Efficient Health Care System Research Articles

Defining the capabilities and competencies of high-performing family physicians: a mixed methods study

IntroductionHigh-performing primary care is recognised as the foundation of an effective and efficient healthcare system. Many medical graduates report they are not prepared for independent practice. To date, no research...

Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications.

In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2022 in the CIS field. The editors follow a systematic approach to gather relevant articles and select the best papers for the section. This year, they updated the query to incorporate the topic of telemedicine and removed search terms related to geographic information systems. The revised query resulted in a larger number of identified papers, necessitating the appointment of a third section editor to handle the increased workload. The editors narrowed the initial pool of articles to 15 candidate papers through a multi-stage selection process. At least seven independent reviews were collected for each candidate paper, and a selection meeting with the IMIA Yearbook editorial board led to the final selection of the best papers for the CIS section. The query was carried out in mid-January 2023 and retrieved a deduplicated result set of 5,206 articles from 1,500 journals. This year, 15 papers were nominated as candidates, and four were finally selected as the best papers in the CIS section.Including telemedicine in the query resulted in a substantial increase in the number of papers found. The analysis highlights the growing convergence between clinical information systems and telemedicine, with mobile health (mHealth) technologies and data science applications gaining prominence. The selected candidate papers emphasize the practical impact of research efforts, focusing on patient-centric outcomes and benefits, including intelligent mobile health monitoring systems and AI-assisted decision-making in healthcare. Looking ahead, the field of CIS is expected to continue evolving, driven by advances in telemedicine, mHealth technologies, data science, and AI integration, leading to more efficient, patient-oriented, and intelligent healthcare systems and overall improvement of global healthcare outcomes.

IoT enabled healthcare environment using intelligent deep learning enabled skin lesion diagnosis model

Melanoma skin cancer is a widely occurring disease over the globe and it is needed to identify the disease at the initial stages. Recent technologies like Internet of Things (IoT) have aimed to design efficient healthcare system. Besides, deep learning and transfer learning are progressively important in the healthcare diagnoses of various diseases. This study focuses on the design of Intelligent Internet of Things with Deep learning Enabled Skin Lesion Diagnosis (IIoT-DLSLD) model for healthcare environment. The proposed IIoT-DLSLD technique enables the IoT devices to capture the dermoscopic images and classify the skin lesions into distinct class labels. In addition, the IIoT-DLSLD technique employs Gaussian filtering (GF) based preprocessing technique to eradicate the presence of noise. Besides, Otsu thresholding with swallow swarm optimization (OT-SSO) algorithm is employed for skin lesion segmentation. For skin lesion classification, MobileNet v2 based feature extraction and emperor penguin optimizer (EPO) based deep wavelet neural network (DWNN) is utilized. The experimental validation of the IIoT-DLSLD technique take place against ISIC dataset and the results portrayed the betterment of the IIoT-DLSLD algorithm over the other state of art skin lesion classifiers.

Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study

Background Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital. Objective This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources. Methods Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number. Results The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization’s information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient’s specific profile. Simulation can also be applied to reproduce the system’s functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block. Conclusions This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system. Trial Registration ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621 International Registered Report Identifier (IRRID) DERR1-10.2196/45477

An efficient privacy-preserving authentication scheme with enhanced security for IoMT applications

The Internet of Medical Things (IoMT), an essential application of Internet of Things (IoT) technology in the health sector, is of great practical importance in improving healthcare quality and reducing healthcare costs and errors. For years, researchers have designed many cryptographic schemes for securing IoMT applications. However, in this work, we reveal that the state-of-the-art cloud-centric IoMT-based smart healthcare system cannot be deployed in real-world scenarios by proposing a concrete signature forgery attack on their underlying pairing-based homomorphic certificateless signature (CLS) scheme. Then we present a new CLS scheme without pairings and formally prove its security under the standard cryptographic assumption. Based on our design, we put forward a new secure and efficient IoMT-based smart healthcare system. The performance comparison results illustrate the practicality of our construction.

