3KEY TERMS AND ACRONYMSAgency for Healthcare Research .docx

3
KEY TERMS AND ACRONYMS
Agency for Healthcare Research and
Quality (AHRQ)
consumer-directed healthcare
evidence-based medicine (EBM)
health savings account
Institute of Medicine (IOM)
knowledge-based management
(KBM)
patient care microsystem
Vincent Valley Hospital and Health
System (VVH)
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EBSCO Publishing : eBook Academic Collection (EBSCOhost)
- printed on 2/28/2018 3:07 PM via TRIDENT UNIVERSITY
AN: 237622 ; McLaughlin, Daniel B., Hays, Julie M..;
Healthcare Operations Management
Account: s3642728
Introduction
The challenges and opportunities in today’s complex healthcare
delivery sys-
tems demand that leaders take charge of their operations. A
strong opera-

tions focus can reduce costs, increase safety, improve clinical
outcomes, and
allow an organization to compete effectively in an aggressive
marketplace.
In the recent past, the success of many organizations in the
Ameri-
can healthcare system has been achieved through executing a
few key
strategies: First, attract and retain talented clinicians; next, add
new tech-
nology and specialty care; and finally, find new methods to
maximize the
organization’s reimbursement for these services. In most
organizations,
new services—not ongoing operations—represented the key to
success.
However, that era is ending. Payer resistance to cost increases
and
a surge in public reporting on the quality of healthcare are
strong forces
driving a major change in strategy. To succeed in this new
environment,
a healthcare enterprise must focus on making significant
improvements
in its core operations.
This book is about how to get things done. It provides an inte-
grated system and set of contemporary operations improvement
tools that
can be used to make significant gains in any organization. These
tools
have been successfully deployed in much of the global business
commu-
nity for more than 30 years (Hammer 2005) and now are being

used by
leading healthcare delivery organizations.
This chapter outlines the purpose of the book, identifies
challenges
that current healthcare systems are facing, presents a systems
view of health-
care, and provides a comprehensive framework for the use of
operations tools
and methods in healthcare. Finally, Vincent Valley Hospital and
Health Sys-
tem (VVH), which is used in examples throughout the book, is
described.
Purpose of this Book
Excellence in healthcare derives from three major areas of
expertise: clinical
care, leadership, and operations. Although clinical expertise and
leadership
are critical to an organization’s success, this book focuses on
operations—
how to deliver high-quality care in a consistent, efficient
manner.
Many books cover operational improvement tools, and some
focus on
using these tools in healthcare environments. So, why a book
devoted to the
broad topic of healthcare operations? Because there is a real
need for an inte-
grated approach to operations improvement that puts all the
tools in a logi-
cal context and provides a road map for their use. An integrated
approach

I n t r o d u c t i o n t o H e a l t h c a r e O p e r a t i o n s
4
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C h a p t e r 1 : T h e C h a l l e n g e a n d t h e O p p o r t
u n i t y 5
uses a clinical analogy—first find and diagnose an operations
issue, then apply
the appropriate treatment tool to solve the problem.
The field of operations research and management science is too
deep
to cover in one book. In Healthcare Operations Management,
only tools and
techniques that are currently being deployed in leading
healthcare organiza-
tions are covered in enough detail to enable students and
practitioners to
“get things done” in their work. Each chapter provides many
references for
deeper study. The authors have also included additional
resources, exercises,
and tools on the website that accompanies this book.
This book is organized so that each chapter builds on the next
and is
cross-referenced. However, each chapter also stands alone, so a
reader inter-
ested in Six Sigma could start in Chapter 8 and then move back

and forth
into the other chapters.
This book does not specifically explore “quality” in healthcare
as
defined by the many agencies that have a mission to ensure
healthcare qual-
ity, such as the Joint Commission, National Committee for
Quality Assur-
ance, National Quality Forum, or federally funded Quality
Improvement
Organizations. The Healthcare Quality Book: Vision, Strategy
and Tools (Ran-
som, Maulik, and Nash 2005) explores this perspective in depth
and provides
a useful companion to this book. However, the systems, tools,
and tech-
niques discussed here are essential to make the operational
improvements
needed to meet the expectations of these quality-assurance
organizations.
The Challenge
The United States spent more than $2 trillion on healthcare in
2007—the
most per capita in the world. With health insurance premiums
doubling every
five years, the annual cost for a family for health insurance is
expected to be
$22,000 by 2010—all of a worker’s paycheck at ten dollars an
hour. The
Centers for Medicare & Medicaid Services predict that within
the next
decade, one of every five dollars of the U.S. economy will be
devoted to

healthcare (DoBias and Evans 2006).
Despite its high cost, the value delivered by the system has been
ques-
tioned by many policymakers. Unexplained variations in
healthcare have been
estimated to result in 44,000 to 98,000 preventable deaths every
year. Pre-
ventable healthcare-related injuries cost the economy between
$17 billion
and $29 billion annually, half of which represents direct
healthcare costs
(IOM 1999). In 2004, more than half (55 percent) of the
American public
said that they were dissatisfied with the quality of healthcare in
this country,
compared to 44 percent in 2000 (Henry J. Kaiser Foundation,
Agency for
Healthcare Research and Quality, and Harvard School of Public
Health
2004).Co
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I n t r o d u c t i o n t o H e a l t h c a r e O p e r a t i o n s6
These problems were studied in the landmark work of the
Institute of
Medicine (IOM 2001), Crossing the Quality Chasm—A New
Health System
for the 21st Century. The IOM panel concluded that the
knowledge to
improve patient care is available, but a gap—a chasm—

separates that knowl-
edge from everyday practice. The panel summarizes the goals of
a new health
system in six “aims.” (Box 1.1)
BOX 1.1
Six Aims of a
New Health
System
Patient care should be
1. Safe, avoiding injuries to patients from the care that is
intended to help
them;
2. Effective, providing services based on scientific knowledge
to all who
could benefit, and refraining from providing services to those
not likely to
benefit (avoiding underuse and overuse, respectively);
3. 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;
4. Timely, reducing wait times and harmful delays for both
those who receive
and those who give care;
5. Efficient, avoiding waste of equipment, supplies, ideas, and
energy; and
6. Equitable, providing care that does not vary in quality

because of personal
characteristics such as gender, ethnicity, geographic location,
and socio-
economic status.
SOURCE: Reprinted with permission from Crossing the Quality
Chasm—A New Health System for the 21st Cen-
tury © 2001 by the National Academy of Sciences, Courtesy of
the National Academies Press, Washington, D.C.
The IOM panel recommended ten steps to close the gap between
care
with the above characteristics and current practice (Box 1.2).
The ten steps to close the gap are:
1. Care based on continuous healing relationships. Patients
should receive
care whenever they need it and in many forms, not just face-to-
face visits.
This rule implies that the healthcare system should be
responsive at all
times (24 hours a day, every day), and that access to care should
be pro-
vided over the Internet, by telephone, and by other means in
addition to
face-to-face visits.
2. Customization based on patient needs and values. The system
of care
should be designed to meet the most common types of needs,
but have
the capability to respond to individual patient choices and
preferences.

BOX 1.2
Ten Steps to
Close the Gap
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u n i t y 7
3. The patient as the source of control. Patients should be given
all relevant
information and the opportunity to exercise whatever degree of
control
they choose over healthcare decisions that affect them. The
health system
should be able to accommodate differences in patient
preferences and
encourage shared decision making.
4. Shared knowledge and the free flow of information. Patients
should have
unfettered access to their own medical information and to
clinical knowledge.
Clinicians and patients should communicate effectively and
share information.
5. Evidence-based decision making. Patients should receive care
based on
the best available scientific knowledge. Care should not vary
illogically
from clinician to clinician or from place to place.

6. Safety as a system property. Patients should be safe from
injury caused
by the care system. Reducing risk and ensuring safety require
greater
attention to systems that help prevent and mitigate errors.
7. The need for transparency. The healthcare system should
make available
to patients and their families information that allows them to
make
informed decisions when selecting a health plan, hospital, or
clinical prac-
tice, or when choosing among alternative treatments. This
should include
information describing the system’s performance on safety,
evidence-
based practice, and patient satisfaction.
8. Anticipation of needs. The health system should anticipate
patient needs
rather than simply react to events.
9. Continuous decrease in waste. The health system should not
waste
resources or patient time.
10. Cooperation among clinicians. Clinicians and institutions
should actively
collaborate and communicate to ensure an appropriate exchange
of infor-
mation and coordination of care.
SOURCE: Reprinted with permission from Crossing the Quality
Chasm—A New Health System for the 21st Cen-
tury © 2001 by the National Academy of Sciences, Courtesy of

the National Academies Press, Washington, D.C.
Many healthcare leaders have begun to address these issues and
are cap-
italizing on proven tools employed by other industries to ensure
high per-
formance and quality outcomes. For major change to occur in
the U.S. health
system, however, these strategies must be adopted by a broad
spectrum of
healthcare providers and implemented consistently throughout
the contin-
uum of care—ambulatory, inpatient/acute settings, and long-
term care.
The payers for healthcare must engage with the delivery system
to find
new ways to partner for improvement. In addition, patients have
to assume a
stronger financial and self-care role in this new system.
Although not all of the IOM goals can be accomplished through
oper-
ational improvements, this book provides methods and tools to
actively
change the system to accomplish many aspects of them.
BOX 1.2
Ten Steps to
Close the Gap
(continued)
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The Opportunity
Although the current American health system presents numerous
challenges,
opportunities for improvement are emerging as well. Three
major trends pro-
vide hope that significant change is possible.
Evidence-Based Medicine
The use of evidence-based medicine (EBM) for the delivery of
healthcare is
the result of 30 years of work by some of the most progressive
and thought-
ful practitioners in the nation. The movement has produced an
array of care
guidelines, care patterns, and new shared decision-making tools
for both
caregivers and patients. The cost of healthcare could be reduced
by nearly 29
percent and clinical outcomes improved significantly if EBM
guidelines and
the most efficient care procedures were used by all practitioners
in the United
States (Wennberg, Fisher, and Skinner 2004).
Comprehensive resources are available to the healthcare
organization
that wishes to emphasize EBM. For example, the National
Guideline Clear-
inghouse (NGC 2006) is a comprehensive database of evidence-
based clini-
cal practice guidelines and related documents and contains more

than 4,000
guidelines. NGC is an initiative of the Agency for Healthcare
Research and
Quality (AHRQ) of the U.S. Department of Health and Human
Services.
NGC was originally created by AHRQ in partnership with the
American
Medical Association and American Association of Health Plans,
now Amer-
ica’s Health Insurance Plans (AHIP).
Knowledge-Based Management
Knowledge-based management (KBM) employs data and
information, rather
than feelings or intuition, to support management decisions.
Practitioners of
KBM use the tools contained in this book for cost reduction,
increased safety,
and improved clinical outcomes. The evidence for the efficacy
of these tech-
niques is contained in the operations research and management
science liter-
ature. Although these tools have been taught in healthcare
graduate
programs for many years, they have not migrated widely into
practice.
Recently, the IOM (Proctor et al. 2005) has recognized the
opportunities
that the use of KBM presents with its publication Building a
Better Delivery
System: A New Engineering/Healthcare Partnership. In
addition, AHRQ and
Denver Health provide practical operations improvement tools
in A Toolkit
for Redesign in Healthcare (Gabow et al. 2003).

