Schaye Verity, DiTullio David J, Sartori Daniel J, Hauck Kevin, Haller Matthew, Reinstein Ilan, Guzman Benedict, Burk-Rafel Jesse
Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
Institute for Innovations in Medical Education, New York University Grossman School of Medicine, New York, NY, USA.
BMC Med Educ. 2025 Apr 22;25(1):591. doi: 10.1186/s12909-025-07191-x.
Objective measures and large datasets are needed to determine aspects of the Clinical Learning Environment (CLE) impacting the essential skill of clinical reasoning documentation. Artificial Intelligence (AI) offers a solution. Here, the authors sought to determine what aspects of the CLE might be impacting resident clinical reasoning documentation quality assessed by AI.
In this observational, retrospective cross-sectional analysis of hospital admission notes from the Electronic Health Record (EHR), all categorical internal medicine (IM) residents who wrote at least one admission note during the study period July 1, 2018- June 30, 2023 at two sites of NYU Grossman School of Medicine's IM residency program were included. Clinical reasoning documentation quality of admission notes was determined to be low or high-quality using a supervised machine learning model. From note-level data, the shift (day or night) and note index within shift (if a note was first, second, etc. within shift) were calculated. These aspects of the CLE were included as potential markers of workload, which have been shown to have a strong relationship with resident performance. Patient data was also captured, including age, sex, Charlson Comorbidity Index, and primary diagnosis. The relationship between these variables and clinical reasoning documentation quality was analyzed using generalized estimating equations accounting for resident-level clustering.
Across 37,750 notes authored by 474 residents, patients who were older, had more pre-existing comorbidities, and presented with certain primary diagnoses (e.g., infectious and pulmonary conditions) were associated with higher clinical reasoning documentation quality. When controlling for these and other patient factors, variables associated with clinical reasoning documentation quality included academic year (adjusted odds ratio, aOR, for high-quality: 1.10; 95% CI 1.06-1.15; P <.001), night shift (aOR 1.21; 95% CI 1.13-1.30; P <.001), and note index (aOR 0.93; 95% CI 0.90-0.95; P <.001).
AI can be used to assess complex skills such as clinical reasoning in authentic clinical notes that can help elucidate the potential impact of the CLE on resident clinical reasoning documentation quality. Future work should explore residency program and systems interventions to optimize the CLE.
需要客观测量方法和大型数据集来确定临床学习环境（CLE）中影响临床推理记录这一关键技能的各个方面。人工智能（AI）提供了一种解决方案。在此，作者试图确定CLE的哪些方面可能会影响由AI评估的住院医师临床推理记录质量。
在这项对电子健康记录（EHR）中的医院入院记录进行的观察性、回顾性横断面分析中，纳入了纽约大学格罗斯曼医学院内科住院医师培训项目两个地点在2018年7月1日至2023年6月30日研究期间撰写了至少一份入院记录的所有分类内科（IM）住院医师。使用监督机器学习模型确定入院记录的临床推理记录质量为低质量或高质量。从记录层面的数据中，计算轮班（白天或晚上）以及轮班内的记录索引（如果一份记录是轮班内的第一份、第二份等）。CLE的这些方面被作为工作量的潜在指标纳入，已证明这些指标与住院医师的表现有很强的关系。还收集了患者数据，包括年龄、性别、查尔森合并症指数和主要诊断。使用考虑住院医师层面聚类的广义估计方程分析这些变量与临床推理记录质量之间的关系。
在474名住院医师撰写的37750份记录中，年龄较大、有更多既往合并症以及患有某些主要诊断（如感染性和肺部疾病）的患者与更高的临床推理记录质量相关。在控制这些及其他患者因素后，与临床推理记录质量相关的变量包括学年（高质量的调整优势比，aOR：1.10；95%可信区间1.06 - 1.15；P <.001）、夜班（aOR 1.21；95%可信区间1.
