help with Assignment HCA 375 paper.docx

help with Assignment HCA 375 paper
Title of Paper Your name HCA375– Continuous Quality Monitoring and Accreditation Type
Instructor Name Here Type Date HCA375 – WEEK 4 ASSIGNMENT PART 1 – DETAIL OF
THE ADVERSE EVENT CHOSEN Refer to the instructions in the Week 4 Assignment of your
online course to understand what is expected in each row. This completed template should
be between eight to ten pages in length. Include APA citations within the description row
where appropriate. List your references in APA format according to the Ashford Writing
Center guidelines on the last page of this template. CONTENT ADVERSE EVENT
HISTORICAL BACKGROUND LEGAL & ACCREDITING AGENCY REQUIREMENTS CQI TEAM
COMMUNICATION DESCRIPTION OPERATIONAL OR SAFETY PROCESSES IMPACT OF THIS
EVENT WEEK 4 ASSIGNMENT PART 2 – GRAPH THE DATA You are tasked with graphing
the data in Excel for your chosen event. The data is located in the classroom under the Week
4 Assignment Directions. Make sure to use only the data for your chosen event. The
directions identify which columns of information to use depending on the chosen adverse
event. Once you complete the graph in Excel, copy/paste your graph below. Include an
analysis of the data in paragraph format. Discuss the frequency of the adverse event as
compared to the increase or decrease of patient discharges. What is the data telling you?
What possible factors in your opinion could be attributed to the change? WEEK 4
ASSIGNMENT PART 3 – CQI TOOL • • • Choose one of the CQI Tools listed below to illustrate
the use of the tool with your chosen Adverse Event. You will be responsible for creating the
CQI Tool, completing the tool, taking a screenshot, and copying/pasting the screenshot into
the space below. If you are unfamiliar with these tools, please refer to the recommended
readings, specifically the article from Week 2, which is listed below. You can locate the
article in the Ashford Library. In addition, as a learning resource, the CQI tools listed below
are hyperlinked to the Institute for Health Care Improvement website, which discusses and
illustrates examples of each type of tool. Siriwardena, A. (2009). Using quality improvement
methods for evaluating health care. Quality in Primary Care, 17(3), 155-159. ISSN: 1479-
1072 PMID: 19622265 • Choose a CQI Tool that best suits your chosen Adverse Event from
the following list. o Fishbone (Cause and Effect) Diagram o Flowchart o Pareto Diagram
WEEK 4 ASSIGNMENT PART 4 – FUTURE PREVENTION APPLYING PDSA – Worksheet
PHASE PHASE ACTIVITIES PLAN Problem Objective Team members Communication Data
collected Pilot phase DO Three possible solutions One solution to implement Result of pilot
(create own scenario) Methods of communication STUDY Summarize data Observations and
problems Comparison of pilot plan to pilot results Revisions needed to meet objective ACT

Revised improvement plan How to Implement the plan hospital wide Plan for monitoring
the improvement plan Checks and balance EXPLANATION References Excluding your
textbook or course readings include at least four scholarly sources in addition to the course
text. The sources should be peer-reviewed sources; that were published within the past five
years. All references must be cited within the rows where the referenced material was used
to support your ideas. The citations and references must be in APA format per the 6th
edition guidelines, which can be found at the Ashford Writing Center. These examples are
correctly-formatted references. Note that they are in alphabetical order. You may NOT use
these examples as your sources. 12/17/2019 Print Data Resources 5 Fuse/Thinkstock
Learning Objectives After reading this chapter, you should be able to do the following:
Illustrate the importance of data in quality improvement. Compare the different types of
data available for quality improvement. Assess internal sources of data for quality
improvement activities. Select appropriate external sources of data for comparative or
benchmarking purposes.
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Introduction Healthcare data can be used to drive improvements in organizations. In fact,
data plays a central role in the quality improvement process. It can help pinpoint areas
where organizations need to improve and then help demonstrate the results of those
quality improvement efforts. Let’s look at an example of how data can show how
organizations are performing and where improvements are needed—in this case, in the
country’s community health centers. Since 2007, the U.S. Health Resources and Services
Administration (HRSA) has made public updated data on the performance of the almost
1,200 community health centers across America. When the government released data for
calendar year 2012, it showed that some of these centers, which serve millions of mostly
poor people, fell short on key measures— with many of the centers failing to take steps such
as vaccinate children and help diabetics control their blood sugar levels. That was troubling
news because more than 21 million people rely on these federally funded community health
centers for their primary medical care (National Association of Community Health Centers,
2014). This role will only grow as the Affordable Care Act (ACA) has significantly expanded
the health center program to meet the increased demand for healthcare that is expected as
millions of the uninsured gain health coverage beginning in 2014. Therefore, evidence of the
quality of care that these centers provide is of major interest. The federal government
provides subsidies to support these centers and in 2007 began tracking quality indicators
for each center. For example, vaccination rates, blood pressure control, and prenatal care
are all quality measures that the federal government monitors at these clinics. With public
release of the data by HRSA, the Kaiser Commission on Medicaid and the Uninsured (2013)
partnered with George Washington University to analyze health center performance and
released a 14-page report. While most research shows the centers have a high performance
when it comes to healthcare standards, this data revealed gaps, and the numbers showed
wide variation in the quality of care delivered by these clinics. For instance, based on the
latest data for calendar year 2012, community health centers in New Hampshire were the
most likely to keep diabetics’ blood sugar under control, while Vermont’s health centers had

the best child immunization rates, and Maine’s centers had the highest percent of pregnant
women getting early prenatal care (Galewitz, 2012). Community health centers in New
England—where rates of insurance coverage are higher, making it more likely people will
seek care when they need it—generally performed better than centers in the South and
West. Mississippi health centers had some of the highest proportion of low birth-weight
babies, which places those infants at risk for certain health problems. Wyoming and Oregon
had some of the lowest child immunization rates. While there were differences in regions
and states, even centers in the same city often performed differently (Galewitz, 2012). One
of the key lessons from the measures collected from these community health centers is that
data plays a key role in quality reporting (i.e., whether institutions achieve certain quality
indicators) and also in the process of quality improvement. Measuring healthcare
performance can result in real improvements (National Committee for Quality Assurance,
2014). The National Committee for Quality Assurance (NCQA) measures performance by
health plans on a variety of factors aimed at improving care and keeping patients healthy. It
has been estimated that quality improvements by health plans in providing beta-blocker
treatment, cholesterol management, blood pressure control, and blood sugar control for
diabetics have saved thousands of lives (NCQA, 2014). Improvements in these healthcare
practices have real benefits for individuals. In
practical terms, smokers who are more consistently advised to quit are more likely to do so.
Heart attack victims are likely to live longer if their blood pressure and cholesterol are
controlled. Immunized children don’t miss as many school days because of illness and grow
to be healthier adults. Therefore, it is essential for students and practitioners of quality
improvement to understand healthcare data and how it can be used to drive change in
healthcare organizations.
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Role of Data in Quality Improvement Data is an essential component of any quality
improvement process. William Thomson (Lord Kelvin), noted in the 19th century that
“when you cannot measure it . . . your knowledge is of a meager and unsatisfactory kind . . .”
(Thomson, 1891, p. 80). The data-driven approach is integral to the major quality
improvement methods used in the United States, including the Plan-Do-Study-Act (PDSA)
model on which others are based. A variety of measurement approaches are also currently
used, but regardless of the specific approach to quality improvement, data assumes a
central role in defining the project work. Increasingly, the performance and reimbursement
of health systems in the United States is based on quality-oriented goals, whereas in the
past health systems focused on providing a high volume of healthcare services (Panzer et
al., 2013). In the past, healthcare providers were reimbursed based almost solely on the
quantity of services they provided, giving hospitals and physicians an incentive to order
diagnostic tests and services for patients. Improving quality relies increasingly on the use of
data; therefore, it is important to understand the roles played by data in any health system
quality improvement enterprise. Science and Society/SuperStock Lord Kelvin stressed the

