Assessing the Impact on Electronic Health Record Burden After Five Years of Physician Engagement in a Canadian Mental Health Organization: Mixed-Methods Study
Bibliographic record
Abstract
Background: The burden caused by the use of electronic health record (EHR) systems continues to be an important issue for health care organizations, especially given human resource shortages in health care systems globally. As physicians report spending 2 hours documenting for every hour of patient care, there has been strong interest from many organizations to understand and address the root causes of physician burnout due to EHR burden. Objective: This study focuses on evaluating physician burnout related to EHR usage and the impact of a physician engagement strategy at a Canadian mental health organization 5 years after implementation. Methods: A cross-sectional survey was conducted to assess the perceived impact of the physician engagement strategy on burnout associated with EHR use. Physicians were invited to participate in a web-based survey that included the Mini-Z Burnout questionnaire, along with questions about their perceptions of the EHR and the effectiveness of the initiatives within the physician engagement strategy. Descriptive statistics were applied to analyze the quantitative data, while thematic analysis was used for the qualitative data. Results: Of the 254 physicians invited, 128 completed the survey, resulting in a 50% response rate. Among the respondents, 26% (33/128) met the criteria for burnout according to the Mini-Z questionnaire, with 61% (20/33) of these attributing their burnout to EHR use. About 52% of participants indicated that the EHR improves communication (67/128) and 38% agreed that the EHR enables high-quality care (49/128). Regarding the physician engagement strategy initiatives, 39% (50/128) agreed that communication through the strategy is efficient, and 75% (96/128) felt more proficient in using the EHR. However, additional areas for improvement within the EHR were identified, including (1) medication reconciliation and prescription processes; (2) chart navigation and information retrieval; (3) longitudinal medication history; and (4) technology infrastructure challenges. Conclusions: This study highlights the potential impact of EHRs on physician burnout and the effectiveness of a unique physician engagement strategy in fostering positive perceptions and improving EHR usability among physicians. By evaluating this initiative in a real-world setting, the study contributes to the broader literature on strategies aimed at enhancing physician experience following large-scale EHR implementation. However, the findings indicate a continued need for system-level improvements to maximize the value and usage of EHRs. The physician engagement strategy demonstrates the potential to enhance physicians' EHR experience. Future efforts should prioritize system-level advancements to increase the EHR's impact on quality of care and develop standardized approaches for engaging physicians on a broader Canadian scale.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".