An overview of the i2b2 clinical research platform, and the implications of connecting Indivo to i2b2 as a source of patient-reported outcomes. Presented at the 2012 Indivo X Users' Conference. By Shawn Murphy MD, Ph.D., Partners Healthcare.

1. Secondary Use of Healthcare
Data for Translational Research
Shawn Murphy MD, Ph.D.
Indivo Conference June 18, 2012

2. Example: PPARγ Pro12Ala and Diabetes
Oh et al.
Deeb et al.
Mancini et al.
Clement et al.
Hegele et al.
Sample size
Hasstedt et al.
Lei et al.
Ringel et al.
Hara et al.
Meirhaeghe et al. Overall P value = 2 x 10-7
Douglas et al.
Altshuler et al.
Mori et al.
Odds ratio = 0.79 (0.72-0.86)
All studies
Estimated risk 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2.0
(Ala allele) 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9
Ala is protective Courtesy J. Hirschhorn

3. High Throughput Methods for supporting Translational
Research
 Setof patients is selected from medical record data in a high
throughput fashion
 Investigatorsexplore phenotypes of these patients using i2b2
tools and a translational team developed to work specifically
with medical record data
 Distributed
networks cross institutional boundaries for
phenotype selection, public health, and hypothesis testing
 Working with Indivo for advancement of patient participation in
Translational Research

4. High Throughput Methods for supporting Translational
Research
 Setof patients is selected from medical record data in a high
throughput fashion
 Investigatorsexplore phenotypes of these patients using i2b2
tools and a translational team developed to work specifically
with medical record data
 Distributed
networks cross institutional boundaries for
phenotype selection, public health, and hypothesis testing
 Working with Indivo for advancement of patient participation in
Translational Research

5. Research Patient Data Registry exists at Partners
Healthcare to find patient cohorts for clinical research
Query construction in web tool
1) Queries for aggregate patient numbers
- Warehouse of in & outpatient clinical data De-
- 6.0 million Partners Healthcare patients identified
- 1.5 billion diagnoses, medications,
procedures, laboratories, & physical findings
Data
coupled to demographic & visit data Warehouse
- Authorized use by faculty status
- Clinicians can construct complex queries Z731984X
Z74902XX
- Queries cannot identify individuals, internally ...
can produce identifiers for (2) ...
Encrypted identifiers
2) Returns identified patient data OR
0000004
2185793
- Start with list of specific patients, usually from (1) ...
0000004
2185793
- Authorized use by IRB Protocol ...
...
- Returns contact and PCP information, demographics, ...
providers, visits, diagnoses, medications, procedures,
Real identifiers
laboratories, microbiology, reports (discharge, LMR,
operative, radiology, pathology, cardiology, pulmonary,
endoscopy), and images into a Microsoft Access
database and text files.

6. Organizing data in the Clinical Data Warehouse
Star schema Binary
Concept DIMENSION Patient DIMENSION
concept_key
concept_text
search_hierarchy
patient_key
patient_id (encrypted)
sex
Tree
Patient-Concept FACTS
age
patient_key birth_date
concept_key race
start_date deceased
end_date ZIP
Encounter DIMENSION practitioner_key
encounter_key
encounter_key value_type
encounter_date numeric_value
hospital_of_service textual_value
abnormal_flag Pract . DIMENSION
practitioner_key start
name search
service
.16 6.0
150 .06
1500 million

7. FINDING PATIENTS
Query items Person who is using tool
Query construction
Results - broken down by number distinct of patients

9. High Throughput Methods for supporting Translational
Research
 Setof patients is selected from medical record data in a high
throughput fashion
 Investigatorsexplore phenotypes of these patients using i2b2
tools and a translational team developed to work specifically
with medical record data
 Distributed
networks cross institutional boundaries for
phenotype selection, public health, and hypothesis testing
 Working with Indivo for advancement of patient participation in
Translational Research

10. The National Center for Biomedical Computing entitled
Informatics for Integrating Biology and the Bedside (i2b2),
what is it?
 Software for explicitly organizing and transforming person-
oriented clinical data to a way that is optimized for clinical
genomics research
 Allows integration of clinical data, trials data, and genotypic data
A portable and extensible application framework
 Software is built in a modular pattern that allows additions without
disturbing core parts
 Available as open source at https://www.i2b2.org

