Bibliographic record
Abstract
Objective To understand the key challenges to adoption of advanced features of electronic medical records (EMRs) in office practice, and to better understand these challenges in a Canadian context. Design Mixed-methods study. Setting Manitoba. Participants Health care providers and staff in 5 primary care offices. Methods Level of EMR adoption was assessed, and field notes from interviews and discussion groups were qualitatively analyzed for common challenges and themes across all sites. Main findings Fifty-seven interviews and 4 discussion groups were conducted from November 2011 to January 2012. Electronic medical record adoption scores ranged from 2.3 to 3.0 (out of a theoretical maximum of 5). Practices often scored lower than expected on use of decision support, providing patients with access to their own data, and use of practice-reporting tools. Qualitative analysis showed there were ceiling effects to EMR adoption owing to how the EMR was implemented, the supporting eHealth infrastructure, lack of awareness or availability of EMR functionality, and poor EMR data quality. Conclusion Many practitioners used their EMRs as “electronic paper records” and were not using advanced features of their EMRs that could further enhance practice. Data-quality issues within the EMRs could affect future attempts at using these features. Education and quality improvement activities to support data quality and EMR optimization are likely needed to support practices in maximizing their use of EMRs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.013 |
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 teacher head, 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".