Healthcare delivery has been slow to adopt information
technologies,
but many organizations are now beginning to aggressively
implement elec-
tronic medical record systems and other automated tools.
Hillestad et al.
(2005) have suggested that broad deployment of these systems
could save up
to $371 billion annually in the United States.Co
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u n i t y 9
A More Active Role for the Consumer
Consumers are beginning to assume new roles in their own care
through the
use of health education and information and more effective
partnering with
their healthcare providers. Personal maintenance of wellness
though a healthy
lifestyle is one essential component. Understanding one’s
disease and treat-
ment options and having an awareness of the cost of care are
also important
responsibilities of the consumer.
Patients will become good consumers of healthcare by finding
and
using price information in selecting providers and treatments.
Many employ-
ers are now offering high-deductible health plans with

accompanying health
savings accounts (HSAs.) This type of consumer-directed
healthcare is likely
to grow and increase pressure on providers to deliver cost-
effective, customer-
sensitive, high-quality care.
The healthcare delivery system of the future will support and
empower
active, informed consumers.
A Systems Look at Healthcare
The Clinical System
To improve healthcare operations, it is important to understand
the systems
that influence the delivery of care. Clinical care delivery is
embedded in a
series of interconnected systems (Figure 1.1).
The patient care microsystem is where the healthcare
professional pro-
vides hands-on care. Elements of the clinical microsystem
include:
FIGURE 1.1
A Systems View
of Healthcare
SOURCE: Ransom, Maulik, and Nash (2005). Based on Ferlie,
E., and S. M. Shortell. 2001. “Improving the
Quality of Healthcare in the United Kingdom and the United
States: A Framework for Change.” The Milbank
Quarterly 79(2): 281–316.
Organization

Level C
Microsystem
Level B
Patient
Level A
Environment
Level D
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• The team of health professionals who provide clinical care to
the
patient;
• The tools the team has to diagnose and treat the patient (e.g.,
imaging
capabilities, lab tests, drugs); and
• The logic for determining the appropriate treatments and the
processes
to deliver this care.
Because common conditions (e.g., hypertension) affect a large
number of
patients, clinical research has determined the most effective
way to treat these
patients. Therefore, in many cases, the organization and

functioning of the
microsystem can be optimized.
Process improvements can be made at this level to ensure that
the
most effective, least costly care is delivered. In addition, the
use of EBM
guidelines can also help ensure that the patient receives the
correct treatment
at the correct time.
The organizational infrastructure also influences the effective
delivery
of care to the patient. Ensuring that providers have the correct
tools and skills
is an important element of infrastructure. The use of KBM
provides a mech-
anism to optimize the use of clinical tools.
The electronic health record is one of the most important
advances in
the clinical microsystem for both process improvement and the
wider use of
EBM. Another key component of infrastructure is the leadership
displayed by
senior staff. Without leadership, effective progress or change
will not occur.
Finally, the environment strongly influences the delivery of
care. Key
environmental factors include competition, government
regulation, demo-
graphics, and payer policies. An organization’s strategy is
frequently influ-
enced by such factors (e.g., a new regulation from Medicare, a
new

competitor).
Many of the systems concepts regarding healthcare delivery
were ini-
tially developed by Avedis Donabedian. These fundamental
contributions are
discussed in depth in Chapter 2.
System Stability and Change
Elements in each layer of this system interact. Peter Senge
(1990) provides a
useful theory to understand the interaction of elements in a
complex system
such as healthcare. In his model, the structure of a system is the
primary
mechanism for producing an outcome. For example, an
organized structure
of facilities, trained professionals, supplies, equipment, and
EBM care guide-
lines has a high probability of producing an expected clinical
outcome.
No system is ever completely stable. Each system’s
performance is
modified and controlled by feedback (Figure 1.2). Senge (1990,
75) defines
feedback as “any reciprocal flow of influence. In systems
thinking it is an
axiom that every influence is both cause and effect.” As shown
in Figure 1.2,
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u n i t y 11
higher salaries provide an incentive for higher performance
levels by employ-
ees. This, in turn, leads to better financial performance and
profitability;
increased profits provide additional funds for higher salaries,
and the cycle
continues. Another frequent example in healthcare delivery is
patient lab
results that directly influence the medication ordered by a
physician. A third
example is a financial report that shows an overexpenditure in
one category
that will prompt a manager to reduce spending to meet budget
goals.
A more formal systems definition with feedback includes a
process, a
sensor that monitors process output, a feedback loop, and a
control that
modifies how the process operates.
Feedback can be either reinforcing or balancing. Reinforcing
feedback
prompts change that builds on itself and amplifies the outcome
of a process,
taking the process further and further from its starting point.
The effect of
reinforcing feedback can be either positive or negative. For
example, a rein-
forcing change of positive financial results for an organization
could lead to
higher salaries, which would then lead to even better financial

performance
because the employees were highly motivated. In contrast, a
poor supervisor
could lead to employee turnover, short staffing, and even more
turnover.
FIGURE 1.2
Systems with
Reinforcing
and Balancing
Feedback+
+
+
–
–
Employee
motivation
Salaries
Financial
performance,
profit
Add or
reduce staff
Actual
staffing
level

Compare actual to
needed staff based
on patient demand
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Balancing feedback prompts change that seeks stability. A
balancing
feedback loop attempts to return the system to its starting point.
The human
body provides a good example of a complex system that has
many balancing
feedback mechanisms. For example, an overheated body
prompts perspira-
tion until the body is cooled through evaporation. The clinical
term for this
type of balance is homeostasis. A clinical treatment process that
controls drug
dosing via real-time monitoring of the patient’s physiological
responses is an
example of balancing feedback. Inpatient unit staffing levels
that drive where
in a hospital patients are admitted is another. All of these
feedback mecha-
nisms are designed to maintain balance in the system.
A confounding problem with feedback is delay. Delays occur
when
there are interruptions between actions and consequences. When
this hap-

pens, systems tend to overshoot and perform poorly. For
example, an emer-
gency department might experience a surge in patients and call
in additional
staff. If the surge subsides, the added staff may not be needed
and unneces-
sary expense will have been incurred.
As healthcare leaders focus on improving their operations, it is
impor-
tant to understand the systems in which change resides. Every
change will
be resisted and reinforced by feedback mechanisms, many of
which are not
clearly visible. Taking a broad systems view can improve the
effectiveness of
change.
Many subsystems in the total healthcare system are
interconnected.
These connections have feedback mechanisms that either
reinforce or balance
the subsystem’s performance. Figure 1.3 shows a simple
connection that
originates in the environmental segment of the total health
system. Each
process has both reinforcing and balancing feedback.
An Integrating Framework for Operations Management
in Healthcare
This book is divided into five major sections:
• Introduction to healthcare operations;
• Setting goals and executing strategy;

FIGURE 1.3
Linkages
Within the
Healthcare
System:
Chemotherapy
Payers want
to reduce
costs for
chemotherapy
New payment
method for
chemotherapy
is created
Chemotherapy
treatment needs
to be more
efficient to meet
payment levels
Changes are made in
care processes and
support systems to
maintain quality
while reducing costs
Environment Organization Clinical microsystem Patient
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u n i t y 13
• Performance improvement tools, techniques, and programs;
• Applications to contemporary healthcare operations issues;
and
• Putting it all together for operational excellence.
This schema reflects the authors’ view that effective operations
man-
agement in healthcare consists of highly focused strategy
execution and orga-
nizational change accompanied by the disciplined use of
analytical tools,
techniques, and programs. The book includes examples of
applications of this
approach to common healthcare challenges.
Figure 1.4 illustrates this framework. An organization needs to
under-
stand the environment, develop a strategy, and implement a
system to effec-
tively deploy this strategy. At the same time, the organization
must become
adept at using all the tools of operations improvement contained
in this
book. These improvement tools can then be combined to attack
the funda-
mental challenges of operating a complex healthcare delivery
organization.
Introduction to Healthcare Operations
The introductory chapters provide an overview of the

significant environ-
mental trends healthcare delivery organizations face. Annual
updates to
industry-wide trends can be found in Futurescan: Healthcare
Trends and
Implications 2008–2013 (Society for Healthcare Strategy and
Market Devel-
opment and American College of Healthcare Executives 2008).
Progressive
organizations will review these publications carefully. Then,
using this infor-
mation, they can respond to external forces by identifying either
new strate-
gies or current operating problems that must be addressed.
Business has been aggressively using operations improvement
tools for
the past 30 years, but the field of operations science actually
began many cen-
turies in the past. Chapter 2 provides a brief history.
Healthcare operations are being strongly driven by the effects of
EBM and pay-for-performance. Chapter 3 provides an overview
of these
trends and how organizations can effect change to meet current
challenges
and opportunities.
FIGURE 1.4
Framework for
Effective
Operations
Management in
Healthcare
Setting goals

and executing
strategy
Performance
improvement
tools,
techniques, and
programs
Fundamental
healthcare
operations
issues
High performance
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Setting Goals and Executing Strategy
A key component of effective operations is the ability to move
strategy to
action. Chapter 4 shows how the use of the balanced scorecard
can accom-
plish this aim. Change in all organizations is challenging, and
formal meth-
ods of project management (Chapter 5) can be used to make
effective, lasting
improvements in an organization’s operations.
Performance Improvement Tools, Techniques, and Programs

Once an organization has in place strategy implementation and
change
management processes, it needs to select the correct tools,
techniques,
and programs to analyze current operations and implement
effective
changes.
Chapter 6—Tools for Problem Solving and Decision Making—
outlines
the basic steps of problem solving, beginning with framing the
question or
problem and continuing through data collection and analyses to
enable
effective decision making. Chapter 7—Using Data and
Statistical Tools for
Operations Improvement—provides a review of the building
blocks for
many of the more advanced tools used later in the book. (This
chapter
may serve as a review or reference for readers who already have
good sta-
tistical skills.)
Some projects will require a focus on process improvement. Six
Sigma
tools (Chapter 8) can be used to reduce the variability in the
outcome of a
process. Lean tools (Chapter 9) can be used to eliminate waste
and increase
speed. Many healthcare processes, such as patient flow, can be
modeled and
improved by using computer simulation (Chapter 10), which
may also be
used to evaluate project risks.

