Dataset Information

Mock Paging and Consult Curriculum to Prepare Fourth-Year Medical Students for Medical Internship.

ABSTRACT:

Introduction

Internship preparation should include curricula to hone key skills such as acute medical management and communication with consulting and interprofessional providers.

Methods

To enhance these skills, we developed an interprofessional mock paging and consult curriculum incorporating direct observation and peer, faculty, and nursing feedback for fourth-year medical students entering medical internships. Our brief mock paging and consult curriculum was designed as part of a larger 2-week internship preparation course. Our curriculum was delivered in two 2-hour sessions by physician and nurse educators. Sessions were conducted in small groups, offering the opportunity for direct observation and feedback from faculty, nurse educators, and peers. Our curriculum was expanded from a pilot for 10-15 students to 60 students after 2 years of a successful pilot.

Results

Mock paging and consult sessions were highly rated by medical students and resulted in significantly enhanced self-assessment of preparedness in key intern skills such as returning pages, interprofessional communication, calling a consult, and managing acute issues for cross-cover patients.

Discussion

We have demonstrated the effectiveness of a brief, interprofessional mock paging and consult curriculum incorporating faculty, nurse educator, and peer feedback. The tenets of our curriculum can be widely adopted for other learner groups.

SUBMITTER: Tischendorf J

PROVIDER: S-EPMC6342441 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Publications

Mock Paging and Consult Curriculum to Prepare Fourth-Year Medical Students for Medical Internship.

Tischendorf Jessica J O'Connor Clare C Alvarez Madelyn M Johnson Sara S

MedEdPORTAL : the journal of teaching and learning resources 20180420

<h4>Introduction</h4>Internship preparation should include curricula to hone key skills such as acute medical management and communication with consulting and interprofessional providers.<h4>Methods</h4>To enhance these skills, we developed an interprofessional mock paging and consult curriculum incorporating direct observation and peer, faculty, and nursing feedback for fourth-year medical students entering medical internships. Our brief mock paging and consult curriculum was designed as part o ...[more]

PMID: 30800908

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Project description:BackgroundArtificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty.ObjectiveWe hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption.MethodsThis elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected.ResultsThis web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential.ConclusionsCourse participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.

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Dataset Information

Mock Paging and Consult Curriculum to Prepare Fourth-Year Medical Students for Medical Internship.

Introduction

Methods

Results

Discussion

Publications

Mock Paging and Consult Curriculum to Prepare Fourth-Year Medical Students for Medical Internship.

Similar Datasets

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