Integrated Interdisciplinary Care for Patients with Chronic Obstructive Pulmonary Disease Reduces Emergency Department Visits and Admissions: A Quality Assurance Study
Bibliographic record
Abstract
BACKGROUND: Dedicated programs for the management of chronic obstructive pulmonary disease (COPD) can reduce hospitalizations and improve quality of life. OBJECTIVE: To investigate whether health care utilization could be reduced by a newly developed integrated, interdisciplinary initiative that included a COPD nurse navigator who educates patients and families, transitions patients through various points of care and integrates services. METHODS: The present quality assurance, pre-post study included patients followed by a COPD nurse navigator from January 25, 2010 to November 5, 2011. Information regarding emergency department visits and hospitalizations, including lengths of stay, were obtained from hospital databases. Diagnoses were classified as respiratory or nonrespiratory, and used primary and secondary hospitalization diagnoses to identify acute exacerbations of COPD (AECOPD). Paired sign tests were performed. RESULTS: The sample consisted of 202 patients. Following nurse navigator intervention, significantly more patients experienced a decrease in the number of respiratory-cause emergency department visits (P<0.05), number of respiratory hospitalizations (P<0.001), total hospital days for respiratory admissions (P<0.001), number of hospitalizations with AECOPD (P<0.001) and total hospital days for admissions with AECOPD (P<0.001). Financial modelling estimated annual savings in excess of $260,000. CONCLUSION: The present quality assurance study indicated that the implementation of an integrated interdisciplinary program for the care of patients with COPD can improve patient outcomes despite the tendency of COPD to worsen over time.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".