Quality Improvement In Geriatric Nursing
Key components of quality improving its challenges and strategies for improvement and characteristics.
Learning Objective
1. Discuss key components of the definition of quality as outlined
by the Institute of Medicine (IOM)
2. Describe three challenges of measuring quality of care
3. Delineate three strategies for addressing the challenges of
measuring quality
4. List three characteristics of a good performance measure
Nursing Care and Effort for Quality Improvement
Nadzam and Abraham (2003) state that. “The main objective of
implementing best practice protocols for geriatric nursing are to stimulate
nurses to practice with greater knowledge and skill, and thus improve the
quality of care to older adults”.
Although improved patient care and
safety is certainly a goal, providers also need to be focused on the
implementation of evidence based practice and on improving outcomes of care.
The implementation of evidenced based nursing practice as a means to providing
safe, quality patient care, and positive outcomes is well supported in the
literature. However, in order to ensure that protocols are implemented
correctly, as is true with the delivery of all nursing care, it is essential to
evaluate the care provided.
Outcomes of care are gaining increased attention
and will be of particular interest to providers as the health care industry
continues to move toward a “pay-for-performance (P4P)/value based purchasing
(VBP)” reimbursement model.
Background Knowledge and Statement of Perform
The improvement of care and clinical outcomes or, as it is commonly
known as Performance Improvement requires a defined, organized approach.
Improvement efforts are typically guided by the organization’s Quality
Assessment (measurement) and Performance Improvement (process improvement)
model.
Some well known models or approaches for improving care and processes
include Plan-Do-Study-Act (PDSA: Institute for Health Care Improvement, see Improvement Methods/Tools
/Plan-Do-Study-Act%20 and Six Sigma.
These
methodologies are simply an organized approach to defining improvement
priorities, collecting data, analyzing the data, making sound recommendations
for process improvement, implementing identified changes, and then reevaluating
the measures.
Through Performance Improvement, standards of care (e.g, Nurses
Improving Care for Health system Elders [NICHE] protocols, in this case) are
identified, evaluated, analyzed for variances, and improved. The goal is to
standardize and improve patient care and outcomes.
Restructuring, redesigning,
and innovative processes aid in improving the quality of patient care. However,
nursing professionals must be supported by a structure of continuous
improvement that empowers nurses to make changes and delivers reliable outcomes
(Johnson, Hallsey, Meredith, & Warden, 2006).
Does Organizational Changes Bring Improvement ?
From the very beginning of the NICHE project in the early 1990s
(Fulmer et al., 2002), the NICHE team struggled with the following questions:
How can we measure whether the combination of models of care, staff education
and development. and organizational change leads to improvements in patient
care?
How can we provide hospitals and health systems that are committed to
improving their nursing care to older adults with guidance and frameworks, let
alone tools for measuring the quality of geriatric care?
In turn, these
questions generated many other questions: Is it possible. to measure quality?
Can we identify direct indicators of quality? Or do we have to rely on indirect
indicators (eg, if 30-day re admissions of patients older than the age of 65
drop, can we reasonably state that this reflects an improvement in the quality
of care)?
What factors may influence our desired quality outcomes, whether
these are unrelated factors (eg, the pressure to reduce length of stay) or
related factors (eg, the severity of illness)? How can we design evaluation
programs that enable us to measure quality without adding more burden (of data
collection, of taking time away from direct nursing care)?
No doubt, the
results from evaluation programs should be useful at the “local” level. Would
it be helpful, though, to have results that are comparable across clinical
settings (within the same hospital or health system) and across institutions
(eg, as quality benchmarking tools)?
Many of these questions remain unanswered
today, although the focus on defining practice through an evidence-based
approach is becoming the standard, for it is against a standard of care that we
monitor and evaluate expected care.
Defining outcomes for internal and external
reporting is expected, as is the improvement of processes required to deliver
safe, affordable, and quality patient care.
Whats is Quality of Care In Nursing
The
concept of performance measures as the evaluation link between care delivery
and quality improvement is introduced.
It also describes
external comparative databases sponsored by Centers for Medicare &
Medic-aid Services (CMS) and other quality improvement organizations. It
concludes with a description of the challenge to selecting performance
measures.