11. i2b2 Cell: The Canonical Software Module
Business Logic i2b2
Data Access
Data Objects
HTTP XML
(minimum: RESTful)

12. An i2b2 Environment (the Hive) is built from i2b2
Cells
“Hive” of software services
provided by i2b2 cells
B
patient_dimension
PK Patient_Num
1 observation_fact 1
PK
visit_dimension
Encounter_Num
A patient_dimension
PK Patient_Num
Birth_Date
Death_Date
Vital_Status_CD
1
PK
PK
observation_fact
Patient_Num
∞ PK Encounter_Num
Concept_CD ∞
1
PK
visit_dimension
Encounter_Num
Start_Date
End_Date
Active_Status_CD
Location_CD*
Start_Date PK Observer_CD ∞
PK Patient_Num
End_Date
Age_Num* ∞ PK Start_Date
Birth_Date ∞ PK Encounter_Num Gender_CD*
∞ ∞ observer_dimension
Death_Date PK Concept_CD ∞ Active_Status_CD Race_CD*
PK Modifier_CD
Vital_Status_CD Location_CD* PK Instance_Num
PK Observer_CD ∞ Ethnicity_CD* PK Observer_Path
Age_Num* ∞ PK Start_Date End_Date
Gender_CD*
Race_CD*
PK Modifier_CD ∞ ∞ observer_dimension ValType_CD Observer_CD
PK Instance_Num TVal_Char Name_Char
Ethnicity_CD* PK Observer_Path NVal_Num
concept_dimension
End_Date ValueFlag_CD ∞
ValType_CD Observer_CD Observation_Blob modifier_dimension
PK Concept_Path
C
TVal_Char Name_Char
NVal_Num ∞ PK Modifier_Path
concept_dimension Concept_CD
ValueFlag_CD ∞ Name_Char
Observation_Blob modifier_dimension Modifier_CD
PK Concept_Path
Name_Char
∞ PK Modifier_Path visit_dimension
Concept_CD
patient_dimension
Name_Char Modifier_CD PK Encounter_Num
PK Patient_Num
1 observation_fact 1
Name_Char
Start_Date
PK Patient_Num
End_Date
Birth_Date ∞ PK Encounter_Num
Death_Date PK Concept_CD ∞ Active_Status_CD
Vital_Status_CD Location_CD*
PK Observer_CD ∞
Age_Num* ∞ PK Start_Date
Gender_CD*
Race_CD*
PK Modifier_CD ∞ ∞ observer_dimension
PK Instance_Num
Ethnicity_CD* PK Observer_Path
End_Date
ValType_CD Observer_CD
TVal_Char Name_Char
NVal_Num
concept_dimension
ValueFlag_CD ∞
PK Concept_Path Observation_Blob modifier_dimension
∞
local
PK Modifier_Path
Concept_CD
Name_Char Modifier_CD
Name_Char
visit_dimension
patient_dimension
PK Encounter_Num
PK Patient_Num
1 observation_fact 1
Start_Date
PK Patient_Num
End_Date
Birth_Date ∞ PK Encounter_Num
Death_Date PK Concept_CD ∞ Active_Status_CD
Vital_Status_CD Location_CD*
PK Observer_CD ∞
Age_Num* ∞ PK Start_Date
Gender_CD*
Race_CD*
PK Modifier_CD ∞ ∞ observer_dimension
PK Instance_Num
Ethnicity_CD*
Data
PK Observer_Path
End_Date
ValType_CD Observer_CD
TVal_Char Name_Char
NVal_Num
concept_dimension
ValueFlag_CD ∞
PK Concept_Path Observation_Blob modifier_dimension
∞
Model
PK Modifier_Path
Concept_CD
Name_Char Modifier_CD
Name_Char
remote
Data Repository Cell

