Asthma electronic medical records in primary care: an integrative review.
Bibliographic record
Abstract
BACKGROUND: Quality management, evaluation, and surveillance of asthma may be enhanced by access to and utilization of an asthma electronic medical record (EMR) in primary care. PURPOSE: To describe the current status, support tools, and utility of asthma EMRs in primary care. METHODS: An integrative review of the literature published between 1996 and 2008 was completed using Ovid MEDLINE, EMBASE, and CINAHL databases. Key search terms included asthma, medical records, computerized, primary health care, primary care, family physician, family practice, chronic disease, COPD, neoplasm, diabetes mellitus, and cardiovascular disease. Articles related to concepts, systems in development, and sources such as acute care and pharmacy EMRs were excluded. Each article was reviewed by two reviewers. RESULTS: Of 309 articles identified, 76 met the inclusion criteria. Twenty-two percent were specific to asthma, 78% pertained to other chronic diseases and/or the overall status of an EMR in primary care. The literature varied in methodology, topics of discussion and value of data. Articles describing an asthma EMR most often reported on decision support tools (n = 3) and/or utility (n = 14), specifically the ability to predict mortality and assess severity and timeliness of diagnosis. A primary care EMR containing a validated asthma minimum data set was not found. Three themes emerged from the review: status (description of users, functionalities and adoption issues), tools (decision support tools to enhance knowledge uptake), and utility (data quality, extraction and outcomes). CONCLUSIONS: There is a paucity of asthma elements in EMRs in primary care, with the exception of discussion of decision support tools and utility. Integration of a more robust asthma EMR in primary care, including a minimum data set, standardized terminology, and validated indicators, may further enhance care and enable outcomes monitoring.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".