Use of a severe asthma algorithm in an asthma education center electronic medical record (EMR)
Bibliographic record
Abstract
<b>Aims:</b> Integrating knowledge translation tools into electronic medical records (EMRs) may improve evidence-based practice and outcomes. We describe the impact of implementing a severe asthma (SA) EMR algorithm on patient care in an asthma education center. <b>Methods:</b> A SA algorithm, based on Canadian Thoracic Society criteria for SA, was programmed into the Airways Management and Outcomes Monitoring System (AMOMS), a data capture system incorporated into the patient care system at a tertiary care centre in Kingston, ON, Canada. The impact of the SA algorithm was assessed using a 16-month observational pre-post implememtation study design. Patients were categorized as having a confirmed or suspected asthma diagnosis. Confirmed asthma was subdivided into severe and non-severe. <b>Results:</b> 675 patient assessments from 388 patients were categorized as having confirmed (n=221) and suspected asthma (n=167). The algorithm identified 42 confirmed asthma assessments as SA; of those, 37 met criteria for uncontrolled SA. Action plan review and revision, device technique optimal, triggers addressed and spirometry increased post implementation of the SA algorithm (Table1). <b>Conclusions:</b> The SA algorithm supported asthma educators9 adherence to best practice guidelines, including the recognition and management of uncontrolled severe asthma. Next steps are to evaluate the impact of implementation of the SA algorithm on primary care EMRs.
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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 | 0.000 | 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) | 0.003 | 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".