Principles of Evaluation
It is important to reaffirm two key principles for the purposes of
evaluating nursing care in this context.
First, at the management level, it is
indispensable to measure the quality of geriatric nursing care; however, doing
so must help those who actually provide care (nurses) and must impact on those
who receive care (older adult patients).
Second, measuring quality of care is
not the end goal; rather, it is done to enable the continuous use of
quality-of-care information to improve patient care.
Assessment of the Problem Quality Health Care Defined
without reflecting on one’s own values and beliefs surrounding quality health
care.
Many have tried to define the concept; but like the old cliché “beauty is
in the eye of the beholder,” so is our own perception of quality. Health care
consumers and providers alike are often asked, “What does quality mean to you?”
The response typically varies and includes statements such as “a safe health
care experience,” “receiving correct medications.” “receiving medications
in a timely manner,” “a pain-free procedure or postoperative
experience,” “compliance with regulation,” “accessibility
to services,” “effectiveness of treatments and medications,”
“efficiency of services,” “good communication among providers,”
“information sharing,” and “a caring environment.”
These are important
attributes to remember when discussing the provision of care with clients and
patients.
Measure of Quality of Care
The IOM defines quality of care as “the degree to which health
services for individuals and populations increase[s] the likelihood of desired
health outcomes and are consistent with current professional knowledge” (Kohn,
Corrigan, & Donaldson, 2000, p. 211) .
Note that this definition does not
tell us what quality is, but what quality should achieve. This definition also
does not say that quality exists if certain conditions are met (eg, a ratio of
x falls to y older orthopedic surgery patients, a 30-day readmission rate of
2).
Instead, it emphasizes that the likelihood of achieving desired levels of
care is what matters. In other words, quality is not a matter of reaching
something but, rather, the challenge, over and over, of improving the odds of
reaching the desired level of outcomes.
Thus, the definition implies the
cyclical and longitudinal nature of quality: What we achieve today must guide
us as to what to do tomorrow-better and better, over and over. The focus being
on improving processes while demonstrating sustained improvement.
The IOM definition stresses the framework within which to
conceptualize quality: knowledge.
The best knowledge to have been research
evidence preferably from randomized clinical trials (experimental studies) yet
without ignoring the relevance of less rigorous studies (non randomized studies,
epidemiological investigations, descriptive studies, even case studies).
Realistically, in nursing, we have limited evidence to guide the care of older
adults. Therefore, professional consensus among clinical and research experts
is a critical factor in determining quality.
Furthermore, knowledge is needed
at three levels: to achieve quality, we need to know what to do (knowledge
about best practice), we need to know how to do it (knowledge about behavioral
skills), and we need to know what outcomes to achieve (knowledge about best
outcomes).
The IOM definition of quality of care contains several other
important elements. “Health services” focuses the definition on the care
itself. Granted, the quality of care provided is determined by such factors as
knowledgeable professionals, good technology, and efficient organizations;
however, these are not the focus of quality measurement.
Rather, the
definition implies a challenge to health care organizations: The system should
be organized in such a way that knowledge-based care is provided and that its
effects can be measured. This brings us to the “desired health
outcomes” element of the definition.
Quality is not an attribute (as in
“My hospital is in the top 100 hospitals in the United States as ranked by US
News & World Report”), but an ability (as in “Only x% of our older adult
surgical patients go into acute confusion; of those who do, y% return to normal
cognitive function within z hours after on ser”).
In the IOM definition, degree
implies that quality occurs on a continuum from unacceptable to excellent. The
clinical consequences are on a continuum as well. If the care is of
unacceptable quality, the likelihood that we will achieve the desired outcome
is nil.
In fact, we probably will achieve outcomes that are the opposite of
what are desired. As the care moves up the scale toward excellent, the more
likely the desired outcomes will be achieved. Degree also implies
quantification.
Although it helps to be able to talk to colleagues about, say,
unacceptable, poor, average, good, or excellent care, these terms should be
anchored by a measurement system.