value of measuring data a century before Six Sigma was created. First, sufficient data must
exist to identify the problem that is the focus of an improvement activity. Having identified
that an opportunity for improvement exists, additional data are typically necessary to
identify potential target(s) in the care process that can be improved. Organizational leaders
will look for the root cause of a problem by asking why the problem is occurring. For
instance, why are infection rates on the rise or patient falls increasing? It’s usually not a
simple answer. Looking for a root cause means delving deeply into what is behind a
particular problem—peeling the onion, so to speak, to determine the real cause.
Subsequently, leaders must use data about specific care processes to plan interventions that
will generate measurable changes. Once these interventions are put into place, health
system leaders use data to measure the impact of the intervention. Finally, data serve a key
monitoring role after a test of change becomes incorporated into routine processes and
procedures, so that improvements in an organization’s processes will be sustained. Use
Data to Identify the Problem Consider the variety of roles that data can play in each phase of
one of the best-known quality improvement models, Dr. William Edwards Deming’s PDSA
cycle, as shown in Table 5.1. The PDSA model is the original quality improvement model
from which later models evolved. As discussed earlier in the book, you will sometimes see
this model referred to as Plan-Do-Check-Act (PDCA), which was the original language.
However, as the model evolved, Deming amended his description of PDCA to emphasize the
importance of not just checking, but using or studying the knowledge to better understand
the process being improved. Deming’s PDSA involves a sequence of four steps that are
intended to provide a structured process, which is to be continuous in quality improvement
efforts. 1. In the “Plan” phase, there is great consideration for the desired outcome(s), and
factors that lead to the desired outcome(s) are generally included in the planning efforts.
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Within the “Do” phase, the plan is then implemented. It is within this step that data is
gathered for the following “Study” phase. 3. The “Study” phase analyzes the data, which is
then converted into information. For instance, the data can provide trending information,
which provides the decision maker with the knowledge and understanding to make
modifications or corrections during the last phase of the cycle, or “Act” phase. 4. The “Act”
phase allows for changes to the initial plan. Questions regarding the effectiveness and
appropriateness of the plan are factored into the equation, and thus help in the
development of a modified plan if necessary. Let’s look more closely at how data play into
the PDSA model. In the Plan phase, data are integrally involved in identifying the problem,
defining the project goal, planning the intervention/target, and developing the
measurement strategy. As a first step in this phase, a problem or area of interest must be
identified. For example, patients who undergo surgery are frequently at risk of developing
blood clots in their legs following the operation, which is a condition known as deep vein
thrombosis (DVT). These blood clots can travel to the lungs (a condition called pulmonary
embolism, or PE), causing severe heart or breathing problems and potentially resulting in
the death of the patient. For this reason, patients typically receive therapy after surgery to
prevent the formation of clots—for example, by taking anticoagulation or anticlotting

medication. The clinical entity of DVT and PE is collectively known as venous
thromboembolism (VTE), and is a leading problem in healthcare quality for hospitalized
patients. Not only is PE the third most common cause of hospital death, but complications of
VTE are the most common preventable cause of hospital death (Ozaki & Bartholomew,
2012). The Centers for Medicare & Medicaid Services (CMS) has six performance measures
related to VTE that hospitals are required to report (Centers for Medicare & Medicaid
Services, 2013f). Therefore, an important and common quality metric for patients
undergoing surgery is whether or not they received appropriate measures to prevent the
occurrence of VTE. These measures might include getting the patient up and walking, using
compression stockings on the legs, or giving anti-clotting medication to patients at higher
risk. Adherence to this routine, particularly after surgical patients leave the hospital to
continue recovery at home, appears low: a recent study noted that only 1.5% of elderly
patients undergoing surgery received appropriate medication to prevent VTE (Merkow et
al., 2013). Therefore, in the planning phase, nurses, clinicians, and administrators need to
know how many patients are taking the steps to prevent blood clots and VTE and how it
compares to the desired rate of use. When VTE prevention measures are below the target
benchmark, clinicians recognize that a problem exists. In this instance, data signal whether
a problem may exist with the delivery of care. The example shown in Table 5.1 describes
how the Plan-Do-Study-Act cycle would be used by a hospital that wishes to decrease the
inpatient readmission rate for patients with heart failure. Table 5.1: Data use by stage of
Plan-Do-Study-Act cycle Stage Data use Example
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Do Study Act Print Data use Identify/define problem Define project objective/goal (target)
Plan intervention Identify metrics and plan data gathering Example Problem: Readmission
rate for patients with heart failure is 35% Goal: Decrease inpatient readmission rate to 20%
Intervention: Outpatient follow-up visit, 7 days Metric: Percent of patients with outpatient
follow-up visit ,7 days after discharge Collect data to evaluate intervention (measure
impact) Data Collection: Outpatient follow-up visits Readmissions Analyze data: Did the
intervention work? Was the goal achieved? (measure impact) Analysis: Track outpatient
follow-up visits weekly Compare readmission rate after 3 months to target If goal not
achieved: Gather data to plan next intervention (reenter Plan stage) If goal achieved:
Monitor data to sustain improvement Goal not reached: Interview patients to determine
why inpatient readmission occurred; plan intervention based on results Goal reached:
Monitor inpatient readmission rate Sources: Adapted from Langley, G. J., Moen, R. D., Nolan,
K. M., Nolan, T. W., Norman, T. L., & Provost, L. P. (2009). The improvement guide (2nd ed.).
San Francisco: Jossey-Bass; Victorian Quality Council, Department of Human Services.
(2008). A guide to using data for health care quality improvement. Melbourne, Australia.
Retrieved from http://www.health.vic.gov.au/qualitycouncil
(http://www.health.vic.gov.au/qualitycouncil) Dashboards are commonly used to alert
healthcare team members to the existence of a potential or actual problem, especially when
multiple metrics are involved. Like the dashboard of an automobile, dashboards in a
healthcare system display key performance indicators in a way that is useful for bringing

providers’ and administrators’ attention to a problem. For example, a car’s dashboard
features temperature gauges that alert the driver if and when the engine becomes too hot.
Note that in this case the dashboard only signals to the driver that something is potentially
wrong with the engine’s temperature; additional investigation (i.e., data gathering) is
required to understand why the engine is overheating. A healthcare dashboard for an
outpatient clinic might include data on several key quality measures, such as immunization
rates, foot examinations for patients with diabetes mellitus, and wait times from patient
check-in until seen by a provider. Dashboards often include real-time data, but they can also
include data that is collected on a more periodic basis (i.e., weekly or monthly). Figure 5.1 is
an example of a dashboard that shows data collected periodically. Figure 5.1: Healthcare
dashboard Example of a healthcare dashboard, which displays summaries of key
performance measures, such as length of stay, in an easily interpretable fashion.
Dashboards provide administrators and clinicians with “at a glance” information about
health system performance.
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shown in Figure 5.1, managers can quickly see such factors as the average length of stay on
each hospital unit, projected occupancy rates, and the emergency department patients
placed in beds on each unit. For instance, occupancy in the intensive care unit is at 100%,
which could lead managers to question whether staff levels are adequate. Dashboard
reports enable organization leaders to quickly look at their data. A dashboard should be
created by choosing key indicators that the organization will follow over a year’s time. It
might include the total number of medical errors, fall rates, restraint rates, and the
percentage of indicator compliance. It’s best to focus on a few vital measures. Managers can
see a rolling 12 months of data in areas that interest them, rather than poring through many
reports. There are multiple ways that dashboards may appear. Organizations may use a
Gantt chart, a type of bar chart that typically illustrates a project schedule; a Shewhart
control chart that can illustrate statistical variations; a box plot that can graphically depict
groups of numerical data; and box-and-whisker diagrams that show the distribution of data
along a number line. The biggest challenge for an organization is to determine a benchmark
standard and then the appropriate variation that it will allow. For instance, a nursing home
might look at how the number of resident falls in its facility compares with other nursing
homes in the state. In other words, they benchmark their rate against others’. Then the
nursing home may decide it will allow a deviation of, for instance, plus or minus 5% from
the state average as an acceptable level. Perhaps the nursing home has many residents with
Parkinson’s disease who are at higher risk of falls and can therefore expect its numbers to
be higher than other facilities. When it comes to benchmarking, an organization must take
into account its location and the demographics of its providers and patients. A large,
metropolitan hospital in northeast America may want to benchmark itself against similar
organizations rather than small, rural facilities in the Midwest. Similarly, a health clinic that
serves many seasonal or migrant farm workers may be expected to have higher rates of
infectious diseases, respiratory conditions, dental diseases, and child health problems
because of the poverty, limited access to healthcare, and hazardous working conditions

these patients face. Identify the Improvement Target(s)
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healthcare leaders and staff recognize a problem exists, they must use data to better
understand the problem and identify potential interventions—again, a critical part of the
Plan phase of the PDSA cycle (Table 5.1). In the case of using medication to prevent VTE,
multiple steps must be taken for a patient to receive the appropriate therapy. Figure 5.2
shows a flow chart that tracks the process of administering VTE medication to a patient.
Figure 5.2: Flow chart A flow chart can help illustrate the number and order of steps
involved in a treatment process. This example flow chart tracks the administration of VTE
prophylaxis to a patient. First, a surgeon or other physician, such as a hospitalist, who is
overseeing care must know that the patient needs the medication post-surgery. Second, the
surgeon or physician must place the order (including appropriate medication and dosing).
Next, the order must be transmitted to the pharmacy, either electronically or in written
form. The pharmacy will confirm the dose and dispense the medication, which is then
administered to the patient. Simply knowing that the VTE medication rate is below target is
only sufficient to signal that a problem exists; further data is required to understand which
step(s) in the process can be optimized in order to improve the target metric. Plan
Intervention(s) Suppose that data gathered about the process of care delivery for VTE
medication suggest that surgeons or hospitalists inconsistently order medication to prevent
clot formation in their patients. When quality improvement leaders press the issue in
interviews with key stakeholders, they discover that surgeons or hospitalists at a particular
hospital are unfamiliar with new guidelines related to the use of medication to prevent VTE,
which results in a number of patients not receiving guideline-adherent care.
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possible intervention in an electronic health record system with its computerized order
entry system would be to provide point-of-care education or a prompt that helps surgeons
or hospitalists identify patients who should receive medication based on current guidelines.
Then, surgeons could either order the appropriate medication, or note the clinical reason
why a patient meets the guideline metric’s exemption criteria—as would, for example, a
patient being monitored for active bleeding. It would be dangerous to give such a patient
medications that prevent blood from clotting. Measure the Impact of Change Once an
intervention is planned (called a “test of change” in the PDSA cycle), data play a critical role
in determining what impact, if any, the intervention had on the metric of interest. This is the
role that data play in the Study phase of the PDSA cycle (Table 5.1). Returning to the
example of medication to prevent VTE for post-surgery patients, assume that the planned
intervention involved use of a computerized prompt to remind surgeons to order VTE
medication on appropriate patients. Not only would it be important to monitor the overall
rate of VTE medication use for the unit (i.e., what proportion of patients receive the
therapy), but a more direct measure of whether providers ordered the medication could be
important. Determining appropriate, responsive, and valid measures to assess impacts of
change and the costs and benefits of different types of measures are discussed further in

