Enhance Healthcare Services Research Articles

Telemedicine integration with cloud-based IoT: A literature review approach

This paper discusses the possibilities of integrating cloud-based IoT with telemedicine as the ways to advance the healthcare industry performance and change its context for the better. This paper aims at exploring how these technologies can be integrated to enhance the existing solutions to the present problems of health care access, patient interactions and use of analytical data in practices. Possible ways in which IoT devices and cloud technology can enhance patient care and assist clinicians in their process of arriving at diagnoses are problems that the research seeks to uncover. The method used is a qualitative systematic review of prior studies related to telemedicine, IoT, and cloud computing, accompanied by a thematic analysis to reveal emerging patterns and the gaps in the literature examined in the study. The new model ‘‘Telemedicine Integration with Cloud-Based IoT,’’ was categorized thematically by differentiation of real-time severity, data privacy, patient interaction and technological difficulties. This model considers layers for acquiring IoT data, storing and analysis, a healthcare remote interface, a machine learning based analytics, and security, which offer a holistic way to enhance healthcare services. Real-time IoT, cloud-based storage, AI for early diagnosis, secure telemedicine platforms, data protection, and complete os compatibility. Based on this study, the authors posited that the convergence of IoT and cloud is capable of redesigning telemedicine by delivering better quality, efficient, and accessible healthcare solutions. However, they lack socio-economic factors for example, the feasibility cost for implementing it for the health companies and difficulties faced by the patient in the less-developed regions. The main limitation observed in the current research is related to the barriers hindering its implementation and future studies are suggested to address these challenges by exploring improved cost-effective approaches to overcome these and other barriers and analysing the issue of data security and patients’ privacy regulation. Studies should also aim at the long-term implications on the user satisfaction in order to introduce or show the long term positive and negative effects of cloud IoTs in telemedicine.

Evaluating Factors Affecting Knowledge Sharing Among Health Care Professionals in the Medical Imaging Departments of 2 Cancer Centers: Concurrent Mixed Methods Study.

Knowledge sharing is a crucial part of any knowledge management implementation. It refers to sharing skills and experience among team members in an organization. In a health care setting, sharing knowledge, whether tacit or explicit, is important and can lead to better health care services. In medical imaging departments, knowledge sharing can be of particular importance. There are several factors that affect knowledge-sharing practices in medical imaging departments: individual, departmental, and technological. Evaluating the importance of these factors and understanding their use can help with improving knowledge-sharing practices in medical imaging departments. We aimed to assess the level of motivation, identify current knowledge-sharing tools, and evaluate factors affecting knowledge sharing in the medical imaging departments of 2 cancer centers, The Christie, United Kingdom, and the Kuwait Cancer Control Center (KCCC). A concurrent mixed methods study was conducted through nonprobability sampling techniques between February 1, 2023, and July 30, 2023. Semistructured interviews were used to validate the results of the quantitative analysis. Data were collected using an electronic questionnaire that was distributed among health care professionals in both cancer centers using Qualtrics. Semistructured interviews were conducted online using Microsoft Teams. The quantitative data were analyzed using the Qualtrics MX software to report the results for each question, whereas the qualitative data were analyzed using a thematic approach with codes classified through NVivo. In total, 56 respondents from the KCCC and 29 from The Christie participated, with a 100% response rate (56/56, 100% and 29/29, 100%, respectively) based on the Qualtrics survey tool. A total of 59% (17/29) of health care professionals from The Christie shared their knowledge using emails and face-to-face communication as their main tools on a daily basis, and 57% (32/56) of health care professionals from the KCCC used face-to-face communication for knowledge sharing. The mean Likert-scale score of all the components that assessed the factors that affected knowledge-sharing behaviors fell between "somewhat agree" and "strongly agree" in both centers, excepting extrinsic motivation, which was rated as "neither agree nor disagree." This was similar to the results related to incentives. It was shown that 52% (15/29) of health care professionals at The Christie had no incentives to encourage knowledge-sharing practices. Therefore, establishing clear policies to manage incentives is important to increase knowledge-sharing practices. This study offered an evaluation of factors that affect knowledge sharing in 2 cancer centers. Most health care professionals were aware of the importance of knowledge-sharing practices in enhancing health care services. Several challenges were identified, such as time constraints, a lack of staff, and the language barrier, which limit knowledge-sharing practices. Therefore, establishing a clear policy for knowledge sharing is vital to practicing knowledge-sharing behaviors and facing any challenges that limit this practice.

