Factors Affecting Patient Portal Use Among Low-Income Pregnant Women: Mixed-Methods Pilot Study
Bibliographic record
Abstract
BACKGROUND: Patient portals offer patients personalized and secure Web access to their medical information and enable patients to manage their health care online. However, there is a lack of information about patient acceptance and use of patient portals among low-income pregnant women. OBJECTIVE: This formative research aims to assess the potential of a patient portal, MyChart, for improving prenatal health care and pregnancy outcomes, and identify the barriers and facilitators of MyChart use among low-income pregnant women. METHODS: A mixed-methods study was conducted with a convenience sample of 18 low-income pregnant women comprising low- and high-risk patients enrolled in a prenatal clinic in eastern North Carolina. MyChart use, patient demographics, and pregnancy information were collected by reviewing electronic medical charts. Health literacy was measured. Reported use and attitudes toward MyChart were collected using a semi-structured interview. RESULTS: Although 39% (7/18) of participants interviewed signed up for MyChart, only 22% (4/18) of them became active users. Another 33% (6/18) had never heard of MyChart or was unsure of how to access it. Users primarily accessed test results and appointment schedules. The main facilitating factors for patient portal use were information and motivation from health care providers and concerns about pregnancy due to a history of miscarriage. Reported barriers were lack of educational resources, lack of care provider encouragement, and technical difficulties possibly exacerbated by low health literacy. Participants also suggested improvements for MyChart, especially the provision of discussion-based support for pregnant women. CONCLUSIONS: The one-time verbal introduction of MyChart does not meet current patients' needs. Data reveal the need for more consistent patient education and support programs, tailored to patients' previous pregnancy histories. The clinic also needs to facilitate better provider-patient communication about the importance of MyChart use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".