# Presentation: 372565

## Open Source Deidentification Software - The SPIN/VSL Experience

- Bruce Beckwith, MD
- Beth Israel Deaconess Medical Center
- Harvard Medical School

## The Tissue Challenge

**Notes:**
Paraffin embedded tissue is increasingly useful in biomedical research as molecular methods improve 
There are millions of paraffin embedded human tissue samples in storage in pathology departments across the country
These post-diagnostic excess tissues are well-characterized by pathologist examination
The pathology reports are available electronically in laboratory information systems

>50 tissue repositories at HMS affiliated institutions
Includes frozen and paraffin embedded tissue
Associated clinical information varies
No standard information storage 
No easy way for investigators to learn about or search these tissue resources

## The Privacy Challenge

- Protection of research subjects
- Institutional Review Board
- HIPAA
- Common rule

## The SPIN/VSL Solution

- Query
- Tool

- HTTPS

- HMS
- Node

- BWH
- Node

- CHMC
- Node

- BIDMC
- Node

- MGH
- Node

- Distributed databases containing de-identified clinical data

## Shared Pathology Informatics Network (SPIN)

- NCI initiative
- 5 year demonstration project
- 2 consortia
- Harvard/UCLA
- Indiana/Pittsburgh
- Built functioning network
- Proof of concept tissue studies ongoing

## Specific Challenges

- Integrate heterogeneous data sources
- Allow local control of information
- Respect patient privacy
- Comply with federal regulations
- Obtaining cooperation of various institutions
- Coordinating the IRB’s of the different HIPAA covered entities
- Signing up tissue repositories

**Notes:**
Pathology reports exist in multiple formats depending upon the LIS and local preferences

Unify access to HMS tissue repositories
Extend SPIN tools to create production network
Cross-institution project
Dana Farber Harvard Cancer Center funded
A DFHCC core facility

## Harvard VSL Network

- BIDMC	      318,883
- BWH	      428,226
- MGH	      100,777
- CHMC	  	23,205
- Total: 	   > 850,000 cases
- Live June 2005

## Slide 10

## Information Pipeline

- Extract pathology reports from LIS
- Convert from the local format into the SPIN XML format
- Remove identifying information
- Automatically code important medical concepts
- Load into local “node” database

## Local view of institution

- SPIN
- Node

- Node
- Tools

- Pathology

- Clinical

- MPI

- UPDATE

- Institutional
- Systems

- Network

**Institutional**
**Firewall**

**Internal**
**Threshold**

## Deidentification

- Pathology reports always contain identifiers
- Header information is trivial to remove since it resides in well defined fields
- Identifying information embedded in text of pathology reports is difficult to completely remove

**Notes:**
Critical step in the pipeline

This is very analogous to spam filtering!

## HIPAA and Deidentification

- 18 categories of information defined
- If all of this information is removed, then it is no longer considered Protected Health Information (PHI)
- Certain non-identifying information may be left in
- Ages (<90 years)
- Locations (state, country)

**Notes:**
All individually identifying information must be removed AND there must be NO REASONABLE BASIS to believe that the person could be identified from the remaining information

## HIPAA Identifiers

- Names
- ALL geographic subdivisions smaller than the state
- All elements of dates smaller than a year
- Ages over 89
- Phone/Fax numbers
- E-mail addresses
- SS numbers
- Medical record number
- Health plan beneficiary number
- Any other account numbers

- Certificate/license numbers
- Vehicle identifiers
- Device identification numbers
- WEB URL's
- Internet IP address
- Biometric identifiers (fingerprint, voice prints, retina scan, etc)
- Full face photographs or comparable images
- Any other unique number, characteristic or code

## Pre-existing Options

- Pittsburgh De-ID
- Proprietary
- Berman – concept match scrubbing
- Alters text
- Other tools had not been tested upon pathology reports

## HMS Scrubber

- An open source software tool for removing direct identifiers from text of pathology reports
- Modular design which is easy to modify
- Multiple development cycles
- Final testing on 1800 cases (600 each from BIDMC, MGH and BWH)

**Notes:**
Designed our own scrubber since no other ones available for use
Open Source – freely usable by anyone without licensing fees
Tuned for use on pathology reports

## Scrubber Design

- Input in SPIN/VSL XML format
- Remove identifiers specified in the header (name, mrn, accession number, etc.)

