1. A Guide to Applying Quality Improvement
to Healthcare: Five Principles

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Healthcare Quality Improvement
The U.S. spends 18 percent of its Gross
Domestic Product (GDP) on healthcare, yet,
by some measures, is the least healthy of all
its peer countries.
Approximately $1 trillion is thought to be
wasteful spending and 14 percent of that
($140 billion) is due to clinical waste.
Healthcare organizations can change this
wasteful trajectory by applying quality
improvement methods to improve their
processes.

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Healthcare Quality Improvement
Since healthcare is complex, many in the
industry believe that the controls and
standardization suggested by quality
improvement methods are difficult for the
industry to adopt.
But general quality improvement
methods—defining quality, developing
improvement measures, identifying
variation, using control charts, and running
Plan-Do-Study-Act (PDSA) cycles—have
been successfully applied to healthcare
processes, healthcare, and health.

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Healthcare Quality Improvement
With the proper application of data
and analytics within the appropriate
quality improvement framework,
healthcare organizations can
approach quality improvement
scientifically—and effectively.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
Quality improvement methods have been
commonly used in agriculture and
manufacturing environments built on
processes, but some believe these methods
can’t be applied to healthcare because of its
craftsmanship nature.
Patient care isn’t typically viewed as a
process that can be improved. Clinicians
rely on their expertise to care for patients,
making tailored decisions one case at a time.
One of biggest barriers to quality
improvement in healthcare is not
understanding that systems and processes
may coexist with personalized care.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
When applying quality improvement methods and tools to
healthcare, there are five guiding principles healthcare
organizations should consider.
1. Facilitate Adoption Through Hands-on Improvement Projects
2. Define Quality and Get Agreement
3. Measure for Improvement, Not Accountability
4. Use a Quality Improvement Framework and PDSA Cycles
5. Learn from Variation in Data

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#1: Facilitate Adoption Through Hands-on Improvement Projects
Simply exposing clinicians to ideas and discussing
case studies around quality improvement doesn’t
motivate them to adopt improvement initiatives.
Quality improvement theory and methodology is
better learned through hands-on improvement
work—applying it to the actual clinical
environment.
Identifying an area that is important to
clinicians and creating the platform for
improvement will facilitate adoption.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#2: Define Quality and Get Agreement
Getting agreement on the definition of quality in any
particular context establishes what to measure and
how to collect data on those measures.
The Institute of Medicine (IOM) developed a quality
framework around six aims for healthcare systems,
but the most salient one for defining quality states
that measures should be patient-centered:
Providing care that is respectful of, and
responsive to, individual patient preferences,
needs, and values and ensuring that patient
values guide all clinical decisions.”

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#2: Define Quality and Get Agreement
Definitions of quality should include what’s important to
the patient (patient-reported outcomes, or PROs).
Are patients with chronic disease getting the
best care? How is their quality of life?
Health systems are still learning how to
routinely measure PROs to understand if
they are focused on improvements that
matter to the patient.
In addition to focusing on the patient-
centered dimension of care, quality
improvement efforts focus on safety,
effectiveness, efficiency, and timeliness.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#2: Define Quality and Get Agreement
The IOM quality framework also defines
quality in terms of healthcare equity:
Providing care that does not vary in quality
because of personal characteristics such as
gender, ethnicity, geographic location, and
socioeconomic status.”
A good operational definition of quality
extends improvement to all population
segments and closes care equity gaps.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#3: Measure for Improvement, Not Accountability
Data and measurement power quality
improvement, but this is where healthcare is
tougher than other industries.
When clinicians first hear about quality
improvement measures, they equate them
with performance measures, which imply
accountability.
It’s important to separate measures for
improvement from measures for
accountability. Measures for accountability
are typically converted to percentages.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#3: Measure for Improvement, Not Accountability
For example, an accountability measure collects
data on the percent of ER patients who waited for
more than 30 minutes.
Management is held accountable for keeping wait
times under 30 minutes.
An improvement measure collects actual wait time
data in minutes to measure system (not people)
performance, so a process can be improved.
Improvement measures create high-value data
that leads to dramatic improvement, saving
time and resources.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
Several frameworks have been adopted for quality improvement in healthcare:
Six Sigma’s DMAIC model (define, measure, analyze, improve, control) examines
existing processes; its DMADV model (define, measure, analyze, design, verify) is
used to develop new processes.
Lean methodology emphasizes value for patients, then focuses improvements on
processes that impact cost and time efficiencies.
The Model for Improvement framework was developed by the Associates in
Process Improvement (API) in 1987 and has become the most prominent
framework in healthcare.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
An improvement team should ask three fundamental questions:
What are we trying to accomplish? This question establishes the aim for
improvement efforts and ensures that collected data is related to patients’
perception of quality.
How will we know that a change is an improvement? This question sets up the
criteria for determining when change results in sustainable improvement.
What changes can we make that will result in improvement? This question leads
to the PDSA cycle, which tests small scale changes, or interventions, to see their
effect on outcomes.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
Goal: The high-level project objective, which is measured by outcome measures.
Example: Reduce 30-day, all-cause chronic obstructive pulmonary disease
(COPD) readmissions.
Aim: An incremental objective that contributes to the overall goal. An improvement
project can have multiple aims, each determined by process measures. Example:
Reliably identify the patient, then activate the COPD care bundle.
Intervention: Changes to system or process designed to improve performance of
outcome and process measures. Example: Train physicians on COPD bundle use.
To answer these questions, the improvement team sets goals, aims, and
interventions in pursuit of high-value improvement measures:

