Keynote Presentation: "Patient Matching Decoded: A Proposed National Patient Matching Framework" by Mariann Yeager

1. Patient Matching Decoded
A Framework for Cross-Organizational Patient Identity Management
1 ©Copyright The Sequoia Project. All rights reserved.
Mariann Yeager, MBA
CEO
www.sequoiaproject.org

2. The Sequoia Project is the trusted, independent
convener of industry and government
Works to address the challenges of secure, interoperable
nationwide health data sharing
2
The Sequoia Project’s Role
NATIONWIDESECURE INTEROPERABLE
© 2015 The Sequoia Project. All Rights Reserved.

3. Acting in the Public Interest
As a nonprofit 501 (c) 3 organization operating in the public
interest, our public-private governance process insures
transparent oversight of this work. The Sequoia Project
serves as a neutral, third party convener.
The practical application of our work:
• Enables consensus agreement on the policies and
standards required to reduce barriers to data exchange
• Advances development and continued support for health
data exchange governance frameworks
• Focuses on real-world implementation issues to advance
interoperability
3 © 2015 The Sequoia Project. All Rights Reserved.

4. Current Sequoia Project Initiatives
The eHealth Exchange is the largest and
fastest growing health data sharing network
in the US
Carequality is a public-private collaborative
building consensus on a nationwide
interoperability framework to inter-connect
networks
4 © 2015 The Sequoia Project. All Rights Reserved.

5. The Sequoia Project and Care Connectivity Consortium
(CCC) Strategic Alliance
• CCC is a collaborative of 5
prominent healthcare organizations:
– Geisinger (PA)
– Intermountain Health (UT)
– Kaiser Permanente (CA, OR, WA,
VA, MD, HI, GA, CO)
– Mayo Clinic (MN, FL, AZ, GA, WI)
– OCHIN (17 states)
• CCC enhances capabilities of
current HIE technologies and allows for
sharing between organizations and
health IT systems
• The CCC aids eHealth Exchange
growth by:
– Serving as a test bed for
new technologies
– Contributing innovations to the
eHealth Exchange community
• The CCC participates in
Carequality and serves on its:
– Steering Committee
– Trust Framework Work Group
– Query Work Group
– Operations Work Group
5 © 2015 The Sequoia Project. All Rights Reserved.

6. Interoperability is a Journey
Successes
• Growing pockets of
interoperability
• Enhanced care coordination is
saving lives and money
• Accountable care and customer
demand fuel data sharing
eHealth Exchange and other
networks are growing
Carequality will bridge networks
Challenges
• Still striving for comprehensive
nationwide footprint
• Exchanges need to get faster
• Need to improve format and
usefulness of data
• Difficulties in patient identity
matching and accuracy
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7. Patient Identity Management
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Identifying and correctly matching patient data across disparate systems and points of care
- Without a shared unique patient identifier
- Comparing demographic information about an individual
- Within an enterprise
- Between organizations
- Precedence in other industries (e.g. financial services, credit bureaus, etc.)
- Very different than matching inside an organization
What is it?

8. Why is Patient Matching Such a Challenge?
• Technical
– Heterogeneous technologies and vendors
– Data exchange latency (e.g. timeouts, lack of consent/authorization)
• Data
– Data quality, completeness, consistency
– Different default values (“John Doe”)
• Policy and legal
– Different legal jurisdictions and requirements (such as for minors)
– Different patient matching rules
– Consent, security, sensitive data sharing
• Process
– Different QA and human and system workflows (latency, variations, definitions, etc.)
– Organizational size, resource allocation, project timelines, commitment, skill levels
– Change management
• The result:
– Match rates ACROSS organizations are frequently unacceptable
– 10% to 30% in several cases

9. Exacting Change: Intermountain Case Study
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10. Intermountain Healthcare
Case Study
• Not-for-profit health system
serving Utah and southeast Idaho
• 22 hospitals
• 1,400 employed physicians at
more than 185 clinics
• 750,000 SelectHealth insurance
plan members
• Highly innovative and progressive
• Member of the CCC
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11. Establishing a Baseline
• Sample trial to establish baseline
• 10,000 patients known to have
been treated by Intermountain
and an exchange partner
• High match rate expected
• Patient analysis demonstrated
only 10% true match rate
• Why?
– Data quality / lack of
normalization
Initial Cross-Organizational
Patient Match Error Rate
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Benchmark Trial

12. Offline Line Performance Measurement and Refinement
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Completeness: At what rate is this trait captured and available?
Validity: Is this trait known to be correct as compared to the known true values?
Distinctiveness: Is the trait able to uniquely identify a person? A trait such as administrative gender,
for example, is not associated to a single individual. A trait such as an enterprise master patient index
(EMPI) number is distinctive.
Comparability: Is the trait readily, programmatically, and accurately matched with the same trait at
another organization? An address is an example of a relatively difficult to compare trait, whereas a
social security number (SSN) can be easier to compare.
Stability: How much does the trait remain constant over a patient’s lifetime? Although examples exist
to the contrary, traits such as gender, birth date and SSN are generally consistent over time.

