1. De-identifying Pathology Reports for Pathology Informatics
James Gardner, Li Xiong
Department of Math and Computer Science
Fusheng Wang, Andrew Post, Joel Saltz
Center for Comprehensive Informatics 1

2. Introduction
The HIPAA Privacy Rule regulates the use and disclosure of Protected Health Information (PHI)
De-identification of pathology reports is of critical importance in order to facilitate secondary use of medical records for research
HIDE (Health Information DE-identification) is an open- source de-id tool based on advanced statistical based de- identification technologies
While statistical learning based techniques have shown promising results for de-identification purposes, few such systems are publicly available.
A comprehensive study evaluating the effects of different feature sets and potential impacts of sampling on extracting PHI from pathology reports.

3. HIPAA Identifiers These identifiers have to be removed or
1. Names;
2. All geographical subdivisions smaller than a state;
3. All elements of dates (except year);
4. Phone numbers;
5. Fax numbers;
6. Electronic mail addresses;
7. Social Security numbers;
8. Medical record numbers;
9. Health plan beneficiary numbers;
10. Account numbers;
11. Certificate/license numbers;
12. Vehicle identifiers and serial numbers;
13. Device identifiers and serial numbers;
14. Web Universal Resource Locators (URLs);
15. Internet Protocol (IP) address numbers;
16. Biometric identifiers, including finger and voice prints;
17. Full face photographic images or comparable images; and
18. Any other unique identifying number, characteristic, or code
These identifiers have to be removed or
Based on the opinion from an qualified statistical expert, the risk of identifying an individual is very small

4. HIDE Overview
Utilizes the state-of-the-art named entity recognition technique, Conditional Random Fields, for extracting PHI
Previous tools such as DE-ID and HMS scrubber use rule- based approaches which are labor intensive and not portable
Provides flexible de-identification options including full de- identification and state-of-the-art statistical de-identification
Previous tools allow simple removal or substitution of the PHI
Provides an easy-to-use web-based interface that utilizes the latest web-technologies
Integrated with caTIES, and caTissue (in progress)

5. PHI Extraction
Utilizes state-of-the-art NLP technique, Conditional Random Fields
High accuracy, easy to train, portable
Combines different feature sets and sampling techniques
Feature sets: dictionary, affix, regular expression and context
Can use default models or custom trained models
Web interface for annotating and training custom models
A set of reports are loaded and manually labeled
The labeled documents will generate a trained model for automatically de-identifying new reports

6. HIDE: De-identification Options
Full de-identification
safe-harbor, all 18 HIPAA identifiers removed or substituted
Partial de-identification
limited dataset, all direct HIPAA identifiers removed or substituted(not for dates, address other than street/P.O.Box)
Configurable de-identification
A configurable set of identifiers removed or substituted
Statistical de-identification
Advanced anonymization that guarantees rigorous statistically acceptable privacy while keeping the utility of the data
An example of utility from statistical de-id results?

7. Statistical De-identification Example
De-identification satisfying k-anonymity (k=2) (every record is indistinguishable in a group of records with size greater than or equal to k)

8. Study 1: PHI Extraction on Emory Pathology Reports
The CRF classifier with a good feature set achieves good attribute extraction accuracy
(100 reports,10-fold cross validation)
Precision: true positives over the sum of true positives and false positives
Recall (sensitivity): true positives over total actual positives
F1: combination: 2*precision*recall/(precision+ recall)

9. Study 2: PHI Extraction on i2b2 Reports
Based on 669 discharge summaries, 10-fold cross validation
Good precision and recall for most individual PHI identifiers
Good overall precision and recall for PHI extraction
I2b2: Informatics for Integrating Biology and the Bedside

10. Study 3: Impact of Different Feature Sets
The context features, the previous word, next work, etc., are the most important.
Regular expression features;
Affix features – prefix and suffix;
Dictionary features (phrase or token);
Context features: previous, next words, and occurrence counts
Dictionary (d), affix (a), regular expression (r) and context (c) features are in order of increasing importance for statistical CRF based PHI extraction

11. Integrating HIDE with caTIES
caTIES (cancer Text Information Extraction System) provides tools for de-identification and automated coding of free-text pathology reports
caTIES provides de-id extensibility through implementing its CaTIES_DeIdentifier interface
HIDEDeIdentifier, which calls HIDE client API
Added HIDE de-id option in caTIES installer
HIDE is bundled with caTIES since release v3.7 (May )

12. Integrating HIDE with caTissue (in Progress)
caTissue uses caTIES V2.x and refactored it into caTissue’s workflow
HIDE integration with caTissue is similar to caTIES
Implementation and evaluation under going
Goal: Integration of pathology reports into caTissue installation at Winship Cancer Institute at Emory University

13. Continue development on HIDE/caTissue integration
Ongoing Development
Continue development on HIDE/caTissue integration
Usability improvement: simplified installation progress
System improvements
Efficiency and scalability of the system
Multiple file formats support
Additional statistical de-identification options

14. HIDE Demo

15. Li Xiong (lxiong@mathcs.emory.edu)
Thank you
Li Xiong