Applications to Contemporary Healthcare Operations Issues
This part of the book demonstrates how these concepts can be
applied to
some of today’s fundamental healthcare challenges. Process
improvement
techniques are widely deployed in many organizations to
significantly
improve performance; Chapter 11 reviews the tools of process
improvement
and demonstrates their use in improving patient flow.
Scheduling and capacity management continue to be major
concerns for
many healthcare delivery organizations, particularly with the
advent of advanced
access. Chapter 12 demonstrates how simulation can be used to
optimize sched-
uling. Chapter 13—Supply Chain Management—explores the
optimal methods
of acquiring supplies and maintaining appropriate inventory
levels.
In the end, any operations improvement will fail unless steps
are taken
to maintain the gains; Chapter 14—Putting it All Together for
Operational
Excellence—contains the necessary tools. The chapter also
provides a more
detailed algorithm that can help practitioners select the
appropriate tools,
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u n i t y 15
methods, and techniques to make significant operational
improvements. It
includes an example of how Vincent Valley Hospital and Health
System (VVH)
uses all the tools in the book to achieve operational excellence.
Vincent Valley Hospital and Health System
Woven throughout the sections described below are examples
designed to
consistently illustrate the tools discussed. A fictitious but
realistic health sys-
tem, VVH, is featured in these examples. (The companion
website,
ache.org/books/OpsManagement, contains a more expansive
description
of VVH.)
VVH is located in a Midwestern city of 1.5 million. It has 3,000
employees, operates 350 inpatient beds, and has a medical staff
of 450 physi-
cians. In addition, VVH operates nine clinics staffed by
physicians who are
employees of the system. VVH has two major competitor
hospitals, and a
number of surgeons from all three hospitals recently joined
together to set
up an independent ambulatory surgery center.
Three major health plans provide most of the private payment to
VVH
and, along with the state Medicaid system, have recently begun
a pay-for-

performance initiative. VVH has a strong balance sheet and a
profit margin
of approximately 2 percent, but feels financially challenged.
The board of VVH includes many local industry leaders, who
have
asked the chief executive officer to focus on using the
operational techniques
that have led them to succeed in their businesses.
Conclusion
This book is an overview of operations management approaches
and tools. It
is expected that the successful reader will understand all the
concepts in the
book (and in current use in the field) and should be able to
apply at the basic
level some of the tools, techniques, and programs presented. It
is not
expected that the reader will be able to execute at the more
advanced level
(e.g., Six Sigma black belt, Project Management Professional).
However, this
book will prepare readers to work effectively with
knowledgeable profession-
als and, most important, enable them to direct their work.
Discussion Questions
1. Review the ten action steps recommended by IOM to close
the quality
chasm. Rank them from easiest to most difficult to achieve, and
give a
rationale for your rankings.

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2. Give three examples of possibilities for system improvement
at the
boundaries of the healthcare subsystems (patient, microsystem,
organi-
zation, and environment).
3. Identify three systems in a healthcare organization (at any
level) that
have reinforcing feedback.
level) that
have balancing feedback.
level) where
feedback delays affect the performance of the system.
References
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10-Year Spending Pro-
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Room for
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2003. A Toolkit for Redesign

in Healthcare. Washington, D.C.: Agency for Healthcare
Research and Quality.
Hammer, M. 2005. “Making Operational Innovation Work.”
Harvard Management
Update 10 (4): 3–4.
Henry J. Kaiser Foundation, Agency for Healthcare Research
and Quality, and Harvard
School of Public Health. 2004. National Survey on Consumers’
Experiences with
Patient Safety and Quality Information. Menlo Park, CA: Kaiser
Family Founda-
tion. [Online information; retrieved 8/28/06.]
www.kff.org/kaiserpolls/
upload/National-Survey-on-Consumers-Experiences-With-
Patient-Safety-and-
Quality-Information-Survey-Summary-and-Chartpack.pdf.
Hillestad, R., J. Bigelow, A. Bower, F. Girosi, R. Meili, R.
Scoville, and R. Taylor. 2005.
“Can Electronic Medical Record Systems Transform Health
Care? Potential
Health Benefits, Savings, and Costs.” Health Affairs 24 (5):
1103–17.
Institute of Medicine. 2001. Crossing the Quality Chasm—A
New Health System for
the 21st Century. Washington, D.C.: National Academies Press.
———. 1999. To Err Is Human: Building a Safer Health
System. Washington, D.C.:
National Academies Press.
National Guideline Clearinghouse (NGC). 2006. [Online
information; retrieved

8/28/06.] www.guideline.gov/.
Proctor, P., W. Reid, D. Compton, J. H. Grossman, and G.
Fanjiang. 2005. Build-
ing a Better Delivery System: A New Engineering/Health Care
Partnership.
Washington, D.C.: Institute of Medicine.
Ransom, S. B., J. S. Maulik, and D. B. Nash, (eds.), 2005. The
Healthcare Quality
Book: Vision, Strategy, and Tools. Chicago: Health
Administration Press.
Senge, P. M. 1990. The Fifth Discipline—The Art and Practice
of the Learning Orga-
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p
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ex
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f
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u n i t y 17
Society for Healthcare Strategy and Market Development and
American College of
Healthcare Executives. 2008. Futurescan: Healthcare Trends and
Implications
2008–2013. Chicago: Health Administration Press.
Wennberg, J. E., E. S. Fisher, and J. S. Skinner. 2004.
“Geography and the Debate
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Al
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d.

M
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y
fo
rm
w
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,
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18
2
CHAPTER
HISTORY OF PERFORMANCE IMPROVEMENT
CHAPTER OUTLINE
Operations Management in
Action
Overview
Background
Knowledge-Based Management
History of Scientific Management
Mass Production
Frederick Taylor
Frank and Lillian Gilbreth
Scientific Management Today
Project Management
Quality
Walter Shewhart
W. Edwards Deming
Joseph M. Juran
Avedis Donabedian
TQM and CQI, Leading to Six

Sigma
ISO 9000
Baldrige Award
JIT, Leading to Lean and Agile
Baldrige, Six Sigma, Lean, and ISO
9000
Service Typologies
Supply Chain Management
Conclusion
Discussion Questions
References
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19
KEY TERMS AND ACRONYMS
agile
Agency for Healthcare Research and
Quality (AHRQ)
Centers for Medicare & Medicaid
Services (CMS)
continuous quality improvement
(CQI)
critical path method (CPM)
Deming’s 14 points for healthcare

enterprise resource planning (ERP)
Institute for Healthcare Improve-
ment (IHI)
ISO 9000
Juran’s quality trilogy
just-in-time (JIT)
knowledge-based management
(KBM)
knowledge hierarchy
Lean
Malcolm Baldrige National Quality
Award
materials requirements planning
(MRP)
plan-do-check-act (PDCA)
plan-do-study-act, a variation of
plan-do-check-act
program evaluation and review tech-
nique (PERT)
service process matrix
service typologies
single-minute exchange of die
(SMED)
Six Sigma
statistical process control (SPC)
supply chain management (SCM)
systems thinking
total quality management (TQM)

Toyota Production System (TPS)
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TRIDENT UNIVERSITY
BHA 320- MGT OF HEALTH PROGRAMS
Module 4 - SLP
HEALTH CARE OPERATIONS AND QUALITY
From the library access the following text: Healthcare
Operations Management (Authors: Daniel B. McLaughlin &
Julie M Hays). Review Chapter 1: The Challenge and the
Opportunity (Introduction to Healthcare Operations).
Then, review common hospital operations problems
at http://www.beckershospitalreview.com/hospital-management-
administration/5-common-hospital-problems-and-suggestions-
for-how-to-fix-them.html.
Select two of the problems identified in the above article and
develop a 2- to 3-page paper assessing the reasons for the
problems and possible solutions (recommended solutions should
include a brief plan of action). In your paper, identity which of
the ten action steps recommended by Institute of Medicine
(IOM) to close the quality chasm is applicable to each selected
problem. The ten action steps can be found on pages 6 and 7 of
the text or at the following
link: http://www.nationalacademies.org/hmd/~/media/Files/Rep
ort%20Files/2001/Crossing-the-Quality-
Chasm/Quality%20Chasm%202001%20%20report%20brief.pdf
SLP Assignment Expectations
1. Conduct additional research to gather sufficient information
to support your identification of problems and recommended
solutions
2. Limit your response to a maximum of 3 pages.
3. Support your SLP with peer-reviewed articles, with at least 2

references. Use the following source for additional information
on how to recognize peer-reviewed
journals: http://www.angelo.edu/services/library/handouts/peerr
ev.php.
4. You may use the following source to assist in your formatting
your
assignment: https://owl.english.purdue.edu/owl/resource/560/01
/.
T h e H e a l t h c a r e Q u a l i t y B o o k92
All Baldrige applicants receive a feedback report evaluating the
strengths and weaknesses of their responses to each of the seven
categories.
The purpose of the feedback report is to document the analysis
of the appli-
cant’s response so that it can be used to evaluate the
organization’s responses
to future applications and identify potential gaps in the
organization’s strate-
gic planning and improvement activities.
The national Baldrige criteria serve as the framework for many
state
and local quality awards. In 2012, eligibility requirements for
the Baldrige
Award were changed; applicants now must have received a “top-
tier award”
from a state or local Baldrige-based award program or meet one
of five condi-
tions related to past national or state-based award performance.

