Phoenix Children's Hospital, 1919 East Thomas Rd, Phoenix, AZ, 85016, United States, 1 6029333635, 1 6029330806.
Department of Biomedical Informatics, The University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States.
JMIR Med Educ. 2024 Nov 6;10:e53337. doi: 10.2196/53337.
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.
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.
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.
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.
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.
基于能力的医学教育需要强有力的数据来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计划中临床暴露的标准轨迹。住院医师项目负责人的任务是使用现有工具，确定和解决每个学员经验中的由此产生的差距。
本研究旨在展示一种可行且高效的方法，从连续性诊所中捕获电子健康记录（EHR）数据，以衡量儿科住院医师的临床经验量和种类；生成个人、班级和毕业水平的基准数据；并为学习者创建一个可视化工具，以快速识别临床经验中的差距。
该试点研究于 2016 年至 2022 年在一个大型城市儿科住院医师项目中进行。通过共识，5 名儿科教师确定了儿科住院医师应在门诊儿科中看到的诊断组，以达到胜任能力。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，从电子病历患者就诊数据中提取信息，作为暴露于特定诊断的指标。活跃住院医师（2020-2022 年班级）看到的频率（量）和诊断类型（种类）与班级和毕业住院医师（2016-2019 年班级）的平均值进行比较。这些数据被转换为百分比，并转化为雷达图可视化，供住院医师快速比较他们当前的临床经验与同龄人及毕业生。住院医师接受了有关使用这些数据和可视化工具来识别培训差距的调查。
从 102 名住院医师（N=52 名毕业生）的临床经验中提取了患者就诊数据。50 名活跃住院医师（n=50）每两年获得一次雷达图报告：2020 年和 2021 年班级各 3 次，2022 年班级 2 次。雷达图清楚地显示了与同学和毕业生相比，在诊断暴露方面的差距。住院医师发现该可视化工具有助于设定临床和学习目标。
本试点研究描述了一种新颖的方法，用于捕获和呈现住院医师临床经验的数据，并与同行和毕业生的基准数据进行比较，以识别医学培训中断或修改可能导致的学习差距。这种方法可以在各专业和机构中汇总，并有可能为基于能力的医学教育提供信息。
