Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.

1. Healthcare Interoperability:
New Tactics and Technology
̶ Sean Stohl

2. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Interoperability: New Tactics and Technology
When it comes to defining interoperability,
there are many perspectives.
Let’s take a high-level look at what some
of those are and at the challenges we
face in “solving” this puzzle.
We’ll also take a look at the Health
Catalyst platform, applications and
methodologies, and how they are able to
achieve some of the requirements of
interoperability in a very pragmatic way.

3. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Where a Lack of Interoperability Hits Hardest
What does interoperability mean?
Simplistically, it’s the ability for multiple
systems to talk to each other and integrate
data to improve patient care.
The Center for Medical Interoperability,
with a device-centric approach, defines it
as the ability to share information across
multiple technologies.
What it ultimately boils down to is the idea
that all players in the healthcare IT space
should play well together.

4. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Where a Lack of Interoperability Hits Hardest
Accountable Care – ACOs exist to improve
the quality and coordination of healthcare
delivered to a defined population.
Care managers within an ACO need access
to information about the patients for which
they are accountable. According to a recent
survey of 68 ACOs, around 70% of ACOs
struggle to collect data.
This struggle is amplified as patients see an
increasing number of specialists and out-of-
network providers who are unable to convey
data back to the initial ACO provider.

5. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Where a Lack of Interoperability Hits Hardest
Population Health – Dale Sanders, Health
Catalyst Executive VP, describes 12
categories of data required for population
health, with the minimal data sets being:
1. Patient reported outcomes
2. Social determinants of health
3. Activity-based costing data
These are all but missing from the
healthcare data landscape today, gaps
that can be attributed to the lack of
interoperability.

6. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Where a Lack of Interoperability Hits Hardest
Precision Medicine – Precision medicine
integrates research with clinical practice to
understand a patient’s individual illness and
deliver the right treatment at the right time.
We’ve been talking about it since 2011,
when the National Research Council first
issued a news release calling for:
…a new data network that integrates emerging
research on the molecular makeup of diseases with
clinical data on individual patients” for the purposes of
developing “a more accurate classification of disease
and ultimately [enhancing] diagnosis and treatment.”

7. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Who Is Impacted and Why?
The biggest concern with the lack of inter-
operability goes back to the patients who
benefit from improved care. Providers can
also suffer because they strive to create
ideal care.
When they don’t have all the information
about their patients, who often undergo
care elsewhere, this makes it difficult to
administer and coordinate overall care.
The added costs of trying to get relevant
data points out of EMRs ultimately trickles
down to the patient.

8. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Who Is Impacted and Why?
Inconsistent data quality can easily make
two different records for one patient look
like two different patients when tracking
them from one system to the next.
This becomes a master data problem.
And from a resourcing and maintenance
perspective, there are other costs in
managing the different technologies
involved with multiple systems, not to
mention the churn and waste.

9. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
What’s the Holdup?
In the past, EMR vendors haven’t had to
worry about interoperability. They’ve had
their own kingdoms.
But lately, there has been enough public
and government pressure to improve
patient care, so they’ve started to work
with one another and with other large
healthcare systems and IT providers.
Where EMRs could afford to worry about
their own needs in the past, this is no
longer the case.

10. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
What’s the Holdup?
Some of the biggest struggles with
interoperability exist with the workflow
and policy differences between
organizations and variability in the data
captured and different contexts of use.
This is where the government has tried to
impose standards, but that still takes time
and can have unforeseen consequences.
One of the top protocol concerns with inter-
operability is around standards and getting
everyone to agree on the one way to send
and receive data.

11. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
What’s the Holdup?
HL7 has been the top interoperability
standard in the past, and used by most
health information exchanges (HIEs)
Continuity of Care Documents are
another standard.
HL7 defines these as fostering:
…interoperability of clinical data by allowing
physicians to send electronic medical information
to other providers without loss of meaning and
enabling improvement of patient care.”

12. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
What’s the Holdup?
There have been frustrations when
dealing with HL7 data because of its
unreliability from both a completeness
and consistency standpoint.
HIE’s also struggle in this area because
of insufficient data standards, inaccurate
data, and different privacy rules.
With the current variety and volume of
technologies there are no easy answers,
but some solutions are beginning to
separate themselves.

13. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
What’s the Holdup?
Application program interfaces (APIs) are
being used more and more to interact
with Health Systems.
An API expresses a software component
in terms of its operations, inputs, and
outputs, which allows definitions and
implementations to vary without
compromising the interface.
According to The Advisory Board:
…the use of a standardized and accessible API is a
critical step in allowing the appropriate flow of
information across health care stakeholders.”

14. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
What’s the Holdup?
Fast healthcare interoperability resources
(FHIR) is an interoperability standard for
electronic exchange of healthcare
information that is the successor to HL7.
FHIR is a healthcare exchange API that
provides a simple and efficient way to
discover and consume information across
distributed systems.
FHIR aims to make the implementation
of the data exchange simpler so more
time can be spent on the non-technical,
hard interoperability issues.

15. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
®
HL7 FHIR®
ARGONAUT PROJECT
What’s the Holdup?
Nurturing these ideas falls under the care of
The Argonaut Project, which seeks to advance
the adoption of interoperability standards:
It’s a joint project between HL7 and some of
the top healthcare organizations in the U.S.,
including many top EMRs in the country.
EMRs are going to leverage FHIR more and
more to both extract data from the EMR and
feed it back into the EMR.
…“to enable expanded information sharing for
electronic health records, documents, and other
health information based on the FHIR specification.”

16. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
The Ultimate Goal: Closed-Loop Analytics
Patients with difficult-to-diagnose conditions
(Parkinson’s, heart failure, cancers) will
sometimes visit multiple physicians, quite often
fragmenting their data ecosystem.
The most subjective data content is contained in
clinical notes and diagnostic reports, so to solve
the subjective problem of diagnoses, we must
get our hands on that text data and start
bouncing it against our discrete data.
We also need 7×24 biometrics, genomic,
familial, and socio-economic data to
reduce the time to accurate diagnoses.

17. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
The Ultimate Goal: Closed-Loop Analytics
While EMR vendors are slow and improving,
the space we want to get to, to truly improve
care, is closed-loop analytics.
Closed-loop analytics is about closing the
loop between analytics and workflow. As a
physician works in the EMR, they are
creating valuable data on a patient.
That data typically is resurfaced in an analytic
environment where complex analyses and
algorithms can create new data insights that
are relevant to clinical care.

18. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
The Ultimate Goal: Closed-Loop Analytics
Traditionally, the analytic insights are
delivered in a siloed analytic environment
accessed separate from the EMR system.
This leads to many valuable analytics
never being used to improve patient care
because accessing the analytics is
cumbersome and not part of the normal
clinical workflow.

19. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
The Ultimate Goal: Closed-Loop Analytics
Closed-loop analytics aims to bring the
analytic insights directly into the clinical
workflow – analytics relevant to patient care
should be displayed directly in the EMR.
To do this requires getting data from the
EMR, feeding it back into the enterprise
data warehouse (EDW) to run algorithms
and analytics, and then feeding the
findings back into the EMR again
to improve workflows and care.

20. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
How to Get On Track with Interoperability
Using a Late-BindingSM Enterprise
Data Warehouse and analytics
applications built with this platform,
health systems can reduce the pain
of getting data out of various systems
and into the EDW.
This allows the healthcare systems
we work with to spend less time on
gathering data and more time
analyzing it and improving care.

21. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
How to Get On Track with Interoperability
One way to improve care is by creating
predictive analytics based EDW data, then
feeding that data back in the EMR so
clinicians can improve care.
For example, in our Congestive Heart
Failure application, which uses a predictive
algorithm to identify patients with the highest
readmission risk.
Providers use this data to follow up with the
highest-risk patients and apply best known
practices that help alleviate readmission risk.

22. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
One Approach to Interoperability
A best practice approach is to bring
multiple source systems into the EDW in
a standardized and very efficient way.
The Health Catalyst Analytics Platform
does this and captures metadata about
the source systems. We use that
metadata to quickly load the data into the
EDW via database direct querying, flat
files, or XML files.
New connectors are being developed
that will provide the ability to insert
streaming HL7 messages, claims EDI
data, and Hadoop data into the EDW.

23. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
One Approach to Interoperability
Regardless of what standards or systems
are on the organization’s side, we can
get that data into the EDW in a pragmatic
way without worrying about getting all the
data at once.
This approach focuses on initially getting
what provides the most analytical value.
If it turns out later that there are other
metrics needed from a new table, it’s
easy to bring that in because of the data
acquisition functionality in our platform.

24. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
One Approach to Interoperability
We work with one large healthcare
system with more than 200 sites of care,
including nine hospitals, many coming
by way of merger and acquisition.
At one point, this was a health system
with a single EMR that suddenly found
themselves contending with records
from Epic, Cerner, Meditech, and some
Centricity data.
We worked with them to bring all this
data into our platform and integrate
everything using Source Mart Designer
and SAM Designer.

25. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
One Approach to Interoperability
ACOs need to integrate various claims
systems so their payers have a master data
record of the patient to work with.
With multiple systems, Health Catalyst
can create this common master record
for the patient or provider, the single
source of truth, from which all analytics
can originate.
We have begun to ‘close the loop’ with our
analytic platform because our applications
have been delivered through the EMRs of
our healthcare systems and directly into the
clinical workflow.

26. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Can We Build a Better Mousetrap?
Time will tell if we can achieve true
interoperability. We’re a good year or two
from seeing the fruits of the FHIR efforts.
Once standards are in place, many small,
ambulatory clinics running obscure EMRs,
without the technical or financial resources
to share data will continue to struggle.
How do you integrate their data? Some will
look to HIEs to help with interoperability, but
there are funding issues.
Data quality is another big issue; the breadth
of data often isn’t enough.

27. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Can We Build a Better Mousetrap?
Building a better mousetraps starts
with the platform, tools, and
approach to get the various source
systems data and start analyzing it.
Patient providers have Master Data
Management tools at their disposal
to help in those scenarios.
Health Catalyst is developing
closed-loop analytics for improving
quality and outcomes to get data
back to providers for making
informed decisions to improve
care for their patients.

28. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

29. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
More about this topic
Link to original article for a more in-depth discussion.
Healthcare Interoperability: New Tactics and Technology
Clinical Data Management: 3 Improvement Strategies
Jane Felmlee, Finance Operations Consultant
Going Beyond Genomics in Precision Medicine: What’s Next
David Crockett, Ph.D., Research & Predictive Analytics, Sr. Director
The Best Way to Optimize Physician Workflow
Dr. Ed Corbett, Deputy CMO
Healthcare Analytics Applications: Why You Need an Out of Box Solution with Customizability
Jason Burke, Technical Director; John Simmons, Senior Data Architect
How to Evaluate a Clinical Analytics Vendor: A Checklist
Dale Sanders, Executive VP of Software

30. © 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Sean Stohl started with Health Catalyst in 2012. He oversees the Data Acquisition
Services Team in Product Development that works with Health Catalyst’s clients to bring
their source system data into their Enterprise Data Warehouse. Sean also works on the
Health Catalyst Analytics Platform and enhancing it to bring in new sources of data into
the EDW. Prior to joining Health Catalyst, Sean worked at Goldman Sachs in the Private
Wealth Management Technology group and Intel Corporation. Sean holds a MS in Information
Systems Management and a BS in Business Management from Brigham Young University.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com