Impacts of an Electronic Medical Records System on Work Practices and Quality of Care at a Specialty Acute Care Hospital: A Mixed Methods Study
Bibliographic record
Abstract
Background: Despite the increasing shift towards implementing hospital information systems (HISs), there is limited evidence of their effects as perceived by physicians following the use of longstanding implemented systems in specialty acute care hospitals, notably in Canada. Additionally, due to its burden on work practices, studies which assessed the impacts of implementations have shown limited or mixed findings on its ability to improve quality of care (QoC) (Tajirian et al., 2020; Trout et al., 2022; Campanella et al., 2016; Upadhyay & Hu, 2022; Price et al., 2022). In 2019, the University of Ottawa Heart Institute (UOHI) and five other Ottawa hospitals launched their use of Epic Systems. An initial pre-post study assessed the impacts of the implementation (Jaana et al., under review). This follow-up study aims to investigate physicians' perceptions of system use effects on work practices and QoC at the UOHI. Methods: This study employed a sequential explanatory mixed methods design consisting of a survey distributed to physicians working at the UOHI, followed by semi-structured individual interviews with key physicians. Survey data were collected (n=31, response rate 42.5%) and analyzed to assess physicians' perceptions of system use, attributes and impacts on QoC, performing descriptive and bivariate analyses. 11 physicians were interviewed, and thematic analysis of transcripts identified six key aggregate dimensions. Results: Physician survey respondents expressed dissatisfaction with system attributes such as functionality, information quality and support. Nevertheless, many expressed Epic's beneficial impact on several items related to care coordination, clinical documentation and clinical workflow efficiency. Some differences in perspectives were observed among groups with different tenure, age, specialty and biological sex. System feature use and satisfaction with system attributes were moderately associated with perceived improvements in documentation, care coordination and guideline adherence. Interviews' codes were aggregated into six dimensions (i.e., Use Behaviours, System Quality, Information Quality, Support Quality, User Satisfaction and Driving Improvements in QoC). These results complemented the quantitative findings and highlighted the poor alignment between system design and physicians' needs, inconsistent documentation practices and a lack of support for using Epic data in QI and research projects on QoC. Conclusion: This mixed methods study provides a comprehensive assessment of how Epic system use influences QoC. Main findings reveal physicians' ability to navigate the system and access relevant and accurate patient information can affect the efficiency, effectiveness and safety of patient care. This study provides benchmarking of system use effects despite familiarity with Epic, informing future and ongoing implementation efforts in Canada. Improving system usability and providing transparent and sustained support to physician end users is critical. Furthermore, customization efforts need to be collaborative and tailored to subspecialty workflows, rather than relying on individual adaptations. This would promote standardized and complete data collection practices across the organization to further support physicians' medical decision-making capacity. Future studies should expand the scope of evaluation to include multiple sites using Epic and explore the experiences of various end-user groups.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".