Such systems enable us to interpret what, for
instance, poor care is by providing us with a range of numbers that correspond
to poor. In turn, these numbers can provide us with a reference point for
improving care to the level of average: We measure care again, looking at
whether the numbers have improved, then checking whether these numbers fall in
the range defined as average.
Likewise, if we see a worsening of scores, we
will be able to conclude whether we have gone from, say, good to average.
Individuals and populations underscore that quality of care is reflected in the
outcomes of one patient and in the outcomes of a set of patients. It focuses
our attention on providing quality care to individuals while aiming to raise
the level of care provided to populations of patients.
In summary, the IOM definition of quality-of-care forces us to
think about quality in relative and dynamic rather than in absolute and static
terms. Quality of care is not a state of being but a process of becoming.
Quality is and should be measurable, using performance measures a quantitative
tool that provides an indication of an organization’s performance in relation
to a specified process or outcome” (Schyve & Nadzam, 1998, p. 222).
Quality improvement is a process of attaining ever better levels of
care in parallel with advances in knowledge and technology. It strives toward
increasing the likelihood that certain outcomes will be achieved.
This is the
professional responsibility of those who are charged with providing care
(clinicians, managers, and their organizations). On the other hand, consumers
of health care (not only patients but also purchasers, payers, regulators, and
accreditors) are much less concerned with the processes in place, as with the
results of those processes.
Clinical Outcomes and Publicly Reported Quality Measures
Although it is important to evaluate clinical practices and
processes, it is equally important to evaluate and improve outcomes of care. Clinical
outcome indicators are receiving unprecedented attention within the health care
industry from providers, payors, and consumers alike.
Regulatory and
accrediting bodies review outcome indicators to evaluate the care provided by
the organization prior to and during regulatory and accrediting surveys, and to
evaluate clinical and related processes.
Organizations are expected to use
outcome data to identify and prioritize the processes that support clinical
care and demonstrate an attempt to improve performance. Providers may use
outcomes data to support best practices by bench marking their results with
similar organizations.
The bench marking process is supported through publicly
reported outcomes data at the national and state levels. National reporting
occurs on the CMS website, where consumers and providers alike may access
information and compare hospitals, home-care agencies, nursing homes, and
managed care plans.
Home Health Compare list outcome indicators relative to
the specific service or delivery model. Consumers may use those websites to
select organizations and compare outcomes, one against another, to aid in their
selection of a facility or service.
These websites also serve as a resource for
providers to benchmark their outcomes against those of another organization.
Outcomes data also become increasingly important to providers as the industry
shifts toward a P4P/ VBP model.
In a P4P/VBP model, practitioners are reimbursed for achieved
quality-of-care outcomes. Currently, the CMS has several P4P initiatives and
demonstration projects for a detailed overview) is
part of the US Department of Health and Human Services’ broader national
quality initiative that focuses on an initial set of 10 quality measures by
linking reporting of those measures to the payments the hospitals receive for
each discharge.
The purpose of the Premier Hospital Quality Incentive
Demonstration was to have improved the quality of inpatient
care for Medicare beneficiaries by giving financial incentives to almost 300
hospitals for high quality.
The Physician Group Practice Demonstration, mandated
by the Medicare, Medicaid, and State Children’s Health Insurance Program
(SCHIP) Benefits Improvement and Protection Act of 2000 (BIPA), is the first
P4P initiative for physicians under the Medicare program.
The Medicare Care
Management Performance Demonstration (Medicare Modernization Act [MMA)] section
649), modeled on the “bridges to excellence” program, is a 3-year P4P
demonstration with physicians to promote the adoption and use of health
information technology to improve the Quality of patient care for chronically
ill Medicare patients.
The Medicare Health Care Quality Demonstration, mandated
by section 646 of the MMA, is a 5-year demonstration program under which
projects enhance quality by improving patient safety, reducing variations in
utilization by appropriate use of evidence-based care and best practice
guidelines, encourage shared decision-making, and using culturally and
ethnically appropriate care.
Interventions and Care Strategies
Measuring quality of care
Schyve and Nadzam (1998) identified several challenges to measuring
quality. First, the suggestion that quality of care is in the eye of the
beholder points to the different interests of multiple users. This issue
encompasses both measurement and communication challenges.
