1. Healthcare Data Quality:
Five Lessons Learned
from COVID-19

2. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Healthcare Data: Lessons Learned from COVID-19
COVID-19’s onset was unexpected, and
its rapid spread across the globe was
unprecedented.
Healthcare providers knew the disease
presented a threat to their patients’ health,
but few fully grasped the extent to which it
would affect other facets of their business.

3. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Healthcare Data: Lessons Learned from COVID-19
Early in the pandemic, providers faced
COVID-19-induced challenges that largely
fell into two categories: analytic strain and
resource limitations.
New COVID-19-specific value sets created
analytic strain, the outbreaks drove urgent
lab code updates, and the Centers for Disease
Control (CDC) and other public health
entities published ever-evolving guidelines.

4. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Healthcare Data: Lessons Learned from COVID-19
Compounding these issues were resource
limitations, spawned as providers had to
furlough staff due to canceled or
postponed surgeries and office visits.
Non-furloughed staff often had to work
from new remote environments, straining
communications and reporting structures.

5. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Healthcare Data: Lessons Learned from COVID-19
Pandemic-driven urgency, variety of data,
and a lack of resources have highlighted
the critical importance of data quality as a
prerequisite for any analytic use case.
The COVID-19 pandemic will not be the
last of its kind.
Organizations must prepare for the next
large-scale emergency by committing
to a systemwide data quality strategy
that produces accurate data at all
organizational levels.

6. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Data Quality Starts from the Ground Up
Data quality, the state of qualitative or
quantitative pieces of information, ranks
as “high” when it helps users make quick
and accurate decisions.
Healthcare providers must cultivate or
adopt a systemwide approach to achieve
and maintain this data quality level.

7. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Data Quality Starts from the Ground Up
For example, health data users can base their
quality approaches on the Toyota Total Quality
Management Approach, a widely accepted
framework that integrates customer-centric
data quality into each business’s facet.
In a healthcare adaptation, all aspects of a
health system work together to ensure the
free flow of data across the organization,
and all are accountable for the quality
of that data.

8. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
The fast and furious nature of COVID-19 has
highlighted areas for improvement in healthcare
data quality that organizations must address in
preparation for future analytic use cases.
To get started, healthcare providers can follow
the guidance of five data quality lessons
learned from COVID-19.
1. Assess Data Quality Throughout the Pipeline
2. Do Not Leave Analysts to Firefight
3. Look Outside the Four Walls of the Organization
4. Data Context and Purpose Matters
5. Use a Singular Vision to Scale Healthcare Data Quality

9. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#1–Assess Data Quality Throughout the Pipeline
End users discover most data quality issues too
late—at the conclusion of the pipeline.
At that point in the process, an analyst or subject
matter expert (SME) must engage in a time-
consuming root cause analysis to determine
where things went awry, further delaying the
delivery of accurate and actionable results.

10. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#1–Assess Data Quality Throughout the Pipeline
Report writers, analysts, and SMEs can move
quality up the pipeline by assessing their data
and inserting quality checks on top of the
model wherever they add data.
For example, imagine a report is created
that lists COVID-19 patients and their primary
care providers (PCPs).
When an analyst looks at that report, she
sees that each patient is listed with every
PCP they have ever encountered, as
opposed to just their current PCP.

11. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#1–Assess Data Quality Throughout the Pipeline
The analyst knows patient and PCP should be
a 1:1 relationship, not a one-to-many
relationship, so she kicks off a root cause
analysis to determine where the error entered
the model.
She may find that when the patient and PCP
tables joined, the analyst hadn’t included a
time component.
She is then able to build a data quality check
on top of the model to ensure that if that 1:1
relationship is broken, it sets off an alert.

12. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#2–Do Not Leave Analysts to Firefight
Analysts are not firefighters.
Organizations shouldn’t rely on them to
quickly address analytic emergencies when
they arise, though this is often the case.
The onset of COVID-19 has created human
resource shortages while also increasing
requests for metrics and reports.

13. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#2–Do Not Leave Analysts to Firefight
Analysts need a framework that allows them to
focus on data quality but should not carry the
full burden of quality maintenance.
SMEs and report writers access the data in the
later stages of its lifecycle and must contribute
to its quality by analyzing it from their unique
perspectives and implementing quality checks
as needed.

14. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#3–Look Outside the Four Walls of the Organization
When analyzing data, team members should make
an effort to look outside their organizational silo with
an eye on the two Vs—verification and validation:
Does the information meet system assumptions? Is local
knowledge represented in the data? If so, the data is verified.
Validation, on the other hand, aims to align data values with
relevant external benchmarks. For example, during a merger
and acquisition, one should compare data quality between the
organizations to ensure no glaring disparities in quality.

15. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#4–Data Context and Purpose Matters
Both context and purpose matter when
determining whether data quality is
sufficient to support decision making,
but they become increasingly important
as data travels up the framework.
Table 1 shows a four-level data quality
framework.

16. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#4–Data Context and Purpose Matters
Definition
Context
Dependent?
Purpose
Dependent?
Level 1 - Structural Database constraints are enforced
including data types, NULLs, primary
keys, and referential integrity.
No No
Level 2 – Content:
Single Subject Area
Values are reasonable within the
context of the domain.
No No
Level 3 – Content:
Multiple Subject Areas
Values are reasonable across multiple
domains.
Yes No
Level 4 – Utility Values represent information
empirically demonstrated to support
better decisions.
Yes Yes
Table 1:Four levels of a data quality framework.

17. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#4–Data Context and Purpose Matters
The data quality framework suggests that
providers think of data as a product and
address its quality as it traverses the system.
Analysts and data users should address
structural data quality before moving on to
more complex challenges

18. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#4–Data Context and Purpose Matters
They can then work with the SMEs who use
the data to define single subject area and
multisubject area data quality use cases,
resulting in a data quality coalition across
the organization.
Overall, the framework helps providers
maintain quick access to accurate data
for use in typical day-to-day operations
or extreme cases such as the response
to COVID-19.

19. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Five Healthcare Data Quality
Lessons Learned from COVID-19
#5–Use a Singular Vision to Scale Healthcare Data Quality
Team members in different departments
across the organization likely spend a small
percentage of their time addressing data
quality, resulting in a low level of commitment
and impact across all resources.
When those team members adopt a singular
vision and framework, they pool all resources,
turning those small percentages into a
scalable, single vision for data quality,
resulting in cohesive insights.

20. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Commit to Data Quality
COVID-19 has highlighted the crucial
importance of embracing a systemic
approach to healthcare data quality.
Analysts and SMEs are necessary pieces
of the puzzle, but they are not the
solution, and just-in-time data is simply
too late when lives are on the line.
By taking these lessons learned from
COVID-19, organizations can build a
reliable data quality framework, prepar-
ing them to save jobs and lives when
the next urgent analytic use case arrives.

21. © 2021 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

22. © 2021 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 Data Quality: Five Lessons Learned from COVID-19
How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
Taylor Larsen, DOS Marts Data Quality, Director
Six Proven Methods to Combat COVID-19 with Real-World Analytics
Health Catalyst Editors
Achieve Data-Informed Healthcare in Eight Steps
Sam McCutcheon, Analytics Engineer
Six Strategies to Navigate COVID-19 Financial Recovery for Health Systems
Health Catalyst Editors

23. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Taylor Larsen joined Health Catalyst in December 2014 as a Data Architect. Prior to
coming to Health Catalyst, he worked for the Colorado Department of Health Care
Policy and Financing as a Budget and Data Analyst. Taylor has a Master’s degree in
Economics from the University of Colorado
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Taylor Lawson

24. © 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
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
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.”