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
Each intervention goes through a PDSA cycle to test
its validity and to adapt it to the specific context.
While it can appear simplistic compared to other
methodologies, the PDSA cycle, in a repeated
application, is the backbone of quality improvement.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
Plan: Establish the objective, determine what questions need to be asked and
what predictions need to be made, and then plan to carry out the cycle.
Example PDSA cycle:
Goal: The high-level project objective, which is measured by outcome measures.
Example: Reduce 30-day, all-cause
Study: Complete the data analysis, compare the data to the predictions, and then
summarize learnings.
Act: Determine what changes will be made and what the next cycle will be.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
An improvement project usually involves several
PDSA cycles. The key to quality improvement
success is understanding that PDSA is an
iterative process.
After each cycle, the improvement team
assesses the success of the associated
intervention. At some point, the intervention is
adopted or abandoned, which indicates the end
of PDSA cycles for that intervention.
Then the team can move to the next intervention.
Reaching the global aim indicates completion of
the overall quality improvement project.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#4: Use a Quality Improvement Framework and PDSA Cycles
This improvement process is well-illustrated in a
recent paper by Zafar and others, which
documents 36 PDSA cycles over 10 interventions
during an improvement project to reduce COPD
readmissions.
This is an excellent example of properly applying
quality improvement in healthcare and shows
how the model works with aims, measurements,
and change theories.
Comprehending variation in data is a vital
component of this study.
REDUCING COPD
READMISSIONS

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
A deep knowledge of the Model for Improvement
framework helps the team accomplish the
improvement goal.
Part of this knowledge comes from understanding
variation in data and the causes of that variation.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Intended and unintended variation in data.
Healthcare processes involve both intended and
unintended variation. Intended variation is
purposely deciding to do something a different way.
It’s what defines patient-centered care. Clinicians
sometimes resist the idea of reducing variation
because it’s part of their everyday practice.
They purposely prescribe one dosage or treatment
to one patient, and another dosage or treatment to
the next. Intended variation is a desirable practice
and part of the job description.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Intended and unintended variation in data.
The theory of variation was proposed for
identifying and removing unintended variation.
Multiple systems with multiple sets of unintended
variation create significant unnecessary costs.
Unintended variation occurs when several
clinicians in the same practice prescribe different
antibiotics for the same problem without a
specific rationale for, or awareness of, the
variation.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Intended and unintended variation in data.
If the variation isn’t thoughtful or it’s out of habit
or convenience, then it’s unintended.
But if each clinician has a rationale behind their
individual choices and the variation continues,
then it’s intended.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Common cause and special cause variation
Walter Shewhart developed the concept of
common cause and special cause variation.
Common causes are an inherent part of a system
or process that impact all people and outcomes.
Special causes arise from specific circumstances
that impact only a subset of people or outcomes.
Understanding common cause and special cause
variation helps health systems identify the
changes they can make to result in improvement.
WALTER SHEWHART
The father of statistical
quality control

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Common cause and special cause variation
Studying common cause and special cause
variation is the cornerstone of improvement
because it shows why the variation occurred
and suggests the most effective approach to
address it.
Shewhart’s control chart method provides
this insight.
WALTER SHEWHART
The father of statistical
quality control

