendations underscore the need for enhanced healthcare technology, occupational safety, and adaptive strategies to support social sustainability across 24 countries from 2016 to 2020. This approach offers a comprehensive efficiency framework and actionable insights to guide future policy development to address European healthcare efficiency gaps.

Innovation ecosystems in health and care: the Andalusian Reference Site as an example

Innovation ecosystems foster collaboration between academia, industry, public bodies, and civil society to drive technological and social advancements. The European Innovation Partnership on Active and Healthy Aging (EIP on AHA), launched in 2012, aimed to extend healthy life years, improve healthcare efficiency, and stimulate economic growth. Reference sites (RSs) and action groups (AGs) were key components, with RSs adopting collaborative approaches to improve health outcomes. Andalusia, Spain, achieved top recognition across multiple EIP on AHA calls for its digital health strategies and strong Quadruple Helix collaboration. In 2022, Andalusia’s self-assessment using the SCIROCCO tool highlighted strengths in digital transformation and citizen empowerment. Andalusia’s innovative practices in health have contributed to regional improvements in healthcare efficiency, life expectancy, and research initiatives.

Healthcare Workers' Knowledge and Attitudes Regarding Artificial Intelligence Adoption in Healthcare: A Cross-sectional Study

BackgroundThe convergence of healthcare and artificial intelligence (AI) introduces a transformative era in medical practice. However, the knowledge and attitudes of healthcare workers concerning the adoption of artificial intelligence in healthcare are currently unknown. AimsThe primary objective was to investigate the knowledge and attitudes of healthcare professionals in Dhaka city, Bangladesh, regarding the adoption of AI in healthcare. MethodsA cross-sectional research design was employed, incorporating a dual-method approach to select participants using randomness and convenience sampling techniques. Validity was ensured through a literature review, content validity, and reliability assessment (Cronbach's alpha = 0.85), and exploratory factor analysis identified robust underlying factors. Data analysis involved descriptive and inferential statistics, including Fisher's exact tests, multivariate logistic regression, and Pearson correlation analysis, conducted using STATA software, providing a comprehensive understanding of healthcare workers' AI adoption in healthcare. ResultsThis study revealed that age was a significant factor, with individuals aged 18-25 and 26-35 having higher odds of good knowledge and positive attitudes (AOR 1.56, 95% CI 1.12–2.43; AOR 1.42, 95% CI 0.98–2.34). Physicians (AOR 1.08, 95% CI 0.78–1.89), hospital workers (AOR 1.29, 95% CI 0.92–2.09), and full-time employees (AOR 1.45, 95% CI 1.12–2.34) exhibited higher odds. Attending AI conferences (AOR 1.27, 95% CI 0.92–2.23) and learning through research articles/journals (AOR 1.31, 95% CI 0.98–2.09) were positively associated with good knowledge and positive attitudes. This research also emphasized the strong correlations between knowledge and positive attitudes (r = 0.89, P < 0.001), as well as negative attitudes with poor knowledge (r = 0.65, P < 0.001). ConclusionsThe study highlights the critical need for targeted educational interventions to bridge the knowledge gaps among healthcare professionals regarding AI adoption. The findings reveal that younger healthcare workers, those in full-time employment, and individuals with exposure to AI through conferences or research are more likely to possess good knowledge and hold positive attitudes towards AI integration. These results suggest that policies and training programs must be tailored to address specific demographic differences, ensuring that all groups are equipped to engage with AI technologies. Moreover, the study emphasizes the importance of continuous professional development, which could foster a workforce capable of harnessing AI’s potential to improve patient outcomes and healthcare efficiency.

