Improving healthcare interoperability is a top priority for health systems today. Fundamental problems around improving interoperability include standardization of terminology and normalization of data to those standards. And, the volume of data healthcare IT systems produce exacerbates these problems. While interoperability regulations focus on trying to make it easy to find and exchange patient data across multiple organizations and HIEs, the legislation’s lack of fine print and aggressive implementation timelines nearly ensures the proliferation of existing interoperability problems. This article discusses the biggest barriers to interoperability, possible solutions to interoperability problems, and why it matters.

1. The Biggest Barriers to
Healthcare Interoperability

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The Biggest Barriers to Healthcare Interoperability
Improving healthcare interoperability is a
top priority for health systems, clinicians,
patients, and even legislators.
The latest governmental efforts to address
interoperability come from the Office of the
National Coordinator for Health IT (ONC),
which issued a proposed interoperability
and information-blocking rule in February
2019.

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The Biggest Barriers to Healthcare Interoperability
The ONC rule defines demands for
healthcare and IT providers around data
sharing and outlines exceptions to the
restrictions on information blocking.
While aspects of this legislation are
promising, previous attempts to improve
interoperability have failed because the
main sources for healthcare data—EMRs—
produce non-standardized, disparate data.

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The Biggest Barriers to Healthcare Interoperability
While the government’s push for Health
Information Exchanges (HIEs) began in
2009 with the passage of the Health
Information Technology for Economic and
Clinical Health (HITECH) Act, the focus
for health systems centered around EMR
adoption rather than interoperability.

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The Biggest Barriers to Healthcare Interoperability
Today, EMR adoption is widespread, with
almost 98 percent of health systems using
a government-certified EMR.
But each of the hundreds of EMR systems
in use today has its own set of technical
specifications, clinical terminologies, and
even unique customizations that prevent
true interoperability and data sharing
across systems.

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The Real Barriers to Healthcare Interoperability
Today’s EMR systems, and the lack
of interoperability between these
systems, reveals that healthcare has
fundamental problems to address to
improve interoperability, including
standardization of terminology and
normalization of data to those standards.
In addition, the volume of data
healthcare IT systems are producing
exacerbate these problems.

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The Real Barriers to Healthcare Interoperability
While the EMR was created by and for a
single provider (Regenstrief Institute), most
systems available today generate tremendous
amounts of data, while lacking the necessary
tools for data analytics and integration.
Expand the number of providers, systems,
and data sources, and the record quickly
becomes a collection of disparate low-quality
data that is a major contributor to provider
discontent and burnout.

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The Real Barriers to Healthcare Interoperability
Add to that, the explosion of data from other
sources—such as wearables, mobile phones,
and genomics—that exacerbates problems of
interoperability.
EMR systems aren’t designed to integrate
data from other sources or to manage data,
making additional integration tools necessary.

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Addressing Healthcare Interoperability Challenges
While the latest interoperability regulations
focus on trying to make it easy to find and
exchange patient data across multiple
organizations and HIEs, the legislation’s lack
of fine print addressing patient matching
challenges and aggressive implementation
timelines nearly ensures the proliferation of
existing interoperability problems.

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Addressing Healthcare Interoperability Challenges
Patient allergies are one such example:
HIEs frequently receive the same allergy
list multiple times from multiple provider
EMR systems that are each coded using
a different standard (e.g., RxNorm,
SNOMED, etc.).
Having different representations of the
same medical concept is confusing to
providers who must try to sift through
and reconcile the list.

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Machine Learning
One answer to interoperability problems that’s
been gaining traction is to use machine
learning (ML) and artificial intelligence (AI) to
sift through the high volume of low-quality data.
Using the patient allergy list example above,
having patient allergy lists in different formats
that are coded using different standards makes
ML more difficult and complex as all of these
terminologies for the same thing must be
mapped together.
For AI to work, providers need high-quality data
sets to train and execute those ML/AI models.

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Blockchain
While many in the healthcare industry
have touted blockchain as the answer to
healthcare’s interoperability issues, its
practical application remains unclear.
Blockchain, the underlying technology
behind bitcoin, is a distributed ledger
system for tracking transactions in a
highly secure system.