Measurement and
analysis methods must generate information about the quality of care that meets
the needs of different stakeholders. In addition, the results must be
communicated in ways that meet these different needs. Second, we must have good
and generally accepted tools for measuring quality.
Thus, user groups must come
together in their conceptualization of quality care so that relevant health
care measures can be identified and standardized. A common language of
measurement must be developed, grounded in a shared perspective on quality that
is cohesive across, yet meets the needs of various user groups.
Third, once the
measurement systems are in place, data must be collected. This translates into
resource demands and logistic issues as to who is to report, record, collect,
and manage data. Fourth, data must be analyzed in statistically appropriate
ways. This is not just a matter of using the right statistical methods.
More
important, user groups must agree on a framework for analyzing quality data to
interpret the results. Fifth, health care environments are complex and dynamic
in nature. There are differences across health care environments, between types
of provider organizations, and within organizations.
Furthermore, changes in
health care occur frequently such as the movement of care from one setting to
another and the introduction of new technology. Finding common denominators is
a major challenge.
Challenges
These challenges are not insurmountable. However, making a
commitment to quality care entails a commitment to putting the processes and
systems in place to measure quality through performance measures and to report
quality-of-care results.
This commitment applies as much to a quality
improvement initiative on a nursing unit as it does to a corporate commitment
by a large health care system. In other words, once an organization decides to
pursue excellence (ie quality), it must accept the need to overcome the various
challenges to measurement and reporting. Let us examine how this could be done
in a clinical setting.
McGlynn and Asch (1998) offer several strategies for addressing the
challenges to measuring quality. First, the various user groups must identify
and balance competing perspectives.
This is a process of giving and taking: not
only proposing highly clinical measures (eg, prevalence of pressure ulcers) but
also providing more general data (eg use of restraints).
It is a process of
asking and responding: not only asking management for monthly statistics on
medication errors but also agreeing to provide management with the necessary
documentation of the reasons stated for restraint use. Second, there must be an
accountability framework.
Committing to quality care implies that nurses.
Several responsibilities and are willing to assume to be held accountable for
each of them:
(a) providing the best possible care to older patients
(b)
examining their own geriatric nursing knowledge and practice
(c) seeking ways
to improve it
(d ) agreeing to evaluation of their practics
(e) responding
to needs for improvement.
Third, there must be objectivity in the evaluation of
quality. This requires setting and adopting explicit criteria for judging
performance, then building the evaluation process on these criteria.
Nurses,
their colleagues, and their managers need to reach consensus on how performance
will be measured and what will be considered excellent (and good, average,
etc.) performance.
Fourth, once these indicators have been identified, nurses
need to select a subset of indicators for routine reporting. Indicators should
give a reliable snapshot of the team’s care to older patients.
Fifth, it is
critical to separate as much as possible the use of indicators for evaluating
patient care and the use of these indicators for financial or non-financial
incentives.
Should the team be cost conscious? Yes, but cost should not
influence any clinical judgment as to what is best for patients. Finally,
nurses in the clinical setting must plan how to collect the data.
At the
institutional level, this may be facilitated by information systems that allow
performance measurement and reporting.
Ideally, point-of-care documentation
will also provide the data necessary for a systematic and goal-directed
quality-improvement program, thus, eliminating separate data abstraction and
collection activities.
Achieving Improvement
The success of a quality improvement program in geriatric nursing
care (and the ability to overcome many of the challenges) hinges on the
decision as to what to measure.
We know that good performance measures must be
objective, that data collection must be easy and as burdenless as possible,
that statistical analysis must be guided by principles and placed within a
framework, and that communication of results must be targeted toward different
user groups.
Conceivably, we could try to measure every possible aspect of
care, realistically, however, the planning for this will never reach the
implementation stage.
Instead, nurses need to establish priorities by asking
these questions: Based on our clinical expertise, what is critical for us to
know? What aspects of our care to older patients are high risk or high volume?
What parts of our elder care are problem-prone, either because we have
experienced difficulties in the past or because we can anticipate problems caused
by the lack of knowledge or resources?