Chapter 6. Maintain Improvement Once a change in a process of care is instituted, and data
suggest that the change is effective in achieving the desired result (i.e., the use of VTE
medication reaches the target threshold for the surgical unit), it becomes important to
stakeholders to sustain the improvement. When a specific care process or quality metric is
the subject of intense focus, such as during a quality improvement project for VTE
prevention, the increased focus and attention can lead to performance changes that fade
over time, as organizational priorities shift to other areas. This is one form of the
Hawthorne effect, which suggests that the mere act of measuring a process can result in
improvement, as stakeholders in the system become aware of measurement and focus
attention on compliance. In this case, dashboards or other forms of continued surveillance
of key quality measures become important for making sure that adherence to VTE use
guidelines remains at or above target levels. If data suggest that compliance has fallen off
several months after a quality improvement effort, this monitoring function serves as an
indicator to re-enter the quality improvement (PDSA) cycle (Table 5.1, Act phase of PDSA
cycle). It is always important for organizations to consider and address any confounding or
influencing variables that may cause a measure to be out of the acceptable range and then
characterize those variables into two categories: those the organization can change and
those it cannot. For example, certain times of year, such as those around the Thanksgiving
and Christmas holidays, are typically associated with higher levels of depression among
patients. Consequently, patient satisfaction with health services may be influenced by that
variable and dip below the tolerance range for November and December. At face value, that
may appear to show a problem as the numbers on patient satisfaction surveys drop. But
really the drop in scores has nothing to do with the current quality of service the
organization is providing.
However, if the satisfaction rate dropped in November and December and then remained at
that level in January and February, there may be a real problem that needs improvement to
increase patient satisfaction to the acceptable level or goal. Confounding variables can also
impact the outcome of a quality improvement project. For instance, a hospital wants to
know how effective surgery is in treating women for incontinence. Variables that may
impact the outcome of that surgery might be the age and activity of the women, whether
they are obese, the severity and duration of their symptoms prior to treatment, or even the
type of surgical procedure. Or a researcher is studying the relation between birth order
(first child, second child, etc.) and the presence of Down Syndrome. A confounding variable
in such a study would be the age of the mother, since a higher maternal age is directly
associated with Down Syndrome in a child. If a researcher were studying the effect of
smoking tobacco on human health, confounding variables might be alcohol intake and diet,
as both are lifestyle activities that can also have an impact on health. Therefore, it is
important to take into account these kinds of factors, which can influence an organization’s
data. Questions to Consider 1. In your opinion, what step(s) of the quality improvement
cycle are most reliant on valid data? Why? 2. Consider a recent customer experience that
you thought could have been better (for example, a long wait for your meal at a chain

restaurant). Write down what data you might use to identify the improvement target, plan
an intervention, measure the impact of a change, and sustain the improvement.
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Data Collection Overview It is clear that quality improvement methods rely on data in all
phases of the improvement cycle. In order to maximize use of these data, it is important to
understand the various types that exist, and when each type may be useful during specific
stages of the quality improvement process. In addition, leaders in healthcare should be
familiar with existing data sources and understand when it may be necessary to collect new
data. Finally, healthcare leaders should be knowledgeable in a variety of methods to collect
new data. Empirical Data Several types of data exist, and each type possesses advantages
and disadvantages, depending on the quality improvement question a healthcare leader
attempts to address. Broadly speaking, data may be considered quantitative or qualitative.
Quantitative data are numerical—for example, a patient’s age, the value of a laboratory test
for the amount of sodium in a patient’s serum, or the charges for an inpatient
hospitalization. Qualitative data include descriptive data, such as patient race, ethnicity, or
gender, which may be readily expressed in data storage systems as numerical data. Other
examples of qualitative data that may be less readily expressed numerically include
opinions, as well as written or verbal descriptions of experiences from the patient
perspective. In order to determine appropriate analytic methods to provide insight into
quality improvement opportunities, let’s consider some of the categories of data. Examples
of continuous data include a patient’s age and the level of a toxin in a patient’s bloodstream.
The key property of continuous data is that the interval between different levels is
consistent. For example, a two-year age difference exists when comparing two patients aged
17 and 19 years as well as two patients aged 63 and 65 years. The difference between age
levels (or other continuous variables) is meaningful on a scale. Categorical data (sometimes
referred to as nominal data), as the name implies, are those in which qualities of the subject
being measured may be sorted into different groups or categories. These categories could
include different racial groups, or patients diagnosed with colon cancer, or those who have
suffered a heart attack. When planning analyses or presentations, it is important to
understand what type of data is being considered, as its type determines the best way to
summarize and present it. Conceptually, the process of summarizing data highlights the
distinction between data and information. Data are essentially raw measures—for example,
the length of hospital stay in days for the last 100 patients discharged from the surgical unit.
It is only when the individual data points are summarized that useful information is
produced. Therefore, it is important to understand a variety of potential summary
measures. For example, quality improvement teams may wish to summarize continuous
data as an average. In contrast, categorical data are usually summarized by frequency or
proportions (i.e., what percent of a group is male or female). age fotostock/SuperStock
Continuous data may include information measured over consistent intervals of time, such
as age or weight. Examples of continuous and categorical data collected in the process of
delivering healthcare abound. Demographic data, including age, sex, and race, are routinely
collected. Other clinical data, such as laboratory tests and vital sign measurements, like

temperature, heart rate, and blood pressure, are routinely available in the healthcare
environment. Information about use of medications is frequently available, as is financial
data. Administrative data, such as
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used to report claims for healthcare services rendered, are also readily available. Almost all
of these data are either categorical or continuous, and therefore it is important to
understand how to identify, summarize, present, and analyze them to best support quality
improvement efforts. We will look more closely at sources of internal data later in this
chapter. Experiential Data In the course of quality improvement activities, data beyond
simple continuous or categorical qualitative information are frequently required. These
may include concepts, thoughts, ideas, or individuals’ opinions about experiences and are
known as experiential data. For example, consider a quality improvement project that is
designed to reduce patient wait times at an ambulatory clinic. Quantitative data, such as
wait time (the time elapsed between when the patient checks in and when she is seen by the
healthcare provider), are the key metric for measuring and monitoring sustainable change.
However, these types of quantitative data reveal little about how patients physically move
through the clinical area (patient flow). The phenomenon of patient flow may best be
represented with a visual technique, such as process mapping (see Figure 5.3). Figure 5.3:
Process map A process map can help illustrate the number and order of steps in a treatment
process, which can vary per patient. Similarly, consider a brainstorming session in which
nurses, physicians, administrators, and other staff members are trying to improve the
timeliness of antibiotic administration for patients with sepsis, a severe, often life-
threatening systemic infection. Timely antibiotic administration reduces the death rate
from sepsis, and is thus an important quality marker. The participants in the brainstorming
session could identify a number of patient, staff, or process factors that contribute to the
problem of delayed antibiotic administration. For example, suppose that intravenous
antibiotics are not readily available in the clinical unit, but instead must be obtained from a
central pharmacy. The process of transmitting and confirming the order, preparing the
antibiotic, and transporting it can pose a significant time delay between when a physician
orders the antibiotic and when a patient actually receives the first dose.
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efficient method exists for capturing these potential causes in a typical empirical or
variable-driven approach. Variable-driven approaches are inefficient because it is difficult
to capture every circumstance that could create a delay. For example, a transport system
could cease functioning, inventory may be low or not immediately available, or competing
demands from another ill patient could interfere with timely antibiotic delivery. Instead of a
variable-driven approach, participant descriptions of the process may better capture
relationships among various components of the phenomenon. A fishbone diagram (cause-
and-effect diagram) can be an effective way to visually communicate the relationships
between various steps in a process, and, more importantly, identify opportunities for
intervening to improve an important metric, such as timeliness of antibiotic administration