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Blockchain
In healthcare, blockchain would enable someone
looking at a patient’s medical record to know and
trust each piece of data in that record.
Blockchain databases are designed
to be read-only and can’t be edited
or deleted, potentially preventing fraud
associated with altering transactional
data in a permanent record.

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Blockchain
However, preventing fraud of a medical
record with low quality data does nothing
to help the case of better interoperability.
Instead, health systems first need to
address their differing levels of maturity
when it comes to data quality and
governance.

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Blockchain
Before healthcare organizations spend
precious technology resources and
dollars on blockchain, they need to fix
the data at the source.
There should be multiple standards for
data and exchange.
Instead, the EMR manufacturers use their
lobbying power or government entities
(e.g., the ONC) that can dominate the
organizations responsible for creating
these standards (e.g., IHE and HL7).

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Blockchain
The industry should focus on creating a
single set of terminology, data format,
and exchange standards for a larger
and more comprehensive patient
health data set.
This patient health data set should
include maternal, perinatal, cardiac,
and social determinants of health data.

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Blockchain
Instead of having to search through
text notes for perinatal age or stroke
index, providers would have access
to data codified using standards-
based terminology and formatted in
a way that can be analyzed by the
advanced IT technology of ML/AI.
This, in turn, would make it easier for
providers to identify critical
information—not just critical data.

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A Solution that Addresses Data at the Source
Enter the Health Catalyst® Data
Operating System (DOS™), which
combines the features of data
warehousing, clinical data
repositories, and HIEs in a single,
comprehensive technology platform.
With DOS, data is kept in its original
source format to preserve the
sanctity of the source data, and
instead of storing data in various
proprietary formats, DOS puts
disparate data into a single, shared
data structure, highly normalizing
and standardizing the data.

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A Solution that Addresses Data at the Source
The DOS platform works with
EMRs to ingest data from
hundreds of data sources and
deliver point-of-care insights
within the clinical workflow.
Having analytics at the point of
care improves healthcare quality.
Low quality data can actually
reduce the accuracy of any
analytics measure, but it is even
more critical at the point of care,
where medical decisions are
being made.

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Bringing the Focus Back to Data Quality and
Standardization
While new technologies like ML and
blockchain hold promise to transform the
healthcare industry, they won’t solve the
biggest barriers to interoperability.
The hype around blockchain is diverting
focus from the real problems surrounding
data quality, while the value of ML is
dependent on high-quality data sets.

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Bringing the Focus Back to Data Quality and
Standardization
Healthcare organizations need to focus their time and resources around
creating high-quality structured, standardized, and normalized data as well
as the completeness of patient data, paving the way for true improvement
in healthcare interoperability.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
The Biggest Barriers to Healthcare Interoperability
Three Must-Haves for a Successful Healthcare Data Strategy
David Grauer, MBA, MHSA, Senior VP Professional Services
Healthcare Data Literacy: A Must-Have for Becoming a Data-Driven Organization
Anna Kleckner, PhD, MPH, Business Consultant
Five Practical Steps Towards Healthcare Data Governance
Stephen Hess
Continuity of Care Documents: Today’s Top Solution for Healthcare Interoperability Demands
Adam Bell, Director of Clinical Advisory and Provider Outreach Services;
Kristen McRae, Senior Product Management Professional
Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True Healthcare Interoperability
Ryan Smith, MBA, Senior VP and Executive Advisor; Mark McCourt; Mike Noke, MBA; Neil Andersen

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Dan joined Health Catalyst in February 2014 as Vice President of Product Management.
Prior to coming to Health Catalyst, Dan worked for Medicity as Vice President of Product
Management. He brings over 25 years of health IT experience at SpaceLabs, Cerner and
Allscript, as well as 12 years of clinical experience at Good Samaritan Hospital in Phoenix.
Dan has a Bachelor’s degree in Engineering from Arizona State University.
Dan Soule

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement
company that helps healthcare organizations of all sizes improve clinical, financial, and operational
outcomes needed to improve population health and accountable care. Our proven enterprise data
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