Healthcare organizations increasingly understand the value of data quality, but many lack a systematic process for establishing and maintaining that quality. However, as COVID-19 response and recovery further underscores the need for timely, actionable data, organizations must take a more proactive approach to data quality. A structured process engages technical and subject matter expertise to define, evaluate, and monitor data quality throughout the pipeline. Health systems can follow a simple, four-level framework to measure and monitor data quality, ensuring that data is fit to drive quality data-informed decisions: Think of data as a product. Address structural data quality first. Define content level data quality with subject matter experts. Create a coalition for multidisciplinary support.

1. How to Run Analytics for More
Actionable, Timely Insights:
A Healthcare Data Quality Framework

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Analytics for More Actionable, Timely Insights
COVID-19 response and recovery
demands data fit to drive timely, actionable
insights at an unprecedented level.
As a result, health systems increasingly
recognize data quality as a prerequisite for
clinical, financial, and operational analytics.

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Analytics for More Actionable, Timely Insights
To quantify data quality, healthcare data
teams can use measurable data attributes
that demonstrate whether it is fit for a
specific purpose.
Good and transparent data quality instills
confidence in the insight provided, which
accelerates sound decision making.
Conversely, poor data quality degrades
confidence, ultimately delaying or leading
to wrong decisions.

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Analytics for More Actionable, Timely Insights
Organizations tend to understand the
value of data quality, but the
fundamentals of a system that
generates quality data and analytics
are complex.
To meet the COVID-19 urgency for
quality data and ongoing data quality
challenges, health systems need an
actionable structure to navigate the
essential phases of a comprehensive
and proactive data quality strategy.

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Analytics for More Actionable, Timely Insights
A framework for healthcare data quality
provides a systematic way to measure,
monitor, and determine if data is “fit for
purpose” (i.e., it can serve its intended
purpose).

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The Four Levels of Healthcare Data Quality
Defining data quality levels helps an
organization understand the current
state of its data quality and whether its
data is improving.
Data users can follow the Four Levels of
Data Quality (Figure 1) to determine
quality checkpoints, including whether
data quality depends on the context of
the data or purpose for its use and
whether defining data quality requires
subject matter expertise.

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The Four Levels of Healthcare Data Quality
Figure 1: The Four Levels of Data Quality.

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A Framework to Measure Quality
Throughout the Data Pipeline
Health systems that follow the Healthcare Data
Quality Framework (Figure 2) will establish a
data quality culture from the ground up and
amass the requisite information to drive
meaningful improvement, react to crises, and
prepare for future emergencies.

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A Framework to Measure Quality
Throughout the Data Pipeline
Figure 2: The Healthcare Data Quality Framework.

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A Framework to Measure Quality
Throughout the Data Pipeline
Think of Data as a Product
In the context of data quality, thinking of
data as a product means that data results
from a process or system that assesses and
treats its quality throughout—similar to how
a car progresses from raw materials to
assembly line to a dealership to expert
magazine review.
To progress successfully through the
automobile manufacturing, sales, and
evaluation process, car makers need quality
raw materials to take their vehicles from
concept to the consumer.
Source: Ford Motor Company

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A Framework to Measure Quality
Throughout the Data Pipeline
Think of Data as a Product
To ensure they are thinking of data as a product, data engineers can ask
themselves the following questions:
Have I defined user
personas or data users
representing people who
will use the data now and in
the future?
Have I defined user stories
or data use cases that
describe a user completing
a task specific to their goal?
Have I defined unit tests or
data quality assessments
that assess whether the
process is behaving as
expected, and the data are
fit for purpose?
Have I deployed data
quality assessments at the
earliest appropriate point in
the data pipeline?
Have I identified and deployed user personas,
user stories, and data quality assessments for the
components of the data production process that
are upstream of my data product?
Have I documented each assessment in a
transparent, accessible, and centralized place—
allowing new, existing, and upstream/downstream
data users to understand what users and use
cases the data supports and how the organization
defines and ensures data quality?

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A Framework to Measure Quality
Throughout the Data Pipeline
Address Structural Data Quality First
Health systems struggle to move to
higher data quality levels if the data is
not first structurally sound.
The levels described above build on
each other, and while content and
utility assessments will expose
structural issues, understanding the
root cause is more efficient when
leveraging specific structural
assessments.

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A Framework to Measure Quality
Throughout the Data Pipeline
Address Structural Data Quality First
For instance, determining whether an
encounter identifier is unique across
encounters and not NULL promotes
referential integrity.
When a data user then leverages that
identifier as a foreign key to link the
flowsheet and encounter data together,
the user can focus on assessing the
quality of the content across these two
subject areas, like whether the flow sheet
recorded date is during the encounter.

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A Framework to Measure Quality
Throughout the Data Pipeline
Define Content Level Data Quality with Subject Matter Experts
Understanding data use cases is extremely
important for defining and ensuring the
quality of the data content because it
requires subject matter expertise and can be
context dependent.
Potentially different from a data user,
organizations must identify a data subject
matter expert (SME) for defining content
level data quality because that expert will
understand the content.

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A Framework to Measure Quality
Throughout the Data Pipeline
Define Content Level Data Quality with Subject Matter Experts
The SME tailor definitions based on the
context (e.g., the heart rate is appropriate
given the patient’s age) to assess whether
data quality is sufficient for the intended
use cases.

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A Framework to Measure Quality
Throughout the Data Pipeline
Create a Coalition
Typically, organizations take a grassroots
approach to data quality by addressing it
within individual projects or department silos.
However, creating a data quality coalition
brings together organizational leaders,
managers, subject matter experts, and
analytics professionals—all with a vested
and shared interest in ensuring data quality
because it facilitates better decisions.

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A Framework to Measure Quality
Throughout the Data Pipeline
Create a Coalition
The team agrees on a standard approach
to advance proven processes and avoid
spending resources reinventing the wheel.
The coalition must have support from
leadership at the highest level for
organizational alignment in terms of
objectives and resources focused
on the work.

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Building Data Quality from the Ground Up
Fit-for-purpose quality data has
established itself as a strategic
imperative as health systems
continue to navigate COVID-19
and prepare for an emergency-
ready future.
The only way to ensure healthcare
organization leaders, managers,
and providers have data fit for
critical decision making is to
establish quality at the beginning
of the data life cycle and maintain
it throughout all processes.

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Building Data Quality from the Ground Up
A structured quality process, such as the
Healthcare Data Quality Framework,
engages technical and subject matter
expertise to define, evaluate, and monitor
data quality throughout the pipeline.
As a result, health systems don’t just
make data-informed decisions—they
make quality data-informed decisions.

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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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Taylor 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 Larsen

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
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