(see Figure 5.4). Figure 5.4: Fishbone diagram A fishbone diagram provides a systematic
framework to identify potential causes of failures in care delivery. Fishbone diagrams
visually represent factors contributing to a defect or failure. Typically, there are five
categories of potential causes of a defect: people, equipment, procedures, measurements,
and materials (Langley et al., 2009). Examples of people factors include inadequate training,
poor communication, or apathy. Examples of equipment factors include breakdown of order
relay systems or transport systems. An example of a procedure breakdown could be if the
antibiotic was improperly mixed (if it was dissolved in water instead of saline solution).
Measurement factors could include improper dosing. Material factors could include
inventory management problems or if an antibiotic spoiled because it was not properly
refrigerated. Questions to Consider 1. Describe a healthcare experience where “just the
numbers” (e.g., empirical data) do not fully capture the opportunity for improvement. What
other factors might come into play in making an improvement? 2. Think about a recent
customer or patient experience where your expectations were not met. Draw a fishbone
diagram that describes how people, equipment, procedures, measurements, and materials
could contribute to the problem you experienced. Which factors were most critical?
Internal Data Sources Quality monitoring and improvement activities are often local, and
the goal is to achieve an internally identified target for a process or outcome measure. In
this case, there is little need for comparison data from other healthcare entities, such as
other hospitals in the surrounding area or state. During the planning phase of any quality
improvement project, leaders must determine whether to use existing internal data sources,
or whether it is necessary to collect new data to successfully implement the project. This
decision is closely tied to the measures selected to track change and monitor sustained
quality improvement. Existing internal data resources possess many advantages over data
that requires a new collection process. First, existing internal data are already being
collected and stored for other purposes within the healthcare system. Claims for
reimbursement from Medicare and private insurers include numerical codes for the
problems treated (diagnoses) and the procedures performed, such as a surgical
intervention. These are routinely captured in Common Procedural Terminology (CPT) and
International Classification of Diseases— Clinical Modification (ICD) codes. Often there are
other internal data used for local purposes, which can include inventory tracking with
barcodes and proprietary systems for tracking lab results, such as the outcomes of tests for
infectious bacteria (e.g., blood or urine cultures to detect bacteria infecting a patient). For
example, a project to reduce the incidence of bloodstream infections, a serious and often
lethal consequence of hospital care, might take advantage of internal data systems that
track and report when a blood culture sample from a patient grows bacteria. Use of existing
internal data resources therefore reduces the cost of quality improvement projects, since
new data collection and storage resources are typically not required, or are minimal.
Existing data resources are often easy to use and analytic work can usually be performed
quickly. The main limitation to using existing data resources is that sometimes the
measures are imperfect, or no data exist that measure the outcome of interest with enough

precision to support the quality improvement project. In other words, the validity of the
existing data may be limited. Collecting new data may be necessary, particularly when no
data exist to adequately describe the phenomenon under study. For example, it is unlikely
that any existing internal data resources adequately describe how patients move through an
ambulatory surgical center, from preoperative preparation through surgery, recovery, and
discharge. The main limitation of collecting new data is the time and expense required to
abstract/observe, record, and store new data. Expertise in database design and
maintenance may also be required if the amount of new data will be substantial, or if it will
be collected for an extended period of time. Health system administrators, physicians, and
other quality improvement leaders must therefore carefully weigh the tradeoffs between
using existing internal data sources and collecting new data to support a local quality
improvement effort. For example, no existing system may capture the number of times each
week that a nursing unit runs out of gauze to dress patient wounds. In this case, new data
collection may be warranted, even if it is takes time or costs money. In contrast, consider a
project screening for bloodstream infections. It would be time consuming to check every
record to determine which patients truly had a bloodstream infection; instead, data for
insurance claims could be coded and examined to find out if a bloodstream infection
occurred. While the claims data may not perfectly identify every patient, it will be much
more efficient to use because it is already being collected electronically. Using Existing
Internal Data Sources Healthcare organizations collect a lot of internal data that is
invaluable and can be used in quality improvement projects. Below we examine the various
types of data that organizations collect from within their own walls.
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Demographic Data Delivery of patient care is a data-intensive process. Demographic data,
such as a patient’s age and sex, are routinely recorded, as are data regarding where a
patient lives (home address, ZIP code) and even where they work. Demographic
information can be important to many continuous quality improvement projects. For
example, a hospital that has a high rate of patient falls may wish to track data on the ages of
those patients. Are the majority of falls occurring in elderly patients, who are at a higher
risk of falls? If so, the hospital can target its improvements to that population of patients.
Socioeconomic Data Healthcare organizations can also collect socioeconomic data on
patients, including factors such as race and ethnic origin, birthplace, language, income
levels, and education levels. This information can help a hospital have a clear picture of who
is using its facility. Is the hospital serving a poorer population, where many patients were
born in a foreign country? Does that create language barriers that can lead to cases where
patients do not take their medications because they cannot understand instructions from
their physicians? What can the hospital do to help these patients? Can nurses better educate
patients before discharge? Can the hospital specifically assign nurses to meet with patients
before they go home and ask them to repeat the information and treatment plan they are to
follow when they leave the hospital? With information in its hands, a hospital can ask if it is
meeting patients’ cultural, religious, and ethnic needs. Clinical Data Healthcare providers
assess patients in many ways, such as recording their height, weight, blood pressure,

temperature, and heart rate. In today’s healthcare environment, laboratory test results are
typically stored and reported electronically. Healthcare organizations are usually paid for
the services they provide, and will track the entity that pays for the services (i.e., a private
insurance company, Medicare, etc.). It is necessary to document which services an
individual patient received, so the healthcare system employs coding systems that
standardize the reporting of diagnoses and procedures. The U.S. healthcare system is
currently moving towards adoption of an updated coding system known as ICD-10. The
code set allows more than 14,400 different codes and permits the tracking of many new
diagnoses. ICD-10 is set to replace the older ICD-9 although the government has changed
the implementation deadline several times. It is now scheduled for the fall of 2015. It is easy
to understand how clinical data can play a role in quality improvement. For example, how
many nursing home residents are diagnosed with Alzheimer’s disease? A nursing home
might track how many of those residents have “sundowning,” a phenomenon where
residents experience a state of confusion at the end of the day and into the night, which can
result in behaviors such as anxiety, aggression, or wandering. What can staff do to help
reduce sundowning, such as trying to occupy residents’ time with another activity or
limiting background noise that residents may find agitating? Financial Data Healthcare
organizations must also track costs and bill for services, so financial information about
charges to patients and insurers are also frequently available electronically. All of these data
sources can be brought to bear on projects to improve the safety or quality of care delivered
to patients.
Example of Data Use As an example, consider how a project to reduce the incidence and
severity of post-biopsy infection in men who undergo a prostate biopsy (to rule out prostate
cancer) might make use of several types of existing data to improve care. Infection after
prostate biopsy is relatively rare (it typically occurs in less than 3% of men undergoing
biopsy), but it can result in a severe, potentially life-threatening bloodstream infection.
Doctors in the urology clinic have recently become interested in this problem because of
national reports of increasing antibiotic resistance in men experiencing this complication.
Given the relatively rare occurrence of this event, it would take a long time for quality
improvement leaders to identify enough patients experiencing the complication to gain
meaningful insight (what is known as the prospective approach, in which researchers
follow patients forward through time and wait for an outcome to occur). Therefore, the
urologists and quality improvement administrators use a retrospective approach (that is,
one that looks backward in time). In order to identify patients at risk for an infection, the
quality improvement team uses existing administrative data, which is collected to document
the care provided to patients for insurers and government payers, to identify all patients in
the past two years who underwent a prostate biopsy. In order to be paid, the clinic must
document when doctors performed a biopsy on a patient, so they will have this information
in their records. Severe infections following prostate biopsies typically occur within two
weeks of the date of biopsy, so hospital administrative data are searched to identify patients
from among those who underwent a prostate biopsy to determine which patients were seen

