Concept-match medical data scrubbing. How pathology text can be used in research.
BACKGROUND
In the normal course of activity, pathologists create and archive immense data sets of scientifically valuable information. Researchers need pathology-based data sets, annotated with clinical information and linked to archived tissues, to discover and validate new diagnostic tests and therapies. Pathology records can be used for research purposes (without obtaining informed patient consent for each use of each record), provided the data are rendered harmless. Large data sets can be made harmless through 3 computational steps: (1) deidentification, the removal or modification of data fields that can be used to identify a patient (name, social security number, etc); (2) rendering the data ambiguous, ensuring that every data record in a public data set has a nonunique set of characterizing data; and (3) data scrubbing, the removal or transformation of words in free text that can be used to identify persons or that contain information that is incriminating or otherwise private. This article addresses the problem of data scrubbing.


OBJECTIVE
To design and implement a general algorithm that scrubs pathology free text, removing all identifying or private information.


METHODS
The Concept-Match algorithm steps through confidential text. When a medical term matching a standard nomenclature term is encountered, the term is replaced by a nomenclature code and a synonym for the original term. When a high-frequency "stop" word, such as a, an, the, or for, is encountered, it is left in place. When any other word is encountered, it is blocked and replaced by asterisks. This produces a scrubbed text. An open-source implementation of the algorithm is freely available.


RESULTS
The Concept-Match scrub method transformed pathology free text into scrubbed output that preserved the sense of the original sentences, while it blocked terms that did not match terms found in the Unified Medical Language System (UMLS). The scrubbed product is safe, in the restricted sense that the output retains only standard medical terms. The software implementation scrubbed more than half a million surgical pathology report phrases in less than an hour.


CONCLUSIONS
Computerized scrubbing can render the textual portion of a pathology report harmless for research purposes. Scrubbing and deidentification methods allow pathologists to create and use large pathology databases to conduct medical research.
Berman
Jules J
JJ
Pathology Informatics, Cancer Diagnosis Program, National Cancer Institute, National Institutes of Health, Rockville, Md 20892, USA. bermanj@mail.nih.gov
D003205:Computing Methodologies; D003628:Database Management Systems / Q000145:classification / Q000295:instrumentation / Q000600:supply & distribution; D016208:Databases, Factual / Q000145:classification / Q000600:supply & distribution; D006801:Humans; D008498:Medical Record Linkage / Q000295:instrumentation / Q000379:methods; D016347:Medical Records Systems, Computerized / Q000145:classification / Q000295:instrumentation / Q000600:supply & distribution; D008501:Medical Records, Problem-Oriented; D010338:Pathology, Clinical / Q000458:organization & administration*; D013358:Subject Headings; D017432:Unified Medical Language System / Q000145:classification / Q000295:instrumentation / Q000600:supply & distribution