Patient Recommendations for the Content and Design of Electronic Returns of Genetic Test Results: Interview Study Among Patients Who Accessed Their Genetic Test Results via the Internet
Bibliographic record
Abstract
BACKGROUND: Genetic test results will be increasingly made available electronically as more patient-facing tools are developed; however, little research has been done that collects data on patient preferences for content and design before creating results templates. OBJECTIVE: This study identifies patient preferences for the electronic return of genetic test results, including what considerations should be prioritized for content and design. METHODS: Following user-centered design methods, 59 interviews were conducted by using semistructured protocols. The interviews explored the content and design issues of patient portals that facilitated the return of test results to patients. We interviewed patients who received electronic results for specific types of genetics tests (pharmacogenetic tests, hereditary blood disorder tests, and tests for the risk of heritable cancers) or electronically received any type of genetic or nongenetic test results. RESULTS: In general, many of participants felt that there always needed to be some clinician involvement in electronic result returns and that electronic coversheets with simple summaries would be helpful for facilitating this. Coversheet summaries could accompany, but not replace, the more detailed report. Participants had specific suggestions for such results summaries, such as only reporting the information that was the most important for patients to understand, including next steps, and doing so by using clear language that is free of medical jargon. Electronic result returns should also include explicit encouragement for patients to contact health care providers about questions. Finally, many participants preferred to manage their care by using their smartphones, particularly in instances when they needed to access health information on the go. CONCLUSIONS: Participants recommended that a patient-friendly front section should accompany the more detailed report and made suggestions for organization, content, and wording. Many used their smartphones regularly to access test results; therefore, health systems and patient portal software vendors should accommodate smartphone app design and web portal design concomitantly when developing platforms for returning results.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".