Lean/Toyota Production System
The Massachusetts Institute of Technology developed the term
Lean in 1987
to describe product development and production methods that,
when com-
pared with traditional mass production processes, produce more
products
with fewer defects in a shorter time. The goal was to develop a
way to specify
value, align steps/processes in the best sequence, conduct these
activities with-
out interruption whenever someone requests them, and perform
them more
effectively (Womack and Jones 2003). Lean thinking,
sometimes called Lean
manufacturing or the Toyota Production System (TPS), focuses
on the removal
of waste (muda), which is defined as anything that is not needed
to produce
a product or service. Taiichi Ohno (cofounder of TPS)
identified seven types
of waste: (1) overproduction, (2) waiting, (3) unnecessary
transport, (4) over-
processing, (5) excess inventory, (6) unnecessary movement,
and (7) defects.
The focus of Lean methodology is a “back to basics” approach
that
places the needs of the customer first through the following five
steps:
1. Define value as determined by the customer, identified by the
provider’s ability to deliver the right product or service at an
appropriate price.
2. Identify the value stream, the set of specific actions required

to bring a
specific product or service from concept to completion.
3. Make value-added steps flow from beginning to end.
4. Let the customer pull the product from the supplier; do not
push
products.
5. Pursue perfection of the process.
Although Lean focuses on removing waste and improving flow,
it
also has some secondary effects. Quality is improved. The
product spends
less time in process, reducing the chances of damage or
obsolescence. The
simplification of processes reduces variation and inventory and
increases the
uniformity of outputs (Heim 1999).
Copying and distribution of this PDF is prohibited without
written permission.
For permission, please contact Copyright Clearance Center at
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AN: 863699 ; Joshi, Maulik, Ransom, Elizabeth R., Nash, David
B., Ransom, Scott B..; The Healthcare Quality Book
: Vision, Strategy, and Tools
Account: s3642728
93C h a p t e r 4 : Q u a l i t y I m p r o v e m e n t 93
Six Sigma

Six Sigma (3.4 defects per million) is a system for improvement
developed
by Hewlett-Packard, Motorola, General Electric, and others over
the course
of the 1980s and 1990s (Pande, Neuman, and Cavanagh 2000).
The tools
used in Six Sigma are not new. The thinking behind this system
builds on
the foundations of quality improvement established in the 1930s
through
the 1950s. What makes Six Sigma appear new is the rigor of
tying improve-
ment projects to key business processes and clear roles and
responsibilities for
executives, champions, master black belts, black belts, and
green belts.
The aim of Six Sigma is to reduce variation (eliminate defects)
in
key business processes. By using a set of statistical tools to
understand the
fluctuation of a process, managers can predict the expected
outcome of that
process. If the outcome is not satisfactory, management can use
associated
tools to learn more about the elements influencing the process.
Six Sigma
includes five steps—define, measure, analyze, improve, and
control—com-
monly known as DMAIC:
1. Define: Identify the customers and their problems. Determine
the key
characteristics important to the customer along with the
processes that
support those key characteristics. Identify existing output

conditions
along with process elements.
2. Measure: Categorize key characteristics, verify measurement
systems,
and collect data.
3. Analyze: Convert raw data into information that provides
insights into
the process. These insights include identifying the fundamental
and
most important causes of the defects or problems.
4. Improve: Develop solutions to the problem, and make
changes to the
process. Measure process changes, and judge whether the
changes are
beneficial or another set of changes is necessary.
5. Control: If the process is performing at a desired and
predictable level,
monitor the process to ensure that no unexpected changes occur.
The primary theory of Six Sigma is that a focus on reducing
variation
leads to more uniform process output. Secondary effects include
less waste,
less throughput time, and less inventory (Heim 1999).
Quality tools
One of the difficult things about quality is explaining how a
tool is different
from a process or a system. We can observe people using tools
and methods
for improvement. We can see them making a flowchart, plotting

a control
chart, or using a checklist. These tools and procedures are the
logical outcomes
written permission.
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Account: s3642728
of process and system changes that people have put in place or
implemented
to make improvements or identify a problem. People may use
several tools
and procedures to make improvements, and these tools may
form one part of
an improvement system. Although we can observe people using
the tools of
the system, the system (e.g., Six Sigma, Lean) itself is invisible
and cannot be
observed. Many of the more than 50 quality tools available
today were devel-
oped to “see” the quality system they are designed to support.
The American
Society for Quality (Tague 2004) has classified quality tools
into six categories:

1. Cause analysis
2. Evaluation and decision making
3. Process analysis
4. Data collection and analysis
5. Idea creation
6. Project planning and implementation
This section of the chapter is not intended to be a
comprehensive
reference on quality tools and techniques but rather highlights
some of the
more widely used tools. The following discussion organizes the
tools into
three categories:
1. Basic quality tools
2. Management and planning tools
3. Other quality tools
Basic Quality Tools
Basic quality tools are used to define and analyze discrete
processes that
usually produce quantitative data. These tools primarily are
used to explain
a process, identify potential causes for process performance
problems, and
collect and display data indicating which causes are most
prevalent.
5 whys
Simple to understand and perform, the 5 Whys exercise was
developed as a
basic method for drilling down through the symptoms of a
process or design
failure to identify the root cause. By asking why or what caused
the problem,

users of this technique can quickly identify possible root causes
and make
improvements that will correct the real problem, not just
address the symp-
toms. Key to successful use of this technique is not to stop the
analysis too
early so as to misidentify the root cause.
Control Chart
Also referred to as statistical process control, control charts are
graphs used to
display data for the purpose of identifying how processes or
outcomes change
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over time. Control charts contain three lines: a central/control
line (aver-
age), an upper control limit, and a lower control limit. These
boundaries are
used to measure and monitor performance to identify
performance tenden-
cies and variation. Control charts also can be used to assess the

impact of a
process change on performance, enabling the user to correct or
identify any
problems that arise (Tague 2004).
Histogram
A histogram is a graphical display of the frequency distribution
of a quality
characteristic of interest. A histogram makes variation in a
group of data
apparent and aids analysis of the distribution of data around an
average or
median value.
Cause-and-effect/fishbone Diagram
Cause-and-effect diagrams are sometimes referred to as
Ishikawa, or fish-
bone, diagrams. In a cause-and-effect diagram, the problem
(effect) is
stated in a box on the right side of the chart, and likely causes
are listed
around major headings (bones) that lead to the effect. Cause-
and-effect
diagrams can help organize the causes contributing to a complex
problem
(ASQ 2014).
Pareto Chart
Vilfredo Pareto, an Italian economist in the 1880s, observed
that 80 percent
of the wealth in Italy was held by 20 percent of the population.
Juran later
applied this principle to other applications and found that 80
percent of the
variation of any characteristic is caused by only 20 percent of
the possible

variables. A Pareto chart is a display of occurrence frequency
that shows this
small number of significant contributors to a problem, enabling
management
to concentrate resources and identify the frequency with which
specific errors
are occurring (Tague 2004).
Checksheet
Checksheets are a generic tool designed for multiple data-
collection purposes.
They are used to capture data measured repeatedly over time for
purposes
of identifying patterns, trends, defects, or causes of defects.
Data collected
using a checksheet can be easily converted into data
performance tools such
as histograms or Pareto charts (Tague 2004).
Management and Planning Tools
Managers use management and planning tools to organize the
decision-
making process and create a hierarchy when faced with
competing priorities.
These tools also are useful for dealing with issues involving
multiple depart-
ments in an organization and for creating an organization-wide
quality
culture.
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Balanced scorecard
Renowned management consultant Peter Drucker is often quoted
as having said
“you can’t manage what you don’t measure.” Developed by Dr.
Robert Kaplan
and Dr. David Norton, the balanced scorecard is used to collect,
measure, and
analyze the strategic planning and management of an
organization. This tool
transfers high-level organizational performance expectations to
the individual
department level to measure the impact of day-to-day operations
and deliver-
ables. Through visual display of performance measures in the
areas of finance,
customers, internal (business) processes, and employee learning
and growth, an
organization can reinforce its priorities and design specific
systems and processes
around its vision and strategy (Balanced Scorecard Institute
2014).
affinity Diagram
Affinity diagrams can encourage people to develop creative
solutions to
problems. For example, the use of an affinity diagram is a way
to create

order out of a brainstorming session. An issue or problem is
identified, and
then individuals record their own ideas about the issue/problem
on small
note cards. As a group, team members study the cards and then
group the
recorded ideas into common categories.
Matrix relations Diagram
The matrix relations diagram helps us answer two important
questions when
sets of data are compared: (1) Are the data related? and (2) How
strong is the
relationship? The House of Quality, a quality function
deployment tool, is an
example of a matrix relations diagram. It lists customers’ needs
on one axis
and an organization’s/product’s capabilities on the second axis.
The diagram
compares what the customer wants with how the vendor will
meet those
expectations. The matrix relations diagram can identify not only
relation-
ships between sets of data but also patterns in the relationships
and serves as
a useful checklist for ensuring that tasks are being completed
(Tague 2004).
stratification
When gathering data from multiple sources or conditions,
researchers may
use the technique of stratification to analyze and determine
whether data
variation exists among the sources. Stratification can help
researchers identify
patterns in the data and prevent misrepresentation of study

findings when
data from multiple sources are presented together.
scatter Diagram
Scatter diagrams enable users to identify whether a correlation
exists between
pairs of numerical data. Also known as a scatter plot or X-Y
graph, the scatter
diagram can be used in a root cause analysis to determine the
cause-and-effect
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relationship that two elements may have. The greater the
correlation between
the two elements, the more the data will display as a tight line
or curve, whereas
two disparate elements will display as a more scattered or
“shotgun” distribution.
Priorities Matrix
Use of a priorities matrix involves the application of a series of
planning tools

built around the matrix chart. When tasks outnumber available
resources,
managers can use this matrix to prioritize work on the basis of
data rather
than emotion. Priorities matrixes enable managers to
systematically discuss,
identify, and prioritize the criteria that most influence their
decisions about
which tasks to complete and to study different possibilities for
prioritizing
tasks (ASQ 2014).
Other Quality Tools
Benchmarking
Organizations use benchmarking to compare the processes and
successes of
their competitors or of similar top-performing organizations to
their own
processes to identify process variation and organizational
opportunities for
improvement.
failure Mode and effects analysis
Failure mode and effects analysis (FMEA) examines potential
problems and
their causes and predicts undesired results. FMEA normally is
used to pre-
dict product failure from past part failure, but it also can be
used to analyze
future system failures. This method of failure analysis generally
is performed
on product design and work processes. By basing their activities
on FMEA,
organizations can focus their efforts on steps in a process that
have the great-
est potential for failure before failure actually occurs.

