Thomas F. Check, MA, and Lorraine M. Fernandes, RHIA, gave this presentation at HIMSS15. Inside you will find info on a number of learning objectives including: 1.Explain how HIE patient-matching technology supports the innovative research infrastructure of NYC-CDRN. 2.Identify privacy issues addressed by HIE participants including how the NYC-CDRN infrastructure supports patient privacy. 3.Describe how consumer, patient consent and other concerns of community stakeholders are addressed. 4.Discuss the value of re-using data from Healthix and the Bronx RHIO including costs and technology infrastructure. 5.Illustrate the information data model’s use within NYC-CDRN and its connection to the PCORnet. Follow @IBM Healthcare on Twitter: https://twitter.com/IBMHealthcare

1. Trust in Regional Exchange
Supports Patient-Centered Research
April 16, 2015
Thomas F. Check,
President & CEO
Healthix, Inc.
Lorraine Fernandes, RHIA,
Global Ambassador, Information Management
IBM
DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2. Thomas F. Check, MA,
No real or apparent conflicts of interest to report
Lorraine M. Fernandes, RHIA
Very limited IBM shareholder
© HIMSS 2015
Conflict of Interest
2

3. 1. Explain how HIE patient-matching technology supports the
innovative research infrastructure of NYC-CDRN.
2. Identify privacy issues addressed by HIE participants including
how the NYC-CDRN infrastructure supports patient privacy.
3. Describe how consumer, patient consent and other concerns
of community stakeholders are addressed.
4. Discuss the value of re-using data from Healthix and the Bronx
RHIO including costs and technology infrastructure.
5. Illustrate the information data model’s use within NYC-CDRN
and its connection to the PCORnet.
Learning Objectives
3

4. Savings
Re-purpose, re-use data and the underlying
HIE infrastructure; potential to expedite
research dissemination and save costs
http://www.himss.org/ValueSuite
Benefits Realized from Health IT in
Patient-Centered Research: STEPS
Satisfaction
Multi-stakeholder collaborative
approach drives governance,
consensus building and trust and
save costs
Treatment/Clinical
Transform research to
generate and disseminate
results more quickly to
inform clinical practice and
benefit patients
Electronic Information/Data
Build on millions of electronic patient
records; innovate solutions to link
patient records across stakeholders
Prevention & Patient Education
Consumer centricity is core
4

5. • Healthix: Enabling Secure Access to Millions of Patient Records
• New York City Clinical Data Research Network (NYC-CDRN)
• Creating a Model of Trust
• Technical Approach
• Progress to Date
• Closing and Key Takeaways
Agenda
5

6. Healthix is a Regional Health
Information Organization (RHIO)
regulated by the New York State
Department of Health
to facilitate the secure exchange
of patient information among
disparate providers to
• support care coordination
• improve clinical outcomes
• promote efficiency
• reduce healthcare costs
Healthix: Enabling Secure Access
to Millions of Patient Records
6

7. Healthix Holds Records of +10 Million
People from Greater New York Area
2 Million Patients Have Given Consent to at Least One Provider
7
150+ healthcare organizations with 550+
facilities, across the continuum of care

8. Healthix Services and Available Data
• Clinical Event Notifications
• Consent Management
• Consulting Services
• EHR Integration and Single
Sign-on
• Patient Record Look-up
• Reporting and Analytics
• Secure Messaging - Direct
• Allergies
• Demographics
• Diagnoses / Problem Lists
• Encounters
• Insurance
• Lab Results
• Medications
• Plans of Care
• Radiology Reports
• Summary Documents
Healthix Services and Available Data
8

9. Bronx RHIO
Creates Universal Interoperability
of Bronx Healthcare Information:
patients’ medical records follow them
throughout their care in the Bronx
Participants Include:
hospitals, health systems, ambulatory
care centers, individual physician offices,
long-term care, home care, and
community organizations
Progress Thus Far:
Consents collected for 865,000
Data on 2.4 million patients
650,000 patients seen at multiple sites
Users accessing data +2,600
70+ Members 200+ Bronx Practices
Supporting population health improvement and delivery system reform
9
Operates a Secure Clinical HIE:
Offers registration alerts, clinical results
delivery, Direct messaging, provider
portal, and advanced analytics

