Audit of documentation proficiency of emergency department patients who are discharged against medical advice before and after implementation of a checklist
Bibliographic record
Abstract
Objective: Documentation of the discharge against medical advice (AMA) is poorly performed in the emergency department (ED). Little is known about the impacts of a checklist on this. Our study aimed to compare the quality of AMA documentation before and after implementation of a checklist.Methods: A retrospective review was conducted followed by a prospective study; each over three months of AMA interactions in our ED pre and post implementation of a checklist. An 11-point checklist was used to determine documentation quality during these two periods. Quality was assessed based on the number of points fulfilled on this tool. Documentation was classified as “good” (8-11), “average” (4-7) and “poor” (0-3). The primary outcome measured was the proportions of discharged AMA records that showed “good”, “average” and “poor” documentation. Secondary outcomes were compliance rates to each of the categories of the checklist before and after its use.Results: 339 and 309 complete records were retrieved from the retrospective and prospective arms respectively. The proportions of case records in the three grades before and after use of the checklist respectively were: poor, 199/339 (59%) vs. 7/313 (2%); fair, 133/339 (39%) vs. 66/313 (21%) and good 7/339 (2%) vs. 240/313 (77%); all p-values were statistically significant. There were also statistically significant differences in compliance rates to each of the categories of the checklist pre and post checklist implementation.Conclusions: This study shows improvement in quality and compliance rates in the audit categories after the implementation of an AMA checklist.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".