The development of carbon nanostructured biosensors for glucose detection to enhance healthcare services: a review

In this review, the forefront of biosensor development has been marked by a profound exploration of carbon nanostructured materials for the specific application of glucose detection. Moreover, this progressive line of inquiry capitalizes on the distinctive attributes of carbon nanostructured materials such as carbon nanotubes, carbon quantum dots, and graphene which exhibit unique characteristics in the development of biosensor engineering design. It also enhanced analytical performances regarding the limit of detection, selectivity, sensitivity, and reproducibility towards glucose detection in biological samples. Most importantly, the strategic integration of carbon nanostructured-based biosensor architectures has played a significant role in advancements, characterized by heightened sensitivity, exquisite selectivity, and augmented stability in glucose detection processes. Furthermore, utilizing these advanced materials has engendered a transformative impact on electrochemical properties, propelling the biosensors to achieve rapid and precise glucose-sensing capabilities. The confluence of carbon nanostructures with biosensor technology has not only elevated the scientific understanding of glucose detection mechanisms. Still, it has also paved the way for miniaturized and portable biosensors. This transformative shift holds great promise for the realization of point-of-care diagnostics, representing a pivotal step towards durability and efficient glucose monitoring in health/medical care. These advancements emphasize the crucial role of carbon nanostructured-based biosensors in opening the way to a new avenue of superiority and effectiveness in diabetes management. Conclusively, the challenges and, in a forward-looking stance, the prospective futures of glucose biosensors anchored on carbon nanostructured frameworks were considered.

Analyzing the Maharashtra Ambulance Service “108”: The Prospect and Challenges

ABSTRACT The utilization of digital health in India is playing a crucial role in enhancing healthcare services by transitioning from the current inadequate public health structure to a more efficient and patient-centric system. Digital health includes various digital tools, such as electronic health records (EHRs), telemedicine, mobile health applications, health information exchange systems, and other technological advancements to improve access, efficiency, and quality of healthcare delivery. This study investigates the prospects and challenges encountered by the newly-digitized Maharashtra Emergency Medical Services (MEMS). Utilizing the 38,823 MEMS calls from November 2022, this study investigates the current status of emergency service delivery mechanisms in Maharashtra. Through spatial analyses, this study also explores the causes behind calls. The findings of the study show that calls for 108 ambulance services were distributed across the districts and had variable service delivery time periods. Current challenges to the system arise from various urban and healthcare infrastructure problems, as well as socio-cultural challenges. Implementation of the digitized MEMS system reveals key factors that influence the service’s success, assisting the policymakers and health administrators in identifying and further improving the service.

Prevalence and Clinico-sociodemographic Factors of Asthmatic Children Aged 1 to 14 Years: A Prospective Descriptive Cross-sectional Study in Gandaki Province, Nepal

