Six Proven Methods to Combat COVID-19 with Real-World Analytics

Six Proven Methods to
Combat COVID-19 with
Real-World Analytics
Health Catalyst Editors

© 2020 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Dale Sanders
Strategic Advisor
Health Catalyst
Adam Wilcox, PhD
Chief Analytics Officer
University of Washington Medicine
This article is based on the webinar presentation, “Real World Analytics:
Advancing Methods and Literacy in Healthcare” by Adam Wilcox, PhD, Chief
Analytics Officer for University of Washington Medicine and Professor of
Biomedical Informatics and Health Education at the University of Washington,
and Dale Sanders, Strategic Advisor at Health Catalyst.
Combating COVID-19 with Real-World Analytics

Historically, to access data, health systems
had to collect data manually from patients
or providers.
Organizations have now moved from
hunting for data to gaining access to
more data than ever before due to
widespread EHR adoption.

However, even with abundant data,
organizations still struggle to leverage
effective methods that result in real-world
analytics.
Health systems may successfully locate
and aggregate data, but they often don’t
advance the data beyond this point and
therefore fail to leverage data to drive
processes, workflows, and decisions.

While health systems have gotten by with
this antiquated approach to data, COVID-
19 demands more actionable strategies.
The rapid onset of the novel coronavirus
has made health systems realize that the
most effective way to fight COVID-19 is
to leverage more than data—real-world
analytics.

However, data approaches that
focus on finding and aggregating
data don’t fully equip health systems
with the timely, comprehensive
information they need to keep up
with the ever-changing virus, making
it more difficult for organizations to
quickly respond to COVID-19.

Real-World Analytics Proves Critical to
Combatting COVID-19
With little data available about COVID-19,
healthcare organizations have had to rely
on each other to collect as much data as
possible and then quickly share that data
to track the evolving coronavirus.
However, if these healthcare
organizations lack sophisticated data
interoperability, the delay in data sharing
can result in worse patient outcomes,
such as higher mortality rates.

Combatting COVID-19
For example, at the beginning of the
pandemic, the Centers for Disease Control
(CDC) requested that a health system with
high volumes of positive COVID-19 cases
submit weekly data reports including
extensive information about where patients
with COVID-19 received care.

Combatting COVID-19
The information included total COVID-19
cases, the number of people tested, and the
number of positive cases.
The CDC requested this data broken down by
race and ethnicity.
Even though this health system had a robust
and skilled team of data analysts, it struggled
to provide this data on a weekly basis due to
the time-consuming nature of the work.

Six Effective Methods to Transform Data into
The above example of collaboration—and
roadblocks to collaboration—between the
CDC and the health system highlights the
importance of prioritizing new methods to
access real-world analytics.

Although organizations can provide easy-to-use
data tools like common data models, query tools,
and analytics applications to increase team member
analytics involvement, the following six methods
push health systems beyond basic data use to gain
a better understanding of data and leverage data to
drive improvement in a day-to-day clinical setting.
1. Create Effective Information Displays
2. Add Context to Data
3. Ensure Data Processes Are Sustainable
4. Identify High-Quality Data
5. Provide Systemwide Access to Data
6. Refine the Approach to Knowledge Management

#1: Create Effective Information Displays
Effective information displays allow
leaders and decision makers to view
data within the scope of the health
system as a whole and avoid seeing
data in silos.
The sum of the parts of the information
display is less meaningful than the
display itself.

#1: Create Effective Information Displays
An insightful display aggregates all of the
data related to a health system’s key
performance indicators (KPIs) into one
place, including peripheral data that adds
to the bigger picture.
Effective displays also provide
accountability, so decision makers at
every level know who is responsible for
which measure.

#2: Add Context to Data
In today’s healthcare world, patients
often receive care from sources inside
and outside the hospital.
Multiple care sources mean health
systems must aggregate data from
each source to provide a full picture
of patient health.
Also, just as crucial as aggregating the
data, health systems must provide
information to the community in ways
healthcare consumers can understand.

