ArticleJournal of the American Medical Informatics Association : JAMIA2013
Federated queries of clinical data repositories: the sum of the parts does not equal the whole.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- Privacy-preserving data sharing infrastructures for medical research: systematization and comparison.BMC medical informatics and decision making · 2021Article
- Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories.Bioinformatics (Oxford, England) · 2021Article
- Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation.Journal of medical Internet research · 2020Article
- Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation.BMC medical informatics and decision making · 2017Article
- Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision.Yearbook of medical informatics · 2016Review
- Absence of evidence for increase in risk for autism or attention-deficit hyperactivity disorder following antidepressant exposure during pregnancy: a replication study.Translational psychiatry · 2016Article
- Federated queries of clinical data repositories: Scaling to a national network.Journal of biomedical informatics · 2015Article
- Article
- Query Health: standards-based, cross-platform population health surveillance.Journal of the American Medical Informatics Association : JAMIAArticle
- Changing the research landscape: the New York City Clinical Data Research Network.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
BACKGROUND AND
objectiveIn 2008 we developed a shared health research information network (SHRINE), which for the first time enabled research queries across the full patient populations of four Boston hospitals. It uses a federated architecture, where each hospital returns only the aggregate count of the number of patients who match a query. This allows hospitals to retain control over their local databases and comply with federal and state privacy laws. However, because patients may receive care from multiple hospitals, the result of a federated query might differ from what the result would be if the query were run against a single central repository. This paper describes the situations when this happens and presents a technique for correcting these errors.
methodsWe use a one-time process of identifying which patients have data in multiple repositories by comparing one-way hash values of patient demographics. This enables us to partition the local databases such that all patients within a given partition have data at the same subset of hospitals. Federated queries are then run separately on each partition independently, and the combined results are presented to the user.
resultsUsing theoretical bounds and simulated hospital networks, we demonstrate that once the partitions are made, SHRINE can produce more precise estimates of the number of patients matching a query.
conclusionsUncertainty in the overlap of patient populations across hospitals limits the effectiveness of SHRINE and other federated query tools. Our technique reduces this uncertainty while retaining an aggregate federated architecture.
Indexed as
Identifiers
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.