in the emergency department or admitted as an inpatient within two weeks of the date of
their biopsy. This process, all performed using search algorithms on existing data, identifies
the number of patients who had the procedure and the number who potentially
experienced the complication the hospital wants to track. However, it is important to note
that not all of the patients that had a prostate biopsy and were subsequently hospitalized
definitely had an infection. Existing diagnostic codes for the hospitalization could help
distinguish between who may have had a post-biopsy infection and who may have been
evaluated for chest pain that coincidentally occurred a week after the prostate biopsy. Thus,
the hospital may want to verify that the patient had a post-biopsy infection by looking
beyond those diagnostic codes at the patient’s clinical data, which is generated in the course
of caring for the patient and may include vital signs, symptoms, and laboratory test results.
Hospital microbiology laboratories typically maintain databases of specimens (i.e., blood or
urine) that grow bacteria when the sample is cultured. In this case, bacterial growth is a
strong indicator that the patient had a severe infection. Therefore, the microbiology data
may validate that -biopsy hospital admission was indeed for an infection, rather than an
unrelated medical problem. Therefore, the quality improvement team can calculate the
incidence of the complication by dividing the number of patients who had a positive urine
or blood culture within two weeks of the biopsy by the total number of patients undergoing
prostate biopsy within a certain timeframe (e.g., two years). Once the quality improvement
team has determined the incidence of the complication, they may wish to analyze
alternative strategies to reduce the incidence or severity of the complication. While
effectiveness of the treatment or prevention strategy is an important consideration, the cost
of the various strategies may also be an important factor. To determine which potential
treatment or prevention strategy would be most cost effective, the team could use existing
financial data that reflects the costs of caring for patients who experience this complication.
Having diagnosed a problem in a well-defined population using existing data, the team can
then decide upon an intervention and prospectively monitor for new occurrences of the
complication going forward; ideally
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intervention will result in a measurable decrease in the frequency or severity of this biopsy
complication. This example demonstrates how quality improvement efforts can rely largely
on existing, internal data rather than using valuable time and resources collecting new data.
Collecting New Data Despite the ready availability of existing internal data in the healthcare
environment, at times it may be necessary to collect new data. In particular, this need arises
when existing data do not provide valid or reliable measurement of an important aspect of
the improvement process, or when descriptive insights into a phenomenon or process are
required. A number of methods exist to gather new data, including surveys, focus groups or
key informant interviews, chart review or abstraction, use of electronic health records, and
direct observation, among others. Surveys Surveys are an important tool for gathering data
from target groups, such as patients, nurses, or physicians, in an efficient and relatively cost
effective manner. A survey is typically a series of questions, frequently with pre-defined
response choices (e.g., yes or no, rate from 1 to 5, etc.), that query some facet of an

individual’s experience. They can provide quantitative or qualitative categorical data, and
through the use of free response questions, also permit the collection of descriptive data.
For example, a survey could produce quantitative data by asking about a participant’s age,
height, or weight. Qualitative data could be generated by querying a participant’s race,
occupation, or sex (among other characteristics). Qualitative data can also include
descriptions of experiences, such as a response to the prompt: “Think about the last time
you had to wait longer than expected to see the doctor. How did that make you feel?” Many
surveys undertaken for quality improvement efforts are not interactive, and therefore lack
the potential for two-way interactions that permit exploring and clarifying individual
responses. Shironosov/iStock/Thinkstock Once the need for a survey is identified, leaders
should have clearly defined objectives for the survey design. Surveys are an efficient and
typically cost Designing a good, reliable survey is a challenging task, effective tool for
gathering data that can and therefore it is wise to consult with a survey design provide
quantitative or qualitative expert early in the design phase. The survey design categorical
data as well as descriptive data. expert should be able to help write good survey questions,
which are specific, focused on a single topic, and use language that is not ambiguous (for
further detail, see Chapter 7). Survey design experts can also help with strategies to
administer the survey and obtain valid, representative information as efficiently as possible.
For example, a sampling strategy may help obtain valid information from a subset of
patients, rather than surveying all patients undergoing a specific procedure. Using or
adapting existing survey questions is one strategy for overcoming design challenges
inherent to surveys. After creating the survey instrument, the quality improvement team
should pilot test the instrument in a target audience. For example, if a survey was intended
to understand medical student knowledge and attitudes toward quality improvement, then
medical students constitute the target audience. It would not
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sense to test the questions in other groups, such as patients or faculty, that would not be
similar to medical students in perspective or experience. Often the pilot group is a small
subset of the larger group, such as a set of 20 randomly selected medical students in a 200-
person class. Other important issues to address are clarity and focus of questions,
confidentiality of responses, protecting the identity of respondents (particularly if sensitive
information, such as illegal drug use, is queried), and data entry and management. While
surveys are commonly used in the healthcare field, sometimes the data collected should be
used with some caution. Depending on how the surveys are constructed and how carefully
the questions are written, the data can have issues in terms of its validity and reliability.
With a written survey, healthcare organizations have the option of creating an original
questionnaire themselves or using a product that they can purchase from an outside
vendor. Some experts recommend using surveys developed by a company because the
product has likely been tested and validated. A healthcare organization can do it itself, but
the process can be time-consuming. There are some surveys that are widely used in
healthcare and found to be valid and reliable based on what is known as Cronbach’s alpha,
which determines the internal consistency or average correlation of items in a survey to

gauge its reliability. Focus Groups Focus groups are an important mechanism to provide in-
depth understanding of individual experiences, and typically comprise key informants or
participants in a process that is the subject of inquiry. For example, a focus group of patients
can provide detailed descriptions of their experiences at an ambulatory surgical center.
Focus groups also permit interactive inquiry, so that the facilitator can explore new
responses that may not have been recognized as important by the quality improvement
team leaders. For example, an important part of patient satisfaction with an ambulatory
surgical experience might be the quality of waiting facilities and amenities for family
members. Unless leaders decide to explicitly query family member satisfaction, a survey
might miss this important aspect of the experience, which could more readily arise in the
give-and-take environment offered by a focus group. Limitations for focus groups include
the relatively higher cost and lower efficiency as compared with surveys. Focus groups
frequently are more costly than surveys because a physical space is necessary for the
meeting, food is often provided, participants must travel to the focus group site to
participate, and it may take more time to explore participant responses. In addition,
successfully running a focus group requires experience and, at times, specific interventions
to minimize the influence of strong opinion leaders within the group. It is important to keep
in mind that focus groups are good for describing the range of experience, but all opinions
expressed may not be representative of the group as a whole. In contrast, surveys can be
done by mail, electronically, or over the telephone, do not require travel, and frequently
have lower perparticipant costs than do focus groups. Health Record Review Even though a
significant amount of clinical data is captured in electronic form, not all of it can be readily
analyzed. Much of the data in the electronic health record is structured, such as the
numerical value of the concentration of glucose (sugar) in a patient’s bloodstream at a
specific time. However, electronic medical records are also replete with unstructured text
data, which can be difficult to analyze without someone actually reading the text—
particularly on the relatively small scale of clinical quality improvement projects.
(Unstructured data elements include physician notes, scanned documents, and other data
that can contain valuable narratives about a patient’s health and the reasons why healthcare
decisions were made).
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example, consider a quality improvement project with the goal of decreasing hospital
readmissions within 30 days of hospital discharge. One of the key pieces of data to
understand is why the patient returned to the hospital. Was the return potentially
preventable? For example, was the patient unable to obtain pain medication prescribed at
discharge and had to go back to the hospital in uncontrolled pain? Was the patient not
scheduled for a follow-up visit within an appropriate amount of time? Answers to these
types of questions will often be contained in the descriptive or narrative part of the medical
record, but are not captured in any administrative or other clinical records. Therefore, it is
necessary for team members to conduct a record review (chart audit), in which they review
and abstract pertinent structured data from the medical record. The process of record
abstraction and database creation can be quite time intensive, so it should only be

undertaken when no other method exists to obtain the necessary information. Direct
Observation Sometimes quality improvement projects will require direct observation of
behaviors. Consider that physicians, nurses, physical therapists, and other care providers
frequently do not wash their hands before and after touching patients, which is one key way
infections are transmitted in the hospital. If staff members are surveyed about how
frequently they wash their hands before and after patient contact, however, they may
overestimate their compliance with this important quality measure. An alternative is to
directly observe them using unobtrusive monitors that watch to see if doctors (and other
providers) actually wash their hands in accordance to policy. This method of data gathering
is very resource intensive, and potentially intrusive, so team leaders should carefully weigh
the risks and benefits of data collection that requires direct observation. Direct observation
may be useful for studying behaviors that individuals may not otherwise accurately report
(intentionally or unintentionally). Indeed, studies comparing direct observation and self-
report of hand washing suggest that healthcare providers do not actually wash their hands
as frequently as they report (Braun, Kusek, & Larson, 2009; Haas & Larson, 2007; Jenner et
al., 2006; O’Boyle, Henly, & Larson, 2001). Questions to Consider 1. Describe tradeoffs
between using existing data and collecting new data to support a quality improvement
effort. 2. What types of information may be considered data to support a quality
improvement effort?
External Data Sources A number of external data sources can also inform the quality
improvement process. External data describe the environment beyond the healthcare
facility, such as the surrounding community or peer institutions. External sources of data
are particularly valuable in the planning phases of quality improvement efforts, as they
permit comparison of local quality metrics with regional or national peer institutions. In
addition, external data can be used for benchmarking—that is, the process of determining
how a local process or outcome compares with a regional or national standard. For
example, hospital readmissions occur when a discharged patient is admitted back to a
hospital, typically within a 30-day window. Hypothetically, these readmissions could
indicate ineffective inpatient treatment at the initial hospital stay, poorly coordinated post-
discharge care, or a number of other factors. Thirty-day readmission rates are currently
used by large payers, such as Medicare, to assess quality of care at hospitals and provide
financial incentives to reduce readmissions. Nationally, the 30-day readmission rate for
patients with heart failure is 24.7% (Centers for Medicare & Medicaid Services, n.d.a). Thus,
a hospital can determine whether its own readmission rate is similar to the national
average, higher, or lower. A number of government and not-for-profit agencies provide
these types of heath data (see Table 5.2). Table 5.2: Data resources Source Description URL
National Institutes of Health (Health Services Research Information Central) Links to high
quality data sources regarding population health and quality of care The Commonwealth
Fund International comparisons of health indicators http://www.commonwealth fund.org
Web-based Injury Statistics Query and Reporting System Centers for Disease Control and
Prevention injury statistics http://www.cdc.gov/injury /wisqars/index.html Centers for

