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Record W4210446921 · doi:10.2196/29706

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

2022· article· en· W4210446921 on OpenAlexvenueno aff
Diane M. Korngiebel, Kathleen M. West

Bibliographic record

VenueJMIRx Med · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
FundersNational Human Genome Research InstituteNational Institutes of Health
KeywordsTest (biology)JargonPatient portalThe InternetHealth careTest designGenetic testingPsychologyMedical educationMedicineComputer scienceInternet privacyFamily medicineApplied psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.281
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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