Three Analytics Strategies to Drive Patient Care

Three Analytics Strategies to
Drive Patient-Centered Care

© 2020 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Katelyn Muir, CPQH, CSM, MPH
Supervisor of Population Health Analytics,
OneCare Vermont
This article is based on a 2020 Healthcare Analytics Summit (HAS 20 Virtual)
presentation by Tyler Gauthier, MHA, CPHQ, CSM, Director of Value-Based
Care, OneCare Vermont, and Katelyn Muir, CPQH, CSM, MPH, Supervisor of
Population Health Analytics, OneCare Vermont, titled, “Prioritizing the Patient
in Value-Based Care: A Data-Informed Approach.”
Tyler Gauthier, MHA, CPHQ, CSM
Director of Value-Based Care,
OneCare Vermont

In an attempt to curb healthcare’s rising
costs, CMS is increasing the pressure on
health systems to deliver and measure
value with value-based care (VBC).
One way CMS is promoting this value-
driven healthcare model is by offering
health systems and providers financial
incentives to work more closely together
and better coordinate care through formal
networks, such as ACOs.

Even without CMS’s financial incentives,
organizations already feel the pressure
to eliminate uncoordinated care, costing
health systems between $27.2 billion
and $78.2 billion a year.

As more health systems shift from fee-
for-service to value-based payments,
they struggle to balance the financial
focus of VBC with the patient-focused
needs of clinical care.
Managing rising costs and meeting
criteria to qualify for reimbursements
and bonuses can easily distract from
prioritizing patients in a value-based
landscape.

However, in spite of competing priorities,
health systems can rely on data to
manage the financial demands of value-
based payment models and ensure
patient-centric care.
Access to accurate, comprehensive
patient data allows care teams to
understand complex patient populations
and tailor care delivery to unique patient
needs while also meeting the criteria for
reimbursement in value-based contracts.

Prioritize Patients with Comprehensive
Data and a Patient-Centered Care Model
With a variety of data sources (e.g.,
claims, clinical, social determinates of
health) across multiple locations, health
systems must first aggregate all of their
data into one place.
Without comprehensive data in one
place, leaders deliver care based on
disjointed, incomplete data.
Having aggregated data in one place
also means every provider can make
decisions based on the same data
(e.g., a shared MRI)—even when
patients receive care at different facilities.

With data driving a patient-centered care
delivery model, and providers and
resources supporting patients, patient
needs guide care priorities and resource
allotment (versus team members’ opinions
or budget numbers driving care decisions).
Health systems should use the patient-
centric support model as a systemwide
reminder that every initiative, strategy,
and goal must focus on the patient.

A patient-centered care model also
allows health systems to identify patient-
centered metrics to help them reach
targets and evaluate current and future
interventions’ effectiveness.
Without patient-focused metrics that align
with the care model, health systems fail
to accurately measure value.

Relying on data-driven insights aligned with
a patient-centered care model helps
organizations meet the stringent standards
of VBC reimbursements (e.g., target rates
for low hospital readmission).
A data-driven approach also helps team
members stay in sync with strategic goals
because they understand the value of data,
the role it plays in delivering patient-
centered care, and how the organization
uses data to define success.

Three Analytics Strategies to Keep the
Spotlight on Patient-Centered Care
As health systems continually learn the best
way to prioritize patient-centered care while
simultaneously managing the financial
demands of VBC, they can rely on three
analytics strategies to keep them on track:
1. Prioritize Patient Outreach by Risk Level
2. Deploy Data Tools to Combat COVID-19
3. Promote Data Literacy

#1: Prioritize Patient Outreach by Risk Level
Leadership teams set targets and goals
based on risk stratification that reveals the
highest-risk patients (e.g., comorbidities by
zip code), who are candidates for outreach.
With data-driven insight into at-risk
populations, care teams can focus on
effective care coordination and early
care management interventions targeted
to these populations’ needs.

Using data to understand the health of
patients is imperative to managing large
populations. Accurate, comprehensive
patient data allows care teams to track
and identify risk trends over time.
Reviewing and identifying data-informed
trends enables health systems to proactively
address health concerns and provide
preventive solutions in at-risk areas.

For example, if a specific group of people based
in one area code shows signs of prediabetes, a
health system could implement preventive
measures and additional health monitoring
in that community.

#2: Deploy Data Tools to Combat COVID-19
Although COVID-19 has brought unpreced-
ented challenges, health systems that
prioritized data- and patient-centric
strategies before the pandemic have been
more prepared to respond to the outbreak.

With team members already analytics
versed, these health systems focused on
deploying data-driven tools to identify
patients with the highest risk of contracting
COVID-19, rather than first educating
team members about the need for data in
identifying at-risk patients and then
developing the tracking tools.