Medicare & Medicaid Services Data regarding quality of care for Medicare beneficiaries
Hospital Compare Data regarding hospital quality for Medicare beneficiaries
http://www.nlm.nih.gov /hsrinfo/datasites.html
(http://www.nlm.nih.gov/hsrinfo/datasites.html) (http://www.commonwealthfund.org)
(http://www.cdc.gov/injury/wisqars/index.html) https://data.medicare.gov
(https://data.medicare.gov) http://www.medicare.gov /hospitalcompare
(http://www.medicare.gov/hospitalcompare) / Kaiser Family Foundation Unbiased
information regarding healthcare and healthcare policy http://www.kff.org
(http://www.kff.org) University HealthSystem Consortium Quality of care data for member
academic medical centers http://www.uhc.edu (http://www.uhc.edu) The Leapfrog Group
Safety and cost data http://www.leapfroggroup.org (http://www.leapfroggroup.org)
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Description URL National Committee for Quality Assurance (NCQA)/Healthcare
Effectiveness Data and Information Set (HEDIS) Data assists health plans, hospitals, and
other healthcare organizations to measure their quality and performance
http://www.ncqa.org (http://www.ncqa.org) American Hospital Association Hospital and
health system data resources American College of Healthcare Executives Resources
providing current information about healthcare, including databases, statistical resources,
and research Medical Group Management Association Benchmarking tools, practice
dashboards, and data tools http://www.aha.org/research /rc/statstudies/data-and -
directories.shtml (http://www.aha.org/research/rc/statstudies/data-and-
directories.shtml) http://www.ache.org (http://www.ache.org) / http://www.mgma.com
(http://www.mgma.com) National Institutes of Health The National Institutes of Health
maintains the Health Services Research Information Central
(http://www.nlm.nih.gov/hsrinfo/datasites.html) website. This site provides links to a
number of high quality data sources with information about several facets of health and
healthcare, including quality of care. It includes links to individual county health rankings
(http://www.countyhealthrankings.org) that describe the health of local populations. The
Commonwealth Fund (http://www.commonwealthfund.org) provides international
comparisons of a variety of health indicators, including mortality and healthcare spending.
Data related to injuries can be found in the Centers for Disease Control and Prevention’s
(CDC) Web-based Injury Statistics Query and Reporting System
(http://www.cdc.gov/injury/wisqars/index.html) . So, for instance, using the CDC statistics,
emergency department staff could find data about the number of traumatic brain injuries
reported or the numbers of burn victims in their region or state. They could use colorcoded
fatal injury maps that show patterns of death rates across national, regional, and state
levels, to help identify populations at high risk of injury. Medicare The Center for Medicare
& Medicaid Services (CMS) coordinates the Medicare Hospital Compare
(http://www.medicare.gov/hospitalcompare) program to © A9999 DB CDC James
Gathany/dpa/Corbis help patients find and understand quality metrics at Websites linked
to government and not-forhospitals that are certified to treat Medicare patients. The profit
agencies, such as the CDC, provide high website allows patients to search by hospital name

or geographic location, and shows how individual hospitals
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compared to the nationwide average. For each of quality data, which allow hospitals to
several quality metrics, hospitals are classified as being compare statistics with their own
figures. either 1) better than the U.S. national rate; 2) no different than the U.S. national
rate; or 3) worse than the U.S. national rate. Each metric is adjusted for the severity of the
patients treated by individual hospitals, so that hospitals treating relatively healthier
patients are not significantly advantaged in the rankings. For example, a hospital treating
patients for heart failure may have worse outcomes if those patients are older, suffering
from diabetes that can cause complications, have chronic obstructive pulmonary disease
(COPD), or have had a previous heart attack. Pneumonia patients with such conditions as
liver disease or prior cardiovascular events are likely to fare poorer than healthier patients.
Comparison data are provided regarding timeliness and effectiveness of care, readmissions,
complications and deaths, use of medical imaging, and patient satisfaction with care. In
addition, information about how much Medicare pays each hospital is reported. For
example, a daughter who lives in the Boston area is searching for a hospital to provide care
for her mother who just suffered a stroke. She can search for data about hospitals in that
city and compare how those facilities scored when it comes to measures for effective stroke
care. Does the hospital ensure that stroke patients needing medicine to lower cholesterol
are given a prescription before discharge? Do they make sure patients or their caregivers
receive written educational materials about stroke care and prevention during the hospital
stay? Do they evaluate stroke patients for rehabilitation services? How well a hospital ranks
on these kinds of measures can help a person determine where they want to go for services.
Or a person who is about to have elective surgery has the option of having the procedure at
two local hospitals. He can use the Hospital Compare program to see what other patients
have to say. How did others rate the responsiveness of hospital staff or how well staff
managed pain? How did patients survey rate the cleanliness of the hospital or how quiet it
was? Were other patients willing to recommend the hospital? Data regarding other services
for Medicare beneficiaries is also publically available (http://data.medicare.gov
(http://data.medicare.gov) ). Hospitals, nursing homes, physicians, home health agencies,
and dialysis facilities can all be compared using this central website. Data can be
downloaded in a variety of formats for analysis, including Microsoft Access and Microsoft
Excel. An online interface also permits exploration of the data if users do not wish to
download it. The website offers tools for creating visual displays of the information, such as
bar graphs or pie charts. Kaiser Family Foundation The Kaiser Family Foundation
(http://www.kff.org (http://www.kff.org) ) is an independent foundation that provides
independent, unbiased information regarding healthcare and healthcare policy. Special
topics covered by the Kaiser Family Foundation include Medicare, Medicaid, changes in the
private insurance market, healthcare in the safety net, and other key topics. Data and
reports are typically provided without charge. The foundation is not affiliated with Kaiser
Permanente or Kaiser Industries, and is supported by its own endowment, so it is free from
vested interests in the healthcare market. University HealthSystem Consortium (UHC)

University HealthSystem Consortium (UHC) comprises 118 academic medical centers and
nearly 300 additional affiliated hospitals and seeks to help its members achieve national
leadership in quality, safety, and cost-effectiveness (http://www.uhc.edu
(http://www.uhc.edu) ). Member hospitals report data on a
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of measures, including hospital admissions, discharges, transfers between facilities,
purchasing, and spending. UHC then provides quality assessment and standardization of the
data, adjusts for differences in the severity of illness of patients treated at each hospital, and
then produces quality, safety, and cost metrics to permit comparisons between facilities. A
web-based interface to the UHC database gives individual hospitals the ability to generate
automated reports that serve a dashboard function. UHC data are typically updated every
three months, which means that this external data source is excellent for monitoring longer-
term progress, particularly in comparison to peer institutions. However, the time lag
inherent in the data processing results in data that is not as useful for day-to-day
monitoring of quality indicators. National Committee for Quality Assurance
(NCQA)/Healthcare Effectiveness Data and Information Set (HEDIS) The National
Committee for Quality Assurance (NCQA) is a not-for-profit organization, dedicated to
improving the quality of healthcare organizations. The NCQA developed the Healthcare
Effectiveness Data and Information Set (HEDIS) to assist health plans, hospitals, and other
healthcare organizations to measure their quality and performance. HEDIS is a tool used by
more than 90% of U.S. health plans to measure performance on measures of care and
service (NCQA, n.d.). HEDIS consists of 81 measures across five domains of care. The data
allows employers to compare the performance of health plans and also allows the health
plans to use the results themselves to see where they need to focus improvement efforts.
HEDIS measures health issues that include asthma medication use, beta-blocker treatment
after a heart attack, controlling high blood pressure, providing comprehensive diabetes
care, breast cancer screening, antidepressant medication management, childhood and
adolescent immunizations, and childhood and adult weight and body mass index
assessment. Data Makes a Case for Energy Savings In many healthcare quality improvement
projects, more than one type of data is used. For instance, a project aimed at saving energy
costs involved the use of both financial and facility data. Facility managers at a healthcare
system decided they needed to make energy reduction and savings a priority, which was no
easy task, as the system operates in more than 15 states and includes over 70 hospitals as
well as other healthcare facilities. Though it was a challenge, by looking closely at its
operations, the system was able to save more than $1 million in energy costs. The health
system used a third-party bill-pay system to gather data, pay utility bills, and work with the
Energy Star Portfolio Manager. The Environmental Protection Agency runs the Energy Star
program that helps businesses and individuals protect the environment through
encouraging energy efficiency. The first step for the healthcare system was reviewing its
energy data. The utility bill-pay service gathered data on the gas, water, and electric use at
the system’s hospitals each month and interfaced with Energy Star Portfolio Manager,
which has the ability to gauge which buildings use energy efficiently and which are less than