An evaluation of the impact of lean management practices on patients’ satisfaction at a small healthcare facility

The study aims to find the impact of Lean Management Practices on Patients’ Satisfaction in a Small Healthcare Facility. The selected sample size of the study is 229. The researcher has used primary data in this investigation. The researcher uses Interviews, questionnaires, and experiments to acquire primary data directly through primary sources in the study. The interview schedule and substance of the question are created with the study’s goal in mind. ANOVA, SPSS tool, and Regression Analysis technique are used in the study. According to the research, lean adoption in healthcare enhances patient satisfaction at small medical facilities. People are central to every efficient lean healthcare system, and their goals and motivations drive change.

Towards a New Paradigm for Digital Health Training and Education in Australia: Exploring the Implication of the Fifth Industrial Revolution

Digital transformation, characterised by advanced digitalisation, blockchain, the Internet of Things, artificial intelligence, machine learning technologies, and robotics, has played a key role in revolutionising various industries, especially the healthcare sector. The adoption of and transition (from traditional) to new technology will bring challenges, opportunities, and disruptions to existing healthcare systems. According to the European Union, we must pursue both digital and green transitions to achieve sustainable, human-centric, and resilient industries to achieve a world of prosperity for all. The study aims to present a novel approach to education and training in the digital health field that is inspired by the fifth industrial revolution paradigm. The paper highlights the role of training and education interventions that are required to support digital health in the future so that students can develop the capacity to recognise and exploit the potential of new technologies. This article will briefly discuss the challenges and opportunities related to healthcare systems in the era of digital transformation and beyond. Then, we look at the enabling technologies from an Industry 5.0 perspective that supports digital health. Finally, we present a new teaching and learning paradigm and strategies that embed Industry 5.0 technologies in academic curricula so that students can develop their capacities to embrace a digital future and minimise the disruption that will inevitably accompany it. By incorporating Industry 5.0 principles into digital health education, we believe students can gain a deeper understanding of the industry and develop skills that will enable them to deliver a more efficient, effective, and sustainable healthcare system.

Facilitators and barriers to the transition from registered nurse to nurse practitioner in Canada.

Understanding factors that affect integration of NPs is likely to address barriers and provide reform strategies that shape a cost-effective, sustainable, accessible, and efficient health care system. There are a limited number of current and high-quality studies examining the transition process of registered nurses (RNs) to nurse practitioners (NPs), especially in Canada. To explore the experiences of RNs transitioning to NPs in Canada. Thematic analysis of audio-recorded semi-structured interviews was conducted to explore the experiences of 17 RNs as they transitioned to NPs. A purposive sampling of 17 participants was completed in 2022. Six main themes emerged from analysis of 17 interviews. The content of themes varied according to NPs' years of experience and the NP school they attended. Peer support and mentorship programs were facilitators in the transition from RN to NP. Conversely, shortcomings in education, financial stressors, and the lack of NP role definition were seen as barriers. Legislation and regulations supportive of NPs, diverse and comprehensive education, and improved availability of mentorship programs may strengthen transition facilitators and help NPs overcome related barriers. Legislation and regulations supportive of the NP role are needed, focusing on defining the NP role and establishing an independent and consistent remuneration structure for NPs. A more in-depth and diversified educational curriculum is needed, with increased support from faculty and educators and continual encouragement of initiation and perpetuation of peer support. A mentorship program is beneficial to reduce transition shock from the role of the RN to the NP.

