Balancing quality improvement and unintended effects: The impact of implementing admission order sets for chronic obstructive pulmonary disease and heart failure at two teaching hospitals
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Local health administrators implemented chronic obstructive pulmonary disease and heart failure admission order sets to increase guideline adherence. We explored the impact of these order sets on workflows and guideline adherence in the internal medicine specialty in two Canadian teaching hospitals. METHODS: A mixed methods study combined shadowing care providers (250 h), meeting observation and interviews (11 h), and patient medical chart audits for heart failure (n = 120) and chronic obstructive pulmonary disease (n = 120) patients. The chart audits analysed details of the admission process and 14 guideline elements associated with heart failure (nine) and chronic obstructive pulmonary disease (five). RESULTS: A subset (10/14) of the evaluated guideline elements were included in the heart failure or chronic obstructive pulmonary disease order sets. Order set use significantly increased adherence to some (4/10) of these elements. However, our qualitative work uncovered a perception that use of these two order sets increased order duplication. Our chart audits supported this perception. Order set use increased order duplication for heart failure (92% vs 43%) and chronic obstructive pulmonary disease (75% vs 43%). CONCLUSION: It is unclear whether, for these two hospitals, the gains brought by implementation of chronic obstructive pulmonary disease and heart failure admission order sets were worth their associated organisational shortcomings. Problems with order set implementation appeared to stem from poor integration with pre-existing complex organisational systems. Health administrators and clinicians interested in implementing order sets within their own hospitals need to remain cognizant of how these tools will fit into existing systems and practices.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".