Willingness to Be Contacted via a Patient Portal for Health Screening, Research Recruitment, and at-Home Self-Test Kits for Health Monitoring: Pilot Quantitative Survey
Bibliographic record
Abstract
Background: Patient portals are being increasingly used by health systems in the United States. Although some patients use portals for clinical use, patient perspectives on using portals for research-related activities, to complete health screenings, and to request at-home self-test kits are unclear. Objective: We aimed to understand patient perspectives on using electronic health portals for research; health-related screenings; and patient-initiated, home-based self-testing. Methods: Patients (N=105) from the Patient Engaged Research Center at a large, urban, midwestern health system completed a 23-item web-based survey on patient portal (MyChart) use and willingness to use the patient portal for research, risk assessments, and self-test kits. Frequencies and percentages were generated. Results: Almost all participants (102/105, 97.1%) had accessed MyChart at least once, with most (44/102, 43.1%) indicating they logged in at least once per month. Participants indicated logging into MyChart to check laboratory results or other health data (89/105, 84.8%), because they received a message to log in (85/105, 81%), and to message their health care professional (83/105, 79%). Fewer participants logged in to see what medications they had been prescribed (16/105, 15.2%) and to learn more about their health conditions (29/105, 27.6%). Most participants indicated logging into MyChart on a computer via a website (70/105, 66.7%) or on a smartphone via an app (54/105, 51.4%). When asked about how likely they would be to participate in different types of research if contacted via MyChart, most (90/105, 85.7%) said they would be likely to answer a survey, fill out a health assessment (87/105, 82.9%), or watch a video (86/105, 81.9%). Finally, participants would be willing to answer risk assessment questions on MyChart regarding sleep (74/101, 73.3%), stress (65/105, 61.9%), diabetes (60/105, 57.1%), anxiety (59/105, 56.2%), and depression (54/105, 51.4%) and would be interested in receiving an at-home self-test kit for COVID-19 (66/105, 62.9%), cholesterol (63/105, 60%), colon cancer (62/105, 59%), and allergies (56/105, 53.3%). There were no significant demographic differences for any results (all P values were >.05). Conclusions: Patient portals may be used for research recruitment; sending research-related information; and engaging patients to answer risk assessments, read about health information, and complete other clinical tasks. The lack of significant findings based on race and gender suggests that patient portals may be acceptable tools for recruiting research participants and conducting research. Allowing patients to request self-test kits and complete risk assessments in portals may help patients to take agency over their health care. Future research should examine if patient portal recruitment may help address persistent biases in clinical trial recruitment to increase enrollment of women and racial minority groups.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".