Browsing by Subject "Clinical reasoning"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Publication
    Open Access
    Asumiendo competencias desde la aplicación NANDA NIC NOC en la práctica clínica.
    (Murcia : Servicio de Publicaciones de la Universidad de Murcia, 2007) Álvarez Rodríguez, T.; Fernández Lamelas, M.A.; Álvarez Aragón, F.; Lopez Vale, C.; Lago Lemos, A.
    Objetivos: La aplicación de las taxonomías NANDA NIC NOC para desarrollar en el alumno diferentes capacidades: reflexión, razonamiento clínico, toma de decisiones, autonomía, son imprescindibles para su futuro desarrollo como profesionales de Enfermería. Material y Método: Se realizó un estudio descriptivo, observacional. El ámbito de estudio corresponde a los cincuenta y un estudiantes de Enfermería de segundo curso matriculados en Enfermería Médico Quirúrgica I, durante el curso académico 2005/06, que realizaron atención integral a los pacientes de las unidades Médicas y Quirúrgicas del Complejo Hospitalario Universitario de Vigo, siendo la duración de las prácticas en dichas unidades de catorce semanas en total y el número de diagnósticos elaborados y desarrollados fue de doscientos cuatro. Resultado: El grado de respuesta ha sido del 100%, ya que cada alumno debía entregar una memoria al finalizar el periodo de prácticas. Consideraron las taxonomías como una buena herramienta y un marco de análisis y reflexión en la toma de decisiones, ayudándoles a conocer mejor las distintas áreas de responsabilidad en el cuidado de los pacientes. Conclusiones: Los estudiantes han comprendido las ventajas de tener un lenguaje común para dar prioridad y planificar los cuidados, adquiriendo mayor autonomía y seguridad al hacer juicios clínicos sintiéndose al mismo tiempo más motivados al comprobar su utilidad.
  • Thumbnail Image
    Publication
    Open Access
    Avaliação do Raciocínio Clínico no Curso de Medicina em Portugal
    (Servicio de publicaciones, Universidad de Murcia, 2025) Ruas, Pedro; Nunes, Célia; Neto, Isabel
    Introdução:O raciocínio clínico é uma competência fulcral para a prática médica. A suaavaliação desempenha um papel importante na prevenção do erro médico; portanto, deve ter porbase as melhores práticas internacionais. Dada a escassez de conhecimento acerca da avaliação doraciocínio clínico nas escolas médicas portuguesas, esta investigação pretendeu aprofundar estemesmo conhecimento, analisando a prevalência de aplicação de diversos métodos de avaliação eidentificando os principais obstáculos associados. Materiais e Métodos:Foi aplicado umquestionário entre maio e julho de 2023 a todos os docentes responsáveis por unidadescurriculares do 4º ao 6º ano do Mestrado Integrado em Medicina em Portugal. Recorreu-se aosoftware SPSS®, versão 28.0 para o Microsoft Windows®. Os dados foram predominantementeanalisados por meio de estatísticas descritivas. Resultados: Foram recolhidas 75 respostas de 8escolas de Medicina em Portugal, representando aproximadamente metade da população-alvo. Amaioria dos docentes tem mais de 30 anos de experiência em avaliação. As perguntas de escolhamúltipla constituem o método de avaliação mais aplicado. Os métodos aplicados em contextosimulado e clínico, por observação direta, estão em défice nos currículos. Entre os principaisobstáculos identificados, destacam-se a falta de tempo e de recursos humanos. Conclusões: Énecessária uma maior implementação de métodos em contexto simulado e em meio clínico,permitindo uma avaliação mais completa e autêntica do raciocínio clínico. Neste sentido, éfundamental um maior investimento em recursos humanos, aumentando a contratação deprofissionais e promovendo a formação em metodologias de avaliação do raciocínio clínico.
  • Thumbnail Image
    Publication
    Open Access
    Comparison of Automatic Item Generation Methods in the Assessment of Clinical Reasoning Skills
    (Universidad de Murcia. Servicio de publicaciones, 2025) Emekli, Emre; Karahan, Betül Nalan; Departamentos
    The use of automatic item generation (AIG) methods offers potential for assessingclinical reasoning (CR) skills in medical education, a critical skill combining intuitive andanalytical thinking. In preclinical education, these skills are commonly evaluated through writtenexams and case-based multiple-choice questions (MCQs), which are widely used due to the highnumber of students, ease of standardization, and quick evaluation. This research generated CR-focused questions for medical exams using two primary AIG methods: template-based and non-template-based (using AI tools like ChatGPT for a flexible approach). A total of 18 questions wereproduced on ordering radiologic investigations for abdominal emergencies, alongside faculty-developed questions used in medical exams for comparison. Experienced radiologists evaluatedthe questions based on clarity, clinical relevance, and effectiveness in measuring CR skills. Resultsshowed that ChatGPT-generated questions measured CR skills with an 84.52% success rate,faculty-developed questions with 82.14%, and template-based questions with 78.57%, indicatingthat both AIG methods are effective in CR assessment, with ChatGPT performing slightly better.Both AIG methods received high ratings for clarity and clinical suitability, showing promise inproducing effective CR-assessing questions comparable to, and in some cases surpassing, faculty-developed questions. While template-based AIG is effective, it requires more time and effort,suggesting that both methods may offer time-saving potential in exam preparation for educators.
