NLM Scrubber: NLM's Software Application to De-identify Clinical Text Documents
Trial Snapshot
- Phase
- Not Applicable
- Status
- Enrolling By Invitation
- Sponsor
- Enrollment
- 50,000
- Locations
- 1
- Primary Endpoint
- The rate of de-identification of PII
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 1 Day to — (Child, Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •No new participant enrollment. Researchers will review data that have already been collected.
Exclusion Criteria
- Not provided
Arms & Interventions
1
Everybody for whom a clinical narrative report is created.
Outcomes
Primary Outcomes
The rate of de-identification of PII
Time Frame: 01/01/2017-01/31/2027
HIPAA Privacy Rule defines 18 types of personally identifying information, that need to be de-identified, which include personal names, addresses, significant dates, numeric identifiers (such as social security number). Our annotators label those words and numbers creating a gold standard and NLM-Scrubber tries to recognize and eliminate all of them. The rate of de-identification of PII refers to success of this outcome measure.
Secondary Outcomes
- The rate of erroneously redacted clinical information(01/01/2017-01/31/2027)