efficient. The healthcare system then had to verify that data. An employee engaged in the
energy management effort went on-site to each building and confirmed the data about
energy use. All the data being fed to the Energy Star Portfolio Manager was verified,
including how many square feet in each building, as well as how many floors and beds.
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healthcare system then began identifying opportunities for energy savings. Hospital
engineering helped in that effort. Managers looked specifically at lighting costs, where there
is a quick return on investment. Each facility was assigned a project to re-lamp existing
lights with more efficient fixtures. Secondly, facility managers made sure there was a good
steam trap program for all buildings that used steam heat. The steam traps capture
condensation and return it to boilers at a higher temperature, allowing them to heat
buildings more efficiently. The project managers had to justify the cost of the
improvements. What system administrators wanted to see —in addition to the outright
expense of the project—was a return on investment. Will the healthcare system end up
saving enough money to pay for the improvements it needs to make? Generally, if a project
can pay for itself in two or three years, it is an attractive project that the administration is
likely to fund. Project managers calculated the return on investment, calculated the present
value of that investment, and proved those numbers. For instance, with the light
replacement project, managers counted fixtures and obtained quotes for the cost of the
work. By comparing past and present energy costs, the managers were able to show the
savings on energy bills. The initial focus was on the hospital buildings, which are the biggest
consumers of energy in the healthcare system. However, now that the energy saving
program is well established, managers are turning attention to how they can save energy at
the system’s long-term care, assisted living, and residential living facilities. Everybody’s
Problem Let’s take a look at an example where a Joint Commission requirement became a
challenge that several different hospital departments needed to solve. The Joint Commission
expects hospitals to address its patient communication standards—a requirement that
resulted in some creative solutions on the part of many emergency management (EM)
directors. The Joint Commission requires that hospitals make provisions for effective
communication with patients with limited or no English proficiency—both during everyday
operations and during emergencies. Effective communication with patients with limited
English proficiency can be a challenge at any time, but especially during a major emergency
or disaster. So how would a hospital with a chemical contamination situation on its hands
provide decontamination instructions to patients with limited English or other barriers to
communication? Joint Commission guidelines state that instructions by staff should not only
be verbal, but may also include posters or other visual aids for patients who are deaf or
speak limited English. To meet this standard, emergency management directors needed to
expand their planning to include how the hospital would communicate in multiple
languages. As with other aspects of EM planning, one of the first steps for hospitals to
comply was to assess the situation. A key was to know the hospital population. What does
the local census show in terms of population-level demographic data to help determine the
needs of the community? What does hospital data show about the race, ethnicity, and

languages, as well as disabilities of patients? Reviewing the demographics of the local
population allows a hospital not only to plan for its communication needs on a daily basis,
but also to anticipate the increased level of need during an emergency. Although a data
review may not identify every small group in a community, it can provide a good overview
of the languages that staff will most likely encounter. The greater the population base, the
greater the number of ethnic groups a hospital is likely to encounter and needs to plan for.
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data in their hands, hospital managers then had to look for solutions. A key for hospitals
was to evaluate existing resources in the facility. Interpreters who were currently used on a
daily basis to assist patients and families in various departments were likely a resource the
hospital could use in an emergency. Interpretive services already in place may be used
when a hospital’s incident command system is activated and requires language translation
or cultural interpretation. In an emergency, a hospital would be able to translate written
communications for patients and families into multiple languages. The hospitals also had to
consider expanding existing services. What will a hospital do if normal services are not
available in the case of a blizzard or hurricane that shuts down roads and communication or
if additional help is needed? One possible resource is the language skills of a hospital’s own
staff, who may be proficient in multiple languages and may serve as translators. Hospitals
can create a database of this information, with each staff member’s assigned department
and work shifts. The database can be used in an emergency when the usual trained
interpreters are not available. One word of caution, however: Identifying other external
interpretive services may be helpful, but healthcare organizations need to keep in mind the
important distinction between someone who is fluent in another language and a medically
trained interpreter who understands the privacy, cultural, and medical aspects of
translating medical information. While the Joint Commission says hospitals must ensure the
competency of their language interpreters and translators, it’s not clear how that would
play out in an emergency. In an emergency situation such as a devastating hurricane or
tornado, hospitals must do the best job that they can under sometimes trying circumstances
and may not be held to the same standard required under ordinary operations. Hospitals
can also consider the use of third-party “language line” services for emergencies. These
over-thephone translation services offer more than 1,000 different languages. Another
option is the free “language banks” offered free by some Red Cross chapters. One issue to
consider, however, is whether translators are aware of the need to protect patient privacy
and are trained to understand medical terminology. International schools, colleges, or
universities might also have instructors or students who can translate between patients and
providers, especially if the size and scale of a disaster overwhelms other available
resources. By brainstorming with other hospital leaders, emergency managers looked for
solutions and came up with plans to satisfy the Joint Commission requirement. Then during
emergency management exercises, they tested whether their plans would be successful.
Using Data Across Settings More and more quality improvement projects are joint efforts
that bring together a number of healthcare settings. Take Cincinnati Children’s Hospital
Medical Center, which partnered with local physician practices, to launch a large-scale

initiative to improve the care of children with asthma (Institute of Medicine, 2012a). The
hospital worked with 38 community-based pediatric practices to improve the health of
patients with asthma, which is one of the most common chronic conditions in children.
Asthma, a respiratory disease characterized by episodes or attacks of impaired breathing,
affects an estimated 6.8 million children in the United States, with many at risk for
emergency department visits and hospitalizations (CDC, 2012a). The project used
population segmentation to target high-risk patients and help deliver the best care through
components such as multidisciplinary-practice quality improvement teams, real-time
patient-practice, and network-level data reporting. Pediatric physician practices are
automatically alerted if any of their patients require an emergency department or urgent
care visit or need to be admitted to the hospital because of their asthma.
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result has been better care for patients, with a 92% adherence to best practices for care
management and with 92% of parents rating their child’s asthma as under control. The
project has also resulted in lower costs, with 92 avoided hospital admissions in one year,
resulting in $322,000 in savings, and 266 avoided emergency department/urgent care
visits. Questions to Consider 1. Describe some of the key uses of external sources of data in
quality improvement activities. How might this data be used in a particular quality
improvement project? 2. Describe some publicly available data sources that could be used
for the following purposes: a. Understanding the implications of proposed health policies. b.
Comparing heart attack survival rates in Medicare beneficiaries. c. Determining if overall
risk-adjusted inpatient mortality rates are lower at hospitals affiliated with an academic
medical center.
Summary & Resources Chapter Summary Data are integral to the process of quality
improvement. Team leaders of quality improvement efforts need to incorporate data into
the planning, intervention, measurement, and monitoring phases of their study. Important
tradeoffs occur between using existing data and collecting new data for a quality
improvement project. The resources available for a specific project, availability of existing
measures, and the validity and reliability of available measures are key drivers in the
decision to collect additional data. A number of types of data exist, and each serves a
particular function in the quality improvement process. Quantitative data allows team
members to take an empirical approach and to perform statistical tests to assess
relationships between variables. However, descriptive data relating to the patient or
healthcare worker experience also play an important role in understanding how care is
delivered and identifying possible areas for intervention. The range of experiences had by
each individual in the health system provides important context for understanding how to
achieve sustainable increases in quality and patient safety. Healthcare environments are
saturated with data, much of which can inform quality improvement efforts. Hospitals and
other healthcare entities routinely collect demographic, clinical, administrative, and
financial data about individual patients that can be used to identify and conduct quality