Introduction Pediatric asthma poses a significant public health challenge, impacting children’s health and quality of life. As asthma prevalence continues to rise globally, understanding its epidemiology is crucial for developing effective management strategies. This study aims to investigate the prevalence and associated demographic, socioeconomic, and clinical characteristics of pediatric asthma among children aged 1 to 14 years. Methods This study employed a prospective descriptive cross-sectional design. Data were collected from a diagnosed asthmatic children attending pediatric department of the GP Koirala National Respiratory Centre and Hospital, Tanahun, Ganaki, Nepal over three months from April 20, 2023 by assessing demographic factors (age, gender, residence), socioeconomic status, clinical history (family history and comorbid conditions), and asthma severity. Data were analyzed with SPSS version 23 using descriptive statistics. Results Prevalence rate of pediatric asthma was 3.08%. The demographic analysis revealed that most participants were aged 1-5 years (53%), predominantly male (64.9%), and resided in urban areas (59.6%). Socioeconomic data indicated that 59.6% came from low-income households, suggesting a potential link between socioeconomic status and asthma prevalence. Clinically, 53% had a family history of asthma, with 26.3% presenting with comorbid allergic rhinitis. Asthma severity assessments showed that 50% had mild asthma, 33.33% had moderate asthma, and 16.67% had severe asthma. Conclusion This study highlights a low prevalence of asthma in the pediatric population at GP Koirala National Respiratory Centre and Hospital, yet emphasizes the burden faced by younger children, especially those from low-income backgrounds. The correlations between asthma prevalence, socioeconomic status, and comorbid conditions underscore the need for targeted public health interventions and comprehensive management strategies. Further research is warranted to deepen understanding and enhance healthcare services for affected children.

Current Advances in Patient-Perceived Quality Assessment Within Romanian Healthcare.

The global healthcare landscape is shifting toward patient-centered care, emphasizing the integration of patient feedback into service delivery. Romania, aligning with this trend, has implemented patient-perceived quality assessment tools to enhance healthcare services and better meet patient needs and expectations. This study aims to review comprehensively the implementation and impact of these tools in Romania, focusing on their role in improving healthcare quality. By examining key assessment instruments such as the Patient Satisfaction Questionnaire (PSQ), the Service Quality (SERVQUAL) model, and the Romanian Healthcare Quality Assessment Survey (RHQAS), the research seeks to understand how these tools have been used to identify areas for improvement and drive advancements in patient care. Employing a comprehensive review methodology, the study will conduct a thorough literature search to identify relevant studies, reports, and publications, analyzing the PSQ, SERVQUAL, and RHQAS in detail to understand their measurement domains, psychometric properties, and application within Romania. Additionally, qualitative data from interviews with healthcare providers and patients may be collected to offer further insights into the use and effectiveness of these tools. The study's findings are expected to provide valuable insights into the role of patient-perceived quality assessment tools in enhancing healthcare in Romania, identifying strengths, weaknesses, and opportunities for improvement. The results will highlight the effectiveness of combining international methodologies with localized adaptations to address the specific needs of the Romanian healthcare system, ultimately contributing to the ongoing efforts to improve patient satisfaction and health outcomes by informing the development and refinement of patient-centered care initiatives in Romania.

Prevailing Practices in Ototoxicity Monitoring in Individuals With Head and Neck Cancer Undergoing Radiotherapy and Chemoradiotherapy: A Scoping Review.

The aim of the current scoping review is to identify the studies reporting ototoxicity monitoring in individuals with head and neck cancer (HNC) undergoing radiation therapy and/or chemoradiation therapy across the world. The specific objectives were to identify and report the test protocol used, identify the most common timeline of follow-up, and identify barriers and facilitators influencing the implementation of the monitoring program. A comprehensive search was conducted across six electronic databases, including PubMed, Embase, Web of Science, Scopus, Google Scholar, and ProQuest. The scoping review method adhered to relevant guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and frameworks. The database search was carried out by two independent researchers, and studies were selected based on specific inclusion and exclusion criteria. This scoping review identified 13 studies that fulfilled the inclusion criteria of this study. Only one study reported findings from the perspective of ototoxicity monitoring. Another study explicitly mentioned that ototoxicity monitoring was a standard of care in their hospital. Only one study reported using the relevant guidelines for monitoring ototoxicity. Specialized tests such as high-frequency audiometry, distortion product otoacoustic emissions, and vestibular tests were rarely used. Ototoxicity monitoring was influenced by awareness-related factors, technical factors, treatment-related factors, and organizational factors. Research on ototoxicity monitoring programs is in its early stages, highlighting the need for standardized practices and multidisciplinary collaboration to enhance health care services for HNC patients. A standardized approach, improved awareness, and the incorporation of patient perspectives are crucial to enhancing ototoxicity monitoring in HNC patients.

