Global Journal of Medical Terminology Research and Informatics
Global Journal of Medical Terminology Research and Informatics is a peer-reviewed journal dedicated to advancing the understanding, innovation, and application of medical terminology in healthcare and related fields. The journal provides a platform for researchers, educators, and practitioners to share original research, reviews, and case studies that explore the development, standardization, and practical use of medical terminology to improve communication, diagnostics, and patient care globally. ISSN-3107-5282 Publication Frequency: 4 Issues per year Publication Language: English Subject: Medical Science (Terminology, Anatomy, Physiology) Journal Starting Year:2023 Publication Mode: Online
Visual Terminology Aids in Diagnostic Imaging: Improving Radiology Report Accessibility
The purpose of this research is to develop and evaluate visual terminology aids in diagnostic imaging with the intention of making radiology reports more user-friendly. The work combines graphics with medical terms to help clinicians and patients understand difficult radiological expressions. Using a modular NLP and image annotation system, we assessed comprehension levels as well as the efficiency of the assessment. We observed significant improvement in both comprehension and diagnostic accuracy with the use of graphic supports. Moreover, the use of visual aids facilitated communication in multidisciplinary teams. This strategy can be tested clinically so that it can facilitate patient participation in radiological diagnosis.
Radiology, Diagnostic Imaging, Visual Terminology, Report Accessibility, Medical Communication, NLP, Patient Comprehension.
Copyright (c) 2024 Global Journal of Medical Terminology Research and Informatics
The Role of Unified Medical Terminology in Reducing Clinical Miscommunication and Errors
The purpose of this work is to analyze the impact of unified medical terminology systems on clinical miscommunication and diagnostic mistakes. Using a mixed-methods approach, we study the impact of clinical standard frameworks such as SNOMED CT, and ICD on seamless clinical communication. Our results make it clear that standardization increases the accuracy of diagnosis and inter-disciplinary documentation while reducing discrepancies among team members. The focus of the study done describes the integration of unified electronic terminologies into electronic health records as crucial for the effectiveness and reliability for health care service delivery.
Unified Medical Terminology, Clinical Communication, Diagnostic Errors, SNOMED CT, ICD, Standardization, EHR Integration, Patient Safety.
The Role of SNOMED CT in Enhancing Clinical Decision Support Systems
This paper considers SNOMED CT's contribution toward the development of Clinical Decision Support Systems (CDSS). An effort was made to integrate SNOMED CT with the CDSS architecture in such a way that semantic interoperability and uniformity of terminology were achieved. The methodology used included mapping clinical data inputs to SNOMED CT concepts, measuring system accuracy and response time, and conducting timing studies. Results showed an increased accuracy in diagnostic support and a marked improvement in standardization and performance of the system. This study illustrates the role SNOMED CT plays in supporting the clinical decision making and his/her informatics with the authority of advanced healthcare technologies.
SNOMED CT, Clinical Decision Support System, Semantic Interoperability, Medical Terminology, Healthcare Informatics, Ontologies, Decision Making, Electronic Health Records.
The Role of Medical Terminology in Public Health Surveillance and Pandemic Preparedness
By simplifying disease reporting and streamlining surveillance systems, standardized medical terminology serves a critical function in bolstering public health monitoring and preparedness for pandemics. The methodology employs a system-based design that embeds controlled vocabularies into data merging tools. Through comparative analysis, our findings demonstrate that standardized terminology significantly enhances the reporting and tracking of disease cases. The system evaluated is more advanced than traditional models in response time and data integration. The study emphasizes the need to standard integrated terminologies towards enhanced health intelligence and crisis response.
Health Informatics, Interoperability, Disease Tracking, Standardization, Pandemic Preparedness, Public Health Surveillance, Medical Terminology, Data Integration.