Insurtech\'s Role in Enhancing Healthcare Insurance Accessibility and Efficiency in India

Abstract: Insurance Technology (Insurtech) is transforming healthcare this paper explores how innovative technologies such as digital on boarding, automated claims processing, wearable health trackers, and predictive analytics are reshaping the industry. These advancements have not only streamlined processes but also brought insurance services to underserved areas, offering personalized solutions that significantly enhance customer satisfaction. Technologies like telemedicine, block chain for secure transactions, and AI-driven risk analysis are playing a transformative role in improving transparency, speeding up claims processing, and delivering tailored insurance products. These developments are creating a more inclusive and efficient insurance system. However, challenges such as data privacy concerns, regulatory hurdles, and low levels of digital literacy remain obstacles to fully realizing the sector's potential. This study also highlights trends in InsurTech funding in India, the increasing demand for health-focused insurance products, and the role of technology in improving policyholder engagement. InsurTech is poised to redefine healthcare insurance in India, driving operational excellence, expanding coverage, and enhancing service quality. The study offers insights into Insurtech and how to reshape healthcare insurance in India.

Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.

Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integrating artificial intelligence (AI) into healthcare requires understanding these data infrastructures and addressing challenges such as data availability, privacy, and governance. Federated learning (FL), a decentralized AI training approach, addresses these challenges by allowing models to learn from diverse datasets without data leaving its source, thus ensuring privacy and security are maintained. This report introduces FL and discusses its potential in transfusion medicine and blood supply chain management. FL can offer significant benefits in transfusion medicine by enhancing predictive analytics, personalized medicine, and operational efficiency. Predictive models trained on diverse datasets by FL can improve accuracy in forecasting blood transfusion demands. Personalized treatment plans can be refined by aggregating patient data from multiple institutions using FL, reducing adverse reactions and improving outcomes. Operational efficiency can also be achieved through precise demand forecasting and optimized logistics. Despite its advantages, FL faces challenges such as data standardization, governance, and bias. Harmonizing diverse data sources and ensuring fair, unbiased models require advanced analytical solutions. Robust IT infrastructure and specialized expertise are needed for successful FL implementation. FL represents a transformative approach to AI development in healthcare, particularly in transfusion medicine. By leveraging diverse datasets while maintaining data privacy, FL has the potential to enhance predictions, support personalized treatments, and optimize resource management, ultimately improving patient care and healthcare efficiency.

14. OCCUPATIONAL STRESS OF HEALTHCARE WORKERS AND SOME ASSOCIATED FACTORS AT DONG DO HOSPITAL IN 2023

Objective: Occupational stress among healthcare workers (HCWs) significantly impacts their health and the quality of healthcare services. This study aimed to assess the prevalence and identify factors associated with occupational stress among HCWs at Dong Do Hospital, Hanoi, in 2023. Methodology: We utilized a cross-sectional study design with 245 HCWs participating through the administration of the Job Content Questionnaire (JCQ). Results: The study found that 24% of HCWs experienced significant job stress, with notable variations across different departments. Factors significantly associated with higher levels of stress included high job demands, low job control, and inadequate support from colleagues. Notably, demographic factors such as age and gender did not show a significant relationship with occupational stress levels. Conclusion and recommendation: Based on the findings, we recommend targeted interventions to improve job control, enhance workplace support, and manage job demands to mitigate stress among HCWs. This study contributes to the understanding of occupational stress in a hospital setting and suggests pathways for interventions to enhance worker well-being and healthcare efficiency.

AI and Digital Twins in Healthcare: Revolutionizing Remote Patient Monitoring and Precision Medicine

Pairing artificial intelligence (AI) with digital twin technology is transforming healthcare by providing real time remote patient monitoring and pushing precision medicine forward. In this study we explore the potential of AI driven digital twins to improve patient outcomes, reduce costs and increase efficiency in healthcare. To accomplish this, the research focuses on developing AI algorithms to monitor and predict patient health remotely, which tackles those major challenges to be able to intervene in time and provide individualized care. The paper accounts for current knowledge of AI using a data driven approach by reviewing existing literature and recent developments and through case studies of practical applications of AI in patient management. Data analysis techniques were then methodologically combined with ethical considerations of using patient data in order to obtain accurate and private patient data. This key finding shows that with the combination of predictive AI algorithms and digital twins of the patient, diagnostic power can be improved and treatments personalised – to the benefit of the patient. This research is novel for its outlook on how AI can contribute to the building of such responsive, real time digital twins for healthcare. Based on the study’s implications, the adoption of digital twin technology together with AI holds promise to redesign the future of healthcare where diagnostic accuracy is enhanced and healthcare facilities are rendered a lighter burden. These insights are vital for doctors, policy makers and researchers working towards a leading role for AI in patient centric care models.