10. New York State’s HIE Landscape
RHIOs in
New York State
are adopting
common standards
to exchange
data in the State
Health Information
Network of
New York
(SHIN-NY)
New York State’s HIE Landscape
10

11. Dec. 2013: Patient-Centered Outcomes Research Institute
(PCORI) awarded contract to Weill Cornell Medical College
for NYC-CDRN to:
New York City Clinical Data Research
Network (NYC-CDRN)
Build
infrastructure
to perform
comparative
effectiveness
research
Facilitate
patient-
centered
research that
can be linked to
a national
network
Collaborate
nationally with
10 other
CDRNS to
develop best
practices and
support joint
studies
Perform two
observational
studies on
diabetes and
cystic fibrosis,
May 2014 –
October 2015
(contract period)
11

12. NYC-CDRN MISSION
12

13. Organization Type NYC-CDRN Members
Health System • Weill Cornell Medical College
• Columbia University College of Physicians and Surgeons
• NewYork-Presbyterian Hospital
• Montefiore Medical Center and Albert Einstein School of Medicine
• Mount Sinai Health System and Icahn School of Medicine
• NYU Langone Medical Center and School of Medicine
• Clinical Directors Network (of FQHCs)
Research Infrastructure • Biomedical Research Alliance of New York (BRANY)
• New York Genome Center (NYGC)
• Cornell NYC Tech Campus
Health Information
Exchange
• Healthix
• Bronx RHIO
Other Stakeholders • American Diabetes Association
• Center for Medical Consumers
• Consumer Reports
• Cystic Fibrosis Foundation
• New York Academy of Medicine
• NYS Department of Health
• Rockefeller University
NYC-CDRN Research Partners
13

14. Goals that Drive NYC-CDRN’s Approach
Create infrastructure
for strong
governance and
business operations
Ensure
accountability
and coordination
Develop
overarching
vision
Establish a legal
foundation that
protects privacy
Build
technical
infrastructure
Embed
research into
practice
Engage
patients and
clinicians
Goals that Drive NYC-CDRN’s Approach
14

15. Prioritizing collaboration, efficiency,
patient and clinician centeredness,
NYC-CDRN turned to Healthix
and the Bronx RHIO as
key resources.
With most of the region’s
health care providers
connected to them,
Healthix and the Bronx RHIO
met technical and privacy challenges.
Health Information Exchange as Key
Enabler of Patient-Centered Research
15

16. It is mutual
trust, even more
than mutual
interest that
holds human
associations
together.
Creating a Model of Trust
- H. L. Mencken
16

17. Health Information Exchange
as a Solution
Master Patient Index (MPI)
of Healthix (IBM-Initiate) and
Bronx RHIO (Optum-Axolotl)
link the patient’s records from
multiple providers into a
de-identified data set
for research while protecting
patient identities
Health Information Exchange
as a Solution
17
NYGC performs patient-centered research using the patient record
aggregated from providers
NYC-CDRN participants then sends fully de-identified data to NYGC

18. Identifying and Minimizing Risk
• Agree on process to de-identify
data that would be sent to a
trusted partner (NYGC)
• Frame the relationship among
all parties and stakeholders
• Establish governance structure
to oversee challenging issues
• Create transparency, especially
for consumers and clinicians
Identifying and Minimizing Risk
In addition to executing Business Associate Agreements,
collaboration was needed to:
18

19. Considerations
Existing Healthix Agreements with its Participants:
• Allow data use primarily for treatment and care coordination.
• Specify guidelines to share data for research.
Recognized need to:
• Build HIE usage into IRB research approval process
• Establish a HIPAA privacy review board
• Draft agreements or amendments between and among
Participants, stakeholders and trusted agents
• Establish parameters for handling research data through HIE
Considerations
19

