RESUMEN
Introducción: la enseñanza de cursos de ciencias básicas en carreras de la salud es un desafío por no estar directa e inmediatamente rela-cionada con el ámbito profesional. Por otra parte, las condiciones de estrés que ha impuesto el trabajo a distancia requiere de metodologías motivantes, y, que a su vez permitan una evaluación significativa. Objetivos: reportar las adaptaciones metodológicas y los resultados de una adaptación local de la metodología de especificaciones de las calificaciones y retroalimentación del trabajo. Métodos: se aplica una metodología de formación basada en la retroalimentación en el curso de Física para estudiantes de Tecnología Médica (N=106) durante un semestre. Las calificaciones promedio de los estudiantes fueron comparadas con las obtenidas en años anteriores. Para evaluar el desempeño docente se realizaron 2 encuestas a los estudiantes. El cumplimiento de los logros de aprendizaje se midió mediante auto-evaluación (escala likert 1 a 5) al inicio y término de cada uno de los cuatro capítulos. Resultados: las reprobaciones y eliminaciones de estudiantes en el curso fueron menores a años anteriores, siendo las notas significativamente mayores subiendo desde 4,89 a 6,29 (escala de 1 a 7, p<0,001). Los estudiantes se mostraron en un 95% satisfechos con el desempeño docente y finalmente, la auto-evaluación de logros de aprendizaje mostró un aumento promedio de 1 punto. Conclusiones: la metodología de evaluación basada en especificaciones adaptada a dos entregas y con evaluaciones en una escala no-binaria mejoró el rendimiento, los logros de los aprendizajes esperados y la motivación de los estudiantes.
Background: Teaching basic science courses in health careers is a challenge because these courses are not directly linked to professional practice. On the other hand, the stressful conditions imposed by distance work require motivating methodologies and a meaningful evaluation. Objectives: To report the methodological adaptations and the results of a local adaptation of the specifications grading and feedback methodology. Methods: A training methodology based on feedback is applied in the Physics course for Medical Technology students (N = 106) during one semester. We compared the students' average grades to those obtained in previous years with the same topics. To evaluate the teaching performance, we conducted two student surveys. We measure compliance with learning achievements by self-assessment (Likert scale 1 to 5) at the beginning and end of each of the four chapters. Results: Failures and eliminations of students in the course were lower than previous years, with significantly higher grades from 4.89 to 6.29 (p <0.001). The students were 95% satisfied with the teaching performance, and finally, the self-evaluation of learning achievements showed an average increase of 1 point. Conclusion: The evaluation methodology based on specifications adapted to two deliveries and evaluations on a non-binary scale improved the performance, expected learning achievements, and students' motivation.
RESUMEN
Background: A significant proportion of the clinical record is in free text format, making it difficult to extract key information and make secondary use of patient data. Automatic detection of information within narratives initially requires humans, following specific protocols and rules, to identify medical entities of interest. Aim: To build a linguistic resource of annotated medical entities on texts produced in Chilean hospitals. Material and Methods: A clinical corpus was constructed using 150 referrals in public hospitals. Three annotators identified six medical entities: clinical findings, diagnoses, body parts, medications, abbreviations, and family members. An annotation scheme was designed, and an iterative approach to train the annotators was applied. The F1-Score metric was used to assess the progress of the annotator's agreement during their training. Results: An average F1-Score of 0.73 was observed at the beginning of the project. After the training period, it increased to 0.87. Annotation of clinical findings and body parts showed significant discrepancy, while abbreviations, medications, and family members showed high agreement. Conclusions: A linguistic resource with annotated medical entities on texts produced in Chilean hospitals was built and made available, working with annotators related to medicine. The iterative annotation approach allowed us to improve performance metrics. The corpus and annotation protocols will be released to the research community.
Asunto(s)
Humanos , Procesamiento Automatizado de Datos , ChileRESUMEN
Background: Free-text imposes a challenge in health data analysis since the lack of structure makes the extraction and integration of information difficult, particularly in the case of massive data. An appropriate machine-interpretation of electronic health records in Chile can unleash knowledge contained in large volumes of clinical texts, expanding clinical management and national research capabilities. Aim: To illustrate the use of a weighted frequency algorithm to find keywords. This finding was carried out in the diagnostic suspicion field of the Chilean specialty consultation waiting list, for diseases not covered by the Chilean Explicit Health Guarantees plan. Material and Methods: The waiting lists for a first specialty consultation for the period 2008-2018 were obtained from 17 out of 29 Chilean health services, and total of 2,592,925 diagnostic suspicions were identified. A natural language processing technique called Term Frequency-Inverse Document Frequency was used for the retrieval of diagnostic suspicion keywords. Results: For each specialty, four key words with the highest weighted frequency were determined. Word clouds showing words weighted by their importance were created to obtain a visual representation. These are available at cimt.uchile.cl/lechile/. Conclusions: The algorithm allowed to summarize unstructured clinical free-text data, improving its usefulness and accessibility.