Exploring Health Informatics for Chronic Disease Management: A Systematic Review of Tools and Outcomes

Chronic diseases represent a significant burden on healthcare systems worldwide, necessitating innovative approaches to enhance patient management and improve health outcomes. Health informatics tools, including electronic health records (EHRs), mobile health applications, telemedicine, and predictive analytics, offer promising solutions for managing chronic conditions by facilitating patient engagement, supporting clinical decision-making, and improving healthcare efficiency. This systematic review evaluates recent literature on health informatics tools used in chronic disease management, examining their impact on patient health outcomes, adherence, and healthcare delivery efficiency. Findings reveal that digital health solutions, particularly mobile health applications and remote monitoring technologies, demonstrate substantial benefits in improving patient engagement and adherence to treatment protocols, leading to better disease management and reduced hospitalization rates. However, challenges such as data privacy, interoperability, and limited digital literacy remain significant barriers to widespread implementation. This review highlights the need for continued research to optimize health informatics applications, address implementation challenges, and further integrate these tools into healthcare systems for improved chronic disease management.

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

Five-year retrospective analysis of Bakri balloon tamponade for obstetric haemorrhage and patient survey at a single tertiary centre

Background Intrauterine balloon tamponade (IUBT), specifically the usage of Bakri balloon tamponade (BBT), is an effective conservative management technique for postpartum haemorrhage (PPH). The primary objective was to evaluate local guidelines and contribute to evidence regarding ideal duration for BBT. The secondary objectives were to consider impacts on maternal-foetal wellbeing, and optimise healthcare efficiency by reducing length of ICU admissions. Methods This five-year retrospective case series analysed 132 cases of obstetric ICU admissions requiring Bakri balloon insertion for PPH within our centre. Additionally, a prospective patient experience survey was conducted over six-month period, involving 22 obstetric patients who required unplanned ICU admissions. Results Bakri balloon insertion was successful in 95%, whilst 5% experienced failure, necessitating further interventions (uterine artery embolisation) or return to theatre (hysterectomy). The success group demonstrated significant reductions in median blood loss (1.8 L vs. 2.5 L, p = 0.016) and difference in median duration of BBT (18.3 vs. 3.92 hours, p = 0.001). The prospective patient survey revealed a high level of satisfaction of care. Approximately 50% breastfed prior to discharge, on average commencing 23.4 hours post-delivery. This study demonstrates a high success rate of BBT, with failures typically occurring within median duration 3.9 hours. Conclusions Considering the separation of mother from baby during the Bakri balloon’s presence in ICU, we propose timely removal at around six hours, as failures are unlikely post this timeframe, or nursing these women in a neonatal care setting to facilitate earlier mother-baby bonding.

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

Natural Language Processing for Electronic Health Record Optimization in Android Applications

This paper aims to demonstrate the development of an Android application designed to improve healthcare data management using Natural Language Processing (NLP) technologies. It develops an Android application that enables healthcare professionals to access patient records anytime, enhancing decision-making. The system uses Optical Character Recognition (OCR) to extract patient data from documents, simplifying data entry for diagnoses and treatments. It also incorporates Quick Response (QR) code generation for accurate data retrieval. By leveraging NLP, the mobile application will enhance efficiency, accessibility, and accuracy in healthcare, particularly benefiting developing countries where digital health records are uncommon..

Regional healthcare resource allocation and decision-making: Evaluating the effectiveness of the three-stage super-efficiency DEA model

This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.