20. Collaboration and Consensus
Through rigorous governance and collaborative process, NYC-CDRN, RHIOs and
research partners:
• Amended Healthix Participant Agreements to authorize using data for research
in NYC-CDRN
• Designated Biomedical Research Alliance of New York (BRANY) IRB
as HIPAA reviewer, and obtained IRB approval
• Identified NYGC as trusted agent to hold data and house overall technical
infrastructure
• Executed agreements, contracts and sub-contracts
‒ between NYGC and RHIOs documenting processes for transmitting and
protecting data
‒ between NYGC and NYC-CDRN participants
‒ with Weill Cornell (lead investigator and primary contractor with PCORI)
Collaboration and Consensus
20

21. Healthix and the Bronx RHIO Collaborate to:
• Reconcile patient identities across
their Participants
• De-identify data by:
– mapping patient identities to
Proxy IDs
– shifting patient dates
(of birth, encounters and death)
Technical Approaches
21

22. Matching Proxy IDsTechnical Approach
MSMC
NYULMC
NYP
William H.
Ryan
Montefiore /
Einstein
Columbia
Doctors
Charles B.
Wang
CHN
Lutheran
Patient MRNs,
Demographic and
Clinical data &
MRN/Proxy ID
Crosswalk tables
 
22
Healthix
MPI -
InfoSphere
MDM
Bronx
RHIO
(BRIC)
Separate
instance
of
Healthix
MPI

23. Matching Proxy IDsTechnical Approach
Patient MRNs,
Demographic and
Clinical data &
MRN/Proxy ID
Crosswalk tables

File of Matched
Proxy IDs that
identify same
patient
New
York
Genome
Center
(NYGC)
MSMC
NYULMC
NYP
William H.
Ryan
Healthix
MPI -
InfoSphere
MDM
Montefiore /
Einstein
Columbia
Doctors
Charles B.
Wang
CHN
Lutheran
Separate
instance
of
Healthix
MPI
23

Bronx
RHIO
(BRIC)

24. Matching Proxy IDsTechnical Approach
Patient MRNs,
Demographic and
Clinical data &
MRN/Proxy ID
Crosswalk tables
File of Matched
Proxy IDs that
identify same
patient
Date shift values

Separate
instance
of
Healthix
MPI


24
MSMC
NYULMC
NYP
William H.
Ryan
Montefiore /
Einstein
Columbia
Doctors
Charles B.
Wang
CHN
Lutheran

25. Matching Proxy IDsMatching Proxy IDsTechnical Approach
Date shift values

MSMC Research database
NYULMC
Research database
NYP Research database
Charles B. Wang Research
database
Montefiore / Einstein
Research database
Columbia Docs Research
database
Weill Cornell Research database
William H. Ryan Research
database
CHN Research database
Lutheran Research database
Clinical data with
Proxy IDs and
dates shifted

New
York
Genome
Center
(NYGC)
Patient MRNs,
Demographic and
Clinical data &
MRN/Proxy ID
Crosswalk tables
File of Matched
Proxy IDs that
identify same
patient


25

26. New
York
Genome
Center
(NYGC)
Healthix
MPI -
InfoSphere
MDM
Matching Proxy IDsTechnical Approach
MSMC
NYULMC
NYP
William H.
Ryan
Montefiore /
Einstein
Columbia
Doctors
Charles B.
Wang
CHN
Lutheran
Bronx
RHIO
(BRIC)
Separate
instance
of
Healthix
MPI
MSMC Research
database
NYULMC
Research database
NYP Research database
Charles B. Wang
Research database
Montefiore / Einstein
Research database
Columbia Docs Research
database
Weill Cornell Research
database
William H. Ryan
Research database
CHN Research database
Lutheran Research
database
26

27. Patient Healthix: NYULMC
Proxy ID
Healthix:
MSMC Proxy ID
Bronx RHIO:
Montefiore /
Einstein Proxy ID
1 123 456 555
2 789 666
3 987 654
File of matched Proxy IDs indicates which Proxy IDs of Healthix
and Bronx RHIO participants are linked by IBM’s Initiate EMPI as
the same person, but does not contain MRNs, demographic or
clinical data:
Matching Proxy IDsMatching Proxy IDs
Healthix sends this table to NYGC, NOT to the Participants
27

