Legal and Ethical Considerations for the Design and Use of Web Portals for Researchers, Clinicians, and Patients: Scoping Literature Review
Bibliographic record
Abstract
BACKGROUND: This study aims to identify a novel potential use for web portals in health care and health research: their adoption for the purposes of rapidly sharing health research findings with clinicians, scientists, and patients. In the era of precision medicine and learning health systems, the translation of research findings into targeted therapies depends on the availability of big data and emerging research results. Web portals may work to promote the availability of novel research, working in tandem with traditional scientific publications and conference proceedings. OBJECTIVE: This study aims to assess the potential use of web portals, which facilitate the sharing of health research findings among researchers, clinicians, patients, and the public. It also summarizes the potential legal, ethical, and policy implications associated with such tools for public use and in the management of patient care for complex diseases. METHODS: This study broadly adopts the methods for scoping literature reviews outlined by Arskey and O'Malley in 2005. Raised by the integration of web portals into patient care for complex diseases, we systematically searched 3 databases, PubMed, Scopus, and WestLaw Next, for sources describing web portals for sharing health research findings among clinicians, researchers, and patients and their associated legal, ethical, and policy challenges. Of the 719 candidate source citations, 22 were retained for the review. RESULTS: We found varied and inconsistent treatment of web portals for sharing health research findings among clinicians, researchers, and patients. Although the literature supports the view that portals of this kind are potentially highly promising, they remain novel and are not yet widely adopted. We also found a wide range of discussions on the legal, ethical, and policy issues related to the use of web portals to share research data. CONCLUSIONS: We identified 5 important legal and ethical challenges: privacy and confidentiality, patient health literacy, equity, training, and decision-making. We contend that each of these has meaningful implications for the increased integration of web portals into clinical care.
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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 | 0.268 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.043 | 0.038 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".