Population health and value-based payment demand data from multiple sources and multiple organizations. Health systems must access information from across the continuum of care to accurately understand their patients’ healthcare needs beyond the acute-care setting (e.g., reports and results from primary care and specialists). While health system EHRs have a wealth of big-picture data about healthcare delivery (e.g., patient satisfaction, cost, and outcomes), HIEs add the clinical data (e.g., records and transactions) to round out the bigger picture of patient care, as well as the data sharing capabilities needed to disseminate the information. By pairing HIE capability with an advanced analytics platform, a health system can leverage data to improve processes in four important outcomes improvement areas: Workflow Machine learning Professional services Data governance

1. Pairing HIE Data with an Analytics Platform:
Four Key Improvement Categories

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Healthcare organizations need data from multiple
sources and multiple organizations to meet population
health and value-based payment (VBP) goals.
Communitywide networks, such as healthcare
information exchanges (HIEs), are increasingly
important in the current healthcare landscape,
as they provide the interoperability modern
healthcare improvement demands:
• Clinical, pharmaceutical, public health, and quality
reporting data.
• Electronic sharing between different information systems
• Clinical exchange in multiple formats
• Cross mapping from technologies, systems, and formats.
Pairing HIE Data with an Analytics Platform

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HIE data and technology alone, however, isn’t
sufficient for organizations aiming to improve
outcomes and lower the cost of care.
This presentation covers the benefits of
using HIE data and technology along with
an advanced analytics platform and how
their combined capabilities meet modern
healthcare challenges.
Pairing HIE Data with an Analytics Platform

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Large integrated delivery networks (IDNs)
that contract for value-based population
models require networks of providers and
services to deliver holistic care.
These networks of affiliated physicians
often take the form of clinically integrated
networks (CINs) or accountable care
organizations (ACOs).
Sharing information for a given patient
population is a critical factor to the success
of the network.
The Case for HIE Data

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Large health systems need data from the
EMRs of affiliated physicians who generally
work in smaller practices.
These IDNs are also commonly missing data
from the full continuum of care, which creates
real problems during transitions of care.
Important clinical information is distributed
across different systems, making it difficult to
have complete data supporting population
health initiatives.
The Case for HIE Data

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Health systems can extract, transform, and
load (ETL) the data from their own EMRs, but
they are unable to ETL data from their affiliate
physicians’ EMRs.
Most often, they rely on claims data as a
surrogate for the affiliate EMR data—along
with the complications claims data carries
(e.g., lag, subpar accuracy, etc.).
HIE connections and services bring in
clinical data from affiliated systems in real-
time to close the gaps that occur with
delayed batched claims data.
The Case for HIE Data

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By delivering more complete data, analytic
dashboards populated with HIE data can
build confidence and close the loop for
clinicians taking on risk-based contracts.
By understanding its performance and
clinical quality measures, a health system
can align its value-based contracting goals
and processes to identify improvement
opportunities while there is still time to react.
Multiple data sources can validate and
improve the accuracy of risk stratification,
thanks to more comprehensive patient
population information.
More Complete Data Builds Confidence
Around Risk-Based Contracting

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By capturing the large amounts of health
data that occur outside of the IDN or health
system setting, HIE data allows clinicians to
engage patients when they are most likely
to benefit from an intervention.
Care coordinators and navigators, who are
often trusted patient advocates, leverage
this data to keep patients fully informed of
medical information from across the
continuum of care and throughout the
extended care team.
Supports Patient Engagement at the Most
Impactful and Teachable Moments

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With HIE connections and services,
healthcare professionals and patients
appropriately access and securely share a
patient’s medical information electronically,
across the continuum of care.
By combining this more complete data with
analytics, health systems and clinicians
can provide safer and more personalized
care, thanks to comprehensive access to
vital patient data.
Gives Organizations Comprehensive
Patient Information for Personalized Care

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For example, emergency department staff
too often perform triage without a patient’s
full history and conditions.
A real-time HIE can quickly provide an
overview of chronic conditions,
medications, allergies, etc.
In addition, analytics systems can leverage
this insight to stratify risk and identify the
most impactable areas for the care team
to focus‒areas with the greatest return
on engagement.
Gives Organizations Comprehensive
Patient Information for Personalized Care

