Patient perspectives on the linkage of health data for research: Insights from an online patient community questionnaire
Bibliographic record
Abstract
OBJECTIVE: To examine the patient perspective on the risks and benefits of linking existing data sources for research. MATERIALS AND METHODS: Between December 2015 and February 2016, we fielded a questionnaire in PatientsLikeMe, an online patient community representing over 2500 health conditions. The questionnaire was developed using subject matter expertise and patient feedback from a concept elicitation phase (N = 57 patients). The final questionnaire consisted of 37 items. RESULTS: Of n = 5741 who opened the email invitation, n = 3516 respondents completed the questionnaire (61.2%). Of these, 73.8% were women, 86.4% were Caucasian, 14.5% were 65 or older, and 44.9% had completed college or post-graduate education. Questionnaire respondents indicated that the most important benefits of sharing data were "helping my doctor make better decisions about my health" (94%) and "helping make new therapies available faster" (94%). The most important data sharing risk identified was health data being "stolen by hackers" (87%). Of 693 patients who were not comfortable with researchers accessing their de-identified data, most reported that their comfort levels would increase if they were able to learn how their data was protected (84%). In general, responders felt more comfortable when unique identifiers such as social security number (90%) and insurance ID (82%) were removed from the data for linkage and research use. DISCUSSION: The majority of patients in a US-based online community are comfortable with researchers accessing their de-identified data for research purposes. CONCLUSIONS: Developing methods to link databases minimizing the exposure of unique identifiers may improve patient comfort levels with linking data for research purposes.
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 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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".