A Smart Diagnostic, e-Prescription and Cryptocurrency Billing System

This study focuses on the development of a smart e-prescription and billing system that integrates blockchain, artificial intelligence (AI), and analytics to enhance healthcare services. The system's core features include an AI-driven symptom checker, appointment scheduling, and cryptocurrency-based billing. A systematic review of secondary data from academic studies and industry-related articles was conducted to assess current practices in drug prescription, dispensation, and existing software solutions. The symptom-checker model was trained using a dataset of 4,963 entries, with an 80/20 split between training and testing sets. A Decision Tree Classifier was employed, showing promising performance. The findings suggest that the e-prescription (eRx) application can reduce healthcare costs, minimize prescription errors, and improve the overall quality and efficiency of care. The system was built using frontend web technologies, with Python supporting backend functionalities. Cryptocurrency was implemented as the primary payment method, while machine learning was used to support appointment scheduling. The study recommends reliable internet access and adequate hardware for optimal system performance.

Critical success factors in the maintenance strategies of public healthcare facilities

PurposeThe United Nations' Sustainable Development Goal of ensuring healthy lives and promoting well-being for people of all ages underscores the vital role of public healthcare facilities (PHFs) in delivering essential healthcare services. However, these facilities often suffer from inadequate maintenance, exacerbated by the insufficient implementation of maintenance strategies. Recognizing the importance of PHFs in enhancing healthcare services, this paper investigates the Critical Success Factors (CSFs) in the maintenance strategies of PHFs in South Africa.Design/methodology/approachThrough semi-structured interviews with nineteen purposively selected maintenance personnel from the Limpopo Department of Health (DoH), this study identified and analyzed the CSFs to enhance maintenance operations in PHFs. Thematic content analysis was employed to derive key insights from the collected data.FindingsThe study's findings highlight adequate maintenance planning and effective leadership as the two overarching CSFs in the maintenance of PHFs. These factors play a pivotal role in addressing challenges that hinder the current maintenance team from meeting maintenance requirements to the satisfaction of both staff and patients within PHFs.Originality/valueThe study offers valuable insights for policymakers to improve the effectiveness of maintenance operations in PHFs. By addressing the identified CSFs, policymakers can enhance maintenance operations in PHFs, positively impacting healthcare service delivery and the well-being of both staff and patients.

Exploring drivers of self-treatment with antibiotics in three agricultural communities of northern Tanzania

BackgroundAntimicrobial resistance (AMR) poses a significant global health threat, particularly in low- and middle-income countries (LMICs). Self-treatment with antibiotics, the practice of using antibiotics without professional guidance, is often considered an important contributor to the emergence and spread of AMR.MethodThis study investigated the drivers of self-treatment in three common types of agricultural communities in northern Tanzania. The research employed a comprehensive array of methods, including cross-sectional surveys (n = 790), interviews (n = 30) and observations (n = 178) targeting both antibiotic (human and animal) providers and users (patients and farmers). Qualitative interview data were analysed using a coding and association matrix, while descriptive analyses were performed on survey and observation data.ResultsSelf-treatment with antibiotics was highly prevalent in all communities. Between 41.0% (self-reported) and 60.3% (observed) of human antibiotics were obtained without a prescription and we observed that veterinary antibiotics were regularly purchased in retail shops without referral by a professional. Structural deficiencies in the healthcare system drove this practice: limited access to healthcare facilities, medication stockouts and prolonged waiting times were identified as key factors. The absence of safety nets like insurance schemes further contributed to self-medication. Retail shops offered a convenient and cost-effective alternative when antibiotics were inaccessible or unaffordable. Notably, informal networks comprising treatment vendors, friends or neighbours, as well as personal experiences played a crucial role in guiding individuals in their self-treatment decisions by providing advice on treatment choice and modalities.ConclusionsAddressing self-treatment requires a multi-faceted approach. Improving the availability and accessibility of antibiotics, enhancing healthcare services and involving retail vendors in antibiotic stewardship are essential. Structural issues like access to diagnostics and medicines must be tackled, alongside reducing barriers and incentivising individuals to use professional healthcare services. Training retail vendors to sell specific first-line antibiotics over the counter with guidance on appropriate usage should be considered. Such bottom-up interventions will enable sustainable promotion of responsible antibiotic use, mitigating AMR emergence and securing a healthier future for all.

