ArticleEGEMS (Washington, DC)2019
Assessing and Minimizing Re-identification Risk in Research Data Derived from Health Care Records.
Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis 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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ethical issues in biomedical research using electronic health records: a systematic review.Medicine, health care, and philosophy · 2021Pooled it
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- LLM research on public biosignals data is needed to protect patients.NPJ digital medicine · 2026Review
- Generalizable multilingual medical text anonymization using generative instruction tuning.Communications medicine · 2026Article
- Re-identification risk for common privacy preserving patient matching strategies when shared with de-identified demographics.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Digital lifelines, data landmines: digital mental health tools for regional and urban families in an age of surveillance.Frontiers in digital health · 2026Article
- A 7-criteria evaluation approach for the responsible use of synthetic medical data.BJR artificial intelligence · 2026Article
- When algorithms infer gender: revisiting computational phenotyping with electronic health records data.Biology of sex differences · 2025Review
- Article
- A survey on UK researchers' views regarding their experiences with the de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets.Clinical trials (London, England) · 2025Article
- Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2025Review
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
- Using publicly available, interactive epidemiological dashboards: an innovative approach to sharing data from the Rakai Community Cohort Study.JAMIA open · 2024Article
- Private commercial companies sharing health-relevant consumer data with health researchers in sub-Saharan Africa: an ethical exploration.Policy studies (Policy Studies Institute) · 2024Article
- Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage.JAMA network open · 2024Article
- Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact.PLOS digital health · 2024Article
- [Re-identification potential of structured health data].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Review
- Privacy-preserving analysis of time-to-event data under nested case-control sampling.Statistical methods in medical research · 2024Article
- Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.The American journal of bioethics : AJOB · 2023Article
- US Residents' Preferences for Sharing of Electronic Health Record and Genetic Information: A Discrete Choice Experiment.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundSharing of research data derived from health system records supports the rigor and reproducibility of primary research and can accelerate research progress through secondary use. But public sharing of such data can create risk of re-identifying individuals, exposing sensitive health information.
methodWe describe a framework for assessing re-identification risk that includes: identifying data elements in a research dataset that overlap with external data sources, identifying small classes of records defined by unique combinations of those data elements, and considering the pattern of population overlap between the research dataset and an external source. We also describe alternative strategies for mitigating risk when the external data source can or cannot be directly examined.
resultsWe illustrate this framework using the example of a large database used to develop and validate models predicting suicidal behavior after an outpatient visit. We identify elements in the research dataset that might create risk and propose a specific risk mitigation strategy: deleting indicators for health system (a proxy for state of residence) and visit year. DISCUSSION: Researchers holding health system data must balance the public health value of data sharing against the duty to protect the privacy of health system members. Specific steps can provide a useful estimate of re-identification risk and point to effective risk mitigation strategies.
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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.