A Study on Role of Health Care Management Streams

Health care is the maintenance or improvement of health care via the prevention, diagnosis, treatment, amelioration, or cure of disease, illness, injury, and other physical and mental impairments in people. Health care is delivered by health care professionals and allied health care fields. Medicine, dentistry, pharmacy, midwifery, nursing, optometry, audiology, psychology, occupational therapy, physical therapy, athletic training, and other health professions are all part of health care. It includes work done in providing primary care, secondary care, and tertiary care, as well as in public healthcare. Access to health care may vary across countries, communities, and individuals, influenced by social and economic conditions as well as health care policies. Providing health care services means “the timely use of personal health care services to achieve the best possible health care outcomes. A limitation to health care services affects negatively the use of medical services, the efficacy of treatments, and overall outcome (well-being, mortality rates). An efficient health care system can contribute to a significant part of a country’s economy, development, and industrialization. Health care is conventionally regarded as an important determinant in promoting the general physical and mental health care and well-being of people around the world, An example of this was the worldwide eradication of smallpox in 1980, declared by the WHO as the first disease in human history to be eliminated by deliberate health care interventions.

Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review

Medical decision support systems (MDSS) utilized for medical diagnosis aim to improve patient care; they do so by simulating the human cognitive process in order to arrive at a valid clinical conclusion. Medical decision support systems play a significant role in promoting an efficient and effective health care system. However, the adoption and usage of these systems in daily medical practice have been slow due to a lack of physician confidence in the systems and concerns about the erosion of professional autonomy, obscure physicians’ role, and diminished physician-patient relationships. Despite successes in the development of decision support systems, there is still a low adoption and utilization of MDSSs. This study reviews the development, adoption, and utilization of MDSSs in diagnosing Febrile Diseases between 2008 - 2020. We carried out a systematic literature review on the adoption and utilization of MDSS in diagnosing Febrile Diseases using guidelines provided in literature for systematic reviews and a PRISMA flow to guide paper selection, inclusion, and exclusion criteria. Three reviewers independently carried out literature searches on selected databases for MDSS to diagnose febrile diseases. After the initial searches, the reviewers cross-checked the results, and disagreements were resolved through mutual agreements. The reviewers extracted the data based on an agreed template, and a separate set of reviewers cross-checked this. The extracted data was then synthesized, analyzed, and split into various themes to answer the research questions. We identified 113 papers on MDSS development and utilization and 15 papers on studies relating to the adoption of MDSS. The analysis revealed that Fuzzy Logic was the most prevalent model of MDSS in use, while most models discussed in the papers were unimplemented. The results show that the main reason for proposing these models was to improve accuracy and precision. The main factors influencing the adoption of these models by medical facilities were usefulness, relative advantage, and ease of use. However, the reasons for adopting MDSS by the medical facilities were not expressed in 50% of the studies. In contrast, most of those who adopted MDSS disclosed that it was used to improve effectiveness and adherence to guidelines. Finally, compared to WHO statistics, it was determined that the number of papers did not correspond to the disease burden in the research continents. Although considerable research has been carried out on the adoption and utilization of MDSS, there is a paucity of research on implemented models of MDSS. Despite the advances in MDSSs and their accuracy in diagnosing diseases, many developed models have remained unimplemented in clinical practice. Future studies should focus on encouraging the actual usage of existing models that have not been implemented and conducting research on diseases with a high burden in their context. Governments and medical facility managers need to implement new policies, guidelines, and a support structure that will encourage the adoption and actual usage of MDSS in daily medical practice.

Management Barriers to Inter-Organizational Collaboration in Preoperative Treatment of Patients with Hip or Knee Osteoarthritis.

