USER PERCEPTIONS AND SATISFACTION OF A CUSTOMIZABLE EMR HOMEPAGE
Bibliographic record
Abstract
BACKGROUND: Healthcare provider burnout is a concern in primary care, necessitating innovative solutions to improve user experience and reduce work-related stress. A novel Homepage feature has been introduced in the TELUS Collaborative Health Record (CHR), an electronic medical record (EMR) solution. The Homepage is tailored to offer existing CHR clients a more customizable and personalized experience. It includes information not typically seen in EMRs, creating a more user-friendly platform for their daily work. PURPOSE: This study evaluates the initial user perceptions and end-user satisfaction of the CHR Homepage. METHODS: TELUS CHR clients who are family physicians and administrative staff working in primary care clinics took part in qualitative semi-structured interviews before the full release of the Homepage (n = 13), and these and other CHR users were asked to complete a mixed-methods cross-sectional survey four weeks after the Homepage launch (n = 12). Data analysis involved thematic analysis of interview texts and questionnaire responses, along with statistical analysis of quantitative data using non-parametric tests. RESULTS: The analysis of interviews and surveys revealed that users perceived the Homepage positively, and most were “moderately satisfied.” However, users suggested further improvements, such as providing more actionable information, expanding customization options, and addressing specific user needs. CONCLUSIONS: The study provided valuable insights into the user experience of the CHR Homepage, informing quality improvements and refinements for future CHR Homepage releases. The findings can inform EMR solution developers when conducting user testing of EMRs by considering customizable features that primary care users desire to enhance their experiences. Understanding user perceptions and incorporating user feedback can help developers address user concerns and improve user satisfaction, ultimately enhancing user experiences in primary care settings.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".