What clinical indicators would be of
interest to other user groups: patients, the general public, management,
payors, accreditors, and practitioners? Throughout this prioritization process,
nurses should keep asking themselves: What questions are we trying to answer,
and for whom?
Selecting Quality Indicators
is a crucial step in evaluating nursing care and is based on two important
factors: frequency and volume.
Clearly, high-volume practices or frequent
processes require focused attention-to ensure that the care is being delivered
according to protocol or processes are functioning as designed.
Problem-prone
or high risk processes would also warrant a review because these are processes
with inherent risk to patients or variances in implementing the process. The
selection of indicators must also be consistent with organizational goals for
improvement.
This provides buy-in from practitioners as well as administration
when reporting and identifying opportunities for improvement. Performance
measures (indicators) must be based on either a standard of care, policy,
procedure, or protocol.
These documents, or standards of care, define practice
and expectations in the clinical setting and, therefore, determine the criteria
for the monitoring tool. The measurement of these standards simply reflects
adherence to or implementation of these standards.
Once it is decided what to
measure, nurses in the clinical geriatric practice setting face the task of
deciding how to measure performance.
There are two possibilities: either the
appropriate measure (indicator) already exists or a new performance measure
must be developed. Either way, there are a number of requirements of a good
performance measure that will need to be applied.
Although indicators used to monitor patient care and performance do
not need to be subject to the rigors of research, it is imperative that they
reflect some of the attributes necessary to make relevant statements about the
care.
The measure and its output need to focus on improvement, not merely the
description of something. It is not helpful to have a very accurate measure
that just tells the status of a given dimension of practice.
Instead, the
measure needs to inform us about current quality levels and relate them to
previous and future quality levels. It needs to be able to compute improvements
or declines in quality over time so that we can plan for the future. For example,
to have a measure that only tells the number of medication errors in the past
month would not be helpful.
Instead, a measure that tells what types of
medication errors were made, perhaps even with a severity rating indicated,
compares this to medication errors made during the previous months, and shows
in numbers and graphs the changes over time that will enable us to do the
necessary root-cause analysis to prevent more medication errors in the future.
Performance measures need to be clearly defined, including the terms used, the
data elements collected, and the calculation steps employed. Establishing the
definition prior to implementing the monitoring activity allows for precise
data collection.
It also facilitates benchmarking with other organizations when
the data elements are similarly defined and the data collection methodologies
are consistent. Imagine that we want to monitor falls on the unit.
The initial
questions would be as follows: What is considered a fall? Does the patient have
to be on the floor? Does a patient slump against the wall or onto a table while
trying to prevent himself or herself from falling to the floor constitute a
fall?
Is a fall due to physical weakness or orthostatic hypotension treated the
same as a fall caused by tripping over an obstacle?
The next question would be
the following: Over what time are falls measured: a week, a fortnight, a month,
a quarter, a year? The time frame is not a matter of convenience but of
accuracy.
To be able to monitor falls accurately, we need to identify a time
frame that will capture enough events to be meaningful and interpretable from a
quality improvement point of view. External indicator definitions, such as
those defined for use in the National Database of Nursing Quality Indicators,
provide guidance for both the indicator definition as well as the data
collection methodology for nursing-sensitive indicators.
The nursing-sensitive
indicators reflect the structure, process, and outcomes of nursing care. The
structure of nursing care is indicated by the supply of nursing staff, the
skill level of the nursing staff, and the education/certification of nursing
staff.
Process indicators measure aspects of nursing care such as assessment,
intervention, and registered nurse (RN) job satisfaction.
Patient outcomes that
are determined to be nursing sensitive are those that improve if there is a
greater quantity or quality of nursing care (eg, pressure ulcers, falls,
intravenous [IV] infiltrations) and are not considered “nursing-sensitive”.
Several nursing
organizations across the country participate in data collection and submission,
which allows for a robust database and excellent bench marking opportunities.
Additional Indicators or Attributes
Additional indicator attributes include validity, sensitivity, and
specificity. Validity refers to whether the measure “actually measures what it
purports to measure” (Wilson, 1989). Sensitivity and specificity refer to the
ability of the measure to capture all true cases of the event being measured,
and only true cases.
