Note-Worthy: A Novel, Collaborative, Iterative Methodology to Analyzing Written Nurse-Physician Communication
Bibliographic record
Abstract
Effective nurse-physician communication is critical for patient safety in hospitals. In this single-center post-acute care study, we investigated a written on-call communication system, known to have risks including illegibility, misinterpretation, and inappropriate triage. We developed a structured approach to analyzing written communication and optimizing on-call processes. A two-pronged methodology was employed: (1) qualitative analysis through physician observations and (2) quantitative analysis of the quality of the communication book entries. Initial findings were used to dynamically update the scoring system using an iterative study methodology. Our results yielded several insights, including demonstration of the utility of an iterative methodology; development of novel scoring criteria for nurse-physician communication; demonstration of benefits and limits of communication templates; and demonstration of limits of the well-known SBAR communication tool, which focuses on information structure, but not content, and in particular may not be helpful for triage. This study highlights the importance of interdisciplinary, iterative collaboration in creating context-specific communication tools and provides a replicable framework for future analyses of hospital communication processes.
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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 | 0.032 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".