为非信息学培训生开设临床信息学和数据科学早期接触课程,以促进对信息学的兴趣和包容

ACI open Pub Date : 2023-07-01 DOI:10.1055/s-0043-1775971
Akshay Ravi, Benjamin Weia, Matthew Sakumoto, Aris Oates, Xinran Liu
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引用次数: 0

摘要

背景:旨在增加医学实习生接触信息学和实用数据分析的课程可以提高他们在临床研究、质量改进和临床操作方面的有效性。临床信息学数据科学(CI-DS)途径是一个跨学科的课程,旨在提高医学学员的信息学接触。我们将介绍这门新课程的发展、首批学员和经验教训。方法CI-DS路径围绕前期信息学教学框架,随后是纵向的体验式培训,重点是指导、临床数据提取/机器学习和卫生技术治理。课程评估是基于学习者完成的课程前和课程后的调查,以及学习者选择的选修活动的记录。结果CI-DS路径吸引了来自12个医学亚专业的19名学习者,从医学生到研究员。基线调查显示,学习者接触信息学的机会有限。完成的前三大纵向活动是参加电子健康记录(EHR)治理会议、数据科学补充课程和指定的指导会议。基线与路径后调查的比较表明,在评估EHR修改单、访问UCSF的未识别数据、使用基本结构化查询语言(SQL)探索数据库、使用SQL提取数据和解释机器学习模型方面,学习者自我报告的信心有了显著提高。结论临床信息学的早期接触课程与数据提取和管理培训可以成功招募各种各样的学习者,并提高对实际信息学技能的信心。我们反思该课程的优点和缺点,并总结经验教训,以指导其他人为非信息学临床医生创建类似的课程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Curriculum for Early Exposure to Clinical Informatics and Data Science for Noninformatics Trainees to Promote Interest and Inclusion in Informatics
Abstract Background Curricula aimed at increasing exposure to informatics and practical data analytics among medical trainees could increase their effectiveness in clinical research, quality improvement, and clinical operations. Objectives The Clinical Informatics Data Science (CI-DS) pathway is a cross-disciplinary curriculum aimed at improving informatics exposure among medical trainees. We describe the development of this novel curriculum, the inaugural cohort, and lessons learned. Methods The CI-DS pathway is framed around upfront informatics didactics followed by a longitudinal, experiential training focused on mentorship, clinical data extraction/machine learning, and health technology governance. The curriculum was evaluated based on pre- and postpathway surveys completed by learners and logs of the elective activities selected by learners. Results The CI-DS pathway attracted 19 learners across 12 medical subspecialties, from medical students to fellows. Baseline surveys showed limited exposure to informatics across learners. The top three longitudinal activities completed were participating in electronic health record (EHR) governance meetings, data science supplemental courses, and designated mentorship meetings. Comparison of baseline with postpathway surveys demonstrated significant improvements in learner self-reported confidence in appraising an EHR modification ticket, accessing UCSF's deidentified data, exploring a database with basic structured query language (SQL), extracting data using SQL, and interpreting machine learning models. Conclusion An early exposure curriculum in clinical informatics with training in data extraction and governance can successfully recruit a diverse array of learners and improve confidence in practical informatics skills. We reflect on the strengths and weaknesses of this curriculum, and summarize the lessons learned to guide others in creating similar curricula for noninformatics clinicians.
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