3 Easy Steps to Improve Your Patient
Matching Accuracy and Reduce Your
Backlog of Potential Duplicates
Joaquim Neto, VP of Healthcare, Verato
Agenda
• High-level overview of Verato
• Learn why patient matching is critical but challenging
• Discuss how matching challenges are magnified at HIEs
• Explain why MPIs can help, but they face their own
challenges, including backlogs of potential duplicates that
must be manually resolved
• Learn about “Referential Matching” and how it can
improve an MPI’s matching capabilities in 3 easy steps
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Verato Overview
Verato offers a cloud-based platform that
enables HIEs, health systems, and payers to:
Turbocharge the matching
performance of existing
master patient indexes (MPIs)
Link patient or member
information across databases
and enterprises
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Verato Overview
Verato offers a cloud-based platform that
enables HIEs, health systems, and payers to:
Turbocharge the matching
performance of existing
master patient indexes (MPIs)
Link patient or member
information across databases
and enterprises
Today’s focus
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Awards and Accolades
“Verato is tackling a
big market opportunity
with an innovative
approach that seems
to be unique in the
industry.”
“Verato is uniquely
positioned in a
growing market. More
importantly, they
tackle an urgent realworld pain point.”
Select Customers
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Patient matching is critical across the health care industry
“All organizations that match electronic health information must have an internal
duplicate record rate of no more than 2% at the end of 2017.”
- ONC Nationwide Interoperability Roadmap
“A patient match error could result in significant patient safety events, corrupt an
organization’s medical records, and put lives at risk.”
- AHIMA White Paper: “Patient Matching in Health Information Exchanges”
“A nationwide patient data matching strategy will assist in matching patient records
in the HIE, as well as improve clinical care delivery, decrease the cost of duplicative
diagnostic tests, link clinical results, provide accurate data for analytics, underpin
research efforts, and establish a foundation for patient-centric care delivery.”
- AHIMA White Paper: “Patient Matching in Health Information Exchanges”
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Accurate patient matching is even more critical within an HIE
An HIE’s Core Services
Analytics/PopHealth
PHR/Consumer Apps
Record Locator Service
Notifications/Alerts
Information Exchange
Accurate Patient Matching
All of an HIE’s core
services rely on highly
accurate patient matching
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Identity data is the key to patient matching
Name
Name
Health
Record
Health
Record
DoB
Addr.
DoB
Addr.
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Identity data is the key to patient matching
✓
Name
Health
Record
Health
Record
DoB
Addr.
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But a person’s identity data changes over time and is often
entered incorrectly
Changes over time
Maiden name
Ambiguities
Name change
Address change
Phone change
Email change
Jr/Sr overlap
Twins
Hispanic and Asian
naming conventions
Default SSN
Missing email
2000 2010 2020
Mis-typed
birthdate
Errors
Incompleteness
Spelling error
Transcription error
Typing error
Homonym error
Missing data
Default entries
Sparse data
Old address
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In fact, 30-40% of health records have errors in their identity data
Changes over time
Name change
Address change
Phone change
Email change
12%
change per
year
Ambiguities
25% of adult
population
Jr/Sr overlap
Twins
Hispanic and Asian
naming conventions
2000 2010 2020
Errors
Incompleteness
Spelling error
Transcription error
Typing error
Homonym error
6% of data
5% of data
Missing data
Default entries
Sparse data
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This makes accurate patient matching very challenging
Health
Record
Health
Record
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This makes accurate patient matching very challenging
No Match
✗
Health
Record
Health
Record
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This challenge is magnified at HIEs for 3 reasons
1
Large, diverse, and geographically
concentrated patient populations
2
Different data governance and quality
standards across participating providers
3
Continuous onboarding of provider sites
and increased adoption of services
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Reason 1: Large, diverse, and geographically concentrated patient
populations
DIVERSITY
CONCENTRATION
Different cultures have
different naming conventions
The same patient may visit many
different providers.
Matching difficulty
SIZE
Patient pop. size
Matching difficulty rises
exponentially with patient
population size
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Reason 2: Different data governance and quality standards across
participating providers
Case study: Two providers at a large, multi-county
HIE covering over 3 million patients collected
patient data differently at registration
Hospital A
Hospital B
Patient information is collected
from a drivers license.
Patient information is
self-reported.
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Reason 3: Continuous onboarding of provider sites and increased
adoption of services
HIEs experience two phases of onboarding, both of which present matching challenges.
Phase 1
Onboarding
new provider
sites
Phase 2
New provider
sites adopt
HIE’s services
More
patients to
match
More scrutiny
of matching
accuracy
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Some HIEs have invested in best-in-class MPI technologies to help
Others rely on MPIs that are baked into their HIE platforms.
OPTION 1: Rely on
baked-in MPI
OPTION 2: Invest in
a best-in-class MPI
Cost
Cost-effective
Expensive
Effort
Low
High
Matching
Accuracy
Low
Medium
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But even best-in-class MPIs face challenges
MASTER PATIENT INDEX
Matching Engine
Index
Incoming
Identity
Data Stewardship Interface
=
?
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Challenge 1: A Backlog of Potential Duplicates
MASTER PATIENT INDEX
Matching Engine
Index
Incoming
Identity
Data Stewardship Interface
=
?
Potential duplicates either (1) aren’t being generated, (2) aren’t being
resolved, or (3) are only being addressed during periodic clean ups.