Prioritization of failure
modes to address and mitigate is based on the detectability of
the potential
failure, its severity, and its likelihood of occurrence.
flowchart
Flowcharts are used to visually display the steps of a process in
sequential
order. Each step in a flowchart is displayed as a symbol that
represents a
particular action (e.g., process step, direction, decision, delay).
For quality
improvement purposes, flowcharts are useful tools for
identifying unneces-
sary steps in a process, developing procedures, and facilitating
communica-
tion between staff involved in the same process (Tague 2004).
spaghetti Diagram
First developed in the manufacturing industry to display the
path of an
item through a factory, spaghetti diagrams are used to identify
unnecessary
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repetition in a process and opportunities for improved
efficiency (i.e.,
removal of unnecessary steps). By visually displaying multiple
simultaneous
processes, spaghetti diagrams can reveal potential causes of
delay or unneces-
sary motion.
5s
The Japanese tool 5S (each step starts with the letter “S”) is a
systematic
program that helps workers take control of their workspace so
that it helps
them complete their jobs instead of being a neutral or, as is
commonly the
case, a competing factor:
1. Seiri (sort) means to keep only items necessary for
completing one’s
work.
2. Seiton (straighten) means to arrange and identify items so
that they can
be easily retrieved when needed.
3. Seiso (shine) means to keep items and workspaces clean and
in working
order.
4. Seiketsu (standardize) means to use best practices
consistently.
5. Shitsuke (sustain) means to maintain gains and make a
commitment to

continue to apply the first four Ss.
Mistake Proofing (Poka yoke)
A concept developed in the 1960s by Japanese industrial
engineer and TPS
cofounder Shigeo Shingo, mistake proofing is the creation of
techniques
and devices to ensure that processes work right from the first
time they are
implemented. Mistake proofing techniques can be used to
address potential
failures identified during FMEA. The goal of mistake proofing
is to make an
error impossible to occur or easily detectable before significant
consequences
result.
Knowledge transfer and spread techniques
A key aspect of any quality improvement effort is the ability to
replicate suc-
cesses in other areas of the organization. Barriers to spread and
adoption
(e.g., organizational culture, communication, leadership
support) exist in
any unit, organization, or system. However, failure to transfer
knowledge
effectively may cause an organization to produce waste,
perform inconsis-
tently, and miss opportunities to achieve benchmark levels of
operational
performance.
The concept of transfer of learning, developed in 1901, explores
how

individuals can apply lessons learned in one context to another
context. The
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theory relies on the notion that the characteristics of the new
setting are
similar enough to those of the previous setting that processes
can be repli-
cated and similar efficiencies can be gained in the new setting
(Thorndike
and Woodworth 1901).
In 1999, the Institute for Healthcare Improvement (IHI)
chartered
a team to create a “framework for spread.” In 2006, IHI
published “A
Framework for Spread: From Local Improvements to System-
Wide Change,”
a white paper that identified “the ability of healthcare providers
and their
organizations to rapidly spread innovations and new ideas” as a
“key factor

in closing the gap between best practice and common practice”
(Massoud et
al. 2006, 1). The report noted the following questions as
important for orga-
nizations to address when attempting to spread ideas to their
target popula-
tions (Massoud et al. 2006, 6):
• Can the organization or community structure be used to
facilitate spread?
• How are decisions about the adoption of improvements made?
• What infrastructure enhancements will assist in achieving the
spread aim?
• What transition issues need to be addressed?
• How will the spread efforts be transitioned to operational
responsibilities?
The following discussion presents techniques that can be used
to
facilitate spread within a department, across an organization, or
throughout
a system. The decision to use any of these techniques depends
on the goals
and complexity of the changes to be disseminated. Like the
group of quality
improvement systems and tools presented earlier in the chapter,
this selection
of knowledge transfer techniques is only a representative
sample of the many
methods available for this purpose.
Kaizen Blitz/Event

Kaizen, translated as “continuous improvement,” was developed
in Japan
shortly after World War II. Kaizen in any organization involves
ongoing
improvement that is supported and implemented at all levels of
an organiza-
tion. The key aspect of Kaizen is the continual focus on
improving a system
or process regardless of how well the system or process is
currently function-
ing. A Kaizen “blitz” or event is a highly focused improvement
effort aimed
at addressing a specific problem. Kaizen events are short in
duration—typi-
cally three to five days. As such, Kaizen blitzes/events are
intended to pro-
duce rapid changes that produce quick results. The approach to
improvement
taken during a Kaizen blitz/event typically involves common
improvement
methodologies (e.g., DMAIC, PDCA, value stream mapping)
and the partic-
ipation of teams with decision-making authority from multiple
departments
and levels of leadership.
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Rapid-Cycle Testing/Improvement
Two important characteristics of an effective spread model are
staff buy-in
and proof that the change will improve performance. Developed
by IHI,
rapid-cycle testing (or rapid-cycle improvement) was designed
to create vari-
ous small tests involving small sample sizes and multiple PDSA
cycles that
build on the lessons learned in a short period while gaining buy-
in from
staff involved in the change (see Exhibit 4.3). Successful tests
are applied to
other units in the organization, whereas unsuccessful tests
continue to be
revised for potential spread and further implementation. Rapid-
cycle testing
is designed to reduce the cycle time of new process
implementation from
months to days. To prevent unnecessary delays in testing or
implementation,
teams or units using rapid-cycle testing must remain focused on
testing solu-
tions and avoid overanalysis. Rapid-cycle testing can be
resource intensive
(i.e., involves high resource consumption in a short period) and
therefore
may require top-level leadership support.
Case Study: Reengineering Discharge in a Community-Wide
Collaborative Project to Reduce Hospital Readmissions

In August 2008, TMF Health Quality Institute initiated Care
Transitions, an
18-month project to reduce 30-day all-cause readmissions in the
Harlingen
referral region of the Lower Rio Grande Valley in South Texas.
The goal
of the project was to engage inpatient hospitals and their
“downstream” or
discharge providers (e.g., home health agencies, long-term care
facilities,
A P
S D
A P
S D
A P S D
D
S
P
A
D
S
P

A
Using Rapid Cycle to Implement Preprinted Orders
Use of orders V.4
by all physicians
and nurses
Will preprinted
orders be useful for
acute myocardial
infarction patients?
Lea
rnin
g
Cycle 5: Implement V.4;
conduct peer review of
documentation and use
Cycle 4: One-week trial of V.3 on the unit
Cycle 3: Two physicians do trial of V.2 for two days
Cycle 2: Dr. A uses V.1 on one patient
Cycle 1: Gather sample orders; have Dr. A provide feedback
EXHIBIT 4.3
Example of
Rapid-Cycle
Testing

Note: V.1, V.2, V.3, and V.4 refer to the consecutive versions
of the preprinted order sets being
tested. Each time the orders are modified during a test, a new
version of the orders is created.
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inpatient rehabilitation facilities) in identifying gaps in care
coordination and
implementing evidence-based interventions to reduce
unnecessary hospital
readmissions. As part of the Centers for Medicare & Medicaid
Services’
Quality Improvement Organization Program’s 9th Scope of
Work, TMF pro-
posed that home health agencies, hospices, skilled nursing
facilities (SNFs),
inpatient rehabilitation facilities (IRFs), and hospitals working
in collabora-
tion with each other and with physicians could achieve the goals
of the Care
Transitions project through

• improved communication during the transition of patients
from one
setting to another,
• use of community and provider-specific data reports to
increase
accountability and feedback on progress toward goals, and
• implementation of provider-specific evidence-based
interventions
focused on improving the quality of care during transitions.
During the recruitment phase of the project, TMF engaged 5
inpa-
tient hospitals, 28 home health agencies, 11 SNFs, and 2 IRFs.
Initial plan-
ning at the participating hospitals involved conducting a
process-of-care
investigation to determine the root causes of their readmission
rates. The
investigation included the following activities:
• Conducting staff interviews and interdisciplinary meetings
to
discuss the current discharge process in comparison to Project
RED
(Re-Engineered Discharge) and to identify barriers and areas for
improvement
• Analyzing project data provided by TMF (calendar year
2007 Medicare
claims), which included the facility’s 30-day readmission rate
and
discharge disposition (i.e., home, SNF, IRF, and long-term
acute care
hospital) in relation to the 30-day readmission rate

