ArticleJournal of bioethical inquiry2017
Biomedical Big Data: New Models of Control Over Access, Use and Governance.
Article in Journal of bioethical inquiry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The ethical and legal landscape of brain data governance.PloS one · 2022Pooled it
- Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies.BMC medical informatics and decision making · 2020Pooled it
- Access, Equity and Ethical Challenges in Assisted Reproductive Technologies in India: An Empirical Bioethics Analysis.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Justice in the governance of personal health data spaces: stakeholder perspectives in Belgium.BMC medical ethics · 2026Article
- The Changing Concept in the History of Schizophrenia.Brain sciences · 2026Review
- Publishing in the Era of Artificial Intelligence: What Do Medical Research Journals Expect?Cureus · 2026Article
- Digital interactions with the pharmaceutical industry: a qualitative focus group study on the perspectives of rheumatology care providers in Germany.BMC rheumatology · 2026Article
- Beyond the dataset: integrating public voices in data science.Research involvement and engagement · 2026Article
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- Digital Biomarkers and AI for Remote Monitoring of Fatigue Progression in Neurological Disorders: Bridging Mechanisms to Clinical Applications.Brain sciences · 2025Article
- Public Involvement and Engagement in Big Data Research: Scoping Review.Journal of participatory medicine · 2024Article
- Sharing sensitive data in life sciences: an overview of centralized and federated approaches.Briefings in bioinformatics · 2024Review
- Mapping the ethical landscape of digital biomarkers: A scoping review.PLOS digital health · 2024Article
- Personalized Approaches to Cardiovascular Disease: Insights into FDA-Approved Interventions and Clinical Pharmacogenetics.Current pharmaceutical design · 2024Review
- Personal data store ecosystems in health and social care.Frontiers in public health · 2024Article
- Data-driven sudden cardiac arrest research in Europe: Experts' perspectives on ethical challenges and governance strategies.Resuscitation plus · 2023Review
- Towards trust-based governance of health data research.Medicine, health care, and philosophy · 2023Article
- Patients' knowledge, preferences, and perspectives about data protection and data control: an exploratory survey.Frontiers in pharmacology · 2023Article
- Use of an Ingestible, Sensor-Based Digital Adherence System to Strengthen the Therapeutic Relationship in Serious Mental Illness.JMIR mental health · 2022Article
- Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.Proceedings of the ACM on human-computer interaction · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.
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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.