28. Date Shifting to De-identify Data
• HIPAA de-identification requires patient identity not to be deducible by dates
of birth, encounters and death.
• Healthix assigns a random number from 1 to 365 (Date Shift Value) to each
person in the Consolidated Proxy ID File
• Proxy IDs from multiple facilities that represent
the same person receive the same Date Shift Value
• Healthix sends each facility a table indicating the
Date Shift Value for each Proxy ID of that facility
• Facility applies this Date Shift Value to all
records it sends NYGC for that patient
Date Shifting to De-identify Data
Healthix sends this table to Participants, NOT to the NYGC
28

29. Additional Elements
• Confidentiality Code
• Creation Date
• Death Indicator
• Drivers License
• Drivers License State
• Facility
• Insurance Numbers, additional
• Mother’s Birth Name
• Race
• Surviving MRN
Note--Does NOT include all data elements
Select data elements that support patient
linking for Healthix and NYC CDRN
29
Algorithm Considers
• Name
• Address
• Birthday
• Day Phone
• Night Phone
• Facility EID
• Gender
• Insurance Number
• SSN

30. Matching Algorithm Considerations
Fuzzy matches are scored against probabilistic weights based
on value frequencies in the Healthix and Bronx data
Phonetics
Mohammed vs.
Mahmoud
Synonyms
Andrew = Andy
George = Jorge
1st = First
NYC = Queens
Abbreviations
IBM = International
Business Machines
Rd = Road
Concatenation
Van de Velde =
Vandevelde
Misalignment
Kim Jung-il =
Kim il Jung
Edit Distance
867-5309 ~
876-5309
Tokenization
Ibrahim
Mohamed =
Mohamed
Ibrahim
Date Similarity
01/01/1973 ~
01/02/1973
Proximity
Geocodes and
great-circle
distance
Noise Words
IBM Co. =
IBM
Matching Algorithm Considerations
30

31. Match Rates Across NYC CDRN
Health
System A B C D E F
A 2,624 41 65 121 44 22
B 41 404 36 327 42 20
C 65 36 1,049 77 94 54
D 121 327 77 1,249 95 43
E 44 42 94 95 1,147 64
F 22 20 54 43 64 560
O V E R L A P C O U N T (In Thousands)
Match Rates Across NYC-CDRN
31

32. Progress to Date
• Created a separate instance of Healthix MPI; added records from
Bronx RHIO; created file of matched Proxy IDs for NYGC; created
date shift values for Participants
• Currently working to amplify and refresh the data feeds to NYGC
• All research committees, NYC-CDRN and national, are robust,
active and cross pollinating
• Health systems that were not already participating in Healthix
are becoming members
• Existing members are looking at the capabilities of HIE with “fresh
eyes,” buying into HIE and how it can support research use cases
Progress to Date
32

33. Privacy Issues Are Manageable
• De-identification requirements met with Proxy IDs and date shifting
• A trust agent may be key to ensuring privacy, stakeholder concerns
• Research and technology can be the bond for trust today and tomorrow
HIE Linkages Inform Research
• Existing HIE linkages can be re-purposed, saving time and money
• Research use case may require further adjustments to technology
• HIE participation can be expanded, yet isolated
Trust and Collaboration Can Flourish with Innovation
• Use a flexible technology model that can be re-used and adapted
• National initiatives can ignite collaboration and innovation
33
Closing/Key Takeaways




34. Questions
Thomas F. Check,
President and CEO
Healthix, Inc.
40 Worth Street
New York, NY 10013
tcheck@healthix.org / 646-432-3672
Lorraine M. Fernandes, RHIA,
Global HC Industry Ambassador
IBM Information Management
lfernand@us.ibm.com
34

35. Learn More
Click Here to learn more about
Healthcare information
solutions from:
Click Here to learn more about
Healthcare information
solutions from:
35