The use of patient health information outside the circle of care: Consent preferences of patients from a large academic cancer centre.
Bibliographic record
Abstract
e14122 Background: Massive volumes of patient health information (PHI) are required to realize the anticipated benefits of artificial intelligence in future clinical medicine. To maintain public trust in medical research however, consent policies must evolve to reflect contemporary patient preferences. Methods: From January-December 2019, patients attending clinics at a large academic cancer centre were invited to complete a 27-item iPad survey on consent preferences. Survey items focused on: (a) broad vs. specific consent; (b) opt-in vs. opt-out approaches for research contact; (c) comfort sharing with different recipients; (d) perceptions on commercialization; and (e) options to track information use and study results. Demographic questions addressed cancer type, treatment stage, age, gender, ethnicity, education level, and household income. Results: A total of 222 participants were included in the analysis (112 males, 108 females; 2 rather not say); 83% were comfortable sharing PHI with researchers at their own hospital. While 56% of patients preferred broad consent, 38% preferred to be contacted with study details and asked to consent every time (specific consent); 6% prefer not to share at all. Younger patients ( < 49 years) most often chose specific consent (50%); significantly more than those 75+ years (24%; p < .05). Younger patients ( < 49 years) were also significantly more uncomfortable than older patients (50+ years) sharing even within their own hospital (13% uncomfortable vs. 1% uncomfortable; p < .05). A significant majority of patients (63%, p = .0001) preferred to be asked for permission before being entered into a contact pool vs. automatic entry with opportunity to opt-out. The majority of patients were uncomfortable sharing PHI with commercial enterprises (51% uncomfortable, 27% comfortable, 22% neutral). A significant majority expressed the desire to track who is using their PHI (61%, p < .0001), and be notified regarding study results (70%, p < .0001). Conclusions: While most patients were willing to share their PHI with researchers at their own hospital, many preferred a transparent and reciprocal consent process. These data also suggest a generational shift, wherein younger patients preferred more informed consent options. Modernizing consent policies to reflect increased patient interest in the exercise of their autonomy is crucial in fostering sustained public engagement in medical research.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".