• Evaluating current Hospital Consumer Assessment of
Healthcare
Providers and Systems scores related to the hospital discharge
process
The hospitals identified the following root causes (TMF 2010):
• A weak or fragmented discharge plan
• Miscommunication or failure to communicate key
information at the
time of transition
• Discharged patients’ unpreparedness for discharge or self-
management
• Inadequate medical follow-up with discharged patients
after discharge
• Inadequate or poor communication with patients and/or
caregivers
when relating information about medicines, tests, and red flags
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Following the process-of-care and root cause investigations, the
par-
ticipating providers reviewed multiple hospital-based
interventions designed
to reduce unnecessary readmissions, such as (TMF 2010)
• Project RED,
• Project BOOST (Better Outcomes for Older adults through
Safe
Transitions),
• Care Transitions program’s Care Transitions Intervention,
and
• IHI’s guide to creating an ideal transition home.
Following review of the interventions, all hospitals
participating in the
Texas Care Transitions project chose to implement components
of Project
RED. Developed from a study conducted by Boston Medical
Center, Project
RED includes 11 components targeting patient education,
discharge plan-
ning, and postdischarge reinforcement:
1. Educate the patient about his or her diagnosis throughout the
hospital
stay.
2. Make appointments for clinical follow-up visits and testing
prior to
hospital discharge.
3. Discuss any tests or studies with the patient that have been
completed

in the hospital, and identify who will be responsible for
following up
on the results.
4. Organize postdischarge services.
5. Confirm the patient’s medication plan.
6. Reconcile the discharge plan with national guidelines and
critical
pathways.
7. Review with the patient the steps he or she should follow if a
problem
arises after discharge.
8. Expedite dissemination of the discharge summary to the
patient’s
physician and other clinicians involved in the patient’s follow-
up care
after discharge.
9. Give the patient a written discharge plan at the time of
discharge.
10. Implement “teach back” of the patient’s discharge plan by
asking the
patient to explain the details of the plan in his or her own
words.
11. Follow up on the discharge plan with the patient via
telephone two to
three days after discharge.
Throughout the Care Transitions project, TMF provided the
follow-
ing support to participating providers:

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• On-site technical support for team leaders, facility
leaders, and Care
Transitions committees
• Regional meetings in which community providers could
work together
across the care continuum to develop region- or community-
specific
solutions
• Reports identifying the percentage of patients readmitted
within 30
days who received a visit from a physician between hospital
discharge
and readmission
• Quarterly data reports and run charts (based on Medicare
claims data)
displaying readmission rate performance

• Medical staff education and provider education sessions
(e.g.,
medication reconciliation and health literacy)
• Data collection tools for monitoring the effectiveness of
the
implemented project components
• A patient discharge survey tool for monitoring the
effectiveness of the
implemented project components and ensuring that discharge
plans
met hospital core measurement requirements and national
guidelines
for patients with acute myocardial infarction, congestive heart
failure,
or pneumonia
Project results from one of the participating hospitals (see
Exhibits 4.4
and 4.5) suggest that the implementation of a community-based
project in
which providers across the patient care continuum work
together can reduce
unnecessary hospital readmissions. Support from leadership,
accountability
for implementation of evidence-based interventions, and
concurrent moni-
toring are critical to sustaining process redesign efforts.
Collaboration among
providers across the community on behalf of the patient fosters
an awareness
of other individual and organizational efforts and successes in
overcoming
21.9%

23.1% 22.3% 22.2%
23.7% 23.0%
21.5%
22.6% 22.3%
19.5%
14.0%
16.0%
18.0%
20.0%
22.0%
24.0%
26.0%
CY 2007 Baseline Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009
Q3 2009 Q4 2009 Q1 2010
VBMC-B Harlingen HRR Target (Q1 2010)
EXHIBIT 4.4
Percentage of
30-Day
Readmissions
at One
Participating
Hospital
(semiannual
rate ending
in the listed
quarter)
Source: TMF Health Quality Institute. Used with permission.

written permission.
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mutual impediments to improvement. Collective problem
solving can expe-
dite the application of evidence-based care practices and the use
of process
redesign methods.
Conclusion
An organization’s success depends on the foundation on which
it was built
and the strength of the systems, processes, tools, and methods it
uses to
sustain benchmark levels of performance and to identify and
improve per-
formance when expectations are not being met. Although
quality improve-
ment theory and methodology have been available since the
early 1900s,
their widespread acceptance and application by the healthcare
industry have
not occurred as rapidly and effectively as in other industries

(e.g., manufac-
turing). The release of two Institute of Medicine publications
(Crossing the
Quality Chasm [IOM 2001] and To Err Is Human [Kohn,
Corrigan, and
Donaldson 2000]) describing significant concerns about the US
healthcare
system incited a movement toward improvement that greatly
increased
healthcare institutions’ focus on better care and patient safety
(Berwick and
Leape 2005). However, because of a combination of technical
complexity,
system fragmentation, a tradition of autonomy, and hierarchical
authority
structures, overcoming the “daunting barrier to creating the
habits and
beliefs of common purpose, teamwork and individual
accountability” neces-
sary for spread and sustainability will require a continual focus
and commit-
ment (Berwick and Leape 2005). Sustainable improvement is
further defined
through will, ideas, and execution. “You have to have the will
to improve,
0.0%
5.0%
10.0%
15.0%
20.0%

25.0%
30.0%
35.0%
HHA Home IRF LTAC SNF Total
Hospital Q1
2008
Hospital Q1
2010
HHRR Q1
2010
EXHIBIT 4.5
Percentage of
Discharges with
a 30-Day
Readmission
to One
Participating
Hospital
Source: TMF Health Quality Institute. Used with permission.
written permission.
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you have to have ideas about alternatives to the status quo, and
then you
have to make it real—execution” (Nolan 2007). The principles
described in
this chapter have demonstrated success in many healthcare
organizations.
As healthcare technology advances and access to care improves,
healthcare
must continue to build on these principles as it strives to reach
and maintain
benchmark levels of performance. Successful coordination of
care across the
healthcare continuum will provide the right care for every
patient at the right
time, every time.
study Questions
1. How would you select and implement one or more of the
approaches
described in this chapter in your own institution?
2. What are some of the challenges to spreading change?
Identify two
key questions/issues that need to be considered when applying
change

concepts in an organization or system.
3. How would a healthcare organization choose elements to
measure and
measurement tools when seeking to improve the quality of care?
4. How would you encourage your organization to work with
other
healthcare organizations across the healthcare continuum? Name
two
factors that are key to ensuring collaboration/coordination
among
healthcare providers.
5. What are some of the key elements common to the different
tools
discussed in this chapter?
6. What is the difference between a quality improvement system
and a
quality improvement tool? Provide examples of each.
references
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Laying the Founda-
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American Society for Quality (ASQ) Quality Management
Division. 1999. The Certi-
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Press.
Balanced Scorecard Institute. 2014. “Balanced Scorecard

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30.
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Brown, J. A. 2003. The Healthcare Quality Handbook: A
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Certain in Uncertain Times.
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———. 2000b. Out of the Crisis. Cambridge, MA: MIT Press.
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McGraw-Hill.
Heim, K. 1999. “Creating Continuous Improvement Synergy
with Lean and TOC.”
Paper presented at the American Society for Quality Annual
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gress, Anaheim, California, May.
Hertz, H. S. (ed.). 2010. Education Criteria for Performance
Excellence (2009–
2010): Baldrige National Quality Program. Darby, PA: DIANE
Publishing.
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Press.

Juran, J. M. 1989. Juran on Leadership for Quality. New York:
Free Press.
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York: McGraw-Hill.
Kilian, C. 1988. The World of W. Edwards Deming. Knoxville,
TN: SPC Press.
Kohn, L.T., J.M. Corrigan, and M.S. Donaldson (eds.). 2000. To
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Academies Press.
Langley, G., K. Nolan, T. Nolan, C. Norman, and L. Provost.
1996. The Improve-
ment Guide: A Practical Approach to Enhancing Organizational
Performance.
San Francisco: Jossey-Bass.
Massoud, M. R., G. A. Nielson, K. Nolan, T. Nolan, M. W.
Schall, and C. Sevin.
2006. “A Framework for Spread: From Local Improvements to
System-Wide
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Institute for
Healthcare Improvement.
Neave, H. R. 1990. The Deming Dimension. Knoxville, TN:
SPC Press.
Nolan, T. W. 2007. “Execution of Strategic Improvement
Initiatives to Produce
System-Level Results.” IHI Innovation Series white paper.
Cambridge, MA:
Institute for Healthcare Improvement.

Pande, P. S., R. P. Neuman, and R. R. Cavanagh. 2000. The Six
Sigma Way: How
GE, Motorola, and Other Top Companies Are Honing Their
Performance. New
York: McGraw-Hill.
QualityGurus.com. 2014. “Armand V. Feigenbaum.” Accessed
January 30. www.
qualitygurus.com/gurus/list-of-gurus/armand-v-feigenbaum.
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http://www.sigma-engineering
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feigenbaum
http://www.qualitygurus.com/gurus/list-of-gurus/armand-v-
feigenbaum
Tague, N. R. 2004. The Quality Toolbox, second edition.
Milwaukee, WI: ASQ Qual-
ity Press.
Thorndike, E. L., and R. S. Woodworth. 1901. “The Influence of

Improvement in
One Mental Function upon the Efficiency of Other Functions.”
Psychological
Review 8: 247–61.
TMF Health Quality Institute (TMF). 2010. Re-Engineering
Discharges in a Com-
munity-wide Project Reduces 30-Day Hospital Readmission
Rate SQUIRE.
Austin, TX: TMF Health Quality Institute.
Womack, J. P., and D. T. Jones. 2003. Lean Thinking: Banish
Waste and Create
Wealth in Your Corporation. New York: Free Press.
written permission.
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written permission.
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PART
II
HeaLtHCare QuaLity at
tHe organization anD
MiCrosysteM LeVeLs
written permission.
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CHAPTER
111
5Data CoLLeCtion
John J. Byrnes
e
verywhere you turn, everyone wants data. What do they really
mean?
Where do you get data? Is chart review the gold standard, the
best
source? Are administrative databases reliable; can they be the
gold stan-
dard? What about health plan claims databases—are they
accurate? What is
the best source for inpatient data that reflects the quality of
patient care from
both a process and an outcome perspective? When working in
the outpatient
environment, where and how would you obtain data that reflect
the level of
quality delivered in physician office practices? These questions
challenge
many healthcare leaders as they struggle to develop quality
improvement and
measurement programs. This chapter clarifies these issues and
common
industry myths and provides a practical framework for obtaining
valid, accu-
rate, and useful data for quality improvement work.