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Common cause and special cause variation
A case study from Cincinnati Children’s Hospital
Medical Center shows the impact of special
cause variation on catheter-associated
bloodstream infections (CA-BSIs).
The hospital had multiple improvement projects
working on the common causes of variation
associated with CA-BSI rates.
It was showing significant improvement over an
eight-year period, and then rates unexpectedly
increased beyond the upper control limit.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Common cause and special cause variation
The hospital conducted a series of investigative studies
that indicated special cause variation in two units.
It discovered that a new medical device had been
introduced that wasn’t part of the system.
Because it was monitoring through control charts,
it was able to pinpoint when the problem occurred
and remove the special cause.
Control charts are one of five tools health systems
can use to learn from variation in data.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Tools for learning from variation in data
The following tools allow improvement teams to see the status of a whole
system and use discoveries—revealed by variation in the data—to review
and change processes:
Control charts: Show the data
associated with special and common
causes and enable teams to improve
the system by removing special
causes or changing common causes.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Tools for learning from variation in data
The following tools allow improvement teams to see the status of a whole
system and use discoveries—revealed by variation in the data—to review
and change processes:
Run charts: Precursor to control
charts that include visual display of
measures over time.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Tools for learning from variation in data
The following tools allow improvement teams to see the status of a whole
system and use discoveries—revealed by variation in the data—to review
and change processes:
Frequency Plots: Used to visualize
the data patterns for continuous data
(e.g., wait times, length of stay, and
cost per case).

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Tools for learning from variation in data
The following tools allow improvement teams to see the status of a whole
system and use discoveries—revealed by variation in the data—to review
and change processes:
Pareto Plots: Used to visualize
qualitative data (e.g., patient
perceptions of care and quality of life)
and focus improvement efforts.

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Applying Quality Improvement in Healthcare:
Five Guiding Principles
#5: Learn from Variation in Data
Tools for learning from variation in data
The following tools allow improvement teams to see the status of a whole
system and use discoveries—revealed by variation in the data—to review
and change processes:
Scatter Plots: Used to visualize
relationships between measures.

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Quality Improvement in Healthcare:
Start Small to Make Big Changes
The complexities of healthcare operations and
the vast amount of variation and waste in U.S.
healthcare make the undertaking of a quality
improvement initiative seem impossible
But healthcare quality improvement is achievable
when systems use the five principles outlined in
this article as their guide, from getting clinician
buy-in to using an improvement framework that’s
based on scientific methodology.
Health systems can change the dynamic and
pace of quality improvement work by testing on a
small scale and learning from those tests.

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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.
A Guide to Applying Quality Improvement to Healthcare: Five Principles
How to Determine the Best Interventions for Clinical Quality Improvement Projects
Kathleen Merkley, Sr. VP, Professional Services; Ann Tinker, VP, Customer Engagements
Quality Improvement in Healthcare: Where Is the Best Place to Start?
Eric Just, Sr. VP of Product Development
Use Well-Crafted Aim Statements To Achieve Clinical Quality Improvements
Cherbon VanEtten, Director of Education
The Top Five Essentials for Outcomes Improvement
Leslie Hough Falk, Sr. VP; Ann Tinker, VP, Customer Engagements
Five IT Must-Haves for Quality Initiatives in Healthcare
Ann Frasher, Sr. Director, Client Engagement; Ann Tinker, VP, Customer Engagements

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
With an education in statistics, Lloyd works as an advisor to organizations, helping them make
improvements in their products and services and increase their capacity to continually learn and
improve. His experience includes consulting in planning, management systems, measurement,
planned experimentation, and other methods for improvement of quality and productivity. Lloyd
has advised clients worldwide in a variety of industries including computers, health care,
chemical, manufacturing, engineering, construction, automotive, electronics, food, transportation,
professional services, retail, education, and government. Through API's partnership with the Institute of
Healthcare Improvement (IHI), Lloyd is a senior fellow and serves as an improvement advisor supporting
IHI's innovation and improvement programs. He serves as faculty for IHI's Improvement Advisor
Professional Development Program and supports IHI's programs in developing countries. Lloyd has a
Bachelor of Science in Statistics from the University of Tennessee and a Master of Science in Statistics
from the University of Florida. He is the author of several papers relating to quality and measurement and
co-author of books on planned experimentation and the science of improvement Quality Improvement
Through Planned Experimentation (2nd edition, McGraw-Hill, 1998) and The Improvement Guide: A
Practical Approach to Enhancing Organizational Performance (Jossey-Bass, 2009). He was the year 2003
recipient of the Deming Medal awarded by the American Society for Quality.
Lloyd Provost