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An HIE impacts patients, clinicians, and health systems in several key ways:
Gives Organizations Comprehensive
Patient Information for Personalized Care
Reduces health-related costs by reducing medical errors and duplicate testing.
Improves quality and safety of patient care by reducing medication and medical
errors associated with incomplete data.
Coordinates the extended care team to promote consumer education and patient
engagement in their own healthcare.
Increases efficiency by eliminating unnecessary paperwork associated with patient
movement between care settings.
Provides caregivers with clinical decision support tools for more effective care
and treatment.
Improves public health reporting and monitoring.
Facilitates efficient deployment of emerging technology and healthcare services.
Provides the backbone of technical infrastructure for leverage by national-
and state-level public health initiatives.
Provides interoperability among individual affiliated physicians and organizational
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Even with the above benefits, HIE data
alone doesn’t support comprehensive
improvement under the population health or
value-based demands of healthcare today.
To meet these modern goals, healthcare
organizations must pair these networks with
a fully interoperable analytics platform.
An optimal modern healthcare data
approach combines HIE data with a cloud-
based, interoperable analytics platform such
as the Health Catalyst® Data Operating
System [DOS™]. (Figure 1)
Gives Organizations Comprehensive
Patient Information for Personalized Care

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Gives Organizations Comprehensive
Patient Information for Personalized Care
Figure 1: DOS

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While HIEs are rich in clinical data they lack
depth in the bigger picture of healthcare
delivery—elements such as financial costs of
care, patient satisfaction and outcomes, and
supply chain data.
HIE data is often limited to standard-based
interfaces and datasets such as the
Consolidated-Clinical Document Architecture
(C-CDA) Continuity of Care Document (CCD).
The CCD, however, was designed for one care
provider to communicate with another about
patient information when transferring that
patient from one facility to another—not for the
broader goals of VBP and population health.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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Using a C-CDA CCD, HIEs can meet about
80 to 90 percent of patient information
sharing needs, but they’re not adequate for
all population health use cases.
Data aggregation in an analytics platform
increases the value of data overall by
combining HIE with health system data (e.g.,
patient satisfaction, claims, and cost).
For example, as an HIE lacks cost data, by
combining HIE and health system resources,
an analytics platform can enable community-
based cost comparisons.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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Pairing HIE data with DOS offers benefits in four
key categories that help health systems meet goals
under VBP and population health:
1. Workflow
2. Machine Learning
3. Professional Services
4. Data Governance
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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1: Workflow
Most healthcare analytics platform
technology falls short in clinical workflows
because it doesn’t provide data at the points
of decision-making or care.
When a patient checks into a primary care or
specialist office, an analytics platform may
not immediately produce critical insight at the
point of care.
For example, if a patient has diabetes and
hasn’t received a hemoglobin A1c or an eye
exam, this information may not appear in
front of the provider in the exam room.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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1: Workflow
An interoperable analytics platform with HIE
data makes insight actionable by putting the
right patient information into the workflow at
the right time.
Information at the point of decision-making,
from patient care to follow up, helps
clinicians and care managers improve
preventive care by addressing gaps
in care and broader improvement goals.
Most HIE data is exchanged in real-
time, and most analytics platforms
don’t support real-time action.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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1: Workflow
Adding HIE technology to an organization’s analytics
platform delivers results within moments, directly
through standard channels, sending insights into
the provider workflow and back to the clinicians
via the EMR.
HIE technology makes it easier for health
systems to get data into the workflow.
The right data at the point of decision
making is vital to improved patient
outcomes and especially to patient safety.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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1: Workflow
In sepsis, for example, fast data access
enables prompt diagnosis, which is critical
to better outcomes.
HIE also helps analytics systems incorporate
medication data, such prescription and fill
history, to look for complications including
adverse drug events, contraindications, and
opioid misuse risk.
In addition, the real-time data capabilities of
HIE technology enable timely notifications
across the continuum of care triggered by
the health system analytics platform.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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1: Workflow
Subscribed clinicians and care managers
can receive a timely alert when a patient
experiences a health-related event, such as
a gap in care warning.
An organizational analytics platform
integrates with a decision-support tool, such
as Health Catalyst Leading Wisely™, to
provide personalized watch-lists,
configurable visualizations, and
customizable alerts and notifications.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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2: Machine Learning
HIE increases the amount and reduces the lag
of data available to analytics platforms, which
provides data scientists a more robust data set
with which to train machine learning models.
Clinicians can more quickly identify risks and
opportunities for intervention as well as predict
behaviors and events across entire patient
populations.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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2: Machine Learning
Predictive analytics can verify patient
compliance with medication using
prescription fill records to see which
medications a clinician has prescribed
versus which have been filled.
As well, predictive algorithms can identify
drug-seeking behavior, using statewide
prescription data as well as socioeconomic
data from the HIE.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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3: Professional Services
Combining HIE data with health system analytics
helps organizations identify top-performing
clinicians for a sustainable, high-performing CIN.
Choosing the right clinician will reduce outcomes
variation in clinical care and build the network
around clinicians with better outcomes (both in
the acute and ambulatory settings).
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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3: Professional Services
An HIE also helps clinicians become top performers
by understanding variation across large populations,
especially in conjunction with a benchmarking,
opportunity analysis, and prioritization analytics
application (e.g., Health Catalyst Touchstone™).
Using HIE data in health system analytics also helps
CIN clinicians get genuine value from healthcare
technology beyond regulatory reporting.
Value from enhanced decision-making tools and
eased clinician reporting burden adds incentive for
clinicians to join the CIN and improves provider
engagement, as combining HIE and analytics
reduces the labor of producing reports.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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4: Data Governance
HIE environments struggle with data governance,
particularly with data quality and ownership, and
organizations without good data governance
struggle when they introduce an HIE.
A commercial analytics platform, and the vendor
expertise behind it, helps organizations define and
comply with regulations.
Vendors, meanwhile, also benefit from HIE around
data governance. Life sciences data, for example,
has strict rules around purpose of use, including
patient consent and disclosure.
HIE data adds patient consent expertise to these
health system platforms.
Four Key Improvement Categories of
Pairing HIE Data with an Analytics Platform

