CRRT documentation education: Increasing compliance on a new electronic health record
Bibliographic record
Abstract
Continuous renal replacement therapy (CRRT) is a modality by which critically ill and hemodynamically unstable patients in the intensive care unit (ICU) can receive hemodialysis. Documentation for CRRT includes many crucial elements and contributes significantly towards the achievement of treatment goals. One of these is ultrafiltrate (UF) removal calculation, which is imperative to addressing fluid volume overload and reducing mortality. Our large academic medical center implemented a new electronic health record (EHR) that revamped CRRT documentation and was rife with opportunities for improvement. A hospital-wide survey sent to ICU staff revealed that most nurses felt they did not receive adequate CRRT documentation education, specifically tailored towards the new EHR. Review of the literature supported the notion that improvements in documentation could be made through enhanced educational offerings. The CRRT nursing curriculum was redesigned to place more emphasis on teaching the purpose and correct implementation of documenting in our EHR. The results of the educational intervention were increased confidence in CRRT documentation as well as improved competency.
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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 | 0.026 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".