Developing the MyCancerGene Digital Health Portal to Improve Patients’ Understanding of Germline Cancer Genetic Test Results: Development, User, and Usability Testing Study
Bibliographic record
Abstract
BACKGROUND: The use of multigene panels has significantly increased the likelihood that genetic testing will leave patients with uncertainties regarding test interpretation, implications, and recommendations, which will change over time. Effective longitudinal care models are needed to provide patients with updated information and to obtain patient and family history updates. OBJECTIVE: To bridge this gap, we aimed to develop a patient- and genetic provider-informed digital genetic health portal (GHP), MyCancerGene, to improve longitudinal patient understanding of and responses to genetic testing. METHODS: We used a 5-step process to develop MyCancerGene. To better understand their interest in and willingness to use a digital GHP, we surveyed 307 patients who completed genetic testing (step 1). We completed qualitative interviews with 10 patients and a focus group with 17 genetic providers to inform the content and function of MyCancerGene (step 2). Next, we developed initial intervention content (step 3) and completed user testing of intervention content with 25 providers and 28 patients (step 4). After developing the prototype intervention, we completed usability testing with 8 patients for their feedback on the final content, functions, and ease of use (step 5). RESULTS: In surveys conducted in step 1, 90% of patients with positive results reported interest in a digital GHP, and over 75% of participants with variants of uncertain significance or uninformative negative results reported similar interest. The most frequently reported advantages among patients were increasing accessibility, convenience, and efficiency (103/224, 46%); keeping genetic information organized (54/224, 24.1%); and increasing or maintaining patient understanding of the information (38/224, 17%). In qualitative interviews (step 2), both patients and genetic providers endorsed the benefit of the tool for updating personal and family history and for providers to share new risk information, test interpretation, or other medical changes. Patient and provider input informed eight key components of the tool: (1) Landing Page, (2) Summary of Care page, (3) My Genetic Test Results page, (4) My Family History page, (5) Provide an Update page, (6) Review an Update page, (7) Resources page, and (8) the Screenings Tracker. They also recommended key functions, including the ability to download and print materials and the inclusion of reminders and engagement functions. Potential challenges identified by patients included privacy and security concerns (67/206, 32.5%) and the potential for electronic information to generate distress (20/206, 9.7%). While patients were comfortable with updates (ie, even variant reclassification upgrades or clinically significant results), 44% (11/25) of genetic providers were uncomfortable sharing variant reclassification upgrades through MyCancerGene. CONCLUSIONS: MyCancerGene, a patient-centered digital GHP, was developed with extensive patient and genetic provider feedback and designed to enhance longitudinal patient understanding of and affective and behavioral responses to genetic testing, particularly in the era of evolving evidence and risk information.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".