The Electronic Asthma Management System (eAMS) improves primary care asthma management
Bibliographic record
Abstract
A high prevalence of suboptimal asthma control is attributable to known evidence–practice gaps. We developed a computerised clinical decision support system (the Electronic Asthma Management System (eAMS)) to address major care gaps and sought to measure its impact on care in adults with asthma. This was a 2-year interrupted time-series study of usual care (year 1)versuseAMS (year 2) at three Canadian primary care sites. We included asthma patients aged ≥16 years receiving an asthma medication within the last 12 months. The eAMS consisted of a touch tablet patient questionnaire completed in the waiting room, with real-time data processing producing electronic medical record-integrated clinician decision support. Action plan delivery (primary outcome) improved from zero out of 412 (0%) to 79 out of 443 (17.8%) eligible patients (absolute increase 0.18 (95% CI 0.14–0.22)). Time-series analysis indicated a 30.5% increase in physician visits with action plan delivery with the intervention (p<0.0001). Assessment of asthma control level increased from 173 out of 3497 (4.9%) to 849 out of 3062 (27.7%) eligible visits (adjusted OR 8.62 (95% CI 5.14–12.45)). Clinicians escalated controller therapy in 108 out of 3422 (3.2%) baseline visitsversus126 out of 3240 (3.9%) intervention visits (p=0.12). At baseline, a short-acting β-agonist alone was added in 62 visits and a controller added in 54 visits; with the intervention, this occurred in 33 and 229 visits, respectively (p<0.001). The eAMS improved asthma quality of care in real-world primary care settings. Strategies to further increase clinician uptake and a randomised controlled trial to assess impact on patient outcomes are now required.
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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 | 0.017 |
| 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 | 0.001 | 0.000 |
| 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".