The Evolution of Anatomical Terminology: A Historical and Functional Analysis
This paper looks at the development and functional importance of anatomy terminology from its classical origins to contemporary efforts at standardization. It studies literature concerning anatomy lexicon acceptance and use in clinics alongside the literature to assess the consequences of changing anatomical references. A framework was designed to evaluate the degree of uniformity and the ease of accessibility of various term arrangement systems. Results indicate some degree of international standardization alongside entrenched local and language-based differences. This study specifically addresses the rigid and static nature of anatomical terms and the lack of distinction in the non-relative dynamic caste frameworks that stand to hamper medical pedagogy, research, and practice.
Anatomical Terminology, Terminologia Anatomica, Medical Nomenclature, History of Anatomy, Lexical Standardization, Clinical Communication, Medical Education, Terminology Evolution.
Terminology Mapping in Health Information Exchanges: A Case Study on ICD and LOINC Integration
This study looks into the concepts mapping between ICD and LOINC with respect to Health Information Exchanges (HIE) towards enhancing semantic interoperability. We developed a hybrid strategy that combines ontology alignment with machine learning approaches. Evaluation with real HIE data demonstrated increased mapping accuracy, reliability, and faster response times to queries. Results indicate that employing ICD in unison with LOINC markedly enhances the exchanges and the overall analytic potential of clinical data. This study's results streamline and enhance automated mapping of medical standards’ terminology, thereby improving data sharing across healthcare systems.
Health Information Exchange, International Classification of Diseases, LOINC, Automated Terminological Mapping, Semantic Interoperability, Clinical Data Standards, Medical Informatics, Ontology Alignment.
Standardized Terminology for Symptom Reporting in Telemedicine Consultations
It is noted that existing research has not focused on the effects of a unified vocabulary on symptom reporting in telemedicine, hence the aim of this research is to fill that gap. The purpose of this study is to evaluate the effectiveness of multimodal virtual consultations and their effect on clinical documentation and patient understanding. A model to overcome the language divide in healthcare is proposed. It was found that using a standard vocabulary to define symptoms positively influences the consistency of clinical documentation retention as well as patients' understanding of health information. This paper demonstrates the significance of achieving the desired level of healthcare remote from health institutions as well as offering a model that can be implemented globally.
Virtual Consultation, Health Information Systems, Health Informatic Standards, Informatic Standards, Telemedicine, Telehealth, Remote Healthcare, Multimodal Consultations, Patient Trust.
Semantic Interoperability in Global Health: Challenges in Cross-Cultural Terminology Integration
This study examines the issues affecting the integration of cross-cultural medical terminologies with particular reference to semantic interoperability within global health systems. Recent literature from the year 2022 to 2023 was analyzed, and a conceptual model on semantic harmonization was applied to obtain system design insights and assess it against the prior approaches. The findings sill inadequacies with current models, as proposed strategies for multilingual terminology alignment were found lacking. This attempt enhances the development of inclusive international standards pertaining to the informatics in health.
Global Health Informatics, Cross-Cultural Terminology, Ontology Mapping, Multilingual Integration, Health Information Exchange, Terminology Standardization, Medical Informatics, and Semantics Interoperability.
Ontology-Driven Approaches for Standardizing Rare Disease Terminology
Rare diseases represent approximately 7000 disease entities that affect almost 3.5 % of the world population. Each rare disease receives inconsistent nomenclature from different medical systems which impedes research, diagnosis and treatment. In this paper, we study the problem of integrating rare disease terminologies using semantic models through an ontology driven approach. Using existing ontological datasets and ontology reasoners, we develop an upper ontology which automates the mapping and alignment of diseases’ names and definitions within databases. The data interoperability, improved retrieve and enhanced framework adaptability presented in the results demonstrate the effectiveness of the system. This study presents an integration approach that gives precise and scalable solution for harmonization of rare disease informatics terminologies.
Rare Diseases Ontology, Semantic Standardization, Harmonization, Medical Informatics, Data Interoperability, Automated Reasoning, Disease Ontology
Natural Language Processing for Automated Extraction of Medical Terms in Electronic Health Records
This research aims to explore application of NLP approaches for the automatic extraction of medical terminologies from Electronic Health Records (EHRs). A system to automate clinical relevant information retrieval through named entity recognition (NER) and transformer models was created. The solution was applied to classical biomedical data and was tested against multiple other systems. The findings showed significant differences in the precision and recall results that were computed. This claim of concept validates the degree of improvements that can be achieved by using NLP on EHRs which are essential parts of clinical decision support systems and automatic data entry systems.
