Learn how to solve your patient identity challenges, including how exploring the human factor and learning your duplicate rate can help.

1. How to Overcome Patient
Identity Challenges
Presenter Name:
Michelle Schneider, Senior Solutions Engineer, Iatric
Systems
Date: April 19, 2016

2. Ideal
One Patient
Unique Name
One ACO
One EMR
One Address
In Sickness, and
In Health
Until Death do you part

3. Real
One Patient
Common/Similar Name
Multiple Patients
Disparate Systems
Many EMRs
Multiple Addresses
In Sickness, and
In Health
Until Death do some of you part

4. Patient Identity across a delivery
network
• A single system has painstakingly identified a
single person. (if previous slide was true)
• The patient is sent elsewhere for additional lab
work, another consult visit or for additional
treatment.
• The delivery network (HIE, IDN) can now have
segregated information in multiple systems for a
single identified “Patient”.
This is where trouble can occur

5. EMPIs

6. EMPI
Situation:
Multiple instances of the same or similar demographic information on
a “patient” can be found in different registration systems, clinical
systems, etc.
Remedy:
• Software employs complex algorithms to determine matching and
non-matching characteristics.
• Processes to manage the work flow of remediation of matched and
un-matched
• Reporting, checks and balances

7. Deterministic Matching – Usually represents an exact match between two pieces of data.
It can include some rules but all rules must be met every time. A large HIE will outgrow this
quickly because there will be too many unmatched, duplicate records, searches/queries.
Probabilistic Matching – A statistical approach to evaluate the probability that two
records represent the same person. By associating algorithms and applying scores to
outcomes, matches can be made with confidence when a threshold is met.
Example:
Common Terminology
EMR 1 EMR 2 Deterministic
Match? (In it’s
purest form)
Probabilistic Match?
(With algorithms in place)
First JOHN JON No Yes
Last SMITH SMYTHE No Yes
Middle R R Yes Yes
Addr 1 110 SOUTH MAIN
ST.
110 S. MAIN STREET No Yes
City SOUTH WINDSOR S. WINDSOR No Yes
State CT CT Yes Yes
Zip 06074 06074 Yes Yes
DOB 19900808 19900808 Yes Yes
WILL THE SYSTEM MAKE THE MATCH? NO YES

8. Deterministic Matching
Advantages
• Exact match
• Quick to implement and test
• Less Risk on the front end
Disadvantages
• Lots of duplicates
• Common registration errors are not overcome
• Overwhelming cleanup/unmerge efforts

9. Probabilistic Matching
Advantages
• Lower duplicate rate
• Algorithms to allow for more efficient
matching
Disadvantages
• Algorithms require care and feeding
• Poorly tuned algorithms may cause
mismatches
• More expensive to implement

10. False Positive – A match between two records that do not represent the same person
False Negative– A match result that fails to match two records that represent the same
person. The records are thought to relate to separate individuals.
Example:
Common Terminology
Match
Score
Partial
Match/
Nickname
Score
Completely
different
Missing
First 8 5 0 0
Last 15 10 -4 -3
Middle 4 1 -2 0
Gender 4 0 -4 0
Addr 1 12 8 0 0
City 6 4 -2 0
State 4 2 -5 0
Zip 6 0 0 0
DOB 8 4 -4 0
SSN 20 6 -10 0
Threshold for Matching 45

11. Why does it matter?
Patient Safety
Analytics – Numbers are not true when
duplicate rate is out of control
Decision Support – If clinical information in
two separate records, missing info can cause
errors
Trust – patients, stakeholders
Legal – identity theft

12. Causes of Incorrect Patient
Matching
1. People – Patients and Healthcare workers
2. Processes
3. Technology

13. Human Factors in inconsistent /
incorrect patient matching
• Critical/Trauma situation
• Language barrier
• Card sharing
• Patients don’t understand the importance of
providing accurate/consistent information
• Registrars don’t understand the importance
of their role
• Policy/Procedure not clearly communicated
• Lack of accountability

14. What can you do next week to
improve your EMPI?
Registrars:
• Do they know their value?
• Share use cases with them
• Educate them
• Monthly Spotlight
• Case Studies
• Analytics – Safety, Outcomes, Time
• CELEBRATE Success

15. What can you do next week to
improve your EMPI?
Policy/Procedure:
• Make sure expectations are documented
(i.e. Name must match name on license / ID)
• Educate/Reeducate
• Hold people accountable
• Have a process in place to handle mismatches

