Understanding interprofessional communication: a content analysis of email communications between doctors and nurses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Clinical communication is recognized as a major source of errors in hospitals. The lack of documentation of communication, especially among verbal interactions, often creates hindrances and impedes improvement efforts. By providing smartphones to residents and encouraging nurses to communicate with residents by email shifted much of the communication to emails which permitted analysis of content. OBJECTIVE: Description on the interprofessional email communication between doctors and nurses occurring on the general internal medicine wards at two academic hospitals. DESIGN: A prospective analysis of email communications between doctors and nurses. SETTING: 34 out of the 67 residents who were on the general medicine clinical teaching units consented to allow analysis of their emails over a 6 month period. MAIN MEASURES: Statistical tabulations were performed on the volume and frequency of communications as well the response time of messages. Two physicians coded the content of randomly selected emails for urgency, emotion, language, type of interaction, and subject content. KEY RESULTS: A total of 13,717 emails were available for analysis. Among the emails from nurses, 39.1% were requests for a call back, 18.9% were requests for a response by email and the remaining 42.0% indicated no response was required from physicians. For the messages requesting a response by email, only 50% received an email response. Email responses had a median response time of 2.3 minutes. Content analysis revealed that messages were predominantly non-urgent. The two most frequent purposes for communications were to convey information (91%) and to request action by the physician (36%). CONCLUSIONS: A smartphone-based email system facilitated the description and content analysis of a large amount of email communication between physicians and nurses. Our findings provide a picture of the communication between physicians, nurses and other healthcare professionals. This work may help inform the further development of information and communications technology that can improve clinical communication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it