Natural Language Processing, Bioinformatics, Healthcare Automation, Electronic Health Records, Named Entity Recognition, Clinical NLP, Medical Term Extraction, Transformer Models.
Medical Terminology Curriculum Design in the Age of AI and Big Data
The objective of the current study is to design a contemporary medical terminology curriculum suitable for the age of Artificial Intelligence and Big Data. It employs a cross-disciplinary approach by embedding elements of Natural Language Processing, machine learning, and data science into medical pedagogy. The methodology comprised a curriculum gap analysis and pilot teaching implementation. Evidence demonstrates enhanced student participation and understanding. The findings of the study emphasize the need to modernize the teaching of medicine in relation to the realities of a rapidly evolving data-centric healthcare system.
Curriculum Creation, Medical Education, Healthcare Artificial Intelligence, Education Technologies, Medical Data, Linguistic Computing, Digital Literacy
Impact of Terminology Standardization on Diagnostic Consistency in Multicenter Studies
This examination looks into the effect of terminology standardization on achieving consistency in diagnosis using multicenter studies. Within the comparative framework of structured and unstructured clinical datasets from various institutions, we applied a terminology normalization process using SNOMED CT and LOINC Interfaces. The outcome demonstrated improved alignment in case diagnoses and consistency in the medical interpretation gaps. The findings justify the use multicenter clinical research and clinical-grade global health data toward the refinement of health data integration by relaying evidence on the reproducibility and decision intricacies from controlled standardized terminological frameworks within multidisciplinary clinical research.
Standardized Nomenclature, Multicentre Studies, SNOMED CT, SNOOS and other Multi-center Research, Automated Diagnosis Systems, Clinical Research Interoperability.
Harmonizing Medical Terminology across Multilingual Healthcare Systems: A Global Framework
This document describes a framework designed to solve problems associated with the lack of multilingual terminology standardization in health systems using ontology alignment, natural language processing, and AI-based cross mapping. The framework is designed to address linguistic and contextual barrier issues related to medical data exchange. A preliminary study showed improved results in cross-language conceptualization consistency and accuracy, which surpassed conventional standards, and had increased efficiency in speed and processing. This framework aids global health informatics by enhancing seamless data capture integration, patient care, and research cooperation across regions with diverse languages.
Medical Terminology, Multilingual Healthcare, Semantic Interoperability, Ontology Mapping, Natural Language Processing, Global Health Informatics, AI in Healthcare, Cross-lingual Systems.
Gamification in Medical Terminology Learning: A Comparative Study of Digital Education Tools
The aim of this study is to determine ‘gamification’ as a pedagogical application in the acquiring and learning of medical terminologies, and the associated outcomes. We evaluated both e-learning modules and interventions that incorporated gamified elements. Controlled trials in medical schools measuring knowledge retention, active engagement, and self-reported satisfaction were part of our approach. Gamebased learning interventions were found to be superior in-relating to the motivational levels and the overall efficiency in achieving the set academic goals. This study highlights how gamification can be purposefully integrated into learning within the medical field, particularly in teaching medical terms, providing a costeffective and efficient teaching solution.
Gamification, Medical Terminology, Digital Educational Resources, Learner Engagement, Educational Technology, Knowledge Retention, E-Learning, Medical Education.
Enhancing Patient Comprehension through Simplified Medical Terminology: A Literacy-based Approach
The research analyzes the impact of using simplified medical terminology on the comprehension of patients, focusing on those with low health literacy. The study uses a comparative literacy-based approach with a controlled setting on different patient groups using traditional and simplified terminology. We employed NLP techniques to deidentify complex medical records and obtained patient comprehension through feedback as well as post-consultation surveys. The results demonstrate a statistically significant improvement in patients’ understanding and recall when simplified terminology is used. The research results support the use of simplified terminology in communication with patients to enhance understanding and to improve health outcomes.
