利用电子健康记录测量住院医师的临床经验并找出培训差距:开发和可用性研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES JMIR Medical Education Pub Date : 2024-11-06 DOI:10.2196/53337
Vasudha L Bhavaraju, Sarada Panchanathan, Brigham C Willis, Pamela Garcia-Filion
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引用次数: 0

摘要

背景:以能力为基础的医学教育需要可靠的数据将能力与临床经验联系起来。SARS-CoV-2(COVID-19)大流行突然改变了医学培训项目中临床接触的标准轨迹。住院医师培训项目主任的任务是利用现有工具找出并解决每个学员的经验差距:本研究旨在展示一种可行且高效的方法,用于采集电子健康记录(EHR)数据,以衡量连续性诊所儿科住院医师临床经验的数量和多样性;生成个人、班级和研究生水平的基准数据;并为学员创建可视化工具,以快速识别临床经验中的差距:该试点项目于 2016 年至 2022 年在一个大型城市儿科住院医师培训项目中开展。通过达成共识,5 位儿科教师确定了儿科住院医师为胜任儿科门诊工作而应看的诊断组别。信息技术顾问使用与每个诊断组相对应的《国际疾病分类第十版》(ICD-10)代码提取电子病历患者就诊数据,作为接触特定诊断的指标。在职住院医师(2020-2022 届)的就诊频率(数量)和诊断类型(种类)与班级和毕业住院医师(2016-2019 届)的平均值进行了比较。这些数据被转换为百分比,并转化为雷达图可视化,以便住院医师将其当前的临床经验与同级住院医师和毕业生进行快速比较。住院医师接受了关于使用这些数据和可视化图表找出培训差距的调查:提取了 102 名住院医师(毕业生人数=52)的临床经验数据。在职住院医师(人数=50)每半年收到一次带有雷达图的数据报告:2020届和2021届各3份,2022届2份。雷达图明显显示了与同学和毕业生相比在诊断暴露方面的差距。住院医师发现,这种可视化方法有助于制定临床和学习目标:本试验介绍了一种创新方法,通过与同学和毕业生的基准进行比较,获取并展示住院医师临床经验的数据,从而找出因医学培训中断或修改而可能导致的学习差距。这种方法可以在各专科和机构间进行汇总,并有可能为基于能力的医学教育提供参考。
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Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study.

Background: Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 (COVID-19) pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee's experiences using existing tools.

Objective: This study aims to demonstrate a feasible and efficient method to capture electronic health record (EHR) data that measure the volume and variety of pediatric resident clinical experiences from a continuity clinic; generate individual-, class-, and graduate-level benchmark data; and create a visualization for learners to quickly identify gaps in clinical experiences.

Methods: This pilot was conducted in a large, urban pediatric residency program from 2016 to 2022. Through consensus, 5 pediatric faculty identified diagnostic groups that pediatric residents should see to be competent in outpatient pediatrics. Information technology consultants used International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding with each diagnostic group to extract EHR patient encounter data as an indicator of exposure to the specific diagnosis. The frequency (volume) and diagnosis types (variety) seen by active residents (classes of 2020-2022) were compared with class and graduated resident (classes of 2016-2019) averages. These data were converted to percentages and translated to a radar chart visualization for residents to quickly compare their current clinical experiences with peers and graduates. Residents were surveyed on the use of these data and the visualization to identify training gaps.

Results: Patient encounter data about clinical experiences for 102 residents (N=52 graduates) were extracted. Active residents (n=50) received data reports with radar graphs biannually: 3 for the classes of 2020 and 2021 and 2 for the class of 2022. Radar charts distinctly demonstrated gaps in diagnoses exposure compared with classmates and graduates. Residents found the visualization useful in setting clinical and learning goals.

Conclusions: This pilot describes an innovative method of capturing and presenting data about resident clinical experiences, compared with peer and graduate benchmarks, to identify learning gaps that may result from disruptions or modifications in medical training. This methodology can be aggregated across specialties and institutions and potentially inform competence-based medical education.

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来源期刊
JMIR Medical Education
JMIR Medical Education Social Sciences-Education
CiteScore
6.90
自引率
5.60%
发文量
54
审稿时长
8 weeks
期刊最新文献
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