For example, Washington Heights/Inwood
Informatics Infrastructure for Community-
Centered Comparative Effectiveness
Research (WICER) conducted a study
focused on public health by distributing
self-assessment health diagrams to
community members.
The health diagram had four areas that
health experts considered necessary for
assessing health.

When researchers delivered the findings to
community members, they provided context by
including information about how each individual
compared to an ideal baseline and their peers.
This context helped individuals understand how
they fared in the bigger picture, common health
challenges within their community, and what
they could improve.
Adding context in this way was critical to helping
community members improve their health and
understand and interpret the community health
data from the health system.

#3: Ensure Data Processes Are Sustainable
When researchers delivered the findings to
community members, they provided context by
including information about how each individual
compared to an ideal baseline and their peers.
This context helped individuals understand how
they fared in the bigger picture, common health
challenges within their community, and what
they could improve.
Adding context in this way was critical to helping
community members improve their health and
understand and interpret the community health
data from the health system.

Sustainable Assets
Figure 1: Sustainable assets to define and measure quality data.

When health experts review their assets, they
often see the data as the most critical asset in
driving change, but over time, experts see that
the data alone becomes less important without
scientific methods and human collaborations.

For example, the WICER research team had
data based on interview responses from
community members but when they stopped
conducting those interviews, the data became
less relevant over time.
However, team member collaborations resulted
in applying the data to scientific methods, such
as leveraging the data in research efforts and
using it to guide community outreach.
These methods helped the data become more
valuable and more sustainable as time went on.

#4: Identify High-Quality Data
Many health systems can identify poor-
quality data but fail to identify high-quality
data.
By creating measures that define high-
quality data, organizations will know when
their data meets their quality threshold.
For example, a care team could define and
measure quality data by completeness,
fidelity, and plausibility.

Historically, data users would write a query,
generate a report, and then consider the
data analysis complete.
Reaching a high data-quality threshold goes
beyond the former process: now, a data user
has to review the report in depth and decide
if the data looks correct, identify variances,
and ensure the data offers insight.

Too often, the data reflects what it can
measure but not necessarily what is
happening in the real world, making a
data user’s understanding of quality data
(e.g., completeness, fidelity, and
plausibility) critical for data to reflect
real-world events and outcomes.

#5: Provide Systemwide Access to Data
Widespread data access—or data
democratization—means team members
at every level have access to data and
can make data-informed decisions.
However close health systems may seem
to data democratization, most still face
significant barriers.
One obstacle is the time it takes to
deliver the right data to the right people.

Data stewards need to understand which
team members have access to what data.
Typically, health systems have a group of
analysts or developers who have access
to all data sets. Team members then
make data requests through the data
analysts.
Sometimes it can take months to access
data because people don’t know where to
find the data sources or don’t know the
process for requesting access.

To improve data access, health systems can
provide instruction, or an analytics curriculum,
that addresses what data is available, how to
request data access, how to access the data,
and finally, how team members can use that
data in their day-to-day tasks.
While an analytics curriculum doesn’t eliminate
the time-consuming processes of accessing
data and generating reports, it can speed up
the process.

Another way to improve data access is to build
query libraries (existing data searches that non-
data experts can use) and data models (existing
common data models, so non-data experts
don’t have to reinvent the wheel).

#6: Refine the Approach to Knowledge Management
In the past, knowledge management focused
on data modeling and hierarchies, with
decision support driving documentation.
Knowledge management has changed
drastically since then, as health systems
focus on storing data, data definitions,
and phenotypes.

For example, phenotypes act as
breadcrumbs that reveal what different data
sources represent.
Rather than decision support driving
documentation, predictive analytics and
pattern recognition should drive decision
support.

Many health systems still revert to their
previous knowledge management
approaches that focus too heavily on
data modeling and data hierarchies.
Health systems need to change their mindset
and see data usage as driving knowledge
management because data-driven knowledge
management is a critical step in advancing
healthcare to real-world scenarios.

