Examining Pediatric Resident Electronic Health Records Use During Prerounding: Mixed Methods Observational Study
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) play a substantial role in modern health care, especially during prerounding, when residents gather patient information to inform daily care decisions of the care team. The effective use of the EHR system is crucial for efficient and frustration-free prerounding. Ideally, the system should be designed to support efficient user interactions by presenting data effectively and providing easy navigation between different pages. Additionally, training on the system should aim to make user interactions more efficient by familiarizing the users with best practices that minimize interaction time while using the full potential of the system's capabilities. However, formal training on EHR systems often falls short of providing residents with all the necessary EHR-related skills, leading to the adoption of inefficient practices and the underuse of the system's full range of capabilities. OBJECTIVE: This study aims to examine the efficiency of EHR use during prerounding among pediatric residents, assess the effect of experience level on EHR use, and identify areas for improvement in EHR design and training. METHODS: A mixed methods approach was used, involving a self-reported survey and video analysis of prerounding practices of the entire population of pediatric residents from a large teaching hospital in the South Atlantic Region. The residents were stratified by experience level by postgraduate year. Data were collected on the number of pages accessed, duration of prerounding, task completion rates, and effective use of data sources. Observational and qualitative data complemented the quantitative analysis. Our study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines, ensuring completeness and transparency of reporting. RESULTS: Of the 30 pediatric residents, 20 were included in the analyses; of these, 16 (80%) missed at least 1 step during prerounding. Although more experienced residents on average omitted fewer steps, 4 (57%) of the 7 most experienced residents still omitted at least 1 step. On average, residents took 6.5 minutes to round each patient and accessed 21 pages within the EHR during prerounding; no statistically significant differences were observed between experience levels for prerounding times (P=.48) or number of pages accessed (P=.92). The use of aggregated data pages within the EHR system neither seem to improve prerounding times nor decrease the number of pages accessed. CONCLUSIONS: The findings suggest that EHR design should be improved to better support user needs, and hospitals should adopt more effective training programs to familiarize residents with the system's capabilities. We recommend implementing prerounding checklists and providing ongoing EHR training programs for health care practitioners. Despite the generalizability of limitations of our study in terms of sample size and specialization, it offers valuable insights for future research to investigate the impact of EHR use on patient outcomes and satisfaction, as well as identify factors that contribute to efficient and effective EHR usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".