Uptake and user characteristics of MyChart within a Canadian community hospital with a diverse patient population: A comparative study
Bibliographic record
Abstract
Patient portals offer a convenient way to access health information and increase patient participation in healthcare. To promote broad accessibility and impact of portals, it is essential to understand uptake patterns across patient populations. This study described the characteristics of patient users of a portal called MyChart and compared them to non-users at a large community hospital. We descriptively analyzed (frequency, counts) patient health records to characterize MyChart users and their usage patterns during the first year of its launch from September 11, 2023, to September 112024. We summarized user demographics along with information about how they activated accounts, accessed MyChart, and utilized its features. Using chi-square and t-tests, we compared MyChart user demographics to non-users who visited the hospital in the same time period. A total of 61,306 patients activated MyChart during the first year it was available. On average, MyChart users were 53 years old, 62% female, 64% predicted to have White ethnicity, and preferred to receive healthcare in English (88%). MyChart users tended to be regular healthcare users, with an average of five annual visits prior to creating an account and logged onto the portal on average five times a month. MyChart users were slightly younger than non-users (an average age of 53.5 vs. 56.9 years) and visited the hospital more often (an average of 5.7 vs. 3.1 annual visits). Many patients activated MyChart during the first year of launch, and users closely resembled the broader patient population. To enhance adoption and potential benefits of patient portals, targeted interventions such as accessible educational information tailored to diverse patient groups (e.g., older adults, different ethnicities) could increase their usage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".