Categories of Data: Case example
Quality measurements can be grouped into four categories or
domains: (1)
clinical quality (including both process and outcome measures);
(2) financial
performance; (3) patient, physician, and staff satisfaction; and
(4) functional
status. To report on each of these categories, one may need to
collect data
from several separate sources. The challenge is to collect as
many data ele-
ments from as few data sources as possible with the objectives
of consistency
and continuity in mind. For most large and mature quality
improvement
projects, teams will want to report their organization’s
performance in all
four domains.
Spectrum Health’s clinical reporting (CR) system illustrates this
point.
The CR system contains more than 50 disease-specific
dashboards that report
performance at the system, hospital, and physician levels (see
Exhibit 5.1). In
Exhibit 5.2, a dashboard for total hip replacement provides
examples of clini-
cal quality and financial performance measures. To produce the
CR system,
Spectrum Health used a variety of data sources, including
extracts from its
finance and electronic health record (EHR) systems. The
decision support
department processed the data, applying a series of rigorous
data cleanup

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algorithms, adjusting for severity, and adding industry
benchmarks. The
resulting report contains measures of clinical processes
(antibiotic utiliza-
tion, deep vein thrombosis [DVT] prophylaxis, beta-blocker
administration,
autologous blood collection, and blood product administration),
financial
performance (lengths of stay, total patient charges, pharmacy
charges, lab
charges, X-ray charges, and intravenous therapy charges), and
clinical out-
comes (DVT, acute myocardial infarction [AMI], and
readmission within 31
days). From more than 200 indicators available in the database,
the total joint
quality improvement team selected these measures as the most
important for
assessing the quality and cost of care delivered. The measures
also include

some Joint Commission core measures.1
To obtain patient satisfaction information, the team uses
industry-
standard patient satisfaction surveys. The outbound call center
administers
these surveys by telephone within one week of a patient’s
discharge. The
results can be reported by nursing unit or physician, are updated
monthly,
and can be charted over the past eight quarters.
1. Chest pain
2. Heart attack
3. PCI
4. Heart failure
5. Pneumonia
6. Normal delivery
7. C-section
8. Bypass surgery
9. Valve surgery
10. Stroke—ischemic
11. Total hip replacement
12. Total knee replacement
13. Hip fracture
14. Abd. hysterectomy—non-CA
15. Abd. hysterectomy—CA
16. Lap hysterectomy
17. Cholecystectomy—lap
18. Cholecystectomy—open
19. Lumbar fusion
20. Lumbar laminectomy
21. Bariatric surgery
22. Colon resection
23. Diabetes and glycemic control

24. DVT
25. COPD
26. Upper GI bleed
27. SCIP
28. Peripheral vascular procedures
29. Pediatric asthma
30. Very low birth weight neonates
31. Pediatric appendectomy
32. RSV/bronchiolitis
33. Pediatric chemotherapy
34. Pediatric VP shunts
35. Pediatric hospitalist conditions
a. Bronchitis and asthma
b. Esophagitis and
gastroenteritis
c. Kidney and UTI
d. Nutritional and miscellaneous
metabolic disorders
e. Otitis media and URI
f. Pediatric pneumonia
g. Seizure and headache
h. Fever of unknown origin
36. NICU, PICU, and adult ICU (medi-
cal, surgical, and burn)
37. AHRQ patient safety indicators
38. Pain management
39. Sickle cell
40. Sepsis
41. 100,000 Lives Campaign

42. 5 Million Lives Campaign
43. National Patient Safety Goals
44. Rapid response team
EXHIBIT 5.1
Spectrum
Health’s Clinical
Reporting
System—
Available
Disease and
Project Reports
written permission.
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C h a p t e r 5 : D a t a C o l l e c t i o n 113
To complete the measurement set, the team includes the results
of
patients’ functional status (following their treatments). This
information
can be obtained from patients’ EHRs (if it has been included in
them) or

by using survey tools during follow-up visits. Many hospital
procedures are
performed to improve patients’ functional status. A patient who
undergoes a
total knee replacement, for example, should experience less
knee pain when
he or she walks, have a good range of joint motion, and be able
to perform
the activities of daily living that most of us take for granted.
For this report,
the team examines patients’ functional status before and after
hospitalization
to demonstrate that their treatments were effective.
In summary, when designing data collection efforts, quality
improve-
ment teams need to maintain a balanced perspective of the
process of care by
collecting data in all four categories: clinical quality, financial
performance,
Administrative Data Process
Coumadin Blood
No. of 1st gen. Low mol. or LMW Beta Autologous prod.
DVT Hip
Name patients Ceph Vancomycin Coumadin Heparin wt.
heparin heparin blocker blood coll. given prophylaxis* revision
BL 617 95.5% 9.9% 14.6% 23.0% 91.2% 96.6% 39.9% 1.8%
33.2% 99.7% 20.4%
BW 136 90.4% 11.8% 5.9% 5.1% 100.0% 100.0% 41.9% 4.4%
30.9% 100.0% 13.2%
SH-GR 753 94.6% 10.2% 13.0% 19.8% 92.8% 97.2% 40.2%
2.3% 32.8% 99.7% 19.1%
Administrative Data Outcome Education

Any Education
No. of readmit AMI participation
Name patients DVT AccPuncLac 30 days 2nd DX Los rate*
BL 617 0.6% 0.0% 4.2% 0.0% 3.67 59.3%
BW 136 0.0% 0.0% 4.4% 0.7% 3.78
** The education rate reflects all total joint replacement
patients who had their
SH-GR 753 0.5% 0.0% 4.2% 0.1% 3.69 surgery within the time
period stated on this dashboard.
JCAHO SCIP JCAHO Surgical Care Improvement Project
No. of Preop dose Antibiotic Selection Postop duration
Name patients (SCIP-INF-1)* (SCIP-INF-2) (SCIP-INF-3)*
SH-GR Varies 96.0% n = 75 100.0% n = 76 97.2% n = 72
Administrative Data Direct Costs
No. of ICU Laboratory OR Pharmacy Radiology R&B
Supplies Therapy Other Total
Name patients cost cost cost cost cost cost cost cost cost cost
BL 617 $71 $180 $2,219 $384 $79 $1,460 $1,944 $394 $217
$6,948
BW 136 $101 $127 $1,140 $405 $101 $1,801 $5,062 $389 $285
$9,410
SH-GR 753 $76 $170 $2,024 $388 $83 $1,521 $2,507 $393
$230 $7,393
Administrative Data Fully Allocated Costs
No. of ICU Laboratory OR Pharmacy Radiology R&B
Supplies Therapy Other Total
Name patients cost cost cost cost cost cost cost cost cost cost
BL 617 $117 $251 $3,711 $492 $162 $3,020 $2,078 $559 $326

$10,715
BW 136 $189 $176 $2,279 $515 $171 $3,215 $5,263 $578 $416
$12,802
SH-GR 753 $130 $237 $3,452 $496 $163 $3,055 $2,653 $562
$342 $11,092
Administrative Data Potential Direct Cost Savings
No. of Total cost
Name patients DVT AccPuncLac AMI 2nd DX (Patients above
average)
BL Varies $51,618 n = 4 $0 n = 0 $0 n = 0 $679,916 n = 189
BW Varies $0 n = 0 $0 n = 0 $9,653 n = 1 $165,825 n = 61
SH-GR Varies $49,770 n = 4 $0 n = 0 $11,614 n = 1 $920,655
n = 270
* Denotes indicators selected for “The Joint Commission”
Prepared June 10, 2007 by the Spectrum Health Quality
Department.
Spectrum Health Clinical Outcomes Report (COR)–Hip
Replacement
March 1, 2006 to February 28, 2007
EXHIBIT 5.2
Clinical Dash-
board—Hip
Replacement
Source: Spectrum Health, Grand Rapids, MI. Copyright 2008
Spectrum Health. Used with
permission.
written permission.

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patient satisfaction, and functional status. Teams that fail to
maintain this bal-
ance may overlook critical information. For instance, a health
system in the
Southwest initially reported that it had completed a series of
successful quality
improvement projects—clinical care had improved, patient
satisfaction was
at an all-time high, and patient outcomes were at national
benchmark levels.
However, subsequent review of the projects identified that some
of the inter-
ventions had negatively affected the system’s financial
outcomes. Revenue had
significantly decreased as a result of several interventions, and
other interven-
tions had increased the cost of care. If financial measures had
been included in
the reporting process, the negative financial effect could have
been minimized
and the same outstanding quality improvements would have
resulted. In the
end, the projects were considered only marginally successful

because they
lacked a balanced approach to process improvement and
measurement.
Considerations in Data Collection
Time and Cost Involved in Data Collection
All data collection efforts take time and money. The key is to
balance the
cost of data collection and the value of the data to your
improvement efforts.
In other words, are the cost and time spent collecting data worth
the effort?
Will the data have the power to drive change and improvement?
Although
this cost–benefit analysis may not be as tangible as it is in the
world of busi-
ness and finance, the value equation must be considered.
Generally, medi-
cal record review and prospective data collection are considered
the most
time-intensive and expensive ways to collect information. Many
reserve these
methods for highly specialized improvement projects or use
them to answer
questions that have surfaced following review of administrative
data sets. Use
of administrative data2 is often considered cost-effective,
especially because
the credibility of administrative databases has improved and
continues to
improve through the efforts of coding and billing regulations,
initiatives,3
and rule-based software development. Additionally, third-party
vendors can
provide data cleanup and severity adjustment. Successful data

