Development and implementation of an electronic asthma record for primary care: integrating guidelines into practice
Bibliographic record
Abstract
RATIONALE: Evidence-based practice may be enhanced by integrating knowledge translation tools into electronic medical records (EMRs). We examined the feasibility of incorporating an evidence-based asthma care map (ACM) into Primary Care (PC) EMRs, and reporting on performance indicators. METHODS: Clinicians and information technology experts selected 69 clinical and administrative variables from the ACM template. Four Ontario PC sites using EMRs were recruited to the study. Certified Asthma Educators used the electronic ACM for patient assessment and management. De-identified data from consecutive asthma patients were automatically transmitted to a secure central server for analysis. RESULTS: Of the four sites recruited, two sites using "stand-alone" EMR systems were able to incorporate the selected ACM variables into an electronic format and participate in the pilot. Data were received on 161 visits by 130 patients aged 36.5 ± 26.9 (mean ± SD) (range 2-93) years. Ninety-four percent (65/69) of the selected ACM variables could be analyzed. Reporting capabilities included: individual patient, individual site and aggregate reports. Reports illustrated the ability to measure performance (e.g. number of patients in control, proportion of asthma diagnoses confirmed by an objective measure of lung function), benchmark and use EMR data for disease surveillance (e.g. number of smokers and the individuals with suspected work-related asthma). CONCLUSIONS: Integration of this evidence-based ACM into different EMRs was successful and permitted patient outcomes monitoring. Standardized data definitions and terminology are essential in order for EMR data to be used for performance measurement, benchmarking and disease surveillance.
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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 | 0.095 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".