Promoting Evidence-Based Asthma Care Using Digital Knowledge Translation Tools: Impact of the Provider Asthma Assessment Form and Severe Asthma Algorithm (PEACKT-PAAF)
Bibliographic record
Abstract
Background: Despite national asthma care guidelines, gaps persist between best practice and clinical practice. Electronic Medical Records (EMRs) provide a unique opportunity to integrate novel eTools at the point-of-care. The Provider Asthma Assessment Form (PAAF) is an electronic asthma management tool with an embedded decision support algorithm for severe/uncontrolled asthma, designed to support evidence-based practice. Purpose: The purpose of this study was to determine whether PAAF integration into a primary care EMR improves evidence-based asthma diagnosis and management. We also aimed to evaluate the perceived utility, provider satisfaction, and barriers/enablers to the implementation of the PAAF. Methods: We performed a single-centre pre-post observational study at an academic family health in Kingston, Ontario. Retrospective baseline data (Jan 2018 - Dec 2019) and post-implementation data (Jan – Dec 2023) were collected. A validated adult asthma EMR case definition was applied to identify asthma cases, on which detailed manual chart abstractions were performed. A data extraction was performed for completed PAAFs (Oct 2022 – July 2024). A survey was administered post-implementation to evaluate perceived utility, provider satisfaction, and enablers/barriers to using the PAAF. Results: Overall, 31.3% in baseline (n=72) versus 23.8% (n=34) post-implementation had confirmed asthma. Significantly more pulmonary function tests (PFTs) were requested after implementation of the PAAF (49.0% post-implementation; 30.9% baseline, p=0.0006). Care as assessed by key Primary Care - Asthma Performance Indicators (PC-APIs©) showed tendencies towards improvement in the post-implementation cohort. A significantly higher average number of asthma control parameters was documented when the PAAF (n=12) was used compared to manual chart abstractions (n=366) (5.4±1.9 PAAF, 2.3±1.2 manual chart abstraction [mean±SD], p=<0.0001). Most providers were satisfied that the PAAF was helpful in clinical practice, aided their decision making, and was user friendly. Several barriers to implementation were identified. Conclusions: There were significant improvements in asthma-specific documentation and adherence with key evidence-based recommendations for care following PAAF implementation. However, uptake was low and key asthma care gaps were still common. Although the PAAF was perceived to be a useful eTool, barriers limited user uptake. Lessons learned from the PAAF can inform the development and implementation of novel eTools in primary care EMRs.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".