Working Off the Record: Physicians??? and Nurses??? Transformations of Electronic Patient Record-Based Patient Information
Bibliographic record
Abstract
BACKGROUND: Electronic patient records (EPRs) are increasingly being used in health care, but little is known about how EPR-based patient information is used in daily care activities, nor about its potential influence on novice training. METHOD: Seventy-two physicians and nurses participated in an eight-month study on a single pediatric ward. Eighty hours of nonparticipant observations and 20 interviews were conducted. Data were analyzed using constructivist grounded theory and visual rhetoric. RESULTS: Three main features of participant interactions with EPR-based information were identified: (1) EPR-based information was routinely transformed into paper documents; (2) these transformations were organized by profession-specific guiding principles; and (3) transformation strategies were learned through an informal curriculum. CONCLUSIONS: This study describes how and why health care professionals work around EPR-based patient information, and suggests that an EPR's visual organization may be incompatible with professional activities. The study addresses the socializing implications of these activities, and highlights their educational potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".