13. I2b2 Software components are distributed as open source

14. Set of patients is selected through Enterprise Repository
and data is gathered into a data mart
Selected
patients
Project
Data directly Data from other Data imported
Specific
EDR from EDR sources specifically for Phenotypic
project
Data
Automated Queries search for Patients and add Data

15. Interogation can occur through i2b2 web client

16. Data is available through the i2b2 Workbench

17. Team support for Projects
Local sources
Ex: BICS
RPDR RPDR Local
EDC
Mart Clinical
Final
Project
DB
Analyst Biostatistician Programmer
Project Manager Local data extract analyst
RPDR Support Programmers

18. Natural Language Processing
SOCIAL HISTORY: The patient is married with four grown daughters,
Smoker
uses tobacco, has wine with dinner.
SOCIAL HISTORY: The patient is a nonsmoker. No alcohol.
Non-Smoker
SOCIAL HISTORY: Negative for tobacco, alcohol, and IV drug abuse.
BRIEF RESUME OF HOSPITAL COURSE:
Past Smoker
63 yo woman with COPD, 50 pack-yr tobacco (quit 3 wks ago), spinal stenosis, ...
SOCIAL HISTORY: The patient lives in rehab, married. Unclear smoking history
from the admission note… ???
HOSPITAL COURSE: ... It was recommended that she receive …We also added Lactinax, oral
form of Lactobacillus acidophilus to attempt a repopulation of her gut.
Hard to pick
SH: widow,lives alone,2 children,no tob/alcohol. Hard to pick

19. NLP Workflow
I2b2 Project Investigators
NLP Specialists

20. Research Investigator Workflow enabled by mi2b2
Query is done Derive new
To find patients data from images
Use i2b2
Request
Study
Images with
Images
Accession #’s
BIRN/XNAT
mi2b2
Images
Retrieved
from Clinical
PACS

21. Builds complex “Custom Study” displays

22. Ontology-driven data organization allows simplistic data
models that paste together
i2b2 DB
[ Enterprise Project 1
Shared
Data ]
i2b2 DB
Shared data Ontology
Project 2
of Project 1
Consent/Tracking
of Project 2 Security
i2b2 DB
Project 3
of Project 3

23. Custom views supports clinical trials in i2b2
A Patient-Centered View is welcomed by clinical researchers
when they review patients for clinical trials
 The Patient-Centered View can be focused to support specific
needs of each clinical trial review
 New Apps can be written to assist eligibility determination
during the viewing of a patient

24. SMART Addition to i2b2
1.
SMART Connect
2. REST
SMART

25. A “Patient-Centered View” enabled by SMART

26. Up close …

27. Patient-Centered View can be edited by user

28. High Throughput Methods for supporting Translational
Research
 Setof patients is selected from medical record data in a high
throughput fashion
 Investigatorsexplore phenotypes of these patients using i2b2
tools and a translational team developed to work specifically
with medical record data
 Distributed
networks cross institutional boundaries for
phenotype selection, public health, and hypothesis testing
 Working with Indivo for advancement of patient participation in
Translational Research