Inter-organizational collaboration among healthcare institutions is widely recognized to improve healthcare services. Because there is a research gap in examining the management barriers to inter-organizational collaboration in countries with less efficient healthcare systems and the reasons for non-implementation of innovations, the aim of this study was to identify key management barriers to inter-organizational collaboration in the preoperative treatment of patients with hip or knee osteoarthritis in Slovenia using a mixed-methods approach with key stakeholders. A cross-sectional study was conducted using multiple methods. An online survey (n = 135) and a multilevel qualitative approach were used, interviewing patients (n = 21), healthcare professionals, and other stakeholders (n = 42). The overall assessment of barriers affecting the integrated approach at the macro, meso, and micro levels revealed that macro-level factors were statistically significantly perceived as the major barriers, while micro-level factors were the minor barriers. There was no significant difference between public and private sector respondents in the barriers at the three levels. However, there were significant differences in the perceptions of different professional groups at the micro and meso levels, but not at the macro level. The analysis of the in-depth interviews confirmed the importance of macro-level barriers. A culture of noncooperation combined with "managerial indecision" prevails in the Slovenian healthcare system due to weak management support for inter-organizational collaboration, with managers and other key stakeholders failing to make urgent decisions due to a lack of autonomy. Interviewees commonly noted that one of the major barriers to inter-organizational collaboration was a lack of resources and staff, particularly of primary care physicians and nurses. In the preoperative treatment of patients with hip or knee osteoarthritis, the culture of non-collaboration and executive indecision are the major macro-level barriers to inter-organizational collaboration in Slovenia.

Challenges and Opportunities of Secondary Use of health Data in Developing Countries

Open and reliable health data platforms can assist developing countries in responding to the many pressing issues confronting their society and economy today. Many countries’ cultural and economic structures have already been affected by the global pandemic, and data has played an important role in addressing these global challenges as well as cross-sector collaboration in public health response. Even before the pandemic, there was a pressing need to improve healthcare systems and better manage limited resources to meet the requirements of rising, and frequently aging, populations. We can boost the value of health data and assist to achieve these advancements by reusing it in diverse ways. Clinical data, such as healthcare incidents and clinical trial data, can be merged with information gathered from other sources, such as illness and insurance claims records, as well as gadgets and wearable technology. This data may then be anonymized and pooled to develop new insights and optimize population health, enhance the health and experiences of patients, build more efficient healthcare systems, and stimulate innovation. While many advanced countries have taken the advantage of secondary use of health data, developing countries like Nigeria are lagging behind due to challenges ranging from inadequate infrastructure, manual data collection and processing, siloed electronic health records, poor data quality, inefficient skills in data use core competencies, just to mention but few. This paper takes a deeper look at the inherent opportunities in the use of secondary health data and barriers toward its use in developing countries.

Values-based healthcare (systematic review)

The current demographic situation is characterized by an increase in life expectancy and gain in the number of chronic non-communicable diseases. Healthcare is faced with the task of preserving the population health in these additional years of life, which, in turn, will lead to an increase in the shortage of resources in healthcare and, in general, negatively affect the economies of states. One of the modern concepts of optimal use of resources, evaluation of the effectiveness of health systems is value-based healthcare. In the described model, the value for the patient is the result of medical care in relation to health, which is important for the patient, the criteria for achieving which are determined by the patient himself. According to the concept of the Center, measuring the results and costs for each patient is part of the strategic program for the transition to a highly efficient healthcare system. 
 The purpose of the study is to investigate the features of value-based healthcare in different countries of the world.
 A systematic review was conducted according to the PRISMA guidelines.
 The literature search was performed in the electronic databases PubMed/MEDLINE, Scopus, eLibrary, CyberLeninka, and Yandex and Google systems among English and Russian-language reports. In the healthcare systems of countries that have implemented a value-based approach, the activities of medical institutions are evaluated according to indicators that reflect the value to the patient. To combine the available resources and coordinate the provision of medical care, multidisciplinary teams of specialists are being created and the patient is provided with a full range of services without delay at all stages. In such models, all processes in medical institutions are standardized, which makes it possible to reduce losses and costs at all stages of medical care and increase the results that are valuable for patients.

