Factors identified as barriers or facilitators to EMR/EHR based interprofessional primary care: a scoping review
Bibliographic record
Abstract
As interprofessional collaboration (IPC) in primary care receives increasing attention, the role of electronic medical and health record (EMR/EHR) systems in supporting IPC is important to consider. A scoping review was conducted to synthesize the current literature on the barriers and facilitators of EMR/EHRs to interprofessional primary care. Four online databases (OVID Medline, EBSCO CINAHL, OVID EMBASE, and OVID PsycINFO) were searched without date restrictions. Twelve studies were included in the review. Of six facilitator and barrier themes identified, the key facilitator was teamwork support and a significant barrier was data management. Other important barriers included usability related mainly to interoperability, and practice support primarily in terms of patient care. Additional themes were organization attributes and user features. Although EMR/EHR systems facilitated teamwork support, there is potential for team features to be strengthened further. Persistent barriers may be partly addressed by advances in software design, particularly if interprofessional perspectives are included. Organizations and teams might also consider strategies for working with existing EMR/EHR systems, for instance by developing guidelines for interprofessional use. Further research concerning the use of electronic records in interprofessional contexts is needed to support IPC in primary care.
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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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".