Clinician preferences for verbal communication compared to EHR documentation in the ICU
Bibliographic record
Abstract
BACKGROUND: Effective communication is essential to safe and efficient patient care. Additionally, many health information technology (HIT) developments, innovations, and standards aim to implement processes to improve data quality and integrity of electronic health records (EHR) for the purpose of clinical information exchange and communication. OBJECTIVE: We aimed to understand the current patterns and perceptions of communication of common goals in the ICU using the distributed cognition and clinical communication space theoretical frameworks. METHODS: We conducted a focus group and 5 interviews with ICU clinicians and observed 59.5 hours of interdisciplinary ICU morning rounds. RESULTS: Clinicians used an EHR system, which included electronic documentation and computerized provider order entry (CPOE), and paper artifacts for documentation; yet, preferred the verbal communication space as a method of information exchange because they perceived that the documentation was often not updated or efficient for information retrieval. These perceptions that the EHR is a "shift behind" may lead to a further reliance on verbal information exchange, which is a valuable clinical communication activity, yet, is subject to information loss. CONCLUSIONS: Electronic documentation tools that, in real time, capture information that is currently verbally communicated may increase the effectiveness of communication.
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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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".