29. Community
United States International
 Arizona State University  Georges Pompidous Hospital, Paris, France
 Beth Israel Deaconness Hospital, Boston, MA  Institute for Data Technology and Informatics (IDI), NTNU, Norway
 Boston University School of Medicine, Boston, MA  Karolinska Institute, Sweden
 Brigham and Women's Hospital, Boston, MA  University of Erlangen-Nuremberg, Germany
 Case Western Reserve Hospital  University of Goettingen, Goettingen, Germany
 Children's Hospital, Boston, MA  University of Leicester and Hospitals, England (Biomed. Res. Informatics Ctr. for
 (Denver) Children's Hospital, Denver, CO Clin. Sci)
 Children's Hospital of Philadelphia, PA  University of Pavia, Pavia, Italy
 Childrens's National Medical Center (GWU)  University of Seoul, Seoul, Korea
 Cincinnati Children's Hospital, Cincinnati, OH
 Cleveland Clinic, Cleveland, OH
 (Weil Medical College of) Cornell, NYC, NY
 Duke Medical College
 Group Health Cooperative
 Harvard Pilgrim Healthcare
 Harvard Medical School, Boston, MA
 Health Sciences South Carolina
 Kaiser Permanente Health
 Kimmel Cancer Center (Thomas Jefferson University)
 Massachusetts General Hospital, Boston, MA
 Maine Medical Center, Portland, ME
 Marshfield Clinic, Wisconsin
 Morehouse School of Medicine, Atlanta, GA
 Ohio State University Medical Center, Columbus, OH
 Oregon Health & Science University, Portland, OR
 Renaissance Computing Institute, Chapel Hill, NC
 South Carolina Clinical and Translational Research Institute
 Tufts Medical Center, Boston, MA
 University of Alabama
 University of Arkansas Medical School
 University of California Davis, Davis, CA
 University of California San Francisco, SF, CA
 University of Chicago
 University of Massachusetts Medical School, Worcester, MA
 University of Michigan Medical Center, Ann Arbor, MI
 University of Pennsylvania School of Medicine, Philadelphia, PA
 University of Rochester Medical Center, Rochester, NY
 University of Texas Health Sciences Center at Houston, Houston, TX
 University of Texas Health Sciences Center at San Antonio, SA, TX
 University of Texas Health Sciences Center Southwestern, Dallas, TX
 Utah Health Science Center, Salt Lake City, UT
 University of Washington, Seattle, WA
 University of Wisconsin Madison
 Veterans Administration Boston and Utah

30. Aggregating across 4 hospitals, 3 i2b2 instances
SHRINE (Shared Research Informatics Network)
= Distributed Queries

31. Clinical data in SHRINE
 10 years (2001-2011)
 4 hospitals
 6 million total patients
 >1 billion medical observations
 Demographics
 Diagnoses (ICD9-CM)
 Medications (RxNorm)
 Labs (LOINC)

32. 2012

34. Query Health = I2b2 queries distributed to hospitals
that may or may not be hosting i2b2

35. High Throughput Methods for supporting Translational
Research
 Setof patients is selected from medical record data in a high
throughput fashion
 Investigatorsexplore phenotypes of these patients using i2b2
tools and a translational team developed to work specifically
with medical record data
 Distributed
networks cross institutional boundaries for
phenotype selection, public health, and hypothesis testing
 Working with Indivo for advancement of patient participation in
Translational Research

36. Integration of i2b2 and Indivo
INDIVO
PATIENT
PUBLISH
DATA
FILTER
PATIENT
MATCH

37. Key new components
PATIENT Allows patients permission to be attached
MATCH to their records in i2b2
DATA Allows only specified parts of the i2b2
FILTER record on the patient to be viewed by the
patient
PATIENT Allows patients to publish their outcomes
PUBLISH to i2b2 = WRITE BACK TO i2b2

38. Advantages for Patients
 Able to see data on themselves from medical record
 Important to learn lessons form Beth Israel – Microsoft Healthvault
venture
 Integration can span across several institutions
 May be able to take advantage of SHRINE infrastructure
 SMART will allow integrated view to focus on patient’s
illnesses and concerns
 Indivo may provide a way to return research results to patients

39. Advantages for Research
 Indivo makes it possible to collect Patient Reported Outcomes
 Can allow engagement of patient for recruitment for Clinical
Trials

40. Collaborators
 RPDR  Medical Imaging (mi2b2)
 Eugene Braunwald  Christopher Herrick
 John Glaser  David Wang
 Diane Keogh  Bill Wang
 Henry Chueh
 i2b2 Driving Biology Projects
 I2b2 and SMART  Vivian Gainer
 Isaac Kohane  Victor Castro
 Susanne Churchill  Raul Guzman
 Griffin Weber  Robert Plenge
 Michael Mendis  Scott Weiss
 Nich Wattanasin  Stan Shaw
 Vivian Gainer  John Brownstein
 Lori Phillips  Qing Zeng
 Rajesh Kuttan  Guergana Savova
 Wensong Pan
 Janice Donahue  Query Health
 William Simons (SHRINE)  Jeff Klann
 Andy McMurry (SHRINE)
 Doug McFadden (SHRINE)
 Ken Mandl (SMART)
 Josh Mandel (SMART)