Using Texting for Clinical Communication in Surgery: A Survey of Academic Staff Surgeons
Bibliographic record
Abstract
BACKGROUND: Text messaging has become ubiquitous and is being increasingly used within the health care system. The purpose of this study was to understand texting practices for clinical communication among staff surgeons at a large academic institution. METHODS: Staff surgeons in 4 subspecialties (vascular, plastics, urology, and general surgery) were surveyed electronically. RESULTS: A total of 62 surgeons from general surgery (n = 33), vascular surgery (n = 6), plastic surgery (n = 13), and urology (n = 10) completed the study (response rate 30%). When conveying urgent patient-related information, staff surgeons preferred directly calling other staff surgeons (61.5%) and trainees (58.8%). When discussing routine patient information, staff surgeons used email to reach other staff surgeons (54.9%) but preferred texting (62.7%) for trainees. The majority of participants used texting because it is fast (65.4%), convenient (69.2%) and allows transmitting information to multiple recipients simultaneously (63.5%). Most felt that texting enhances patient care (71.5%); however, only half believed that it enhanced trainees' educational experiences. The majority believed that texting identifiable patient information breaches patient confidentiality. CONCLUSIONS: Our data showed high adoption of text messaging for clinical communication among surgeons, particularly with trainees. The majority of surgeons acknowledge security concerns inherent in texting for patient care. Existing mobile communication platforms fail to meet the needs of academic surgeons. Further research should include guidelines related to texting in clinical practice, educational implications of texting, and technologies to better meet the needs of clinicians working in an academic surgical settings.
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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 | 0.005 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".