Patient Experiences and Perspectives When MyChart is Introduced in a Large Community Hospital: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Patient portals, or secure websites linked to electronic medical records, have emerged as tools to provide patients with timely access to their health information. To support the potential benefits of patient portals such as improved engagement in health care, it is essential to understand how patients and caregivers experience these portals. OBJECTIVE: This study aimed to explore patient and caregiver experiences, facilitators, and barriers to accessing and using a patient portal called MyChart during the initial stages of its implementation. METHODS: We applied explanatory sequential mixed methods to conduct a web-based questionnaire and semistructured interviews with MyChart users and nonusers at a large community hospital in Ontario, Canada. Among users, we explored user satisfaction with MyChart, its impact on care, and areas for improvement. For nonusers, we explored barriers to MyChart access and willingness to use it in the future. Descriptive statistics and thematic analysis were used for data analysis. RESULTS: A total of 5651 patients and caregivers completed the web-based questionnaire and 18 (12 users and 6 nonusers) participated in interviews. MyChart users primarily learned about the portal through email (n=1288, 39%), after-visit summaries (n=953, 29%), and hospital staff (n=408, 12%). Nonusers cited lack of awareness (n=1291, 59%) and registration difficulties (n=707, 32%) as some barriers to activation and adoption, but the majority would consider activating and using MyChart if they could learn more about it (n=1126, 54%). Users valued MyChart for preparing for health care encounters but expressed dissatisfaction with limited features and access to medical history and test results, whereas nonusers tended to be unsure about the benefits of using MyChart, especially if they were infrequent health care users. CONCLUSIONS: Patient portals offer benefits, but barriers to access and limited functionality can hinder widespread use. To enhance the adoption and potential benefits of patient portals, targeted outreach and comprehensive access to health information are essential to promote positive and seamlessly integrated health care experiences.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".