We want to make sure that a performance measure identifies
true cases as true, and false cases as false, and does not identify a true case
as false or a false case as true. Sensitivity of a performance measure is the
likelihood of a positive test when a condition is present.
Lack of sensitivity
is expressed as false positives: The indicator calculates a condition as
present when in fact it is not. Specificity refers to the likelihood of a
negative test when a condition is not present. False-negatives reflect lack of
specificity: The indicators calculate that a condition is not present when in
fact it is.
Depression in Older Adults, to use the Geriatric Depression Scale, in which
a score of 11 or greater is indicative of depression. How robust is this cutoff
score of 11?
What is the likelihood that someone with a score of 9 or 10 (ie
negative for depression ) might actually be depressed (false-negative)?
Similarly, what is the likelihood that a patient with a score of 13 would not
be depressed (false positive)?
Reliability of Measures
Reliability means that results are reproducible, the indicator
measures the same attribute consistently across the same patients and across
time. Reliability begins with a precise definition and specification, as
described earlier.
A measure is reliable if different people calculate the same
rate for the same patient sample. The core issue of reliability is measurement
error, or the difference between the actual phenomenon and its measurement: The
greater the difference, the less reliable the performance measure.
For example,
suppose that we want to focus on pain management in older adults with end-stage
cancer. One way of measuring pain would be to ask patients to rate their pain
as none, a little, some, quite a bit, or a lot.
An alternative approach would
be to administer a visual analogue scale, a 10-point line on which patients
indicate their pain levels. Yet another approach would be to ask the pharmacy
to produce monthly reports of analgesic use by type and dose. Generally
speaking, the more subjective the scoring or measurement, the less reliable it
will be.
If all these measures were of equal reliability, they would yield the
same result. Concept of reliability, particularly inter-rate reliability,
becomes increasingly important to consider in those situations where data
collection is assigned to several staff members.
It is important to review the
data collection methodology and the instrument in detail to avoid different
approaches by the various people collecting the data.Several of the examples given earlier imply the criterion of
interpretability.
A performance measure must be interpretable; that is, it must
convey a result that can be linked to the quality of clinical care. First, the
quantitative output of a performance measure must be scaled in such a way that
users can interpret it.
For example, a scale that starts with 0 as the lowest
possible level and ends with 100 is a lot easier to interpret than a scale that
starts with 13,325 and has no upper boundary except infinity. Second, we should
be able to place the number within a context.
Suppose we are working in a
hemodialysis center that serves quite a large proportion of patients with
end-stage renal disease (ESRD) and are older than the age of 60-the group least
likely to be fit for a kidney transplant yet with several years of life
expectancy remaining.
We know that virtually all patients with ESRD develop
anemia (hemoglobin (Hb) level less than 11 g/dL), which in turn impacts on
their activities of daily living (ADL) and independent activities of daily
living (IADL) performance.
In collaboration with the nephrologists, we initiate
a systematic program of anemia monitoring and management, relying in part on
published best practice guidelines.We want to achieve the best practice
guideline of 85% of all patients having Hb levels equal to or greater than 11
g/dL .
We should be able to succeed because the central laboratory provides us
with Hb levels, which allows us to calculate the percentage of patients at Hb
of 11 g/dL or greater.
The concept of risk-adjusted performance measures or outcome
indicators is an important one. Some patients are sicker than others, some have
more comorbidities, and some are older and frailer. No doubt, we could come up
with many more risk variables that influence how patients respond to nursing
care.
Good performance measures take this differential risk into consideration.
They create a “level playing field” by adjusting quality indicators on the
basis of the (risk for) severity of illness of the patients. It would not be
fair to the health care team if the patients on the unit are a lot sicker than
those on the unit a floor above.
The team is at greater risk for having lower
quality outcomes, not because they provide inferior care, but because the
patients are a lot sicker and are at greater risk for a compromised response to
the care provided.
The sicker patients are more demanding in terms of care and
ultimately are less likely to achieve the same outcomes as less ill patients.
Performance measures must be easy to collect.
The many examples cited earlier
also refer to the importance of using performance measures for which data are
readily available, can be retrieved from existing sources or can be collected
with little burden.
