Evidence map›Paper›PMID 27405787›Full record

ArticleMethods of information in medicine2016

Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification.

David S Carrell, David J Cronkite, Bradley A Malin, John S Aberdeen, Lynette Hirschman

Abstract read
In one paragraph

Article in Methods of information in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Scalable Iterative Classification for Sanitizing Large-Scale Datasets.IEEE transactions on knowledge and data engineering · 2017
    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

5 authors.

David S CarrellDavid S. Carrell, PhD, Group Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, USA, E-mail: carrell.d@ghc.org.
David J Cronkite
Bradley A Malin
John S Aberdeen
Lynette Hirschman

Funding

Variation, Function, and Disease Supplement ProgramU01HG008657 · NHGRI · UNIVERSITY OF WASHINGTON · PI David Russell Crosslin, Gail Pairitz Jarvik · 2015 to 2026
$13.4M
Scalable and Robust Clinical Text De-Identification ToolsR01LM011366 · NLM · KAISER FOUNDATION HEALTH PLAN OF WASHINGTON · PI CARRELL, DAVID S. · 2012 to 2014
$827k
NHGRI NIH HHS U01 HG008657NLM NIH HHS R01 LM011366
6 · The paper itself

Abstract

backgroundClinical text contains valuable information but must be de-identified before it can be used for secondary purposes. Accurate annotation of personally identifiable information (PII) is essential to the development of automated de-identification systems and to manual redaction of PII. Yet the accuracy of annotations may vary considerably across individual annotators and annotation is costly. As such, the marginal benefit of incorporating additional annotators has not been well characterized.

objectivesThis study models the costs and benefits of incorporating increasing numbers of independent human annotators to identify the instances of PII in a corpus. We used a corpus with gold standard annotations to evaluate the performance of teams of annotators of increasing size.

methodsFour annotators independently identified PII in a 100-document corpus consisting of randomly selected clinical notes from Family Practice clinics in a large integrated health care system. These annotations were pooled and validated to generate a gold standard corpus for evaluation.

resultsRecall rates for all PII types ranged from 0.90 to 0.98 for individual annotators to 0.998 to 1.0 for teams of three, when meas-ured against the gold standard. Median cost per PII instance discovered during corpus annotation ranged from $ 0.71 for an individual annotator to $ 377 for annotations discovered only by a fourth annotator.

conclusionsIncorporating a second annotator into a PII annotation process reduces unredacted PII and improves the quality of annotations to 0.99 recall, yielding clear benefit at reasonable cost; the cost advantages of annotation teams larger than two diminish rapidly.

Indexed as

Cost-Benefit AnalysisData MiningElectronic Health RecordsHumansPatient Identification Systemscost analysisdata sharingnatural language processingPatient data privacy

Identifiers

PMID27405787
PMCPMC5194214

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Registered trials

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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.