Evaluating a post-implementation electronic medical record training intervention for diabetes management in primary care
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
OBJECTIVE: This study evaluated the potential for electronic medical record (EMR) video tutorials to improve diabetes (type 1 and 2) care processes by primary care physicians (PCP) using OSCAR EMR. DESIGN: A QUAN(qual) mixed methods approach with an embedded design was used for the overall research study. EMR video tutorials were developed based on the chronic care model (CCM), value-adding EMR use, best practice guidelines for designing software video tutorials and clinician-led EMR training. RESULTS: =0.286). CONCLUSION: This small-scale efficacy study demonstrates the potential of CCM-based EMR video tutorials to improve EMR use for chronic diseases, such as diabetes. A larger-scale effectiveness study with a control group is needed to further validate the study findings and determine their generalisability. The demonstrated efficacy of the intervention suggests that EMR video tutorials may be a cost-effective, sustainable and scalable strategy for supporting EMR optimisation and the continuous learning and development of PCPs. Health informatics practitioners may develop video tutorials for their respective EMR/electronic health record software based on theory and best practices for video tutorial design. For patients, EMR video tutorials may lead to improved tracking of processes of care for diabetes, and potentially other chronic conditions.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it