The goal is to gather good data quickly without running the
risk of having “quick and dirty” data. We begin the process of deciding how to
measure by reviewing existing measures. There is no need to reinvent the wheel,
especially if good measures are out there.
Nurses should review the literature,
check with national organizations, and consult with colleagues. Yet, we should
not adopt existing measures blindly. Instead, we need to subject them to a
thorough review using the characteristics identified previously. Also, health
care organizations that have adopted these measures can offer their experience.
It may be that after an exhaustive search, we cannot find measures that meet
the various requirements outlined previously. We decide instead to develop our
own in-house measure. The following are some important guidelines:
1. Zero in on the population to be measured. If we are measuring an
undesirable event, we must determine the group at risk for experiencing that
event, then limit the denominator population to that group.
If we are measuring
a desirable event or process, we must identify the group that should experience
the event or receive the process. Where do problems tend to occur? What
variables of this problem are within our control?
If some are not within our
control, how can we zero in even more on the target population? In other words,
we exclude patients from the population when good reason exists to do so (eg
those allergic to the medication being measured).
2. Define terms. This is a painstaking but essential effort. It is
better to measure 80% of an issue with 100% accuracy than 100% of an issue with
80% accuracy.
3. Identify and define the data elements and allowable values
required to calculate the measure This is another painstaking but essential
effort. The 80/100 rule applies here, as well.
4. Test the data collection process. Once we have a prototype of a
measure ready, we must examine how easy or difficult it is to get all the
required data.
Implementing the Performance Improvement
Program
Successful Performance Improvement programs require an
organizational commitment to implementation of the Performance Improvement
processes and principles outlined in this chapter.
Consequently, this
commitment requires a defined, organized approach that most organizations
embrace and define in the form of a written plan. The plan outlines the
approach the organization uses to improve care and safety for its patients.
There are several important elements that must be addressed in order to
implement the Performance Improvement program effectively. The scope of
service, which addresses the types of patients and care that is rendered,
provides direction on the selection of performance measures.
An authority and
responsibility statement in the document defines who is able to implement the
quality program and make decisions that will affect its implementation. Finally,
it is important to define the committee structure used to effectively analyze
and communicate improvement efforts to the organization.
The success of the
Performance Improvement program is highly dependent on a well-defined structure
and appropriate selection of performance measures. The following is a list of
issues that, if not addressed, may negatively impact the success of the quality
program:
1. Lack of focus: a measure that tries to track too many criteria
at the same time or is too complicated to administer, interpret, or use for
quality monitoring and improvement
2. Wrong type of measure a measure that calculates indicators the
wrong way (eg, uses rates when ratios are more appropriate; uses a continuous
scale rather than a discrete scale; measures a process when the outcome is
measurable and of greater interest)
3. Unclear definitions: a measure that is
too broad or too vague in its scope and definitions (e.g. population is too
heterogeneous, no risk adjustment, unclear data elements, poorly defined
values)
4. Too much work: a measure that requires too much clinician time
to generate the data or too much manual chart abstraction
5. Reinventing the wheel: a measure that is a reinvention rather
than an improvement of a performance measure
6. Events not under control: measure focuses on a process or
outcome that is out of the organization (or the unit’s) control to improve
7. Trying to do research rather than quality improvement: data
collection and analysis are done for the sake of research rather than for
improvement of nursing care and the health and well-being of the patients
8 Poor communication of results: the format of communication does
not target and enable change
9. Uninterpretable and underused: uninterpretable results are of
little relevance to improving geriatric nursing care
In summary, the success of the Quality Assessment Performance
Improvement Program’s ability to measure, evaluate, and improve the quality of
nursing care to health system elders is in the planning.
First, it is important
to define the scope of services provided and those to be monitored and
improved.
Second, identify performance measures that are reflective of the care
provided. Indicators may be developed internally or may be obtained from
external sources of outcomes and data collection methodologies.
Third, it is
important to analyze the data, pulling together the right people to evaluate
processes, make recommendations, and improve care. Finally, it is important to
co -mmunicate findings across the organization and celebrate success.