Dataset Information

Four Web-Based Interactive Endocrine Case Studies for Use in Undergraduate Medical Education.

ABSTRACT:

Introduction

This resource is a collection of four case-based exercises intended to provide medical students with structured and focused opportunities to link basic science with clinical application. The cases are designed to help students self-evaluate their knowledge and develop a robust and well- integrated understanding of endocrine physiology and pathophysiology in the context of a representative range of endocrine disorders involving adrenal cortical, thyroid, and reproductive function. Although these cases were designed for, and used by, first-year students, they are also suitable for more advanced students.

Methods

Each case opens with a brief vignette containing a patient presentation and a history of present illness. The student first formulates a differential diagnosis and then sequentially narrows the differential by selecting from lists of diagnostic tests; correct answers with feedback are provided at each step. A diagnosis is ultimately required, and the student may be prompted to propose a treatment plan.

Results

End-of-course survey results from 128 first-year medical students suggest that the use of these interactive case studies was considered to be a worthwhile use of study time, and that knowledge gained in the correlate endocrine course was required to work through the cases. Students indicated that the levels of case and task complexity, along with feedback, were appropriate and helpful.

Discussion

These cases provide a resource for meeting the need for clinically relevant scenarios in the preclerkship years.

SUBMITTER: Zachow R

PROVIDER: S-EPMC6354716 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Publications

Four Web-Based Interactive Endocrine Case Studies for Use in Undergraduate Medical Education.

Zachow Rob R Schneider Stephen S Lebeau Robert R Galt James J

MedEdPORTAL : the journal of teaching and learning resources 20170510

<h4>Introduction</h4>This resource is a collection of four case-based exercises intended to provide medical students with structured and focused opportunities to link basic science with clinical application. The cases are designed to help students self-evaluate their knowledge and develop a robust and well- integrated understanding of endocrine physiology and pathophysiology in the context of a representative range of endocrine disorders involving adrenal cortical, thyroid, and reproductive func ...[more]

PMID: 30800781

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Project description:BackgroundMedical postgraduates' demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important.ObjectiveThis study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school.MethodsWe developed a medical data mining course called "Practical Techniques of Medical Data Mining" for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms-Rain Classroom, Tencent Meeting, and WeChat-were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience.ResultsIn total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students' characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated "very positive" or "positive"), and MedHub received the best feedback, both in function (80/83, 96% chose "satisfied") and teaching effect (80/83, 96% chose "satisfied"). The case study showed that our course was able to fill the gap between student expectations and learning outcomes.ConclusionsWe developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

Project description:BACKGROUND:Telemedicine has grown exponentially in the United States over the past few decades, and contemporary trends in the health care environment are serving to fuel this growth into the future. Therefore, medical schools are learning to incorporate telemedicine competencies into the undergraduate medical education of future physicians so that they can more effectively leverage telemedicine technologies for improving the quality of care, increasing patient access, and reducing health care expense. This review articulates the efforts of allopathic-degree-granting medical schools in the United States to characterize and systematize the learnings that have been generated thus far in the domain of telemedicine training in undergraduate medical education. OBJECTIVE:The aim of this review was to collect and outline the current experiences and learnings that have been generated as medical schools have sought to implement telemedicine capacity-building into undergraduate medical education. METHODS:We performed a mixed-methods review, starting with a literature review via Scopus, tracking with Excel, and an email outreach effort utilizing telemedicine curriculum data gathered by the Liaison Committee on Medical Education. This outreach included 70 institutions and yielded 7 interviews, 4 peer-reviewed research papers, 6 online documents, and 3 completed survey responses. RESULTS:There is an emerging, rich international body of learning being generated in the field of telemedicine training in undergraduate medical education. The integration of telemedicine-based lessons, ethics case-studies, clinical rotations, and even teleassessments are being found to offer great value for medical schools and their students. Most medical students find such training to be a valuable component of their preclinical and clinical education for a variety of reasons, which include fostering greater familiarity with telemedicine and increased comfort with applying telemedical approaches in their future careers. CONCLUSIONS:These competencies are increasingly important in tackling the challenges facing health care in the 21st century, and further implementation of telemedicine curricula into undergraduate medical education is highly merited.

Dataset Information

Four Web-Based Interactive Endocrine Case Studies for Use in Undergraduate Medical Education.

Introduction

Methods

Results

Discussion

Publications

Four Web-Based Interactive Endocrine Case Studies for Use in Undergraduate Medical Education.

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