1. De-identification using Harvard Scrubber Umit Topaloglu, Ph.D.

2. Agenda Introduction Harvard Scrubber at UAMS Methodology De-identification Sample References Questions

3. Introduction EPF JCAPS HL7 Msg. Folder Original Messages Research Portal WEB-GUI Transform Messages Backup Messages Pulling Concepts Indexing Concepts Storing De-id Reports Harvard Scrubber caTissue De-id Reports

4. DEIDENTIFICATION at UAMS Harvard Scrubber (SPIN scrubber) [1] Open Source De-identification software package. Efficiency: An evaluation showed that it’s efficiency was greater than 98% at removing HIPAA identifiers [2]. Customizable through the regular expression list. Specifications: Operating System: Platform independent. Programming Language: Java. License: GNU GPL. Any restrictions to use by non-academics: None. Project Home Page: http://spin.nci.nih.gov.http://spin.nci.nih.gov

5. Harvard Scrubber at UAMS The Cancer Text Information Extraction System (caTIES) [3] Harvard Scrubber is the default de-identification package. Bundled with caTIES v3.6 Customization: Mirth Connect [4] is used to extract and mark the Patient Health Information (PHI) in the message based on the HL7 header. (e.g. [[ name ]] Added ~ 15 regular expression rules in addition to the build-in expressions. A pathologist name list is added, which is constantly updated by our Tissue Bank.

6. Methodology HL7 Pipeline De-id Pipeline (Harvard Scrubber) Tie Pipeline Remove PHI based on HL7 header Remove PHI pattern Remove Pathologists’ Name Regular Expression List Pathologist Name List De-identification Processes: 1.Remove PHI known to be associated with patient based on the HL7 header. E.g. Name, MRN, Accession #, etc. 2.Predictable PHI patterns removal using a series of regular expression clauses. E.g. SSN=[^a-z^A-Z^0- 9]+[0-9]{3}-[0-9]{2}-[0-9]{4} 1.Remove pathologist names that exists in the pathologist name list. caTIES

7. De-identification Sample [CLINICAL DATA] CLINICAL DATA: The patient is a female; abnormal uterine bleeding. Date of operation: xxxDATE7xxx (yr:2009) Name of operation: Endometrium biopsy. Preoperative diagnosis: Not given. xxxx PROVIDER_NAME xxxx DOCTOR_NAME xxxx [DIAGNOSIS] DIAGNOSIS: A) Endometrium, biopsy: Disordered proliferative pattern endometrium; no hyperplasia or carcinoma identified. [SPECIMENS SUBMITTED] SPECIMENS SUBMITTED: ENDOMETRIUM BIOPSY [GROSS DESCRIPTION] GROSS DESCRIPTION: Specimen A is received in formalin labeled with the patient's name [ xxxx PATIENT_NAME4 xxxx ] [ xxxx PATIENT_NAME4 xxxx ] medical record xxxx MRN xxxx and ENDOMETRIAL BIOPSY. The specimen consists of multiple fragments of red-brown, soft tissue cores measuring 2.0 x 1.8 x 0.4 cm in aggregate. The specimen is submitted entirely in 1 yellow cassette labeled A1. xxxx PATHOLOGYISTL1 xxxx xxxx PATHOLOGYISTF1 xxxx xxxDATE7xxx (yr:2009) Electronically signed on xxxDATE7xxx (yr:2009) xxxx TIME1 xxxx * I have reviewed the pertinent gross findings, any and all microscopic slides and the Resident's/ Fellow's interpretations. I have made appropriate editorial changes and have rendered the final diagnosis.

8. References 1.Beckwith BA, Mahaadevan R, Balis UJ, Kuo F. Development and evaluation of an open source software tool for deidentification of pathology reports. BMC Med Inform Decis Mak 2006;6:12. 2.Drake TA, Braun J, Marchevsky A, Kohane IS, Fletcher C, Chueh H, et al.: A system for sharing routine surgical pathology specimens across institutions: the Shared Pathology Informatics Network. 2007, 38(8):1212. 3.http://caties.cabig.upmc.edu/Wiki.jsp?page=Homehttp://caties.cabig.upmc.edu/Wiki.jsp?page=Home 4.http://www.mirthcorp.com/community/mirth-connecthttp://www.mirthcorp.com/community/mirth-connect

9. Thanks for listening Questions? Contact information: Umit Topaloglu Ph.D. utopaloglu@uams.edu 501- 686 - 7238