Comparative Analysis of Supervised Machine Learning Algorithms for Diabetes Prediction

Diabetes Mellitus is a chronic disease with far-reaching consequences, necessitating innovative diagnostic approaches. Current methods have limitations, highlighting the need for advanced techniques. This research leveraged machine learning algorithms to predict diabetes using the Pima Indian Diabetes Dataset. Six algorithms were developed and compared: Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Random Forest, and Decision Tree. Standard evaluation metrics assessed their performance. The Random Forest Classifier emerged as the top-performing algorithm, achieving an impressive 91% accuracy and demonstrating exceptional reliability. This study showcases machine learning's potential to transform diabetes diagnosis and management. The developed web application provides a user-friendly interface for predicting diabetes, facilitating timely intervention and improved health outcomes. The findings contribute significantly to the development of intelligent disease prediction systems, promoting early detection and enhanced patient care. By harnessing machine learning, this research pioneers a new frontier in diabetes diagnosis, paving the way for improved patient outcomes and enhanced healthcare services.

Measuring spatial inequalities in maternal and child mortalities in Pakistan: evidence from geographically weighted regression

BackgroundIn developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts.MethodThis study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI.ResultsThe inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H–H clusters of maternal mortality and DMI were in Baluchistan, and the H–H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores.ConclusionThe findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Medical Tourism Management

Medical tourism, the practice of traveling abroad for medical treatment, has gained substantial attention in recent years due to its implications for healthcare delivery and tourism industry dynamics. This paper investigates the management strategies employed in medical tourism destinations, focusing on enhancing healthcare services and tourism competitiveness. Utilizing secondary data from scholarly articles, reports, and industry publications, the research synthesizes existing knowledge on medical tourism management frameworks, destination competitiveness factors, patient experiences, and healthcare quality metrics. The study employs a qualitative analysis approach to distill key insights and patterns from the literature, shedding light on effective management practices and their impact on patient outcomes and destination attractiveness. Findings reveal the importance of strategic planning, collaboration between healthcare providers and tourism stakeholders, quality assurance mechanisms, and marketing strategies in optimizing medical tourism experiences. The paper discusses implications for policy development, industry regulations, and sustainable growth strategies in medical tourism destinations. By leveraging secondary data sources, this research contributes to a comprehensive understanding of medical tourism management dynamics, offering practical insights for healthcare providers, policymakers, and tourism professionals navigating this evolving landscape. Keywords- Medical tourism, Management strategies, Destination competitiveness, Healthcare quality

MoCab: A framework for the deployment of machine learning models across health information systems

Background and Objective:Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others. Methods:The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records. Results:The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings. Conclusions:We demonstrate MoCab’s potential in promoting the interoperability of machine learning models and enhancing its utility across various EHRs. Despite facing challenges like FHIR adoption, MoCab addresses key challenges in adapting machine learning models within healthcare settings, paving the way for further enhancements and broader adoption.

