Benefits, facilitators, and barriers of electronic medical records implementation in outpatient settings: A scoping review
Bibliographic record
Abstract
This scoping review examined the breadth and depth of evidence on Electronic Medical Record (EMR) implementation benefits in outpatient settings. Following PRISMA guidelines for scoping reviews, five databases were searched, and 24 studies were retained and reviewed. Benefits, facilitators, and barriers to EMR implementation were extracted. Direct benefits included improved communication/reporting, work efficiency, care process, healthcare outcomes, safety, and patient-centredness of care. Indirect benefits were improved financial performance and increased data accessibility, staff satisfaction, and decision-support usage. Barriers included time and financial constraints; design/technical issues; limited information technology resources, skills, and infrastructure capacity; increased workload and reduced efficiency during implementation; incompatibility of existing systems and local regulations; and resistance from healthcare professionals. Facilitators included training, change management, user-friendliness and alignment with workflow, user experience with EMRs, top management support, and sufficient resources. More rigorous, systematic research is needed, using relevant frameworks to inform healthcare policies and guide EMR projects in outpatient areas.
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 | 0.047 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".