## Example XML

**Notes:**
The identifiers are easy to interpret elements.  These elements are not loaded into any nodes!
Note the duplication of the text fields

## Scrubber Design

- Input in SPIN/VSL XML format
- Remove identifiers specified in the header (name, mrn, accession number, etc.)
- Search for information based on predictable patterns
- Dr. Xxxx
- Mrs. Yyyy
- Nn/nn/nnnn
- Dates, accession numbers
- Use a list of prohibited words or phrases
- Names, locations, etc

## Regular Expresssions

- About 50 currently
- Predictable patterns to locate potential identifiers
- Tweaked based on training sets
- May not work well on other datasets

## Names Lists

- Census Names
- _[http://](http://www.census.gov/genealogy/names/names_files.html)__[www.census.gov/genealogy/names/names_files.html](http://www.census.gov/genealogy/names/names_files.html)_
- Census Gazetteer
- _[http://](http://www.census.gov/geo/www/gazetteer/gazette.html)__[www.census.gov/geo/www/gazetteer/gazette.html](http://www.census.gov/geo/www/gazetteer/gazette.html)_
- Optional sources
- Institution specific lists
- Other

## Scrubber Performance

|  | Dept. A | Dept. B | Dept. C | Total |
| --- | --- | --- | --- | --- |
| Reports | 600 | 600 | 600 | 1800 |
| Reports with any identifier | 415 | 239 | 600 | 1254 |
| Unique identifiers | 1079 | 338 | 2082 | 3499 |
| Unique identifiers per report | 1.8 | 0.6 | 3.5 | 1.9 |

- BMC Med Inform Decis Mak 2006 6:12

**Notes:**
OVERSCRUBBING: Abbreviations CD20, L4-5, HMB-45
Words that are also person or place names: Brown, white, tan, toto
UNDERSCRUBBING: Misspelled names, accession numbers, ages

## Distribution of Identifiers

## Scrubber Performance

|  | Dept. A | Dept. B | Dept. C | Total |
| --- | --- | --- | --- | --- |
| Reports | 600 | 600 | 600 | 1800 |
| Reports with any identifier | 415 | 239 | 600 | 1254 |
| Unique identifiers | 1079 | 338 | 2082 | 3499 |
| Unique identifiers per report | 1.8 | 0.6 | 3.5 | 1.9 |
| Unique identifiers removed | 1057 | 320 | 2062 | 3439 |
| Unique identifiers remaining, total | 22 | 18 | 20 | 60 |
|      Unique HIPAA identifiers     remaining | 11 | 1 | 7 | 19 |
| % Unique identifiers removed | 98.0% | 94.7% | 99.0% | 98.3% |

- BMC Med Inform Decis Mak 2006 6:12

**Notes:**
OVERSCRUBBING: Abbreviations CD20, L4-5, HMB-45
Words that are also person or place names: Brown, white, tan, toto
UNDERSCRUBBING: Misspelled names, accession numbers, ages

## BMC Med Inform Decis Mak 2006 6:12

| Identifier | Identifier Type | In-house Cases | Consult Cases | Total |
| --- | --- | --- | --- | --- |
| Accession number | HIPAA | 0 | 10 | 10 |
| Pt name misspelled | HIPAA | 5 | 2 | 7 |
| Pt name correctly spelled | HIPAA | 0 | 0 | 0 |
| Medical record number | HIPAA | 1 | 0 | 1 |
| Date | HIPAA | 1 | 0 | 1 |
| HIPAA subtotal |  | 7 | 12 | 19 |
| Institution address, partial | Non-HIPAA | 0 | 17 | 17 |
| Age <90 | Non-HIPAA | 16 | 0 | 16 |
| Health care organization name | Non-HIPAA | 0 | 6 | 6 |
| Doctor name | Non-HIPAA | 1 | 1 | 2 |
| Non-HIPAA subtotal |  | 17 | 24 | 41 |
| Grand total | HIPAA and Non-HIPAA | 24 | 36 | 60 |

