Variations in Nursing Documentation Time in a Mental Health Setting: A Retrospective Observational Study of EHR Usage Data
Bibliographic record
Abstract
Abstract Nurses are the largest group of electronic health record (EHR) users in Canada, yet their experiences with documentation burden remain underexplored. While EHR-generated usage data, such as audit logs and time-motion metrics, have been used to quantify documentation time, they are rarely used to better understand EHR inefficiencies and identify potential changes for nursing documentation and workflows. This approach may help address instances of documentation demands detracting from direct patient care and contributing to burnout, which has been largely reported by nurses. This study aimed to: (1) examine EHR utilization patterns and time spent by nurses across clinical venues and nurse types; (2) identify EHR areas contributing most to nursing workload; (3) determine predictors of EHR time; and (4) assess differences in usage patterns across venues. We analyzed 12 months of EHR usage data from nurses at Canada's largest academic mental health hospital using Cerner Advance (Oracle Health). Seven metrics were selected in collaboration with a Nursing Advisory Council. Regression and least-squares means comparisons were conducted using R, with venue and nurse type as predictors. Data from 840 nurses revealed significant differences in EHR usage across venues and nurse types. Mean active time per patient per shift was highest in inpatient (19.3 minutes), followed by emergency (14.8 minutes), and ambulatory settings (6.3 minutes). Registered Practical Nurses (RPNs) averaged more active EHR time (20.1 minutes) than Registered Nurses (16.4 minutes). Documentation time per patient was significantly different across venues (F [3,832] = 71.97, p < 0.001) and nurse types (p = 0.0018). PowerForms time also varied significantly (F [3,818] = 102.1, p < 0.001). These findings support targeted EHR optimization efforts based on clinical context and role. Significant variation exists in how nurses interact with EHRs, with documentation representing a substantial time burden, especially for RPNs and inpatient settings. These findings emphasize the need for venue and role-specific optimization strategies and underscore the importance of including nurses' voices in EHR design and quality improvement initiatives.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".