Enabling asthma management and outcomes monitoring through standardized EMR data and eTools
Bibliographic record
Abstract
Rationale: Electronic Medical Records (EMRs) can support and enable asthma management and outcomes monitoring, using standardized data elements. Aim: To demonstrate the ability of an asthma EMR system to enable best practice and patient and program evaluation. Methods: An Asthma Management and Outcomes Monitoring System (AMOMS) aligned with guidelines and provincial data standards was programmed, integrated into the hospital’s EMR, and connected seamlessly to a patient/provider portal (AsthmaLife®), which houses asthma assessment eTools. De-identified electronic data was extracted from AMOMS for all asthma visits (January 2009 to February 2016) at Kingston General Hospital and 7 Primary Care Asthma Program (PCAP) sites. Results: Data were analyzed on 1846 patients (1327 adults (≥18 years of age), 53.34±16.2 years [Mean±SD], 68% female; and 519 children, 7.6±4.4 years of age, 41% female) seen at the Asthma Education Centre (69.4%), specialist clinic (7.4%) or PCAP sites (23.2%). 1057 (57%) of patients received an electronically-generated asthma action plan. Asthma diagnosis was confirmed by objective measures (38%) or suspected (48%). 97% of patients had asthma control assessed at each visit. The proportion of patients with controlled asthma increased from 13.4% (Visit 1) to 32.5% (≥ 3 visits). eTools supporting patient care and self-management were utilized 977 times and seamlessly linked to AMOMS data. Conclusion: Asthma patient and program reporting was feasible using standardized, extractable asthma data elements entered electronically at the point of care. Collection of defined, standardized data is enabling performance measurement and benchmarking and continuous quality improvement.
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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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".