ArticleJournal of the American Medical Informatics Association : JAMIA2016
Preserving temporal relations in clinical data while maintaining privacy.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- The AIR·MS data platform for artificial intelligence in healthcare.JAMIA open · 2025Article
- A real-world data challenge: guidance for aligning data privacy compliance and fit-for-purpose usability.Health affairs scholar · 2025Article
- Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records.NPJ digital medicine · 2025Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Development of Common Data Elements for Organ Transplantation.JAMA network open · 2025Article
- Machine Learning-Based Prediction of No-Show Telemedicine Encounters.Telemedicine reports · 2025Article
- Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM.BMJ health & care informatics · 2024Review
- Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR).Heliyon · 2023Article
- The Detection of Date Shifting in Real-World Data.Applied clinical informatics · 2023Article
- A Path to Real-World Evidence in Critical Care Using Open-Source Data Harmonization Tools.Critical care explorations · 2023Article
- Timing errors and temporal uncertainty in clinical databases-A narrative review.Frontiers in digital health · 2022Review
- Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset.Applied clinical informatics · 2021Article
- Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics.Genetics · 2020Article
- Broadening horizons: the case for capturing function and the role of health informatics in its use.BMC public health · 2019Article
- The role of ethics in data governance of large neuro-ICT projects.Journal of the American Medical Informatics Association : JAMIA · 2018Review
- Secondary Use and Analysis of Big Data Collected for Patient Care.Yearbook of medical informatics · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveMaintaining patient privacy is a challenge in large-scale observational research. To assist in reducing the risk of identifying study subjects through publicly available data, we introduce a method for obscuring date information for clinical events and patient characteristics.
methodsThe method, which we call Shift and Truncate (SANT), obscures date information to any desired granularity. Shift and Truncate first assigns each patient a random shift value, such that all dates in that patient's record are shifted by that amount. Data are then truncated from the beginning and end of the data set.
resultsThe data set can be proven to not disclose temporal information finer than the chosen granularity. Unlike previous strategies such as a simple shift, it remains robust to frequent - even daily - updates and robust to inferring dates at the beginning and end of date-shifted data sets. Time-of-day may be retained or obscured, depending on the goal and anticipated knowledge of the data recipient.
conclusionsThe method can be useful as a scientific approach for reducing re-identification risk under the Privacy Rule of the Health Insurance Portability and Accountability Act and may contribute to qualification for the Safe Harbor implementation.
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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.