16. What can you do next week to
improve your EMPI?
I fixed my mismatch….. NEXT!!
• Before moving on – consider the ripple effects
• Alias identity – editing the name isn’t enough
• Good when example is maiden name
• Bad when example is incorrect match
• That incorrect identity can cause trouble
• Increased matching score
• Incorrect matches
• Must UNmerge, REMOVE match

17. What can you do next week to
improve your EMPI?
Fixing a mismatch
EMR
Match to Michele Snyder
Michelle Schneider
Registered for care
Michele Snyder is now
Michelle Schneider
OOPS!
Time to fix
Behind the scenes: Messages sent to
downstream apps. Michelle
Schneider now has alias of Michele
Snyder. Insurance companies,
disability, public health

18. What can you do next week to
improve your EMPI?
I fixed my mismatch….. NEXT!!
• Downstream systems must be considered
• Educate team on process for correction
• Remove aliases if possible
• Both accounts must be corrected

19. What can you do next week to
improve your EMPI?
Patients:
• Education
• Tell them WHY
• Outreach
• Set Goals
• Follow up
• Reeducate
• Goals for patients: Always carry id, biometric
registry, share information with family, check
portal/paper for accuracy

20. Why does it matter to the
patient?
Decision Support – If clinical information in
two separate records, missing info can cause
errors
Patient Outcomes
Patient Safety – Allergy on wrong account
Patient Portal – One record
Insurance and Military disqualifications

21. This is where technology
comes into play
Electronic Best practices include focus on:
• Data governance across the enterprise: consider who has
the final say in what is a link if remediation is occurring
• Process and reporting – Understanding your data and what
systems provide what information.
• SSN – do 2 people share the same SSN but are not the same
person?
• Hospital re-admits or drug seekers at multiple locations?
• Report of “significant” changes
• First name
• Gender
• Date of birth
• SSN – that was not all 9’s for example
• Establish consistency

22. Problems Associated with EMPI Issues
• Disparate and incomplete clinical information on patients
across an enterprise.
• Billing duplications
• Incorrectly matching records within a facility or across
an enterprise. For example:
• Mislabels due to incorrect link
• Father/son confusion due to Jr./Sr. information
• Confusion between individuals with similar information from
the same address (assisted living site)
• Incorrectly NOT matching records within a facility or
across an enterprise
• Deterministic matching can present a risk

23. How can we use Analytics to
affect change?
1. Duplicate Rate – compelling number!
2. Matched Records and Record Sets
3. Generic Records – Names, SSN, DOB
(i.e. baby girl/boy, 999-99-9999, 1/1/1901)
4. Field Level Analysis
(i.e. mother’s maiden, prev address)
To request the Free Patient Matching Assessment or see example
reports, click here:
http://pages.iatric.com/patient-matching-quality-assessment

24. How can we use Analytics to
affect change?
5. Geographic / Regional challenges uncovered
6. Human errors uncovered
7. Can show improvements over time
a) Provide feedback
b) Adjust processes as necessary
c) Celebrate Success

25. Understand your system
• Where is all the data coming from?
• Acute care/Ambulatory EHRs
• Interfaces:
• ADT
• EMR
• Physician Practices
• Ancillary Orgs –Labs, Rads, SNF
• Flat Files
• Order Messages
• CCD
• Have you ever converted from another EMR?
• What do those records look like?
• Are the MRNs formatted differently?

26. Is the data “good” data
• Data quality – Strive for complete, accurate and
consistent data across your exchange.
• Registration
• Interfaces
• CCD
• All sources
• Think proactively – personal device integration
• Do you have an EMPI?
• No –As you add sources, you increase your risk
• It will be difficult to add sources and maintain
this model
• Yes – If your duplicate rate is high and false
matches are frequent, consider analysis and
remediation

27. Get the facts
• Know your duplicate rate
• Potential Duplicates
• Close but not quite right
• How often does this happen?
• Why does this happen?
• Simple name differences
• Nickname
• Missing information from a source
• SSN
• Baby Boy/Girl
• Consider the sources
• Reeducation of registration staff
• Require certain fields from particular source
• Establish procedures – SSN, Baby naming, Multiples

28. Start at the beginning with each new
source
• Get samples of data types and compare
• Examples
• requirements from one ADT to another
• How do unknown SSNs get processed?
• Understand the rules from each source.
• Gather documentation from all sources
• Multiple MRNs for one person
• Understand merge process in all sources

29. I Can Help!
Michelle Schneider
Senior Solutions Engineer
Iatric Systems, Inc.
Phone: (978) 805-4143
E-mail: Michelle.Schneider@iatric.com
Connect with me on LinkedIn: MichelleSchneider
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