Measuring Electronic Health Record Use in Primary Care: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Simple measures of electronic health record (EHR) adoption may be inadequate to evaluate EHR use; and positive outcomes associated with EHRs may be better gauged when varying degrees of EHR use are taken into account. In this article, we aim to assess the current state of the literature regarding measuring EHR use. OBJECTIVE: This article conducts a scoping review of the literature to identify and classify measures of primary care EHR use with a focus on the Canadian context. METHODS: We conducted a scoping review. Multiple citation databases were searched, as well as gray literature from relevant Web sites. Resulting abstracts were screened for inclusion. Included full texts were reviewed by two authors. Data from the articles were extracted; we synthesized the findings. Subsequently, we reviewed these results with seven EHR stakeholders in Canada. RESULTS: Thirty-seven articles were included. Eighteen measured EHR function use individually, while 19 incorporated an overall level of use. Eight frameworks for characterizing overall EHR use were identified. CONCLUSION: There is a need to create standardized frameworks for assessing EHR use.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it