**Notes:**
>99% of HIPAA identifiers removed
Performance varied by institution
Style differences important
Consult cases the most problematic
Need to continually validate to catch changes in style
This scrubber may be easily modified to handle other types of reports

Accession numbers are problematic due to variety of formats in use
Misspellings hard to correct, but easy for reader to interpret
Some institutions routinely dictate personal identifiers into the text of reports, especially the gross descriptions
Scrubber needs to be customized to particular institution

## Overscrubbing

|  | Dept. A | Dept. B | Dept. C | Total |
| --- | --- | --- | --- | --- |
| Unique Identifiers removed | 1057 | 320 | 2062 | 3439 |
|  |  |  |  |  |
| Unique Overscrubs | 1126 | 961 | 2584 | 4671 |
| Unique Overscrubs   per report | 1.9 | 1.6 | 4.3 | 2.6 |
|  |  |  |  |  |
| % of unique phrases removed that were identifiers

 | 48.4% | 25.0% | 44.4% | 42.4% |

- BMC Med Inform Decis Mak 2006 6:12

## Version 1 Scrubber

- Written in Java
- Requires input in SPIN XML format
- Requires JDOM and MySQL
- Relatively hard to implement

## Version 2

- Unrestricted input format
- Increased modularity and flexibility
- Only requires Java
- Faster
- However, not yet tested in production

## Another Open Source Scrubber

- Indiana University (Gunther Schadow)
- Used for loading their SPIN node
- http://aurora.regenstrief.org/~schadow/text/
- Would like to combine code in future

## Scrubber Summary

- >99% of HIPAA identifiers removed
- Performance varied by institution
- Style differences important
- Consult cases the most problematic
- Need to continually validate to catch changes in style
- This scrubber may be easily modified to handle other types of reports

## SPIN/VSL Accomplishments

- Designed open source peer to peer network for medical data sharing
- Defined standard XML schema for representing pathology information
- Created software which allows for safe use of information from pathology reports
- Built national demonstration network
- Fully approved functional network launched at Harvard in 2005

**Notes:**
Cheap, easy to implement
Can be used for much more than pathology data

Demonstration project ends May 2006
Future funding is unknown at this point
Even if not specifically refunded, the tools will be utilized in CaBIG project

Cancer BioInformatics Grid (CaBIG)
NCI funded effort to develop multiple reusable tools that can interoperate over a network
Comprehensive project with vast scope
SPIN/VSL tools and technology are being used as a starting point for parts of CaBIG

## Acknowledgements

- VSL Core Directors
- Isaac Kohane (CH)
- Chris Fletcher (BWH)
- VSL Team
- Connie Gee (DF)
- Frank Kuo (BWH)
- Ulysses Balis (MGH)
- Antonio Perez-Atayde (CH)
- Andrew McMurry (CH)
- Raji Mahaadevan (HMS)
- Elizabeth Sands (MGH)

- SPIN
- MGH Lab of Computer Science
- Henry Chueh,
-  Roger Berkowitz,
- Ana Holzbach
- Indiana
- Clem McDonald
- Gunther Schadow
- Univ. of Pittsburgh
- Michael Becich
- Rebecca Crowley
- UCLA
- Jonathan Braun
- Tom Drake
- And Many Others!

**Notes:**
There are too many people who have helped to be able to list them all here

## Websites

- Shared Pathology Informatics Network
- _[http://](http://spin.nci.nih.gov/)__[spin.nci.nih.gov](http://spin.nci.nih.gov/)_
- HMS Scrubber
- Version 1 (For SPIN format XML only)
- http://spin.nci.nih.gov/SPIN/content
- Version 2 (Unresticted input format)
- https://sourceforge.net/projects/spin-chirps