Effect of intervention on a quality measure of pain management at Medstar Washington Cancer Institute.
Bibliographic record
Abstract
144 Background: Medstar Washington Cancer Institute (MWCI) has participated in Quality Oncology Practice Initiative (QOPI) since 2008. Adherence to pain assessment and intensity documentation was high, but lower in plan of care for moderate/severe pain documentation (69%, compared to QOPI aggregate of 79%) during the fall 2011 round. One potential explanation for the discrepancy was lack of communication between the nursing staff assessing the pain and the physician treating pain. We hypothesized that the use of pain card can improve the communication between nurses and doctors, as well as prompt physicians to document the plan of care for moderate/severe pain. Methods: MWCI created a team of physicians, nurses, quality resources, and administrative staff in December 2011. We abstracted up to 10 patients charts per oncologist for those patientswho reported moderate to severe pain (pain score of more than 3 of 10 on numeric rating scale) each quarter during 2012.We used data for quarter 1 and 2 as a baseline. We implemented the use of pain card by nurses to report pain for these patients to the physician in quarter 3 and 4. Chi square test was used to compare documentation rate in the first two quarters and last two quarters. Results: The total number of charts evaluated, pain documentation as well as confidence intervals for each quarter are shown in the table. Our results show significant improvement in pain documentation by physician in last two quarters compared to first two quarters ( p = 0.0007). Conclusions: Our study demonstrates pain card improved communication between nurse and physician resulting improved documentation of pain by physician. [Table: see text]
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".