Ward Health System in Nigeria: An Assessment of the Awareness and Role of Community Members

Ward Health System is an implementation strategy of Primary Health Care in Nigeria. It was introduced in 2001 and is aimed at promoting community mobilization and participation in health care, thereby enhancing an effective, efficient and sustainable health care system. This study assessed the awareness and role of community members in the Ward Health System. A semi-structured interviewer administered questionnaire was used to conduct an exit poll of 240 Primary Health Care facility clients. Approximately 12%, 13% and 29% of the respondents had ever heard of the terms Ward Health System, Village Development Committee and Ward Development committee respectively, while up to 73% have no idea of the role of any of the stakeholders. Inadequate knowledge and involvement of the community members in the Ward Health System constitutes a big challenge to the effective and efficient implementation of Primary Health Care in Nigeria. Efforts towards community education on Ward Health System should be intensified.

Trends of repeated emergency department visits among adolescents and young adults for substance use: A repeated cross-sectional study.

Emergency Department (ED) visits for substance-related concerns among young people have been increasing in recent years. Understanding the factors related to repeated ED visits (two or more ED visits per year) for substance use concerns among young people is critical to developing a more efficient mental healthcare system that does not overburden ED and that provides efficient care for substance use patients. This study examined trends of substance use-related ED visits and factors related to repeated ED visits (two or more ED visits per year, in comparison to one ED visit per year) among adolescents and young adults (aged 13 to 25 years) in the province of Ontario, Canada. Binary logistic regression models were conducted to examine associations between hospital-related factors (hospital size, urbanicity, triage level, ED wait time) and visit status (2+ vs 1 ED visit/year), controlling for patient characteristics (age/sex). A population-based, repeated cross-sectional data over a 10-year period (2008, 2013, and 2018) was used. The proportion of substance use-related repeated ED visits significantly and consistently increased in the year 2013 and 2018 compared to 2008 (2008 = 12.52%, 2013 = 19.47%, 2018 = 20.19%). Young adult, male, medium-sized hospital, urban location, wait times longer than 6 hours, and symptom severity was associated with increased numbers of repeated ED visits. Furthermore, polysubstance use, opioid use, cocaine use, and stimulant use were strongly associated with repeated ED visits compared with the use of substances such as cannabis, alcohol and sedatives. Current findings suggest that repeated ED visits for substance use concerns could be reduced by policies that reinforce evenly distributed mental health and addiction treatment services across the provinces in rural areas and small hospitals. These services should put special efforts into developing specific (e.g., withdrawal/treatment) programming for substance-related repeated ED patients. The services should target young people using multiple psychoactive substances, stimulants and cocaine.

Clinical Outcome of Revascularization Deferral With Instantaneous Wave-Free Ratio and Fractional Flow Reserve: A 5-Year Follow-Up Substudy From the iFR-SWEDEHEART Trial.

Background Although physiology-based assessment of coronary artery stenosis using instantaneous wave-free ratio (iFR) and fractional flow reserve (FFR) are established methods of guiding coronary revascularization, its clinical outcome in long-term deferral needs further evaluation, especially with acute coronary syndrome as a clinical presentation. The aim was to evaluate the long-term clinical outcome of deferral of revascularization based on iFR or FFR. Methods and Results This is a substudy of the iFR-SWEDEHEART (Instantaneous Wave-Free Ratio Versus Fractional Flow Reserve in Patients With Stable Angina Pectoris or Acute Coronary Syndrome) randomized clinical trial, where patients deferred from revascularization from each study arm were selected. Nine hundred eight patients deferred from coronary revascularization with iFR (n=473) and FFR (n=435) were followed for 5 years. The national quality registry, SWEDEHEART (Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies), was used for patient data collection and clinical follow-up. The end point was major adverse cardiac events and their individual components all-cause death, cardiovascular death, noncardiovascular death, nonfatal myocardial infarction, and unplanned revascularization. No significant difference was found in major adverse cardiac events (iFR 18.6% versus FFR 16.8%; adjusted hazard ratio, 1.08 [95% CI, 0.79-1.48]; P=0.63) or their individual components. Conclusions No differences in clinical outcomes after 5-year follow-up were noted when comparing iFR versus FFR as methods for deferral of coronary revascularization in patients presenting with stable angina pectoris and acute coronary syndrome. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02166736.

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke.

