NLM Scrubber: NLM s Software Application to De-identify Clinical Text Documents

Study Overview
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Brief Summary
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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
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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.

Official Title
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NLM Scrubber: NLM's Software Application to De-identify Clinical Text Documents

Conditions
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Personally Identifiable Information

Participation Criteria
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Eligibility Criteria
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No new participant enrollment. Researchers will review data that have already been collected.

Ages Eligible for Study
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Minimum Age: 1 Day

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| 1<br>Everybody for whom a clinical narrative report is created. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 01/01/2017-01/31/2027 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 01/01/2017-01/31/2027 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Address, Natural History