learn how to overcome patient identity challenges

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

Ideal

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

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

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

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

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

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

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

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

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

Causes of Incorrect Patient Matching

1.  People – Patients and Healthcare workers 2.  Processes 3.  Technology

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

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


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


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


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


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


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

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

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

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

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

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

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?

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

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

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

I Can Help!

Michelle Schneider Senior Solutions Engineer

Iatric Systems, Inc.

Phone: (978) 805-4143 E-mail: [email protected]

Connect with me on LinkedIn: MichelleSchneider

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