Health Literacy, Medical Terminology, Patient Comprehension, NLP, Simplified Language, Patient-centered Care, Health Care Communication, Clinical Documentation
Development of a Real-Time Multilingual Medical Terminology Translator for Emergency Settings
This work describes the design of a real-time translator of medical terms into multiple languages for use in urgent care situations. The system employs natural language processing (NLP), machine translation, and medical ontologies to translate important medical terms quickly and accurately in a contextsensitive manner. Our approach combines custom translation algorithms with specialized terminology datasets which enhances communication between medical practitioners and patients who speak different native languages. This shift helps mitigate language barriers in emergency medical situations and may aid in improving patients’ clinical outcomes and the risks they face in multilingual healthcare systems. Outcomes suggest a bi-directional increase in translation accuracy and speed over existing tools.
Healthcare Communication, Medical Informatics, Multilingual Translation, Natural Language Processing, Terminology of Medicine, Real-Time Systems, Emergency Medicine, Machine Translation.
Copyright (c) 2023 Global Journal of Medical Terminology Research and Informatics
Bridging Traditional and Modern Medical Terminologies Integrative Perspectives from Ayurveda and Allopathy
This work focuses on the fusion of the Allopathic and Ayurvedic terminologies aimed at improving holistic healthcare. It was achieved through comparative analysis parsing termed ontology mapping, semantic juxtaposition, and cross-terminology blending which aims at achieving ontology fusion. Results show that there exists a large congruency in the constructs of various diagnosed sub divisions and also the general aim of treatments even when language and treatment concepts differ. The model presented in this research serves as a basis for incorporating unified terms across various disciplines which enhances crossdisciplinary interaction and patient-sensitive approaches to healthcare.
Ayurveda, Allopathy, Medical Terms, Ontology Mapping, Integrative Medicine, Semantic Non-compatibility, Healthcare Communication, Ontology Alignment.
Blockchain for Terminology Traceability in Decentralized Health Systems
This work analyzes the application of blockchain technology in improving the tracability of medical terms in decentralized health systems. The research proposes a new methodology to ensure crossinstitutional uniformity and verification of medical term application by utilizing the blockchain's transparency and permanence features. These methodologies include the application of smart contracts and decentralized ledgers for audit trails. Results analysis indicates improvements in sepersistency and data integrity of the system, as well as consistency in the use of terminologies. Ascertains, along with evidence from multiple studies, that indeed blockchain offers unprecedented opportunities for the governance of health data worldwide, especially in settings with many languages and institutions.
Blockchains Traceability Terminology, Health Information Exchange, Smart Contracts, Medical Informatics, Decentralized Health System, Interoperability, Proof of Data Integrity.
Assessing Terminology Gaps in Global Health Guidelines: AWHO Terminology Audit
This study analyzes discrepancies in international health guides and documents using a systematic terminology audit based on WHO standards. Several reports and memoranda released by WHO between 2020 and 2024 were analyzed with machine learning and expert linguists for language inconsistencies, and ambiguous wording was detected. This method combines automated document analysis with detailed forensic linguistics at the individual document level. Analysis showed critical gaps in term standardization which raises profound implications concerning the formulation and adherence to policies. This study exemplifies the urgent requirement of a unified foundational language in medicine that is essential for the effective implementation of health policies and guidelines worldwide.
Global Health, Gap in Terminology, WHO Guidelines, Standardization, Health Policy, Linguistic Audit, NLP, Medical Semantics.
AI-powered Medical Coding: Improving Accuracy and Efficiency in Health Data Classification
This in an attempt to enhance the effectiveness of health data classification, the role of Artificial Intelligence (AI) in automating medical coding processes will be explored in this paper. We built an AI model which utilizes NLP and deep learning for ICD-10 code assignment of clinical documentation. Evaluation results showed notable increases in coding accuracy as well as processing speed. The reduction of manual coding errors and operational expenses, as well as the modernization of healthcare information systems through AI's impact on revenue cycle acceleration, reinforces the value of AI in mas turning healthcare information systems.
