Patient Matching Decoded A Framework for Cross-Organizational Patient Identity Management

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Mariann Yeager, MBA CEO

www.sequoiaproject.org

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

NATIONWIDE SECURE INTEROPERABLE

© 2015 The Sequoia Project. All Rights Reserved.

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.

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

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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

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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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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?

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

Exacting Change: Intermountain Case Study

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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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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

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.

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

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

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

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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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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Cross-Organizational Minimal Acceptable Principles

Overview of Proposed Framework

Traits & Identifiers

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Matching Algorithms Exception Handling

Cross-Organizational Maturity Model

Overview of Proposed Framework

Level 0 • Ad hoc

• No oversight

• Unpredictable 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

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

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Questions?

For more information: www.sequoiaproject.org

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