13. Identifying Best Patient Match Attribute
Patient Attributes Analysis
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Attribute Name Completeness Validity Distinctiveness Comparability Stability
EMPI # 100% -- 100% Very High Very High
Last Name 99.85% 99.84% 5.1% Medium High
First Name 99.85% 99.33% 3.1% Medium High
Middle Name 60.54% 60.54% 2.6% Medium High
Suffix Name 0.08% 0.08% 0.08% Medium Medium
SSN 61.40% 60.92% 98.0% High High
Sex (Admin Gender) 99.98% 99.98 0.00008% High High
Date of Birth 98.18% 97.38% 0.8% High Very High
Date of Death 3.36% 3.36% 3.4% High Very High
Street Address
(1 or 2)
95.00% 94.61% 44.4% Low Low
City 94.84% 94.83% 0.8% High Low
State 94.81% 94.39% 0.8% High Low
Facility MRN 99.90% 99.90% 99.90% High Low
Postal Code 92.31% 92.0% 0.6% High Low
Primary Phone
Number
90.68% 87.26% 51.6% High Medium
Work Phone
Number
20.28% 19.79% 51.6% High Low
Ethnicity 25.25% 25.25% 0.0003% High Very High
Race 76.25% 76.25% 0.0001% High Very High

14. Applying Updated Algorithms
Rules Created from Analysis Results
1. First name, last name, date of
birth, gender & telephone
number
2. First name, last name, date of
birth, gender & zip code
3. First name, last name, date of
birth, gender & last 4 digits of SSN
4. First name, last name, date of
birth, administrative gender &
middle name
5. First name, last name, date of
birth, administrative gender &
first character of middle name
Updated Algorithm
Expected Result: Cross-Organizational Patient
Match Error Rate
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This was the expected match
rate for the benchmark trial

15. Examining the Remaining Error Rate
Detailed Analysis of Error Rate
Actual Result: Updated Algorithm and Data Quality
Cross-Organizational Patient Match Error Rate
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Analysis of error rate uncovers
actually 15% errors; other issues
triggered failures but were
unrelated to patient matching
errors

16. Optimizing Patient Identity Management
Additional Strategies for Raising the
Bar Nationwide
• Re-use knowledge
• Apply results of prior work
• Standardize formats is biggest and fastest
opportunity
• Determine minimal acceptable match
rate
• Proactively manage fragile identities (e.g.
with partial info)
• Improve the human workflow
• Involve patients in managing their
identities
• Data integrity (99.99%) requires
supplemental identifier
• Leverage CCC Shared Services
Final Cross-Organizational Patient
Match Error Rate
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17. Is Your Patient Identity Management Working?
Top Questions to Ask Your Organization
• Are our staff trained and actually capturing
high-quality patient identity data?
• Are all our patient demographics data as
complete as possible?
• Are we capturing the telephone type as well
as the number itself?
• How do we handle patient consent with
respect to patient matching?
• More topics covered in the white paper
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18. Cross-Organizational Minimal Acceptable Principles
Overview of Proposed Framework
Traits & Identifiers
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Matching Algorithms Exception Handling

19. Cross-Organizational Maturity Model
Overview of Proposed Framework
Level 0
• Ad hoc
• No oversight
• Unpredictabl
e matching
results
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Level 1
• Basic
processes
• Limited
oversight
• Becoming
predictable
Level 2
• Increasing
algorithm
use
• Quality
metrics
gathered
• Consistent
quality
Level 3
• Advanced
technologies
• Management
controls
quality metrics
• Highly
optimized
Level 4
• Ongoing
optimization
• Active
management
• Innovating
• Leading
industry

20. Next Steps
• Review patient identity management white paper with study findings,
principles and maturity model
• Provide public comment
• Register for the webinar and learn the top 10 things you should be asking
your organization to make strides in patient matching
• Be part of the Sequoia community!
• For more information, or to receive a copy of the paper, email us at:
– admin at sequoiaproject dot org
20 ©Copyright The Sequoia Project. All rights reserved.

21. Questions?
For more information: www.sequoiaproject.org
21 ©Copyright The Sequoia Project. All rights reserved.