ArticleJournal of personalized medicine2021
Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Pooled it
- Preferences for genetic testing among populations underrepresented in genomic research: a systematic review.European journal of human genetics : EJHG · 2025Pooled it
- Translating genomic data into healthcare practice with the Singapore National Precision Medicine program.Nature genetics · 2026Review
- Capturing attitudes towards research and data sharing in down syndrome (CARDS-DS): Piloting a novel parent-report measure.Social sciences & humanities open · 2026Article
- Attitudes Towards the Use of Artificial Intelligence in Healthcare: A Conjoint Analysis Survey in Singapore.Asian bioethics review · 2026Article
- An Ethical Analysis of Public Attitudes towards Controlled Human Infection Studies in Singapore: Acceptability and Payment.Asian bioethics review · 2026Article
- A survey of public attitudes toward secondary research governance oversight: Evidence from Singapore's TRUST platform.International journal of population data science · 2026Article
- "Your Tumour Can Save Lives": Re-examining Singapore's Consent Procedures for the Use of De-identified Bio-specimens in Research.Asian bioethics review · 2025Article
- Enabling secure and self determined health data sharing and consent management.NPJ digital medicine · 2025Review
- Worldwide willingness to share health data high but privacy, consent and transparency paramount, a meta-analysis.NPJ digital medicine · 2025Article
- The Public Knowledge of Precision Medicine and Genomic Research: A Survey in the Aosta Valley.Journal of personalized medicine · 2025Article
- Stakeholders' perceptions of personal health data sharing: A scoping review.PLOS digital health · 2024Article
- Cross-jurisdictional Data Transfer in Health Research: Stakeholder Perceptions on the Role of Law.Asian bioethics review · 2024Article
- Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust.Healthcare (Basel, Switzerland) · 2023Article
- An ethical code for collecting, using and transferring sensitive health data: outcomes of a modified Policy Delphi process in Singapore.BMC medical ethics · 2023Article
- Personalization and localization as key expectations of digital health intervention in women pre- to post-pregnancy.NPJ digital medicine · 2023Article
- Patient Perspectives and Preferences for Consent in the Digital Health Context: State-of-the-art Literature Review.Journal of medical Internet research · 2023Review
- Public preferences towards data management and governance in Swiss biobanks: results from a nationwide survey.BMJ open · 2022Article
- Conjoint Analysis: A Research Method to Study Patients' Preferences and Personalize Care.Journal of personalized medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundPrecision medicine (PM) programs typically use broad consent. This approach requires maintenance of the social license and public trust. The ultimate success of PM programs will thus likely be contingent upon understanding public expectations about data sharing and establishing appropriate governance structures. There is a lack of data on public attitudes towards PM in Asia.
methodsThe aim of the research was to measure the priorities and preferences of Singaporeans for sharing health-related data for PM. We used adaptive choice-based conjoint analysis (ACBC) with four attributes: uses, users, data sensitivity and consent. We recruited a representative sample of
resultsOf the 1000 respondents, 52% were female and majority were in the age range of 40-59 years (40%), followed by 21-39 years (33%) and 60 years and above (27%). A total of 64% were generally willing to share de-identified health data for IRB-approved research without re-consent for each study. Government agencies and public institutions were the most trusted users of data. The importance of the four attributes on respondents' willingness to share data were: users (39.5%), uses (28.5%), data sensitivity (19.5%), consent (12.6%). Most respondents found it acceptable for government agencies and hospitals to use de-identified data for health research with broad consent. Our sample was consistent with official government data on the target population with 52% being female and majority in the age range of 40-59 years (40%), followed by 21-39 years (33%) and 60 years and above (27%).
conclusionsWhile a significant body of prior research focuses on preferences for consent, our conjoint analysis found consent was the least important attribute for sharing data. Our findings suggest the social license for PM data sharing in Singapore currently supports linking health and genomic data, sharing with public institutions for health research and quality improvement; but does not support sharing with private health insurers or for private commercial use.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.