  • Thumbnail Image
    Publication
    Open Access
    Evaluating the Performance of DeepSeek 3, Claude Sonnet 4, and Gemini 2.5 in the Chilean Medical Licensing Examination: Observational Study.
    (Servicio de Publicaciones. Universidad de Murcia, 2025) Jerez Yañez, Oscar; Edgardo, Vicente Alberto; Silva Arroyo, Jesús; Vera Cartes, Marcos Jeremías Giovanny; Herrera Alcaíno, Alvaro Andrés; Lancellotti Guajardo, Anaís Aracelly; Sin departamento asociado
    Introduction: Artificial intelligences and their continuous improvement have revolutionized medical education, but their performance in specific evaluative contexts still requires further exploration. Methods: This study qualitatively evaluated and compared the performance of three state-of-the-art language models — Claude Sonnet 4, Gemini 2.5, and DeepSeek 3 — in simulations of the National Medical Knowledge Examination (EUNACOM) in Chile. Three mock exams with 180 questions each were used, covering various medical areas and question types, including those based on clinical cases. Results: The results show that all AI models consistently passed the exams, with Claude Sonnet 4 achieving the highest overall performance (89% accuracy) and the greatest consistency across attempts. Clinical case-based questions were answered more accurately than theoretical knowledge questions, highlighting the models' strength in contextual clinical reasoning. Claude excelled in Internal Medicine and Psychiatry, DeepSeek in Surgery, and Gemini demonstrated balanced performance. However, specific gaps were identified in areas such as Public Health and clinical follow-up, suggesting the need for model-specific adjustments. Conclusion: The findings support the educational potential of these tools but also emphasize the importance of their ethical, supervised, and complementary use alongside traditional medical training. This study contributes to understanding the emerging role of artificial intelligence in professional assessments, as well as its limitations and opportunities within the Chilean medical context.
  • Thumbnail Image
    Publication
    Open Access
    Using Large Language Models to Generate Script Concordance Test in Medical Education: ChatGPT and Claude
    (Universidad de Murcia. Servicio de publicaciones, 2025) Kıyak, Yavuz Selim; Emekli, Emre; Departamentos
    Using Large Language Models to Generate Script Concordance Test in Medical Education: ChatGPT and ClaudeYavuz Selim Kıyak1 *, Emre Emekli21Department of Medical Education and Informatics, Gazi University Faculty of Medicine, Ankara, Turkiye; yskiyak@gazi.edu.tr, 0000-0002-5026-32342Department of Radiology, Faculty of Medicine, Eskişehir Osmangazi University, Eskişehir, Turkiye; 0000-0001-5989-1897* Correspondence: yskiyak@gazi.edu.trReceived: 4/11/24; Accepted: 2/12/24; Published: 3/12/24AbstractWe aimed to determine the quality of AI-generated (ChatGPT-4 and Claude 3) Script ConcordanceTest (SCT) items through an expert panel.We generated SCT items on abdominal radiology usinga complex prompt in large language model (LLM) chatbots (ChatGPT-4 and Claude 3 (Sonnet) inApril 2024) and evaluated the items’ quality through an expert panel of 16 radiologists. Expertpanel, which was blind to the origin of the items provided without modifications, independentlyanswered each item and assessed them using 12 quality indicators. Data analysis includeddescriptive statistics, bar charts to compare responses against accepted forms, and a heatmap toshow performance in terms of the quality indicators. SCT items generated by chatbots assessclinical reasoning rather than only factual recall (ChatGPT: 92.50%, Claude: 85.00%). The heatmapindicated that the items were generally acceptable, with most responses favorable across qualityindicators (ChatGPT: 71.77%, Claude: 64.23%). The comparison of the bar charts with acceptableand unacceptable forms revealed that 73.33% and 53.33% of the questions in the items can beconsidered acceptable, respectively, for ChatGPT and Claude. The use of LLMs to generate SCTitems can be helpful for medical educators by reducing the required time and effort. Although theprompt provides a good starting point, it remains crucial to review and revise AI-generated SCTitems before educational use. The prompt and the custom GPT, “Script Concordance TestGenerator”, available at https://chatgpt.com/g/g-RlzW5xdc1-script-concordance-test-generator,can streamline SCT item development.