The Role of Healthcare Workers in Modern Medicine: A Comprehensive Review of Challenges, Contributions, and Future Directions

Healthcare workers are essential to the functioning and sustainability of modern healthcare systems. This review explores the diverse roles, contributions, and challenges faced by healthcare professionals, including physicians, nurses, pharmacists, allied health professionals, and support staff. Each role contributes uniquely to patient care quality, system efficiency, and overall health outcomes. However, healthcare workers encounter numerous obstacles, including high workloads, resource shortages, mental health strain, and interdisciplinary coordination challenges. This review also highlights significant contributions that healthcare workers make toward enhancing patient safety, operational efficiency, and public trust. Through case studies and analysis, we examine strategies for supporting healthcare workers, including training, mental health resources, and improved resource allocation. Finally, we provide recommendations for future directions to bolster healthcare worker resilience and efficiency, ensuring that healthcare systems remain adaptable and capable of delivering quality care. This article emphasizes the need for holistic support systems to sustain a resilient and effective healthcare workforce.

Season-dependent characteristics of shock-related injuries in the Arctic zone of the Arkhangelsk region

Relevance. Studying the dynamics of injury rates, injury types and severity, as well as the treatment quality across various country regions, in particular the Arctic, is a prerequisite for managerial, healthcare, and strategic efficiency prior to and during hospital stay, taking into account the climate, geography, social and economic characteristics of every Russian Federation constituent.The objective is to analyse shock-related injuries in the Arctic zone of the Arkhangelsk region to understand season-related injury dynamics.Methods. Retrospective case series description parameters were used to study 140 patients with traumatic shock admitted to the Arkhangelsk Regional Clinical Hospital (ARCH) - a tier 1 trauma center. The analysed medical registration records included patient ambulance record and token (form 114/u), inpatient medical record (form 003/u), surgery logbook (form 008/u), patient radiographs, CT and nuclear MRI reports, as well as laboratory results. The study was conducted in accordance with ethical standards of the Declaration of Helsinki and European Community Directives (8/906 EC) and was approved by the local ethics committee of the Northern State Medical University of the Russian Ministry of Healthcare, Arkhangelsk (protocol no. 02/4-15 dated 04/08/2015). Statistical application package STATA ver.12 was used for statistical data processing.Results and discussion. The research demonstrated that the incidence of shock trauma is 4 times higher in men than in women. The vast majority of the injured are of working age. Seasonal distribution analysis per type of injuries and shock severity, including due to alcohol abuse, was carried out. The number of shock injuries is highest in winter, mainly due to road accidents which constitute 75.6 % of all injuries in winter. Throughout the year, over 50 % of shock injuries are combined injuries, that outperform isolated and multiple injuries in number. In particular, numerous combined injuries occur in winter with a responsible for almost 60 % among all other injuries. The majority the injured with shock-related trauma presented with stage 2 shock (49.3 %), whereas stage 1 and stage 3 shock was diagnosed in 27.9 % and in 21.4 % respectively. In winter, one in four patients with a shock-related injury presented with stage 3 shock. Alcohol-associated shock injuries are more numerous in summer (35.3 %), showing almost the same incidence as road traffic, domestic, and street injuries.Conclusion. The analysis of various types of injuries and season-related incidence of shock injuries in the Arctic territory of the Arkhangelsk region provides evidence to justify preventive measures to reduce the injury rates. Intensified efforts are required to minimise road traffic accidents in winter and combat alcohol abuse responsible for most injuries in summer. Since the vast majority of shock injuries occur in winter, in cold months ambulances must be equipped with local heating facilities, such as evacuation thermal bags with autonomous electric heating, thermal blankets, infusion warmers for blood transfusion and infusion sets.