improvement projects. When necessary, a number of techniques exist to gather additional
data. The technique for data collection should be well matched to the project’s purpose and
available resources so as to collect valid and reliable data. When necessary, experts in a
specific data gathering technique should be consulted prior to implementing the data
collection. Finally, the Internet contains a vast trove of data about healthcare topics and
healthcare institutions, which can be useful for providing context and comparators for local
quality improvement efforts. Mini Case Study You have recently been hired at a community
health clinic where medication non-adherence is a major issue. Patients either delay taking
their medication or do not take the medication that their doctor prescribes. Some patients
say a lack of insurance is the reason for not taking their medication. The clinic wants to
increase the prescription drug adherence rate of patients to 90% or higher. The rate is
currently 75%. Following Deming’s PDSA model, here are the steps the clinic will take. Plan.
The plan is to develop an application for cell phones so that patients will be reminded to
take their pills at specific times. The study target for the project is 1,000 patients. The
measure for the study is that the clinic decides to monitor patients’ compliance rate with
the cell phone application. Do. The clinic downloads the application onto 1,000 patients’ cell
phones. For three months, staff collect information from patients’ cell phones in order to
determine the average compliance rates. Study. Clinic staff analyze the compliance data and
compare results to the pre-study period. They must determine if there is at least 10%
improvement from the previous statistics. The 10% threshold is randomly selected for the
purpose of this study, but it may be adjusted for different organizations or programs.
Generally, this rate is determined based on the consensus of individuals involved in the
project. Act. Clinic staff follow up with patients who did not follow the recommended
schedule for taking their prescriptions. They identify the three most important common
reasons for non-compliance and develop
additional plans for future improvements, such as: Offering cell phones with a basic paid call
plan Offering prescription drug cards for discounts Offering vouchers and/or samples In
this case, the PDSA model is applied to a simple problem in order to improve healthcare
quality. Desired outcomes and factors leading to outcomes are set forth in the “Plan” phase,
implemented in the “Do” phase, analyzed in the “Study” phase, and followed by changes that
are made in the “Act” phase. This cycle enables the clinic to address barriers to the desired
outcomes, modify the plan, and evaluate its effectiveness. Discussion Questions 1. 2. 3. 4. 5.
What was the problem and how did it affect patient care? What data sources were useful in
identifying and defining the problem? How did the use of a cell phone application facilitate
or hinder the quality improvement process? What are some barriers to success in getting
patients to take their medication? What stakeholders would be important in developing
support for possible solutions to this problem? Key Terms administrative data Data
collected to document for insurers and government payers the care that was provided.
categorical data Data that describes qualities of an individual in discrete groups, which are
frequently mutually exclusive. clinical data Medical data generated in the course of caring
for individual patients; examples include vital signs, symptoms, and laboratory test results.

continuous data Data that are on a continuous scale, such as blood pressure or serum
glucose levels. dashboard A visual display of data for the purpose of monitoring current
conditions and detecting when processes may be going awry. direct observation A data
collection technique that involves watching individuals perform a task, such as washing
their hands. experiential data Information, typically qualitative, that individuals provide to
describe how they perceive or experience a phenomenon, such as a clinic visit. external data
Information that describes the environment outside of the healthcare organization. financial
data Information regarding the cost or charges for patient care. fishbone diagram (cause-
and-effect diagram)
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visual technique for identifying contributory factors to lapses in care delivery. focus group A
forum typically of a small group of individuals who provide qualitative information
regarding experiences or perceptions. Hawthorne effect A term describing the phenomenon
that merely observing a behavior (such as hand washing) may improve performance if
those being observed become aware of which behaviors are being monitored. prospective
An approach in which data collection is initiated prior to when an outcome, such as a
patient safety event, occurs. qualitative data Data describing characteristics or experiences
of patients. quantitative data Numerical data, such as weight, time elapsed, or many
laboratory test values. record review (chart audit) A data collection process whereby
medical records are examined in order to extract information regarding care of individual
patients. retrospective An approach in which one looks backward in time. survey A series of
queries designed to gather data, often provided in written or electronic format. Click card to
see term Data collected to document for insurers and
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Thinking Questions 1. In your opinion, what are the most important roles for data in the
healthcare quality improvement process? 2. How should healthcare providers balance the
need to provide care to patients and the need to collect and manage data for quality
improvement processes? 3. How might use of internal data sources impact the cost of
healthcare delivery for a hospital or clinic? 4. Many external data sources provide rankings
or other information that is also available to the public. Describe some of the risks and
benefits of providing this information to patients and families. Suggested Websites Agency
for Healthcare Research and Quality (AHRQ): http://www.qualityindicators.ahrq.gov
(http://www.qualityindicators.ahrq.gov) Federal agency website with several resources
describing common indicators of quality and safety in healthcare delivery. Health Services
Research Information Central (HSRIC): http://www.nlm.nih.gov/hsrinfo
(http://www.nlm.nih.gov/hsrinfo/) / Website with links to a wide variety of data sources
regarding epidemiology, public health, and care delivery. Institute for Healthcare
Improvement: http://www.ihi.org (http://www.ihi.org) Not-for-profit organization that
provides many useful resources regarding quality improvement techniques, including data
collection and management tools. Medicare: https://Data.Medicare.gov
(https://Data.Medicare.gov) Portal for public access to data resources regarding quality of

care for Medicare beneficiaries. National Guideline Clearinghouse:
http://www.guideline.gov (http://www.guideline.gov) Searchable database of clinical
guidelines for a variety of health conditions. National Quality Measures Clearinghouse:
http://www.qualitymeasures.ahrq.gov (http://www.qualitymeasures.ahrq.gov) / Website
with evidence-based quality metrics, as well as a link to dataset of measures being used by
the U.S. Department of Health and Human Services for quality measurement and reporting.
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Measuring Performance Stefano Lunardi/iStock/Thinkstock Learning Objectives After
reading this chapter, you should be able to do the following: Discover the roles of
measurement in quality improvement. Compare three different types of quality measures in
the Donabedian framework. Explain the validity and reliability of a quality measure.
Summarize key considerations in selecting measures for quality improvement projects.
Describe sampling strategies and explain when they should be employed.
Introduction You may have heard the adage, “If you can measure it, you can improve upon
it.” Measurement certainly plays a central role in the process of quality improvement. Let’s
take an example. Summit Medical Group is a physician-owned primary care group with over
53 clinics operating in 11 counties around Knoxville, Tennessee. Since 2008, the physician
group began benchmarking its diabetic care processes with the National Committee for
Quality Assurance (NCQA) Diabetic Recognition Program (McBride & Hensley, 2013). The
program focuses on the care provided to diabetic patients. Diabetes is a serious disease that
can lead to many health complications. It’s important that diabetics control their blood
sugar levels, as too high or low levels can lead to health complications that include problems
with eyesight and even amputations when an infection occurs in the hands or feet that
cannot heal. These complications also result in serious costs to the healthcare system.
Therefore, groups such as the National Committee for Quality Assurance (NCQA) focus on
care given to diabetic patients, as does the government, with its quality measure that looks
at how well the country’s community health centers help diabetics control their blood sugar
levels and its measures that focus on diabetes care on the Physician Compare website
(https://data.medicare.gov/data/physiciancompare
(https://data.medicare.gov/data/physician-compare) ). But how does a medical practice
judge whether its doctors are providing good care to their diabetic patients? What are the
measures to determine that? Some of the measures that Summit Medical Group focused on
include whether patients have annual eye examinations, regular foot exams, and screening
for kidney disease to prevent possible health complications. Given that diabetes is the
seventh leading cause of death in the United States, one of the diseases that the NCQA has
focused on is diabetes care. The NCQA has established Comprehensive Diabetes Care
measures as part of its Healthcare Effectiveness Data and Information Set (HEDIS)
measures used for health plan performance reporting. HEDIS is a tool to measure
performance on care and service and its diabetes care measures assess whether patients
with diabetes receive care as recommended by guidelines and achieve control levels for

their blood sugar, cholesterol, and blood pressure. Using various tools that include
checklists and templates, Summit created a systematic approach focused on continuous
quality improvement to ensure that all of the physicians in its clinics provide quality of care
to diabetic patients. The processes it set up enable Summit to incorporate effective
coordination of care and collaboration among medical professionals across the continuum
of care and provide evidence-based, patient-centric guidelines focused on patient health.
Summit’s continuous quality improvement (CQI) team works with management at each
clinic, individual physicians, and their staff members to identify gaps in clinical quality care
from the standards Summit has set and establish processes to close those gaps. Summit uses
an approach called FOCUS (which stands for Find a problem, Organize a team, Clarify the
problem, Understand the problem, Select an intervention) Rapid Cycle PDSA (or Plan-Do-
Study-Act model, which was described in Chapter 5). Data collection is essential to CQI
projects, with data providing the CQI team with feedback and support. Once Summit had
established standards of care for its diabetic patients, it could look for inconsistencies in
diabetes care related processes and procedures and address those problem outcomes
through its CQI process. In 2008, most of Summit’s practice locations were still using paper
medical charts and had not yet converted to an electronic health record. Therefore, Summit
created a paper diabetic checklist. A care team member prepared for a diabetic patient’s
visit by reviewing his or her chart and then prepared the patient for the
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in the exam room. The completed diabetic checklist was scanned into the patient’s medical
record, filed in the chart, or incorporated into the physician’s progress notes. The checklist
included measures that were identified across the organization and ensured the scheduling
of annual eye exams, foot exams, and screening for kidney disease. As Summit’s physician
practices moved to adopt electronic health records (EHR), a physician-led workgroup
designed a template within the EHR to ensure diabetic foot exams. The organization trained
nurses to prepare patients for a foot exam prior to the physician entering the exam room; in
some cases, the nurses did a preliminary exam, with follow-up by the doctor if any problems
were seen. As a result, many physicians who had previously failed to perform and document
a foot exam on diabetic patients were able to meet the CQI measure (McBride & Hensley,
2013). The EHR also alerts doctors to the need for regular screenings, such as eye exams
and kidney screening. Summit also created care guides that allow physicians to quickly
place reminders and orders simultaneously. As laboratory tests on a patient flow back into
the EHR, the system automa…

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