Medical Coding, Artificial Intelligence, NLP, Deep Learning, Health Informatics, ICD-10, Automation, Clinical Documentation.
Developing a Medical Coding Curriculum for Surgery Students by Resolving Inconsistencies among Physician and Student Records
Proficiency in Medical Code (MC) is essential for operation. The research posited that primary surgery students possess insufficient trust in the MC and that introducing targeted didactics will enhance student trust and understanding. A medical curriculum was presented to significant surgery students encompassing the Global Procedure Phase (GPP), assessment and administration code, and Hospital Payments and Quality Measures (HPQM). A 20-item survey was created to evaluate student proficiency in coding skills. The effectiveness of the MC program was assessed by anonymized documentation-based Pre-assessment and Post-assessment questionnaires. Pre-assessment results (n = 60) indicated that students experienced discomfort with MC. After three MC classes, the post-assessment (n = 25) revealed substantial enhancements in student familiarity with MC (p < 0.002) and student achievement in the areas of GPP (p = 0.017), assessment and administration code (p < 0.002), and HQPM (p = 0.030). Families are apprehensive about the MC lacking proper schooling. This research advocates for a targeted curriculum to equip students for professional activity.
Medical Coding, Surgery, Physician, Student Records.
Copyright (c) 2025 Global Journal of Medical Terminology Research and Informatics
Big Data Integration with Machine Learning Towards Public Health Records and Precision Medicine
Contemporary biological science studies associate the term 'precision' with health care and public health (PH) with concepts such as big data, data integration, and Machine Learning (ML). Technological breakthroughs enable the aggregation and integration of extensive heterogeneous information from many sources, including genomic sequences, social media postings, Public Health Records (PHR), and wearable devices. Moreover, sophisticated algorithms facilitated by high-performance computers enable the converting of big data into intelligence. Notwithstanding this progress, several obstacles persist in realizing Precision Medicine (PM) and PHR for the betterment of individuals and populations. A primary objective of PM is to incorporate and integrate big data from various PHRs regarding the cellular and ecological roots of disease into analytical backgrounds, facilitating the creation of personalized, context-specific diagnosis and treatment plans. In this context, artificial intelligence (AI) and ML methodologies may be used to develop analytical representations of intricate diseases to predict individualized health situations and outcomes. These frameworks must include the extensive variability among people regarding their genetic predispositions and contextual and social variables. Computational methodologies in medicine must effectively handle, display, and incorporate big data, including organized and unorganized forms. Effective big data integration and organization are essential for the effective use of ML methodologies in PM. Numerous obstacles emerge in developing effective medical big data analytics systems, given the present stringent performance requirements in PM, alongside limitations in time, computing resources, and bioethical considerations.
Machine Learning, Big Data, Precision Medicine, Data Integration, Public Health Records.
Securing Multi-Modal Medical Data Management System using Blockchain and the Internet of Medical Things
The Internet of Medical Things (IoMT) refers to a network of interconnected smart medical devices, apps, medical systems, and institutions. The smart medical gadgets and apps are interconnected over the Internet with healthcare frameworks. Confidentiality and safety of patient information, scalability, and information availability are the most intricate difficulties of IoT, especially within the Internet of Medical Things (IoMT), and must be addressed. Blockchain (BC) can potentially transform existing methods of information admission, interchange, gathering, regulator, and involvement. This study presents a Secured Multi-Modal Medical Data Management System (SMMDM) using BC and the IoMT. The proposed SMMDM ensures safe data management among individual servers and sensor-based therapeutic devices, as well as among cloud and individual servers. The IoMT-based safety architecture employs BC to provide secure data transfer and management across interconnected nodes. The BC key has been employed in a healthcare application network, enabling the safe generation of alerts from patient health data for verified healthcare practitioners.
Internet of Medical Things, Blockchain, Data Management, Multi-Modal Medical Data, Cloud servers, Security.