Certain health systems responded to the
virus better than others by developing data-
informed tools and processes soon after the
onset of COVID-19.
The fast response prevented the spread
of COVID-19 and proactively protected
communities by monitoring exposure through
pandemic support tools, such
as contact tracing.

Because COVID-19 is novel, there is very little
data or information about the virus, making data
sharing across organizations more critical than
ever.
Health systems that didn’t have a data-centered
culture and a data-friendly infrastructure were
ill-equipped to share data and gain more
insights about the virus as it evolved.
This deficit delayed patient care, leading to
worse outcomes.

Inversely, health systems with an existing
data infrastructure had already prepared for
effective data interoperability, continually
learning more about the virus and, in turn,
adapting patient care and emergency
planning according to those changes.
With constant, updated data coming
in, health systems can continually pivot
their strategy to fight COVID-19 and
target metrics to stay patient-centric
amid constant change.

Leveraging data throughout the pandemic
has allowed health systems to understand
capacity, utilization, and the resources they
should allot for different healthcare delivery
methods, such as telemedicine.
Using data to understand how to continue
care delivery prioritizes patients over
processes in any circumstance.

#3: Promote Data Literacy
Health systems can improve how they use
data to make patient-centric decisions and
improve outcomes by cultivating data literacy.
With a patient-centered model and patient-
centered metrics, team members need the
literacy tools to understand and apply data
throughout the care delivery process.
Data-literate team members who can access
actionable data understand how that data
drives patient-centered care during the
COVID-19 crisis and future challenges.

Data teams can start by developing various
in-person and virtual data literacy training
opportunities (especially with team members
working remotely due to COVID-19).
A data literacy program can begin with a data
readiness checklist that defines a literacy
baseline. Organizations can use that base-
line to measure progress.
Health systems can also improve data
literacy by creating data literacy champions
to promote data-informed processes and
decision making throughout the health system.

For example, health systems can train
physicians, nurses, and administrators as
data champions within their teams, units,
and departments.
Leveraging existing leaders as data
champions extends the data beyond
analysts and leaders, increasing the
likelihood that team members will model
the behavior of their managers and also
turn to data before making decisions
about patient care.

Data Powers Patient-Centered Care in
Value-Based Care
When health systems leverage analytics
insight to understand a patient’s complete
health, they can use that insight to guide
quality care that qualifies for the demanding
CMS reimbursement standards of VBC (e.g.,
cost, quality, and provider performance).

Value-Based Care
Without actionable insight, care teams can’t effectively
identify at-risk patients, or ensure they are delivering
high-quality, cost-effective care.
Analytics is essential to VBC because it prevents
care teams from wasting resources, spending too
much time on the wrong interventions, or failing to
prioritize the patient.

Value-Based Care
Detailed information from comprehensive data
sets allows patient needs to drive care
interventions, essential to patient-centered
care in VBC.
Whether health systems are facing populations
with chronic diseases like diabetes or respond-
ing to COVID-19, a culture that values data in
the care delivery process will have the tools to
deliver sustainable VBC.

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

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Tyler Gauthier, MHA currently serves as the Director of Value-Based Care for OneCare
Vermont and has been with OneCare for nearly three years. In this role, he provides
leadership and strategic direction to the Quality, Innovation, and Analytics team as well
as a range of healthcare reform and population health programs across the state. Prior
to joining OneCare in 2017, Tyler managed primary care clinics at the University of
Vermont Medical Center, where he was responsible for the day to day operations,
financial performance, and effective Population Health management. His work included
expanding Primary Care access for patients and increasing available services. Tyler is a Navy veteran
with a list of Achievement & Commendation medals from both the Navy and the Army, from his 11+
years of military service and participation in Operation Iraqi Freedom. After discharging from the Navy in
2011 Gauthier began leading technical teams in the healthcare information technology space to improve
care and outcomes for patients. Gauthier earned his Master’s Degree in Healthcare Administration from
Champlain College and is a Certified Professional in Healthcare Quality (CPHQ) as well as Certified
Scrum Master (CSM).
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Tyler Gauthier, MHA, CPHQ, CSM

Katelyn Muir (Katie) is the Supervisor of Population Health Analytics, managing the
Information and Population Health Analysts. She and her team build tools and reports
on cost and utilization metrics, quality measure and quality improvement as well as
care coordination data. She started with OneCare in 2016. Katie’s background is in
Public Health. She earned her MPH from NYU with a focus in international and
community health. Prior to working at OneCare Katie worked for a consulting firm
managing public health research projects. Katie is a Certified Professional in
Healthcare Quality (CPHQ) and a Certified Scrum Master (CSM).
Katelyn Muir, CPQH, CSM, MPH

Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement
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Three Analytics Strategies to Drive Patient Care

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