Integrating User Preferences for Asthma Tools and Clinical Guidelines Into Primary Care Electronic Medical Records: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Asthma is a chronic respiratory disease that poses a substantial burden on individuals and the health care system. Despite published national guidelines for the diagnosis and management of asthma, considerable care gaps exist. Suboptimal adherence to asthma diagnosis and management guidelines contributes to poor patient outcomes. The integration of electronic tools (eTools) into electronic medical records (EMRs) represents a knowledge translation opportunity to support best practices. OBJECTIVE: The purpose of this study was to determine how best to integrate evidence-based asthma eTools into primary care EMRs across Ontario and Canada to improve adherence to guidelines as well as measure and monitor performance. METHODS: In total, 2 focus groups comprising physicians and allied health professionals who were considered experts in primary care, asthma, and EMRs were convened. One focus group also included a patient participant. Focus groups used a semistructured discussion-based format to consider the optimal methods for integrating asthma eTools into EMRs. Discussions were held on the web via Microsoft Teams (Microsoft Corp). The first focus group discussed integrating asthma indicators into EMRs using eTools, and participants completed a questionnaire evaluating the clarity, relevance, and feasibility of collecting asthma performance indicator data at the point of care. The second focus group addressed how to incorporate eTools for asthma into a primary care setting and included a questionnaire evaluating the perceived utility of various eTools. Focus group discussions were recorded and analyzed using thematic qualitative analysis. The responses to focus group questionnaires were assessed using descriptive quantitative analysis. RESULTS: Qualitative analysis of the 2 focus group discussions revealed 7 key themes: designing outcome-oriented tools, gaining stakeholder trust, facilitating open lines of communication, prioritizing the end user, striving for efficiency, ensuring adaptability, and developing within existing workflows. In addition, 24 asthma indicators were rated according to clarity, relevance, feasibility, and overall usefulness. In total, 5 asthma performance indicators were identified as the most relevant. These included smoking cessation support, monitoring using objective measures, the number of emergency department visits and hospitalizations, assessment of asthma control, and presence of an asthma action plan. The eTool questionnaire responses revealed that the Asthma Action Plan Wizard and Electronic Asthma Quality of Life Questionnaire were perceived to be the most useful in primary care. CONCLUSIONS: Primary care physicians, allied health professionals, and patients consider that eTools for asthma care present a unique opportunity to improve adherence to best-practice guidelines in primary care and collect performance indicators. The strategies and themes identified in this study can be leveraged to overcome barriers associated with asthma eTool integration into primary care EMRs. The most beneficial indicators and eTools, along with the key themes identified, will guide future asthma eTool implementation.
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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 | 0.045 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".