Personally Identifiable InformationNCT02795806
NLM Scrubber: NLM s Software Application to De-identify Clinical Text Documents
An observational study of Personally Identifiable Information, run by National Library of Medicine (NLM) at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 1 day and older
- Conditions the study is for
- Personally Identifiable Information
- How many people
- 50,000 participants
- Where
- 1 siteUnited States
- Study dates
- 25 May 2016to 31 Jan 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 day and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Everybody for whom a clinical narrative report is created.
- No new participant enrollment. Researchers will review data that have already been collected.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: NLM Scrubber: NLM's Software Application to De-identify Clinical Text Documents
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.
Read more
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.
Read the 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.
- Conditions
- Personally Identifiable Information
- Keywords
- Address, Natural History
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 1 | Everybody for whom a clinical narrative report is created. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The rate of de-identification of PII
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.
Measured: 01/01/2017-01/31/2027
Secondary outcomes (1)
The rate of erroneously redacted clinical information
While NLM-Scrubber tries to eliminate only PII elements while preserving non-identifying study data, it inadvertently deletes some of the non-identifying study data elements (non-protected health information) as well. The rate of erroneously redacted clinical information refers to the failure of NLM-Scrubber in preserving non-identifying health information.
Measured: 01/01/2017-01/31/2027
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jun 2016 (estimated)
- Study start25 May 2016
- Primary completion31 Jan 2027 (estimated)
- Study completion31 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Personally Identifiable InformationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 50,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Library of Medicine (NLM)
- Sponsor type
- US National Institutes of Health
- Collaborators
- National Cancer Institute (NCI), National Institutes of Health Clinical Center (CC)
- Responsible party
- Sponsor
- Investigators
- Mehmet M Kayaalp, Ph.D., Principal investigator, National Library of Medicine (NLM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02795806
- Sponsor's protocol ID
- 999916122
- Other IDs
- 16-LM-N122
- Status last verified
- 16 Dec 2025
- First submitted
- 9 Jun 2016
- Last updated
- 26 Oct 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We receive patient data, protected health information (PHI), from our collaborating data sources with the promise that we would protect PHI to the full extent and not share it with third parties.
Publications
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.
PubMed 28903886Kayaalp 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.
PubMed 25954383Kayaalp 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.
PubMed 24026308
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- NLM Scrubber: NLM's Software Application to De-identify Clinical Text Documents
- ClinicalTrials.gov ID
- NCT02795806
- Acronym / sponsor ID
- 999916122
- Conditions
- Personally Identifiable Information
- Keywords
- Address, Natural History
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- National Library of Medicine (NLM)US National Institutes of Health
- Collaborators
- National Cancer Institute (NCI), National Institutes of Health Clinical Center (CC)
- Study start
- 25 May 2016
- Primary completion
- 31 Jan 2027 (estimated)
- Study completion
- 31 Jan 2027 (estimated)
- First posted
- 10 Jun 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 1 | – | –Everybody for whom a clinical narrative report is created. |
Who may qualify
- Ages
- 1 day and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Everybody for whom a clinical narrative report is created.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
1 secondary outcome
The rate of erroneously redacted clinical information
Time frame: 01/01/2017-01/31/2027
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Oct 2026.
- Background: PubMed 28903886Kayaalp 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.
- Background: PubMed 25954383Kayaalp 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.
- Background: PubMed 24026308Kayaalp 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.
- Individual participant dataSharing planned: No. We receive patient data, protected health information (PHI), from our collaborating data sources with the promise that we would protect PHI to the full extent and not share it with third parties.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Mehmet M Kayaalp, Ph.D. principal investigator
National Library of Medicine (NLM)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Personally Identifiable Information, run by National Library of Medicine (NLM) at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 26 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The rate of de-identification of PII (time frame: 01/01/2017-01/31/2027). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02795806, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Oct 2026.