PROMOTING EVIDENCE-BASED ASTHMA DIAGNOSIS AND SURVEILLANCE USING ELECTRONIC TOOLS
Bibliographic record
Abstract
Background: In Canada, asthma is one of the most common chronic conditions. It is estimated that approximately 3.8 million people in Canada suffer from asthma. The majority of asthma diagnoses and care take place in primary care settings. Electronic tools (eTools) offer an opportunity to utilize technology to improve asthma diagnosis and surveillance. Purpose: The purpose of this study was to create and validate a case definition or separate case definitions for suspected and confirmed asthma in primary care electronic medical records (EMRs) for use in eTools and to understand how to develop and scale a primary care surveillance system for quality improvement of asthma diagnosis and care. Methods: A respirologist, family physician, and trainee manually classified 776 adult patient charts from a primary care practice EMR in Kingston, Ontario as suspected asthma, confirmed asthma, or not asthma using Canadian Thoracic Society criteria. Case definitions based on billing codes, clinical data elements and medications were applied to the site’s Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data for the same charts and compared to abstractor classifications to determine each case definition’s measurement properties. Opportunities and barriers to developing and scaling eTools for asthma were determined through two focus groups using a mixed-methods approach. Results: The prevalence of suspected and confirmed asthma were 7.3% (n=54) and 2.4% (n=18) respectively in this study. Case definitions based on this EMR dataset could not distinguish between suspected and confirmed asthma. One case definition consisting of billing, clinical, and medication elements had the best measurement properties for suspected or confirmed asthma with a sensitivity of 81%, specificity of 90%, positive predictive value of 45%, negative predictive value of 98%, and Youden’s Index of 0.71 for combined suspected or confirmed asthma cases. A total of 7 key themes important to the development of a surveillance tool were identified through mixed-methods analysis. Conclusions: An EMR case definition for suspected or confirmed adult asthma has been validated for use in a CPCSSN database. Implementation of this case definition in conjunction with developing new eTools that address the key themes identified in this study will enable the development of a primary care surveillance system for quality improvement in adult asthma care. Adoption of EMR data elements that document asthma diagnosis status is warranted and would greatly enhance such a system
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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 | 0.169 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".