Quality Improvement Initiatives to Optimize the Management of Chronic Obstructive Pulmonary Disease in Patients With Lung Cancer
Bibliographic record
Abstract
Purpose: Patients with lung cancer (LC) frequently have chronic obstructive pulmonary disease (COPD), the optimization of which improves outcomes. A 2014 Queen’s University Hospitals audit demonstrated that COPD was underdiagnosed and undertreated in outpatients with LC. We sought to improve the diagnosis and management of COPD in this population. Methods: We implemented change using a Define/Measure/Analyze/Improve/Control (DMAIC) improvement cycle. Data were obtained by chart review from the Cancer Care Ontario database and e-Patient System for patients with newly diagnosed LC, including patient characteristics, pulmonary function test (PFT) data, and bronchodilator therapies. Improvement cycle 1 included engaging stakeholders and prioritizing COPD management by respirologists in the Lung Diagnostic Assessment Program. Improvement cycle 2 included physician restructuring and developing a standard work protocol. Data were analyzed monthly and presented on statistical process control P-charts, which assessed differences over time. The χ 2 and McNemar tests assessed for significance between independent and dependent groups, respectively. Results: A total of 477 patients were studied (165 patients at baseline, 166 patients in cycle 1, and 127 patients in cycle 2). There was no change in PFT completion over time, although respirology-managed patients were significantly more likely to undergo a PFT than patients who were not managed by respirology (56.7% v 96.1%; P < .00001). The proportion of respirology-managed patients with LC with airflow obstruction receiving inhaled bronchodilator significantly increased (baseline, 46.3%; cycle 1, 51.0%; and cycle 2, 74.3%). By cycle 2, patients with airflow obstruction were more likely to receive a long-acting bronchodilator if managed by respirology (74.3% v 44.8%; P = .0009). Conclusion: COPD is underdiagnosed and undertreated in outpatients with LC. A DMAIC quality improvement strategy emphasizing COPD treatment during LC evaluation in the Lung Diagnostic Assessment Program significantly improved COPD management.
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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 | 0.000 | 0.000 |
| 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".