Patient Perspectives and Preferences for Consent in the Digital Health Context: State-of-the-art Literature Review
Bibliographic record
Abstract
BACKGROUND: The increasing integration of digital health tools into care may result in a greater flow of personal health information (PHI) between patients and providers. Although privacy legislation governs how entities may collect, use, or share PHI, such legislation has not kept pace with digital health innovations, resulting in a lack of guidance on implementing meaningful consent. Understanding patient perspectives when implementing meaningful consent is critical to ensure that it meets their needs. Consent for research in the context of digital health is limited. OBJECTIVE: This state-of-the-art review aimed to understand the current state of research as it relates to patient perspectives on digital health consent. Its objectives were to explore what is known about the patient perspective and experience with digital health consent and provide recommendations on designing and implementing digital health consent based on the findings. METHODS: A structured literature search was developed and deployed in 4 electronic databases-MEDLINE, IEEE Xplore, Scopus, and Web of Science-for articles published after January 2010. The initial literature search was conducted in March 2021 and updated in March 2022. Articles were eligible for inclusion if they discussed electronic consent or consent, focused on the patient perspective or preference, and were related to digital health or digital PHI. Data were extracted using an extraction template and analyzed using qualitative content analysis. RESULTS: In total, 75 articles were included for analysis. Most studies were published within the last 5 years (58/75, 77%) and conducted in a clinical care context (33/75, 44%) and in the United States (48/75, 64%). Most studies aimed to understand participants' willingness to share PHI (25/75, 33%) and participants' perceived usability and comprehension of an electronic consent notice (25/75, 33%). More than half (40/75, 53%) of the studies did not describe the type of consent model used. The broad open consent model was the most explored (11/75, 15%). Of the 75 studies, 68 (91%) found that participants were willing to provide consent; however, their consent behaviors and preferences were context-dependent. Common patient consent requirements included clear and digestible information detailing who can access PHI, for what purpose their PHI will be used, and how privacy will be ensured. CONCLUSIONS: There is growing interest in understanding the patient perspective on digital health consent in the context of providing clinical care. There is evidence suggesting that many patients are willing to consent for various purposes, especially when there is greater transparency on how the PHI is used and oversight mechanisms are in place. Providing this transparency is critical for fostering trust in digital health tools and the innovative uses of data to optimize health and system outcomes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".