Gaps in the Inpatient Management of Chronic Obstructive Pulmonary Disease Exacerbation and Impact of An Evidence-Based Order Set
Bibliographic record
Abstract
BACKGROUND: Evidence-based, guideline-recommended practices improve multiple outcomes in patients admitted with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), but are incompletely implemented in actual practice. Admission order sets with evidence-based diagnostic and therapeutic guidance have enabled quality improvement and guideline implementation in other conditions. OBJECTIVE: To characterize the magnitude of care gaps and the effect of order sets on quality of care in patients with AECOPD. METHODS: The authors prospectively designed a standardized chart review protocol to document process of care and health care utilization before and after implementation of AECOPD order sets at an academic hospital in Toronto, Ontario. RESULTS: A total of 243 total AECOPD admissions and multiple important care gaps were identified. There were 74 admissions in the pre-order set period (January to June 2009) and 169 in the order set period (October 2009 to September 2010). The order set was used in 78 of 169 (46.2%) admissions. In the order set period, we observed improvements in respiratory therapy educational referrals (five of 74 [6.8%] versus 48 of 169 [28.4%]; P<0.01); venous thromboembolism prophylaxis prescriptions (when indicated) (15 of 68 [22.1%] versus 100 of 134 [74.6%]; P<0.01); systemic steroid prescriptions (55 of 74 (74.3%) versus 151 of 169 [89.4%]; P<0.01]); and appropriate antibiotic prescriptions (nine of 24 [37.5%] versus 61 of 88 [69.3%]; P<0.01). The mean (± SD) length of stay also decreased from 6.5 ± 7.7 days before order sets to 4.1 ± 5.0 days with order sets (P=0.017). CONCLUSIONS: Care gaps in inpatient AECOPD management were large and evidence-based order sets may improve guideline adherence at the point of care. Randomized trials including patient outcomes are required to further evaluate this knowledge translation intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".