collection
strategies often combine both code- and chart-based sources
into a data col-
lection plan that capitalizes on the strengths and cost-
effectiveness of each.
The following situation illustrates how the cost-effectiveness of
an
administrative system can be combined with the detailed
information available
in a medical record review. A data analyst using a clinical
decision support sys-
tem (administrative database) discovered a higher-than-expected
incidence of
renal failure (a serious complication) following coronary artery
bypass surgery.
The rate was well above 10 percent for the most recent 12
months (more
than 800 patients were included in the data set) and had slowly
increased over
the past six quarters. However, the clinical decision support
system did not
written permission.
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contain enough detail to explain why such a large number of
patients were
experiencing this complication—whether this complication
resulted from the
coronary artery bypass graft procedure or was a chronic
condition present
on admission. To find the answer, the data analyst used chart
review to (1)
verify that the rate of renal failure as reported in the
administrative data sys-
tem was correct, (2) isolate cases of postoperative incidence, (3)
identify the
root cause(s) of the renal failure, and (4) answer additional
questions about
the patient population that were of interest to the physicians
involved in the
patients’ care. In this example, the analyst used the
administrative system to
identify unwanted complications in a large patient population (a
screening or
surveillance function) and reserved chart review for a much
smaller focused
study (80 charts) to validate the incidence and determine why
the patients
were experiencing the complication. This excellent example
shows effective
use of two common data sources and demonstrates how the
analyst is able to
capitalize on the strengths of both while using each most
efficiently.
Collecting the Critical Few Rather than Collecting for a Rainy
Day
Many quality improvement efforts collect every possible data

element in case
it might be needed. Ironically, justification for this approach is
often based
on saving time—the chart has already been pulled, so one might
as well
be thorough. This syndrome of stockpiling “just in case” versus
fulfilling
requirements “just in time” has been studied in supply chain
management
and proven to be ineffective and inefficient. It also creates
quality issues
(Denison 2002). This approach provides little value to the data
collection
effort and is one of the biggest mistakes quality improvement
teams make.
Rather than provide a rich source of information, this approach
unnecessarily
drives up the cost of data collection, slows the data collection
process, creates
data management issues, and overwhelms the quality
improvement team with
too much information.
For all quality improvement projects, it is critical to collect
only the
data required to identify and correct quality issues. As a rule in
ongoing
data collection efforts, quality improvement teams should be
able to link
every data element collected to a report, thereby ensuring that
teams do
not collect data that will not be used (James 2003). In the
reporting project
discussed earlier, the hospital team was limited to selecting no
more than 15
measures for each clinical condition. It also selected indicators

that (1) have
been shown by evidence-based literature to have the greatest
effect on patient
outcomes (e.g., in congestive heart failure, the use of
angiotensin converting
enzyme [ACE] inhibitors and beta blockers and evaluation of
left ventricular
ejection fraction); (2) reflect areas in which significant
improvements are
needed; (3) will be reported in the public domain (Joint
Commission core
measures); and (4) together provide a balanced view of the
clinical process of
care, financial performance, and patient outcomes.
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Inpatient Versus Outpatient Data
The distinction between inpatient and outpatient data is an
important con-
sideration in planning the data collection process because the
data sources
and approaches to data collection can be different.

The case of a team working on a diabetes disease management
project
illustrates this point. First, disease management projects tend to
focus on
the entire continuum of care, so the team needs data from both
inpatient
and outpatient settings. Second, the team needs to identify
whether patients
receive the majority of care in one setting or the other and
decide whether
data collection priorities should be established with this setting
in mind. For
diabetes, the outpatient setting has the most influence on patient
outcomes,
so collection of outpatient data is a priority. Third, the team
must select the
measures that reflect the aspects of care that have the most
influence on
patient outcomes. Remembering to collect the critical few (as
discussed in the
previous section), the team would consult the American
Diabetes Association
(ADA) guidelines for expert direction. Fourth, the team must
recognize that
the sources of outpatient data are much different from the
sources of inpa-
tient data, and outpatient data tend to be more fragmented and
harder to
obtain. However, with the advent of outpatient EHRs and
patient registries,
the ease of collecting outpatient data is improving.
To identify outpatient data sources, the team should consider
the fol-
lowing questions:

• Are the physicians in organized medical groups that have
outpatient
EHRs? Can their financial or billing systems identify all
patients with
diabetes in their practices? If not, can the health plans in the
area
supply the data by practice site or individual physician?
• Some of the most important diabetes measures are based
on laboratory
testing. Do the physicians have their own labs? If so, do they
archive
the lab data for a 12- to 24-month snapshot? If they do not do
their
own lab testing, do they use a common reference lab that would
be
able to supply the data?
Once the team answers these questions, it will be ready to
proceed
with data collection in the outpatient setting.
sources of Data
As just discussed, the sources of data for quality improvement
projects are
extensive. Some sources are simple to access, while accessing
others is com-
plex; some data sources are inexpensive to use, while others are
expensive. In
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the average hospital or health system, data sources include
medical records,
prospective data collection, surveys of various types, telephone
interviews,
focus groups, administrative databases, health plan claims
databases, cost
accounting systems, patient registries, stand-alone clinical
databases, EHRs,
and lab and pharmacy databases.
The following objectives are essential to a successful quality
improve-
ment project and data collection initiative:
• Identify the purpose of the data measurement activity
(i.e., for
monitoring at regular intervals, investigation over a limited
period, or a
onetime study).
• Identify data sources that are most appropriate for the
activity.
• Identify the most important measures to collect (the
critical few).
• Design a common-sense strategy that will ensure

collection of
complete, accurate, and timely information.
By following these steps, project teams will gather actionable
data and
the information required to drive quality improvements.
Medical Record Review (Retrospective)
Retrospective data collection involves identification and
selection of a patient’s
medical record or group of records after the patient has been
discharged from
the hospital or clinic. Records generally cannot be reviewed
until all medical
and financial coding is complete because codes are used as a
starting point
for identifying the study cohort.
For several reasons, many quality improvement projects depend
on
medical record review for data collection. First, many
proponents of medical
record review believe it to be the most accurate method of data
collection.
They believe that because administrative databases have been
designed for
financial and administrative purposes rather than for quality
improvement,
the databases contain inadequate detail, many errors, and “dirty
data”—that
is, data that make no sense or appear to have come from other
sources.
Second, some improvement projects rely on medical record
review

because many of the data elements are not available from
administrative data-
bases. For example, most administrative databases do not
contain measures
that require a time stamp, such as administration of antibiotics
within one
hour before surgical incision.
Third, several national quality improvement database projects—
including the Healthcare Effectiveness Data and Information Set
(HEDIS),
Joint Commission core measures, Leapfrog Hospital Survey,4
and National
Quality Forum’s (NQF) National Voluntary Consensus
Standards for Hos-
pital Care—depend on retrospective medical record review for
collecting a
written permission.
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significant portion of the data elements required to be reported.
The records
not only contain measures requiring a time stamp but, for some

measures,
also require the data collector to include or exclude patients on
the basis of
criteria that administrative databases do not capture
consistently. The per-
centage of patients with congestive heart failure who are
receiving an ACE
inhibitor is an example of this type of measure. The use of ACE
inhibitors
in this population is indicated for all patients with an ejection
fraction of less
than 40 percent. The ejection fraction is not part of the typical
administrative
database. Sometimes this information is contained in a generally
inaccessible,
stand-alone database in the cardiology department, or it may be
contained
only in a transcribed report in the patient’s medical record.
Hence, accurate
reporting of this measure, one of the most critical interventions
that a patient
with congestive heart failure will receive, depends completely
on retrospec-
tive chart review. A consensus document presented to NQF5
suggested that
clinical importance should rate foremost among criteria for
effectiveness and
that measures that score poorly on feasibility6 because of the
burden of medi-
cal record review should not be excluded solely on that basis if
their clini-
cal importance is high (NQF Consumer, Purchaser, and
Research Council
Members 2002).
Fourth, focused medical record review is the primary tool for

answer-
ing the “why” of given situations (e.g., why patients were
experiencing a
particular complication, why a certain intervention negatively
affected patient
outcomes). Medical record review continues to be a key
component of many
data collection projects, but it needs to be used judiciously
because of the
time and cost involved.
The approach to medical record review involves a series of
well-
conceived steps, beginning with the development of a data
collection tool
and ending with the compilation of collected data elements into
a registry or
electronic database for review and analysis.
Prospective Data Collection, Data Collection Forms, and
Scanners
Prospective data collection also relies on medical record review,
but it is com-
pleted during a patient’s hospitalization or visit rather than
retrospectively.
Nursing staff, dedicated research assistants, or full-time data
analysts com-
monly collect the data. The downside to asking nursing staff to
collect data is
the effort involved; it is a time-consuming task that can distract
nurses from
their direct patient care responsibilities. A better approach
would be to hire
research assistants or full-time data analysts who can collect the
data and be
responsible for data entry and analysis. Because this job is their

sole respon-
sibility, the accuracy of data collection is greater. If they also
are responsible
for presenting their analyses to various quality committees, they
are likely to
review the data more rigorously.
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Module 4 - Case
HEALTH CARE OPERATIONS AND QUALITY
Assignment Overview
According to the Agency for Healthcare Research and Quality
(2002), “a central goal of healthcare quality improvement is to
maintain what is good about the existing healthcare system
while focusing on the areas that need improvement” (para. 2).
This assignment will familiarize you with the quality
improvement (QI) approaches and models that health care
administrators can effectively apply.
Case Assignment
Use the library to access the following book: The healthcare
quality book: vision, strategy, and tools. Review Chapter
4, Quality Improvement: Foundation, Processes, Tools, And
Knowledge Transfer Techniques. There are six
approaches/models of quality improvement discussed in Chapter
4.

Create an 8- to 10-slide PowerPoint (PPT) to discuss three of
the six approaches/models of quality improvement discussed.
Your presentation should address the following explicitly:
1. Explanation and/or reasoning for the importance of using
quality improvement as a health care administrator.
2. The steps, stages, or processes of each selected
approach/model.
3. Example of health care administrator’s applicable use of each
selected approach/model.
Assignment Expectations
1. Speaker notes, citations, and a reference slide are required.
2. Conduct additional research to gather sufficient information
to support the information presented in PPT.
3. Support your case with peer-reviewed articles, with at least 2
references (you can use the book as one reference). Use the
following source for additional information on how to recognize
peer-reviewed
journals: http://www.angelo.edu/services/library/handouts/peerr
ev.php.
4. You may use the following source to assist in formatting your
assignment: https://owl.english.purdue.edu/owl/resource/560/01
/

3KEY TERMS AND ACRONYMSAgency for Healthcare Research .docx

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