Exploring the impact of an electronic health record implementation on user experiences across clinical programmes in a large Canadian community hospital: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: This study explored experiences with implementing and using the Epic electronic health record (EHR) across different clinical programmes within a single Canadian hospital system and specifically examined how local configuration decisions and implementation of its features and functionalities integrated well or introduced friction within workflows. DESIGN: Qualitative description methodology involving semistructured interviews analysed using thematic analysis. SETTING: A large community hospital in Canada. PARTICIPANTS: Healthcare providers, administrative staff and clinical leaders from seven clinical programmes. RESULTS: 66 individuals participated in interviews. Participants described that Epic's implementation impacted communication and teamwork, workflow and efficiency, and patient care, with these impacts varying across different programme settings. Participants reported that Epic improved inpatient care and safety, communication and teamwork, workflow and efficiency. However, several programmes also experienced challenges, including information overload and increased clerical tasks, impacting workflow efficiency. In programmes with an outpatient component, such as surgery and oncology, there were additional difficulties, such as connecting with external partners, user interface complexities that hindered task completion and concerns about potential compromises in patient care quality. CONCLUSION: Health systems must consider the diverse needs of various clinical programmes when implementing an EHR. Customising the system interface and iteratively codesigning how health system staff incorporate the technology into their workflows are crucial to ensure an EHR seamlessly integrates across different settings, fosters high-quality care delivery and minimises user friction.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".