Boosting Telemedicine Healthcare Assessment Using Internet of Things and Artificial Intelligence for Transforming Alzheimer's Detection
The emergence of Internet-of-Things (IoT)-based telemedicine platforms has initiated a new technological age that enables early detection and treatment for remote patients. This is especially vital for serious conditions like Alzheimer's Disease (AD), which involves decreased memory and cognitive impairment that greatly disrupts everyday functioning, requiring prompt medical intervention. The influx of information from automated systems, originating from many sources, has increased complexity and reduced diagnostic precision. This study enhances Telemedicine Healthcare Assessment by integrating IoT and Artificial Intelligence to advance Alzheimer's Detection (THA-AI-AD). The paper outlines the fundamental principles and key elements of multifaceted sensing data acquisition, the design and execution of medical tracking using an AI platform, robotics-driven multifaceted data sensing and gathering, and the long-term acquisition of physiological signals with high comfort through smart clothing. The study presents an innovative technique for the automated identification of AD using Convolutional Neural Networks (CNN). Consisting of two subsystems, one focused on AD detection with an accuracy of 97.12% using CNN and another for AI-based telemedicine therapy, yielding exceptional outcomes.
Telemedicine, Alzheimer's Detection, Healthcare, Convolutional Neural Networks, Artificial Intelligence, Sensing Data Acquisition
Artificial Intelligence-Enabled Analytical Framework for Optimizing Medical Billing Processes in Healthcare Applications
This article explores how Artificial Intelligence (AI) can completely alter how medical billing systems work worldwide. AI can help medical facilities make payments faster, more accurately, and more profitable as things get harder and fewer resources are available. Artificial intelligence can make the payment process much more manageable, saving healthcare workers time and effort. Therefore, they can concentrate more on giving better care to their patients. Medical records are checked by AI-powered coding accuracy solutions that provide the correct billing codes. This lowers the number of mistakes and rejected claims. AI can improve repayment plans by looking at old data and finding patterns to ensure doctors get the correct payment rates. A bigger problem is medical scams. Therefore, AI systems can examine massive datasets, spot odd trends, and alert authorities to possible scams, which conserves funds. Using AI to bill hospitals has a lot of perks. Problems include data protection, algorithmic bias, muscular systems, and in-depth training. An increasing number of successful cases from healthcare systems worldwide demonstrate the real advantages of AI apps, including fewer mistakes in billing, faster payment processes, and happy patients. Healthcare centers can be more productive, make more money, and give patients a better experience using AI technologies.
Artificial Intelligence, Medical Billing, Healthcare, Analytics
A Secure Blockchain-Enabled Framework for Healthcare Record Management and Patient Data Protection
Blockchain (BC)-based technologies can help solve many problems in the healthcare (HC) industry, mainly when it comes to managing growing amounts of health information technology (HIT) data safely and efficiently. This article talks about a new BC-based way to improve many areas of healthcare administration. These include data accuracy, prescription drugs, pregnancy information, supply chain oversight, risk data administration, Electronic Health Record (EHR) administration, data on safe access, permanent record-keeping, and effective data sharing. The study offers a way to safely handle patient medical records in the cloud that uses blockchain-based technology and a Hyperledger system in Amazon Web Services (AWS). The established structure is in a Virtual Private Cloud (VPC) to ensure limited access and efficient resource use. It improves access, safety, accountability, and sharing, addressing significant problems in healthcare data management and providing a scalable, effective, and safe way for EHR management in modern healthcare settings.
Blockchain, Healthcare, Security, Data Protection
Integrating Telemedicine and Pharmacists in Chronic Gastrointestinal Diseases: A Critical Role During the COVID-19 Pandemic
New developments have made it easier for Telehealth to connect healthcare services in faraway places. Telehealth (TH) has become essential during the COVID-19 pandemic, changing how healthcare is provided. It has been vital in providing care to patients and making it easy for them to get to while following social distancing and quarantine rules for people with chronic stomach diseases. By sending medical care directly to people's homes, TH makes it safer for patients and healthcare workers. During the outbreak, TH made it easy to get medical supplies to people who needed them, putting COVID-19 care first and handling non-COVID health needs through remote methods. Quickly responding, doctors have started using TH for initial evaluations, cutting down on pointless emergency room trips. This change shows that TH will now play a more significant role in health care, including specialized and urgent treatments, making it much more helpful. Some of TH's drawbacks are that it can be hard to do thorough physical exams and get diagnostic tests and images. These problems show that TH needs to keep improving and developing new ideas. The 2020 pandemic significantly affected telemedicine, but it also led to some significant advances and laid the groundwork for future medical care in chronic gastrointestinal illnesses. To fully utilize TH's potential in modern medical care, healthcare professionals must face its limits and ongoing problems.