Real-World Analytics Arm Health Systems for
COVID-19 Battle
For health systems to quickly react to COVID-
19 with the best defense—comprehensive,
actionable data—they must go beyond basic
data use and processes and derive real-world
analytics from their data.
Relying on old data approaches that prioritize
finding and collecting data delays effective
analytics use and creates an environment in
which team members depend on analysts to
query thousands of data elements for each
report and then wait weeks or months for the
information.

COVID-19 Battle
With the novel coronavirus threatening
long-term financial strain (and collapse, in
some cases), draining resources, and
halting typical revenue-generating
procedures (e.g., elective surgery), real-
world analytics are now paramount for
health systems to survive the pandemic.

COVID-19 Battle
While data maximization strategies are continually
evolving, healthcare organizations can advance
real-world analytics use by applying the six data
methods described above and foster a culture that
uses data to drive meaningful improvement—in a
pandemic and beyond.

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

More about this topic
Link to original article for a more in-depth discussion.
Six Proven Methods to Combat COVID-19 with Real-World Analytics
Four Strategies Drive High-Value Healthcare Analytics for COVID-19 Recovery
The Fight Against COVID-19: A National Patient Registry
Sadiqa Mahmood, DDS, MPH, General Manager & Senior VP, Life Sciences Business
The Healthcare Analytics Summit™: Top Data Discoveries and Insights from HAS 20 Virtual
Six Ways Health Systems Use Analytics to Improve Patient Safety

Dr. Wilcox completed a double major in 1995 in Physics and Mathematics at the
University of Utah. He then obtained his PhD in 2000 in Medical Informatics at
Columbia University (Advisor George Hripcsak). Dr. Wilcox worked with the
informatics group at Intermountain Healthcare under Paul Clayton, where he led the
development and implementation of primary care and emergency medicine systems,
while also researching the effectiveness of care managers in an advanced practice
model that was a precursor to the patient-centered medical home. Most recently he
led Intermountain’s clinical decision support efforts and directed its analytic health
repository. At Intermountain, he was also faculty at the University of Utah, where he taught courses
and lectures in research design and decision support. While faculty of Columbia University, where he
directed the legacy clinical information system, clinical data repository and data warehouse, and was
also principal investigator of the Washington Heights/Inwood Informatics Infrastructure for
Comparative Effectiveness Research (WICER) project, one of the country’s first population health
research databases, funded by AHRQ.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Adam Wilcox, PhD

Dale has been one of the most influential leaders in healthcare analytics and data
warehousing since his earliest days in the industry, starting at Intermountain
Healthcare from 1997-2005, where he was the chief architect for the enterprise data
warehouse (EDW) and regional director of medical informatics at LDS Hospital. In
2001, he founded the Healthcare Data Warehousing Association. From 2005-2009,
he was the CIO for Northwestern University’s physicians’ group and the chief
architect of the Northwestern Medical EDW. From 2009-2012, he served as the CIO
for the national health system of the Cayman Islands where he helped lead the implementation of
new care delivery processes that are now associated with accountable care in the US. Prior to his
healthcare experience, Dale had a diverse 14-year career that included duties as a CIO on Looking
Glass airborne command posts in the US Air Force; IT support for the Reagan/Gorbachev summits;
nuclear threat assessment for the National Security Agency and START Treaty; chief architect for the
Intel Corp’s Integrated Logistics Data Warehouse; and co-founder of Information Technology
International. As a systems engineer at TRW, Dale and his team developed the largest Oracle data
warehouse in the world at that time (1995), using an innovative design principle now known as a late
binding architecture. He holds a BS degree in chemistry and minor in biology from Ft. Lewis College,
Durango Colorado, and is a graduate of the US Air Force Information Systems Engineering program.
Dale Sanders

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.”

Six Proven Methods to Combat COVID-19 with Real-World Analytics

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Six Proven Methods to Combat COVID-19 with Real-World Analytics

Similar to Six Proven Methods to Combat COVID-19 with Real-World Analytics (20)

More from Health Catalyst

More from Health Catalyst (20)

Recently uploaded

Recently uploaded (20)

Six Proven Methods to Combat COVID-19 with Real-World Analytics