Impact of a Palliative Care Checklist on Clinical Documentation
Bibliographic record
Abstract
PURPOSE: Checklists are used in many different settings for the purpose of standardization and reduction of preventable errors in practice. Our group sought to determine whether a palliative care checklist (PCC) would improve the clinical documentation of key patient information. METHODS: An initial review of 110 randomly selected medical records dictated by 10 physicians was performed. The authors identified portions of the dictated medical records that were included regularly, as well as those that were frequently missed. A PCC was drafted after final approval was obtained from the 13 faculty members. Dictations from 13 clinical faculties in the supportive care center were reviewed. A χ(2) test or Fisher's exact test was applied to assess the difference in overall checked rates before and after checklist use. A paired t test was used to examine the difference in the average complete rate and checked rates before and after checklist use. RESULTS: There were improvements in the documentation before and after the checklist for scores on the Cut-down, Annoyed, Guilty, Eye-opener questionnaire for alcoholism (79% v 94%; P ≤ .0001), psychosocial history (69% v 95%; P ≤ .0001), Eastern Cooperative Oncology Group performance status (38% v 81%; P ≤ .0001), advance care planning (28% v 41%; P = .0008), and overall (78% v 95%; P ≤ .0001). There was no significant improvement in the documentation for opioid-induced neurotoxicity (37% v 37%; P = .9492) or the Edmonton Symptom Assessment Scale (98% v 99%; P = .4511). CONCLUSION: Our study showed that the use of a PCC improved the quality of the documentation of a patient visit in an outpatient clinical setting.
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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 | 0.031 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".