Clinical trial NCT02795806: NLM Scrubber: NLM s Software Application to De-identify Clinical Text Documents
Official title: NLM Scrubber: NLM's Software Application to De-identify Clinical Text Documents
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Personally Identifiable Information
Keywords: Address
Natural History
Lead sponsor: National Institutes of Health Clinical Center (CC)
Sponsor class: NIH
Collaborators: name: National Cancer Institute (NCI)
name: National Institutes of Health Clinical Center (CC)
First posted: 2016-06-10
Enrollment: 50000
Sex: ALL
Minimum age (years): 1
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Everybody for whom a clinical narrative report is created.

Brief summary: Background: Electronic health records contain a vast amount of data about diseases and treatments. Researchers could use this data to test their ideas, but they would need to use records from more than just their own group of patients. But access to those records is restricted to ensure patient privacy.

U.S. National Library of Medicine (NLM) has created a computer tool called NLM Scrubber. This program recognizes and deletes personal information from health records. The researchers who developed this program now need access to the original records. This will allow them to see how well the program removes personal information from patient records and how they can make it more accurate.

Objectives:

To find ways to improve clinical text de-identification.

Eligibility:

No new participants. Researchers will review data that have already been collected.

Design:

Researchers will collect a random sample of reports. These will be from different doctors in different fields.

Researchers will manually remove personal information from the records.

Researchers will also automatically remove personal information from original records using NLM-Scrubber.

Researchers will compare the results of the computer program versus the manual changes. They will note when the program has not been removing personal information correctly. They will also note when the program has been deleting nonpersonal health information incorrectly.

Researchers will use the results to revise the program. They will keep testing it until the de-identification process is complete.

Detailed description: This study is about the quality assessment, improvement, and monitoring of an automatic clinical text de-identification software application called NLM Scrubber, which has been developed at the National Library of Medicine (NLM). The application has been developed so that clinical reports can be used in secondary scientific studies (i.e., for secondary use) without breaching patient privacy. Research on methods for protecting patient privacy and on the development of NLM Scrubber have been conducted by following the guidelines of and in compliance with HIPAA and the Privacy Act.

In order to further develop and improve NLM Scrubber and assess its de-identification performance effectively, the investigators require the original / unredacted samples from all potential clinical report types and sources. To this end, NLM investigators have been

collaborating with entities within NIH, namely, NIH Clinical Center, BTRIS, and NCI as well as outside entities, Kentucky State Registry administered by University of Kentucky and researchers from the University of Pittsburgh, who stated their interest in integrating NLM

Scrubber to their application called Text Information Extraction System. These entities collect samples of various types of clinical reports for assessing and improving NLM Scrubber performance. However we also need access to the original data in order to assess

potential problems and improve the accuracy of NLM Scrubber.

Eligibility criteria: * No new participant enrollment. Researchers will review data that have already been collected.

Primary outcomes:
measure: The rate of de-identification of PII
timeFrame: 01/01/2017-01/31/2027
Secondary outcomes:
measure: The rate of erroneously redacted clinical information
timeFrame: 01/01/2017-01/31/2027
Other outcomes:
Locations:
city: Bethesda
country: United States
facility: National Library of Medicine
state: Maryland
Study officials:
affiliation: National Library of Medicine (NLM)
name: Mehmet M Kayaalp, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Kayaalp M. Patient Privacy in the Era of Big Data. Balkan Med J. 2018 Jan 20;35(1):8-17. doi: 10.4274/balkanmedj.2017.0966. Epub 2017 Sep 13.
citation: Kayaalp M, Browne AC, Dodd ZA, Sagan P, McDonald CJ. De-identification of Address, Date, and Alphanumeric Identifiers in Narrative Clinical Reports. AMIA Annu Symp Proc. 2014 Nov 14;2014:767-76. eCollection 2014.
citation: Kayaalp M, Browne AC, Callaghan FM, Dodd ZA, Divita G, Ozturk S, McDonald CJ. The pattern of name tokens in narrative clinical text and a comparison of five systems for redacting them. J Am Med Inform Assoc. 2014 May-Jun;21(3):423-31. doi: 10.1136/amiajnl-2013-001689. Epub 2013 Sep 11.