Exploring Emotional Intelligence in Jordan’s Artificial Intelligence (AI) Healthcare Adoption: A UTAUT Framework

The integration of Artificial Intelligence (AI) has been reshaping healthcare globally. However, the AI adoption in Jordan is met with cautious progress. AI has shown substantial potential to enhance healthcare services and foster Emotional Intelligence (EI), especially in advanced economies. Despite its proven effectiveness elsewhere, the Jordanian populace is reluctant to adopt AI in the healthcare sector, with predictions for hospitalizations, medical consultations, and treatment recommendations being sluggish to gain acceptance. This study investigates the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model. While UTAUT typically considers performance expectancy, effort expectancy, social influence, and facilitating conditions as key determinants of technology acceptance, this study argues that emotional intelligence, including self-regulated, self-awareness, motivation, empathy, and social skills, should be integrated as direct determinants of behavioural intention. In this study, a quantitative approach has been employed, whereby questionnaires were delivered through email and messaging apps to evaluate the impact of emotional intelligence on Jordanians’ willingness to adopt AI technology in the healthcare sector. The findings suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct, particularly in developing countries like Jordan, where user models for AI adoption are less explored. The implications of the study extend to healthcare planners and developers in Jordan, providing insights into factors, which influence the successful adoption of AI technologies among diverse user groups. This study has provided valuable recommendations for developers of AI-based healthcare systems, enabling them to align their assistance with the perceptions and behaviours of Middle Eastern users. By doing so, they can foster increased acceptance of AI-based healthcare systems in the Middle East and other developing regions to improve healthcare services.

Network-integrated medical chatbot for enhanced healthcare services

Technology has a significant role in improving medical care, such as enhancing communication between patients and healthcare virtual assistants. By including artificial intelligence and natural language processing (NLP), the main aim of this research paper is to design a medical chatbot framework, and application design prototype, and use the Dialog Flow tool to build an auto-response system known as a chatbot. The goal is to provide medical services and meet patients' needs. It will communicate with the user in the language they follow. With the help of keywords such as symptoms entered by the patients, it will help to identify the disease. Moreover, the application provides the patients with the ability to record their medicine to remind them to take it. Additionally, it will make it easier for patients to learn more about their disease and motivate them to take the necessary precautions to stay healthy.

Trust: How It Affects the Use of Telemedicine in Improving Access to Assistive Technology to Enhance Healthcare Services.

Modern telemedicine (TM) technologies play a crucial role in enhancing access to Assistive Technology in healthcare services. However, the full benefits of this technology will not be realized unless it is widely accepted among service users (patients). This study aimed to investigate the impact of patient trust and perceived risk on the acceptance of TM for Assistive Technology in healthcare. A comprehensive survey instrument was developed and refined through expert feedback and a pilot study, leading to data collection from 917 participants. The theoretical framework guiding this research was based on the Trust factors in TM, which helped in conceptualizing the factors influencing patient acceptance of TM. The study revealed a significant gap in patient trust in TM and highlighted the multifaceted nature of perceived risk, emphasizing the need to consider individual risk factors separately. Results also indicated that trust in technological reliability and the perceived effectiveness of TM were critical factors influencing its adoption. The findings underscore the importance of building trust among service users and promoting the reliability of TM for achieving desirable medical outcomes. In conclusion, to facilitate widespread acceptance of TM for Assistive Technology, a multi-faceted approach involving healthcare providers, organizations, and governments is essential to address patient concerns, enhance trust, and promote the benefits of this technology.

Pengembangan Sistem Pelayanan Klinik Budhi Pratama Gedong Berbasis Web dengan Metode Rapid Application Development (RAD)

Budhi Pratama Gedong Clinic faces significant challenges in the manual handling of patient data and report recording, leading to inefficiencies and slow operational processes. To address these issues, the development of a computerized system is essential for collecting, storing, and processing data to generate actionable information that can enhance healthcare services. This computerized system is also expected to reduce operational costs by minimizing the reliance on paper-based records. Located at Jalan Raya Tengah No. 26 RT 6/RW 1 Gedong Pasar Rebo, East Jakarta, the clinic provides various health services, including medication procurement, which currently relies on manual processes. The implementation of a web-based service system using the Rapid Application Development (RAD) methodology with XAMPP is projected to streamline operational processes and improve data reporting. The objectives of this study include facilitating easier patient registration, expediting medical record retrieval, simplifying medication management, and increasing the accuracy of patient payment processing. Furthermore, the developed system aims to integrate a comprehensive database to efficiently document and manage data on patients, doctors, clinic staff, and medications, thereby providing a user-friendly platform for healthcare providers and enhancing overall patient care

