THE MORE WE know about patients, the easier it is to match
them. Or at least that’s the theory. Clinical data, patient-gener-ated data, biometrics, social determinants of health: all of this
should make patient matching in health information exchanges
(HIE) a breeze, right?
“As the volume of data grows, the matching burden grows as
well,” says Shaun Grannis, MD, MS, FAAFP, director of the Center for Biomedical Informatics at the Regenstrief Institute, a collaborative research and learning organization that develops and
evaluates innovative solutions for improving patient care. HIEs are
caught in the crossfire, trying to make sense of the data and ultimately provide accurate information to its participating providers,
according to Grannis.
Experts agree that challenges related to data quality and completeness as well as a lack of data standardization make patient
matching in HIEs more difficult today than ever before. However, these challenges present unique opportunities for health
information management (HIM) professionals to advocate for
solutions that improve patient identity management.
Data Challenges Persist
One challenge is that healthcare providers feeding information
into the HIE don’t collect the same data, nor do they collect that
data in a consistent format, says Kelly Thompson, CEO at the
Strategic Health Information Exchange Collaborative (SHIEC).
Some provider organizations, for example, collect data for legal
names while others use nicknames (e.g., Jim instead of James).
Some electronic health record (EHR) vendors enable registration staff to capture the middle initial only, while others can accommodate the entire middle name.
There’s also no data standardization among other entities—
departments of health, departments of transportation, Medicaid, and other agencies—upon which some HIEs rely for matching purposes. For example, some states only permit the choice
of male or female for gender on their driver's licenses, while
others allow additional options.
These differences that make it difficult to match patients within a single organization or health system are only magnified at
the HIE level, says Thompson.
Dan Cidon, chief technology officer at NextGate, an identity
management vendor, agrees. “Having to arbitrate these different internal and external sources to determine the source of
truth—that’s the challenge,” he says.
Emerging Best Practices for Patient Matching
Deciding to join an HIE is the easy part. Providing that HIE with
accurate and complete data? Not so much.
“HIEs complain that the data they receive is so horrendous,”
says Cidon. “If an organization is looking for an HIE to magically
solve all of its identity problems internally, well, it’s not going to
do that.” Instead, he argues, organizations must establish best
practices before joining an HIE—and continue to follow those
practices after becoming a participating provider.
Consider the following:
1. Make identity management a priority within the organi-
zation. “Few organizations think about patient matching until
their accounts receivable goes past 90 days, and they start to re-
alize they have bad patient demographic data because bills are
being sent to the wrong address,” says Grannis.
Organizations may be tempted to solve the problem of identity management by building their own rudimentary matching
algorithm, says Cidon; however, this strategy will only achieve
a modest matching rate. It also won’t provide the added data
management tools of a commercial enterprise-wide master
patient index (EMPI) that can include a variety of internal and
external sources to construct a single high-quality best record
from the data streams, he adds.
Anything organizations can do to clean up their data before joining the HIE is helpful, says Kim Chaundy, senior director of operations at Keystone Health Information Exchange (KeyHIE), which
uses an EMPI to link more than 10 million EHR records across 59
counties in Pennsylvania and New Jersey. The EMPI helped the
HIE reduce its duplicate record rate to less than one percent.
All participating providers focus on registration performance
improvement during the development and testing phase of
KeyHIE integration. “We call upon the HIM department to constantly increase the quality of the data,” says Chaundy. “They
need to understand the importance of certain data elements because that’s what enables us to match patients correctly.” Even
after joining the HIE, participating providers continue to monitor data quality and foster performance improvement, she adds.
2. Standardize data collection. For example, Neysa Noreen,
MS, RHIA, inpatient coding and CDI manager at Children’s Minnesota in Minneapolis, MN, says her organization implemented
standard formatting for patient names and suffixes (e.g., Jr. vs.
II or two separate last names vs. one name with hyphens), but
hasn’t joined an HIE yet because it wants to focus on internal
data quality and standardization first.
In addition, health systems using multiple EHRs should also
ask their vendors to alter or add registration data fields, if necessary, to ensure consistent data collection—again for the benefit of the organization as well as any HIE to which the health
system belongs, says Noreen. For example, one EHR may use
“street” in the address field while another abbreviates it as “St.”
Standardizing addresses systemwide can also greatly help
with patient matching. One recent study conducted by Indiana
University and supported by Pew Charitable Trusts, found that
standardizing addresses using the US Postal Services standard
can improve match rates by up to three percent, translating to
correct matches for tens of thousands of records or more per
day. 1 When standardizing addresses and last names, match
rates improve by up to eight percent.
Such standardization would be critical in HIEs. The Office of
the National Coordinator for Health Information Technology
(ONC) recently issued draft regulations that propose to remove
the Common Clinical Data Set definition from the 2015 certification criteria and replace it with the United States Core Data
for Interoperability standard that requires patient address and
phone number as part of the demographic information that’s
exchanged. 2 Address standardization would enable HIEs to
match patients more easily.
Close Doesn’t Count: Patient
Matching Challenges in HIEs