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To meet quality measures under VBP as well as
population health goals, healthcare organizations
must have the comprehensive patient and
community data to power advanced analytics
capabilities that only an HIE-and-analytics-
platform approach can deliver.
Combined, the data and analytics entities
provide greater understanding of patient health
across the continuum of care and move insights
into the workflow at the point of decision making.
Meeting Today’s Payment and Population Health Goals
Requires Both HIE and an Analytics Platform

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With real-time communitywide and individual
data, clinicians gain a broader context for
the patient, allowing more timely, informed,
and accurate decisions.
This allows for better care and outcomes
and builds trust among health systems in
taking on risk-based contracts and
population health models.
Meeting Today’s Payment and Population Health Goals
Requires Both HIE and an Analytics Platform

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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.
Pairing HIE Data with an Analytics Platform: Four Key Improvement Categories
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Ann Tinker, MSN, RN, Professional Services, SVP; Dan Hopkins, Professional Services, VP
The Missing Ingredient in Healthcare Analytics: The Executive Sponsor
Ryan Smith , SVP, Professional Services
Addressing Healthcare Waste Through Centralization
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A Framework for High-Reliability Organizations in Healthcare
Health Catalyst Editors

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Adam Bell brings 20+ years of population health management experience working with
payers, providers and hospital systems to Health Catalyst. Adam's many accomplishments
include developing an Advisory Services Dept. with offerings that spanned from analyzing data
(HL7 and C-CDA) for population health to clinical program enablement, leading a state-wide
Chronic Care Management Program responsible for more than 80,000 Medicaid beneficiaries that
improved quality and reduced costs, and developing a comprehensive Wellness Program for small to mid-
size commercial populations. This program included biometric screening, customized online health risk
assessment, telephonic coaching programs and reporting on outcomes.
Dan Soule 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.

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Other Clinical Quality Improvement Resources
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Carol Owen is the SVP of the Health Catalyst Interoperability team. Carol joined Health
Catalyst in 2018 when Health Catalyst acquired Medicity where Carol worked for 18 years.
While at Medicity, Carol held a wide variety of responsibilities ranging from directing product
management, technology and operations. Most recently Carol was SVP of Operations
reporting directly to the CEO. In this role she successfully operationalized client implementations,
scaled the hosting and support functions to meet client demand and matured processes delivering
more predictable revenue back into the business. Prior to Medicity Carol held a variety of roles
including leading product strategy for a start-up EHR vendor, professional and technical recruiting for
a larger ambulatory physician group, and managed the enrollment files of a HMO (and yes is
embarrassed to admit managed member rosters using microfiche). Carol attended the University of
Utah in Community Health focused on Patient Education.

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Other Clinical Quality Improvement Resources
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Eric Crawford joined Health Catalyst after the acquisition of Medicity where he held
numerous roles within Product Management, with the latest being Head of Product. In
addition to product management and strategy, he has developed a strong repertoire of data
mining, analysis, statistics and data presentation. Prior to Medicity, he worked as a consultant
for Intermountain Healthcare’s Institute of Healthcare Delivery Research and directed the data
collection and analysis for the nation’s largest Hospital Engagement Network (HEN). He was a key
player in Intermountain's Big Data and Advanced Analytics teams and chartered and led the ROI
Steering Committee. Eric has a background in human factors, quality improvement and training in
statistical methods and modeling. He is a graduate of a Lean/Six-Sigma black belt course,
Intermountain's Advanced Training Program and has an MBA/MHA.

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Other Clinical Quality Improvement Resources
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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 warehouse
(EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more
than 65 million patients for organizations ranging from the largest US health system to forward-thinking
physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”