Integrating asthma care guidelines into primary care electronic medical records: a review focused on Canadian knowledge translation tools
Bibliographic record
Abstract
INTRODUCTION: Asthma is one of the most common chronic respiratory diseases globally. Despite national and international asthma care guidelines, gaps persist in primary care. Knowledge translation (KT) electronic tools (eTools) exist aiming to address these gaps, but their impact on practice patterns and patient outcomes is variable. We aimed to conduct a nonsystematic review of the literature for key asthma care gaps and identify limitations and future directions of KT eTools optimised for use in electronic medical records (EMRs). METHODS: The database OVID Medline was searched (1999-2024) using keywords such as asthma, KT, primary healthcare and EMRs. Primary research articles, systematic reviews and published international/national guidelines were included. Findings were interpreted within the knowledge-to-action framework. RESULTS: Key asthma care gaps in primary care include under-recognition of suboptimal control, underutilisation of pulmonary function tests, barriers to care delivery, provider attitudes/beliefs, limited access to asthma education and referral to asthma specialists. Various KT eTools have been validated, many with optimisation for use in EMRs. KT eTools within EMRs have been a recent focus, including asthma management systems, decision support algorithms, data standards initiatives and asthma case definition validation for EMRs. CONCLUSIONS: The knowledge-to-action cycle is a valuable framework for developing and implementing novel KT tools. Future research should integrate end-users into the process of KT tool development to improve the perceived utility of these tools. Additionally, the priorities of primary care physicians should be considered in future KT tool research to improve end-user uptake and overall asthma management practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".