Evidence map›Paper›PMID 35896508›Full record

ArticleApplied clinical informatics2022

Real-World Matching Performance of Deidentified Record-Linking Tokens.

Elmer V Bernstam, Reuben Joseph Applegate, Alvin Yu, Deepa Chaudhari, Tian Liu, Alex Coda, Jonah Leshin

Abstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
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  13. Observational
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  15. Review
  16. Review
  17. Article
  18. Privacy preserving record linkage for public health action: opportunities and challenges.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Elmer V BernstamSchool of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, United States.
Reuben Joseph ApplegateSchool of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, United States.
Alvin YuDatavant, Inc., San Francisco, California, United States.
Deepa ChaudhariSchool of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, United States.
Tian LiuDatavant, Inc., San Francisco, California, United States.
Alex CodaDatavant, Inc., San Francisco, California, United States.
Jonah LeshinDatavant, Inc., San Francisco, California, United States.

Funding

Convalescent Plasma to Limit Coronavirus Associated ComplicationsUL1TR003167 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KARP, DANIEL D, MCPHERSON, DAVID D · 2019 to 2023
$45.3M
Harnessing the power of CTSA-CDRN data networks: Using social determinants of health, frailty and functional status to identify at-risk patients and improve risk adjustmentU01TR002393 · NCATS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI SHIREMAN, PAULA K · 2018 to 2021
$3.1M
NCATS NIH HHS U01 TR002393NCATS NIH HHS UL1 TR003167
6 · The paper itself

Abstract

objectiveOur objective was to evaluate tokens commonly used by clinical research consortia to aggregate clinical data across institutions.

methodsThis study compares tokens alone and token-based matching algorithms against manual annotation for 20,002 record pairs extracted from the University of Texas Houston's clinical data warehouse (CDW) in terms of entity resolution.

resultsThe highest precision achieved was 99.9% with a token derived from the first name, last name, gender, and date-of-birth. The highest recall achieved was 95.5% with an algorithm involving tokens that reflected combinations of first name, last name, gender, date-of-birth, and social security number. DISCUSSION: To protect the privacy of patient data, information must be removed from a health care dataset to obscure the identity of individuals from which that data were derived. However, once identifying information is removed, records can no longer be linked to the same entity to enable analyses. Tokens are a mechanism to convert patient identifying information into Health Insurance Portability and Accountability Act-compliant deidentified elements that can be used to link clinical records, while preserving patient privacy.

conclusionDepending on the availability and accuracy of the underlying data, tokens are able to resolve and link entities at a high level of precision and recall for real-world data derived from a CDW.

Indexed as

ConfidentialityPrivacyAlgorithmsHumans

Identifiers

PMID35896508
PMCPMC9474266

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.