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Challenge 2: Existing Duplicates
MASTER PATIENT INDEX
Matching Engine
Index
Incoming
Identity
Some
duplicates
exist that the
MPI has not
even flagged
for manual
resolution.
Data Stewardship Interface
=
?
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Challenge 3: New Duplicates Being Created
MASTER PATIENT INDEX
Matching Engine
Index
Incoming
Identity
New duplicates may be
created when identity data
entered at registration is
recently updated,
misspelled, mistyped, or
incomplete.
Data Stewardship Interface
=
?
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HIEs can “surround” their MPIs with Verato to overcome these
challenges in 3 easy steps
MASTER PATIENT INDEX
Matching Engine
Index
Incoming
Identity
Data Stewardship Interface
=
?
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Step 1: Reduce your backlog of potential duplicates by automating
data stewardship
MASTER PATIENT INDEX
Matching Engine
Index
Incoming
Identity
Data Stewardship Interface
=
?
Automatically resolve 50-75% of tasks
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Step 2: Discover Duplicates Your MPI Has Missed
MASTER PATIENT INDEX
Index
Incoming
Identity
Data Stewardship Interface
=
?
Discover missed duplicates
Matching Engine
Automatically resolve 50-75% of tasks
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Step 3: Validate Incoming Identities
Matching Engine
Index
Data Stewardship Interface
=
?
Discover missed duplicates
Incoming
Identity
Validate incoming identity data
MASTER PATIENT INDEX
Automatically resolve 50-75% of tasks
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Verato leverages a totally new,
next-generation approach to
patient matching called
Referential Matching.
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Verato Referential Matching leverages a comprehensive selflearning database of US identities called CARBON™
CARBON
EXTENSIVE
Over 300M identities with historical data
CURRENT
Over 60M identity updates/month
AUTHORITATIVE
Combines databases from credit, telco, and gov’t
SELF-CORRECTING
Every new data update corrects errors in previous data
SELF-LEARNING
Every query makes CARBON smarter
INTELLIGENT
CARBON contains more “metadata” than data
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CARBON creates composite identities using identity fragments
from commercially available sources
CARBON
These big data databases
aggregate identity data from
various sources:
Credit header data
Telco record identity data
Gov’t & legal record identity data
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Verato uses this database as a reference to match two identities
even if they contain very different data
CARBON
✓Match
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Example: An existing patient registers with a married name and
new address
Matching Engine
✗No Match
N: Jane Smith
A: 123 Main St.
DOB: 2/3/1980
✗
✗
✓
N: Jane Jones
A: 456 Elm Rd.
DOB: 2/3/1980
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Example: An existing patient registers with a married name and
new address
Verato Referential Matching
CARBON
✓
N: Jane Smith
A: 123 Main St.
DOB: 2/3/1980
✓
✓
Names:
Jane Smith
Jane Jones
Addresses:
123 Main St.
456 Elm Rd.
✓
✓
✓
N: Jane Jones
A: 456 Elm Rd.
DOB: 2/3/1980
DOBs:
2/3/1980
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Success Story: Using AUTO-STEWARD at SDHC
Challenge
• EMPI with 3.2M patients from a range of
providers across San Diego
• The EMPI had auto-linked 244K of those records
• But the EMPI had 187K data stewardship tasks
awaiting manual review and resolution
Solution
• Verato AUTO-STEWARD automatically resolved
142K data stewardship tasks
• Verato also found an additional 127K duplicates
the MPI had missed.
Benefits
• Automated 75% of data stewardship tasks
• Increased total MPI links by 110%
• 16,000 man-hours saved
75%
reduction
45K tasks
187K data
stewardship
tasks
110%
more MPI
links
512K total
MPI links
244K total
MPI links
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Healthix is a publicly funded HIE that connects
hundreds of healthcare organizations and
thousands of facilities across NYC and Long Island.
Challenge
•
•
EMPI with 16M patients
But the EMPI had millions of data stewardship tasks
awaiting manual review and resolution
PERFORMANCE
Success Story: Using AUTO-STEWARD at Healthix
1,000,000
Match decisions per day
100,000
Match tasks resolved per day
•
Verato AUTO-STEWARD is working through 100,000
tasks per day
Benefits
• Healthix went live with Verato after 6 weeks
• After just 4 months, Healthix saw a reduction of
3.5M patient identities in its EMPI
IMPLEMENTATION
Solution
2 WEEKS
Healthix begins making live
calls to Verato services
6 WEEKS
Healthix goes live
with Verato
• Healthix is catching up on years worth of manual
effort in months
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Verato is cloud-based, highly secure, and can be rapidly deployed
FAST TO IMPLEMENT
Weeks, not months
SIMPLE
SECURE
Accessed
using simple
APIs
Currently HIPAA, PCI,
EI3PA certified;
Undergoing HITRUST
and SOC 2 audits/certs
SCALABLE
Can scale to
millions of
transactions
COST EFFECTIVE
No hardware, minimal
maintenance
HIGHLY ACCURATE
Referential Matching
EHR
MPI
HIE
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Get started today, and be live with Verato in as little as 6 weeks
AUTO-STEWARD
LINK
Automatically resolve up to 75%
of your data stewardship tasks.
Leverage the most accurate patient
matching platform as an MPI.
If you have an MPI, Verato can
be deployed in weeks to begin
automatically resolving your
potential duplicates.
If you need an MPI, Verato can be
deployed as a cloud-based
lightweight MPI that leverages its
Referential Matching technology.
Learn more
www.verato.com
[email protected]
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