ers, and health information exchanges.
Terri Godar, director of technical operations and eligibility at Advocate Health System, based in Chicago, IL, says that healthcare
organizations can’t solve the patient identity challenges within their
four walls. “They need to innovate by infusing richer data that comes
from external sources that may include credit bureaus and government programs. In addition, we have found that demographic data
from payers, which is increasingly important in the priorities of today’s vast healthcare ecosystem, is outdated and often incomplete,”
Godar says. “Higher data quality and matching can also be achieved
by implementing stronger patient registration, patient access, and
patient search processes.”
Using Cloud-based Data Services to Gain Trust in
The mainstream acceptance of cloud computing has opened an
avenue to incorporate secure external data services into critical
business processes such as patient registration, data exchange,
and patient identification.
Cloud-based data services enable the infusion of referential or
authoritative data that may come from large public databases
outside healthcare, such as credit bureaus, loan servicing organizations, or telecommunications. These non-healthcare databases and associated business processes capture and validate
identity data, update it continuously with each transaction, and
retain the history of the person’s demographics.
A common challenge in healthcare is that a patient’s demographics can change between encounters at different facili-ties. Demographic data recorded at Facility A in January, while
accurate at that time, can differ from demographic data recorded at Facility B in June—also accurate at that time—if the patient’s actual demographic information changed between those
two dates. If the data from Facility A and Facility B do not closely
match, the two identities may not automatically be linked. Referential identity data can help resolve these issues.
Real-time automation of patient matching with external data
also addresses a critical latency issue associated with manual
stewardship efforts, which typically don’t resolve the ambiguous linkages/tasks (those records not automatically linked by an
algorithm) until days or months after a patient presents for care.
Health information exchanges (HIEs) must deliver high-value
and integrated data to their stakeholders despite data challenges.
Tom Check, CEO of Healthix, a public HIE based in New York,
says his organization faced very significant numbers of potential
matches that weren’t strong enough to automatically match given
the conservative threshold that had to be applied (see the sidebar
above). “Healthix needed more comprehensive, actionable information about an individual to serve value-based payment needs,
and to provide trusted information to its providers, care managers and care coordinators, and research,” Check says.
A linked and comprehensive view of patient/consumer data is
needed to support quality and financial reporting for programs
such as HEDIS and CORE Measures, as well as care delivery and
predictive risk modeling. “By augmenting Healthix data with external referential data we are creating complete and current data,”
Check says. “The whole record, not fragments we previously encountered, is essential to tracking and predicting risks so providers can intervene early, thus enhancing quality and reducing
costs.” Additionally, Check pointed out the following healthcare
Big Data and automation imperative: “The volume of data and
automation applied to analytics necessitates timely, trusted, and
comprehensive views of the patient/consumer data.”
Auto-Stewarding with Neural Networks
Neural networks technology (machine learning) has been
around for over 50 years and used in biometric operations like
fingerprint analysis and facial recognition. It has enjoyed a resurgence in the past five years due in part to the explosion of Big
Data, proliferation of large-scale networks, and the maturing of
practical application of this technology.
The shift from a linear data model to a neural data model has
proven to be the key to major neural network improvements
that can be applied to patient identification. In his article “A
Resurgence of Neural Networks in Machine Learning,” Google
research scientist Dan Gillick explains, “in machine learning,
‘training’ refers to the process of automatically choosing the
weights given examples of the input where you know what the
output should be.” 2 Training the neural network through sufficient sample data is the key to producing the desired results, but
in the context of healthcare this is quite practical.
As Graham Jones, MD, business development director at Kestrel Consulting Services, noted in his recent blog post, “the
production platform that implements the neural network, once
trained, is often very simple and cost effective.” 3
Applying Innovation to the
Patient ID Challenge
How Healthix HIE Handles Patient Identification
HEALTHIX IS THE largest public health information exchange (HIE) in the nation, serving a comprehensive range of organizations in the greater New York City area.
Before applying external referential data to augment identity reconciliation, Healthix had received: 51. 1 million medical
record numbers (MRNs) from their provider organizations and resolved them to 25. 4 million actual identifiers (persons).
Over a four-month period, Healthix applied referential identity data to the whole database. In the four months, MRNs
continued to increase to 54. 1 million, but referential data resolved them to 21. 9 million identities.
The number of MRNs per identity increased by 22 percent, from 2.01 to 2. 47.
The 21. 9 million unique individual records are now available to meet the key clinical and operational needs.