Isu Etika dalam Pemanfaatan Tenaga Kesehatan Warga Negara Asing di Indonesia: Perspektif Keberlanjutan Pelayanan Kesehatan

The phenomenon of globalization has transformed the world's landscape, connecting nations, and inspiring the concept of Special Economic Zones (SEZs). The utilization of Foreign National Health Workers (FHNWs) has become a strategy to address the shortage of medical personnel and enhance healthcare services, particularly in remote regions. However, the implementation of this policy also faces several ethical, cultural, and linguistic challenges. The ethical aspect takes center stage in the utilization of FHNWs. Patient rights, professionalism, and cross-cultural collaboration are crucial focal points in healthcare service practices. Despite its benefits, the utilization of FHNWs encounters communication hurdles, especially when the employed language is unfamiliar. Additionally, the integration of local cultural values becomes a pivotal aspect in delivering sensitive and patient-centered care. This paper analyzes policy documents related to the utilization of Foreign National Health Workers (FHNWs) in Indonesia through normative and predictive approaches. The objective is to comprehend the concepts and regulatory implementations concerning the ethics of FHNW utilization within the context of Indonesian healthcare services. The phenomena of globalization and efforts to expedite economic growth through Special Economic Zones (SEZs) have influenced these policies. The analysis involves various policy documents relevant to FHNW utilization. In facing the ethical dilemmas arising in the practice of healthcare services by foreign medical personnel, it is imperative for them to adapt to the local culture without compromising the principles of medical ethics. Moreover, competition within the job market and cultural disparities between foreign and local medical personnel also present challenges that need to be addressed. In dealing with competition, a holistic approach involving the government, relevant institutions, and professional training for local medical personnel is required. Overall, the utilization of FHNWs holds potential benefits in addressing medical workforce shortages and enhancing healthcare access. However, this utilization must be carried out with ethical responsibility, respecting patient rights, and considering cultural and professional aspects. With a prudent approach, the utilization of FHNWs can contribute to improving the quality of healthcare services and the well-being of local medical personnel, in line with ethical principles and national interests.

Building Medication Profiles in the Elderly: a Qualitative Study Based on Medication Information Literacy in a Long-Term Care Facility.

Long-term care facilities are increasingly challenged with meeting the diverse healthcare needs of the elderly population, particularly concerning medication management. Understanding medication information literacy and behavior among this demographic is imperative. Therefore, this qualitative study aims to explore medication information literacy and develop distinct medication profiles among elderly long-term care residents. In this study, we conducted in-depth semi-structured interviews with 32 participants aged 65 or older residing in a long-term care facility. The interviews were designed to explore participants' understanding of medication information, medication management practices, and experiences with healthcare providers. Thematic analysis was employed to analyze the interview data, allowing for the identification of common patterns and themes related to medication-taking behavior among the elderly residents. The thematic analysis revealed four distinct medication behavior profiles among the elderly long-term care residents: (1) Proactive Health Self-Managers, (2) Medication Information Adherents, (3) Experience-Based Medication Users, and (4) Nonadherent Medication Users. These findings provide valuable insights into the diverse approaches to medication management within long-term care facilities and underscore the importance of tailored interventions to support the specific needs of each profile. This study highlights the necessity for tailored medication education and support to optimize medication management for the elderly. With the aging population expansion, addressing the unique medication challenges within long-term care facilities becomes increasingly critical. This research contributes to ongoing endeavors to enhance healthcare services for the elderly, striving for safer and more effective medication-taking behavior.