Measuring chronic obstructive pulmonary disease (COPD) quality indicators using primary care electronic medical records (EMRs) in Ontario, Canada
Bibliographic record
Abstract
RATIONALE: Quality management standards are available for chronic obstructive pulmonary disease (COPD), but how often they are followed in community settings is uncertain.OBJECTIVES: We sought to measure the adherence to standard quality of care criteria for COPD management in primary care using primary care electronic medical records as an indicator for quality of COPD management.METHODS: We conducted a cross-sectional study using EMR data from Ontario and previously validated set of COPD quality indicators previously developed by the Ontario COPD Population Health Network. We analyzed how often the COPD quality indicators were met for patients with COPD at the population-level and at the family physician-level.MEASUREMENTS AND MAIN RESULTS: Five quality indicators were assessed at population- and family physician (FP)-levels. We included 6995 patients with COPD under care of 247 FPs. The highest performing quality indicator was the recording of patients’ smoking history in the EMR. FPs varied in their rates of provision of smoking cessation support to current smokers, recording of spirometry, administration of pneumococcal and seasonal influenza vaccines. Five additional health care or medication utilization rates were assessed for all patients with COPD regardless of disease severity, including prescriptions for short-acting and long-acting bronchodilators, combined inhaled corticosteroids and long-acting bronchodilators, evidence of pulmonary rehabilitation and oxygen therapy use.CONCLUSION: EMR data can be a useful data source to study COPD care, and there are opportunities for improvement in several areas of COPD management in primary care as well as standardization of EMR use for COPD care.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".