Pharmacogenetic testing: current state of the issue

Aim. The purpose of this work is to emphasize the importance and practical benefits of pharmacogenetics in medicine, demonstrating how the integration of pharmacogenetics into clinical practice can improve treatment outcomes and enhance the safety of therapies. Pharmacogenetics is a rapidly developing branch of pharmacology that studies how genetic variation affects an individual’s response to drugs. By understanding these genetic differences, healthcare providers can tailor drug therapy to maximize efficacy and minimize adverse drug reactions. The field aims to move away from the traditional one-size-fits-all approach and instead personalize medical treatment based on a person’s genetic makeup. The ability to predict a person’s response to medication based on their genetic profile has profound implications. For example, pharmacogenetic testing can identify patients at risk for life-threatening adverse drug reactions, such as drug-induced liver injury. This proactive approach can prevent adverse events, improving patient safety and the overall quality of care. In addition, pharmacogenetics aids in the development of new drugs by identifying genetic markers associated with drug responses, optimizing the drug development process, and reducing the time and costs associated with bringing new drugs to market. Key advances in pharmacogenetics include the identification of genetic polymorphisms in enzymes, such as cytochrome P450, that affect drug metabolism, thereby influencing both safety and efficacy. Pharmacogenetic testing allows doctors to predict the best drug and dosage for a patient, improving treatment outcomes and reducing the risk of adverse drug events. Despite its potential, the integration of pharmacogenetics into clinical practice faces challenges, including a lack of reliable clinical evidence, inadequate physician education, and the need for a comprehensive health IT infrastructure to support the use of genetic data. Ethical and legal issues, such as patient privacy and the risk of genetic discrimination, also present significant obstacles. However, continued research, the development of genetic testing technologies, and interdisciplinary collaborations are paving the way for more widespread adoption of pharmacogenetics, which promises to significantly improve patient care and healthcare efficiency. Key resources such as PharmGKB, CPIC, and the NIH provide valuable information and guidance for clinicians, researchers, and students, helping bridge the gap between genetics research and clinical application. Conclusions. Pharmacogenetics represents a significant advancement in personalized medicine, offering the potential to tailor drug therapies to individual genetic profiles. Although the field has made substantial progress, challenges remain in the form of insufficient clinical evidence, implementation barriers, and ethical concerns. Continued research and collaboration among stakeholders are essential to fully realize the benefits of pharmacogenetic testing in clinical practice.

The moderating role of paternalistic leadership in the relationship between job satisfaction and job stress among health workers in Gaziantep, Turkey.

Adopting an effective leadership style is of critical importance in increasing healthcare efficiency and therefore determining patient satisfaction. For example, although there are many new and effective leadership types today, the paternalistic leadership approach can still play an effective role in developing countries. The aim of this study is to examine the moderating role of paternalistic leadership in the relationship between job satisfaction and job stress. A total of 466 healthcare workers completed measures of perceived paternalistic leadership, job stress, and job satisfaction. The research was conducted in the center of Gaziantep, one of the largest cities in Turkey. Regression analysis was used to evaluate the moderate variable. Dawson slope were used to visualize the results. A negative relationship was found between job satisfaction and job stress. Additionally, it has been determined that the perception of paternalistic leadership has a moderating role. It has been observed that as the perception of paternalistic leadership increases, the negative relationship between job satisfaction and job stress decreases. It has been concluded that the paternalistic leadership approach can play a critical role in increasing the productivity of healthcare workers and reducing the negative effect of stress on satisfaction. It has been suggested that policies be developed to develop appropriate strategies for creating work environments that will increase employees' perceptions of paternalistic leadership.

Effectiveness of Integrated Marketing and Financial Strategies in Healthcare

In recent years, the healthcare sector has experienced a shift toward integrated marketing and financial strategies to drive better patient outcomes, streamline operational costs, and improve service delivery. This paper examines the effectiveness of these strategies by exploring how they align to optimize healthcare systems. It focuses on the benefits, challenges, and implications of integrating financial planning with marketing outreach, emphasizing the role of data analytics, technology, and consumer engagement in enhancing both patient satisfaction and financial performance. Through a detailed analysis of current literature, industry examples, and statistical insights, the paper aims to provide a comprehensive overview of how an integrated approach influences revenue cycles, operational efficiency, and patient engagement in healthcare.