Functional recovery and the response to rehabilitation interventions after stroke are highly variable. Understanding this variability will promote precision rehabilitation for stroke, allowing us to deliver targeted interventions to the right person at the right time. Capitalizing on large, heterogeneous data sets, such as those generated through clinical care and housed within the electronic health record, can lead to understanding of poststroke variability. However, accessing data from the electronic health record can be challenging because of data quality, privacy concerns, and the resources required for data extraction. Therefore, creating infrastructure that overcomes these challenges and contributes to a learning health system is needed to achieve precision rehabilitation after stroke. We describe the creation of a Precision Rehabilitation Data Repository that facilitates access to systematically collected data from the electronic health record as part of a learning health system to drive precision rehabilitation. Specifically, we describe the process of (1) standardizing the documentation of functional assessments, (2) obtaining regulatory approval, (3) defining the patient cohort, and (4) extracting data for the Precision Rehabilitation Data Repository. The development of similar infrastructures at other institutions can help generate large, heterogeneous data sets to drive poststroke care toward precision rehabilitation, thereby maximizing poststroke function within an efficient healthcare system.

AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling

AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling

Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers.

The utilization of artificial intelligence (AI) in clinical practice has increased and is evidently contributing to improved diagnostic accuracy, optimized treatment planning, and improved patient outcomes. The rapid evolution of AI, especially generative AI and large language models (LLMs), have reignited the discussions about their potential impact on the healthcare industry, particularly regarding the role of healthcare providers. Concerning questions, "can AI replace doctors?" and "will doctors who are using AI replace those who are not using it?" have been echoed. To shed light on this debate, this article focuses on emphasizing the augmentative role of AI in healthcare, underlining that AI is aimed to complement, rather than replace, doctors and healthcare providers. The fundamental solution emerges with the human-AI collaboration, which combines the cognitive strengths of healthcare providers with the analytical capabilities of AI. A human-in-the-loop (HITL) approach ensures that the AI systems are guided, communicated, and supervised by human expertise, thereby maintaining safety and quality in healthcare services. Finally, the adoption can be forged further by the organizational process informed by the HITL approach to improve multidisciplinary teams in the loop. AI can create a paradigm shift in healthcare by complementing and enhancing the skills of healthcare providers, ultimately leading to improved service quality, patient outcomes, and a more efficient healthcare system.

Transparency of social-economic determinants in developing the efficient public healthcare system

In the modern society, public healthcare and safe life are key tasks of any country worldwide. Social health and security are the highest and the most desired values. They are integral conditions to reach sustainable development goals. Healthcare is an important policy component of separate states and supernational communities worldwide. The issue of global approaches to healthcare and environment protection is one of the most outstanding scientific achievements of the 20th century. The current article reviews arguments in researching the public healthcare system. The main study purpose is a detailed analysis of the social-economic public healthcare determinants and their transparency in the 2013-2023 Scopus sources. The research object is public healthcare. The research topic is bibliometric analysis of the 2013-2023 Scopus sources about public healthcare. The bibliometric analysis of big data provides us with those necessary research results that cannot be obtained without special tools. To conduct the public healthcare bibliometric analysis, we applied the VOSviewer computer software. It engages wide functional possibilities of smart text processing, which generates and visualises joint networks of the most significant terms and notions in a certain branch. A thorough bibliometric analysis of the social-economic public healthcare determinants and their transparency in the 2013-2023 Scopus sources is conducted stepwise. Firstly, we select a list of keywords for the studied issue. Secondly, the Scopus search engine is activated. Thirdly, a range of sources is defined. Fourthly, we analyse a sample of Scopus publications. Fifthly, we conduct the VOSviewer cluster analysis of the Scopus sources (arranged by citation number, co-authorship frequency, countries with the given problem). Sixthly, we examine bibliometric maps with visualised clusters. The obtained research results may definitely contribute to further healthcare studies and bibliometric analysis of indexed scientific sources.