Pharmacists, Telemedicine, Chronic Gastrointestinal Diseases, COVID-19.
Comprehensive Review of Evidence-Based Methods in Preventive Cardiology Education: Perspective from Analytical Studies
Evidence-based Methods (EBM) are essential for guaranteeing patient safety. Despite recommending teaching tactics to improve EBM understanding and abilities, new research suggests that nurses lack adequate preparation to implement EBM. A proposed three-tiered framework for the instruction of EBM emphasizes the necessity of interactive clinical experiences within EBM teaching methodologies. The article delineates the pedagogical strategies in undergraduate nursing courses to impart EBM expertise and abilities in cardiology. The research delineates the perspectives of pupils and teachers regarding learning outcomes and obstacles. Six qualitative investigations and one mixed-method study fulfilled the inclusion requirements and were rigorously assessed using the Critical Assessment Skills Course. The seven articles were analyzed inductively and conceptually using Braun and Clarke's six processes to identify themes. Four topics of teaching techniques were found, each encompassing subtopics: interactive instructional strategies, merged interactive and clinical teaching methods, results from learning, and impediments. While four studies had an ambiguous emphasis on instructing EBM rules, they all used research utilization and interactive pedagogical methodologies. Documented learning outcomes encompass improved analytical and critical thinking abilities and research application to guarantee patient security. Four of the seven examined studies exhibited an ambiguous emphasis on the implementation of EBM teaching methodologies. Interactive methods of instruction are employed, although the primary focus is on locating and critically evaluating research for practical application. Despite encompassing a limited body of literature, this study suggests a necessity for additional qualitative work focused on interactive and clinically integrative teaching methodologies to augment further EBM knowledge and abilities among first-year cardiology nursing pupils.
Evidence-Based Methods, Cardiology, Education, Healthcare.
Effective Pedagogical Strategies for Oncology Medical Students on Healthy Lifestyles
The paper highlights the significance of instructional elements in a physician's training and practical endeavors. An Oncology physician engaged in teaching, medical care and testing, and ethics-driven outreach should utilize modern technology for education. The research uses a competence-based strategy to examine practice-oriented and person-centered strategies for training medical experts. This research used several study techniques: research review and analysis in psychology and education, synthesis, simulation, contrast, and generalization. The inference is drawn that the worldwide objective for advanced oncology medical schooling is to justify contemporary new pedagogical methods developed through a personality-oriented perspective. These approaches are quite successful in fostering healthy habits.
Pedagogical Strategies, Oncology, Lifestyles, Healthcare
Developing an Effective Training System for Interventional Pulmonology Education through Digital Learning
This research aimed to provide a competency paradigm for Pulmonology Education students, serving as the initial stage in implementing outcome-based curricula. The current investigation was executed in three steps. Initially, the perspectives of 100 participants, comprising learners, alumni, teachers, and educational administrators, were examined through the Nominal Group method, surveys, and phone conversations. The collected codes were categorized into subcategories and sections using an inductive content evaluation method. In phase two, the results were transformed into competency elements, sub-domains, and competency categories within working subgroups. The generated elements' intellectual relevance was assessed using qualitative and quantitative methods. Five hundred thirty-five codes, twelve sub-categories, and seven categories were retrieved from stakeholders' perspectives and further refined into sixty-five competency elements, twelve sub-domains, and seven competency domains within group discussions. A competency structure is crucial for creating a thorough and cohesive curriculum. Implementing a competency-based program for Pulmonology Education is feasible by developing a competency structure.
Pulmonology, Digital Learning, Healthcare, Education