ArticleNeurology. Education2026
Education Research: Educational Informatics: Characterizing Child Neurology Resident Clinical Experiences Using Electronic Health Record Data.
Article in Neurology. Education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Reframing Patient-Centered Medical Education.Neurology. Education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objectives: Gaps in preparedness for independent practice are common among recent graduates. Clinical experiences serve as the foundation for learning during residency, yet the breadth and variability of clinical exposure of child neurology residents remain poorly characterized. To inform programmatic curricular development and precision medical education (PME), we must better characterize learners' clinical exposure during training. We aimed to use electronic health record (EHR) data to catalog resident clinical experiences, linking diagnostic codes to educational benchmarks. Methods: We performed a retrospective cohort study of 11 child neurology residents at a single institution in their neurology years of training between July 2024 and May 2025. We used an established algorithm to determine which patient encounters were most meaningful for resident education, based on EHR interaction, and extracted International Classification of Disease (ICD)-10 codes from these encounters. We mapped ICD-10 categories to American Board of Psychiatry and Neurology (ABPN) child neurology certification examination content areas. We identified the most frequent ICD-10 categories seen in inpatient and outpatient settings. We also identified the frequency of ABPN content areas seen across encounters and compared with their relative frequency on the certification examination. Results: We identified 3,109 meaningful outpatient and 4,469 meaningful inpatient encounters over an 11-month period. The crosswalk between ICD-10 categories and ABPN content areas contains 385 ICD-10 categories mapped to ABPN content domains. There was 90.0% ICD-10 category capture for outpatient encounters and 73.8% for inpatient encounters. The most frequent ICD-10 categories were "epilepsy and recurrent seizures" and "migraine" (outpatient) and "cerebral infarction" and "convulsions" (inpatient). More than half of ABPN content areas were underrepresented in clinical exposure compared with the certification examination. There was overrepresentation of 3 ABPN content areas: "epilepsy and episodic disorders," "vascular neurology," and "headache and pain disorders." Discussion: A novel crosswalk between ICD-10 categories and ABPN content areas for child neurology enables transformation of EHR-coded data to educationally meaningful information. Using this crosswalk to characterize resident experiences from EHR data may inform rational curricular design and PME. Programs may use these data to optimize rotations and target experiential learning gaps at individual and program levels, resulting in better preparation for autonomous practice.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.