Impacts on quality of care following electronic health record implementation within a large Canadian community hospital: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to describe how healthcare providers perceived the impacts of implementing and using an electronic health record (EHR) on quality, safety and person-centredness of care. DESIGN: A qualitative descriptive design using semistructured interviews. SETTING: In October 2020, a large Canadian community hospital implemented a new EHR system (Epic) across three sites, transitioning from a previously fragmented (combination of paper-based and electronic) system. PARTICIPANTS: Sixty-two healthcare providers and clinical leaders. RESULTS: Participants shared their experiences regarding the impact of EHR implementation on quality of care, which were analysed into common themes including task efficiency, information management, patient interactions and patient safety. While the system significantly altered their routines and introduced new responsibilities like additional documentation requirements, it also facilitated adherence to clinical guidelines, improved information visibility and enhanced documentation, benefiting overall quality of care and patient safety. Participants reported that EHR implementation led to increased efficiency, freeing up time for patient care and improving communication with patients and other providers. CONCLUSION: EHRs have the potential to improve quality of care and patient safety, but this depends on their perceived value and how well healthcare providers can integrate their various features into clinical routines.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".