Benefits and Risks in Secondary Use of Digitized Clinical Data: Views of Community Members Living in a Predominantly Ethnic Minority Urban Neighborhood
Bibliographic record
Abstract
BACKGROUND: There is potential to increase the speed of scientific discovery and implement personalized health care by using digitized clinical data collected on the patient care experience. The use of these data in research raises concerns about the privacy and confidentiality of personal health information. This study explored community members' views on the secondary use of digitized clinical data to (1) recruit participants for clinical studies; (2) recruit family members of persons with an index condition for primary studies; and (3) conduct studies of information related to stored biospecimens. METHODS: A qualitative descriptive design was used to examine the bioethical issues outlined from the perspective of urban-dwelling community members. Focus groups were used for data collection, and emergent content analysis was employed to organize and interpret the data. RESULTS: Thirty community members attended one of four focus groups ranging in size from 4 to 11 participants. Five critical themes emerged from the focus-group material: (1) perceived motivators for research participation; (2) objective or "real-life" barriers to research participation; (3) a psychological component of uncertainty and mistrust; (4) preferred mechanisms for recruitment and participation; and (5) cultural characteristics that can impact understanding and willingness to engage in research. CONCLUSIONS: The overriding concern of community members regarding research participation and/or secondary clinical and nonclinical use of digitized information was that their involvement would be safe and the outcome would be meaningful to them and to others. According to participants, biospecimens acquired during routine clinical visits or for research are no longer possessions of the participant. Although the loss of privacy was a concern for participants, they preferred that researchers access their personal health information using a digitized clinical file rather than through a paper-based medical record.
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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 | 0.036 | 0.203 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.012 |
| 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; both teacher heads agree on what is shown here.
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".