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Using data to improve patient safety is not only required by governmental agencies but has become a recent focus of media attention. The reason regulatory agencies require data about falls,

Topic:
Case Study Analysis

Review the case study below. Write
a 1,750- to 2,100-word paper that addresses the following regarding the case
study:

• What measures are used to
monitor and revise quality program implementation? Are the measures appropriate
considering the circumstances? Why do you think this?

• What regulatory and
accreditation standards exist? What strategies are used for meeting these
standards? Are the strategies appropriate considering the circumstances? Why do
you think this?

• What barriers may interfere
with implementing or revising the quality measures described in the study? How
could the organizations overcome those barriers?

Format your paper consistent with
APA guidelines. No plagiarism on the paper. Keep it confidential.

Please Read the Case Study below and write a paper based on the
requirements posted above. Use at least four peer review references.

CASE Study: FALLS

Using data to
improve patient safety is not only required by governmental agencies but has
become a recent focus of media attention. The reason regulatory agencies
require data about falls, for example, is that falls are prioritized as a
high-risk problem that can result in fractures,

surgery, or
worse. Because falls are a patient safety concern, if safety is a high priority
for the organization, part of its stated mission, then preventing falls is
important.

Nursing staff collect information about
falls: incident reports record

the time,
place, date, frequency, and reason for the fall. Patient assessment

and H&P
(history and physical) target certain patients as highly susceptible to
falling. Falls have an impact on LOS, especially when the resulting injuries
require tests and treatment. Patients who fall, and their families, complain
about their care in a formal way, such as through satisfaction surveys or
complaints to the organization, suggesting that better

care would
have prevented the fall from occurring. Patients and their

families have
instituted lawsuits as a result of falls.

Malpractice suits are increasingly
being brought after falls, because they are thought to be preventable and can
result in serious injury. Jury awards for these perceived “unnecessary”
complications have been high.

Why is it
that hospitals cannot prevent patient falls? The methodological explanation is
that the “fall prevention” ranking (that is, a given patient’s likelihood of
falling) is perceived to be a nursing assessment issue. This perception is
itself a problem, due to the conflicting desires to show not only that the
rates are low but also to illustrate to regulatory agencies that the measure,
which they require, is being used. In fact, the report of low rates is based on
poorly defined measures.

A valid measure defines a set of events
that occurs in a circumstance where there were opportunities for that type of
event to occur. Figure 2.2 graphically
illustrates how to define a quality measure. The number of events is thenumerator
of the measure, and the number of

opportunities
for that event to occur is thedenominator.

For example, if you are interested in
examining how many falls resulted

in fractures,
the numerator of the measure would be exactly that—the number of patient falls
that resulted in fractures. The denominator would encompass the totality of all
falls. If 20 falls resulted in fractures, and there were 100 falls in total,
the numerator (20) is a subset of the denominator (100). The measure of the
falls is calculated as a rate, in this case, 20/100, or 20 percent. The
numerator, orNof a measure, defines what
you want to study or what question you want to investigate or which hypothesis
you want to test. Therefore theNcan be as specific or as general as appropriate. If you were
interested in determining the influence of medication on falls, you might want
to know the rate of medicated patients who fell. The measure would be

events/opportunities,
orN/D—in this case the number of patients on sedatives who fell/the
total number of patients who fell (see Figure 2.3).

Quality Measure =Event =
Numerator

Opportunity denominator

Event = Number of Sedated patients who
Fell

Opportunity Total
Number of patient Falls

Because major falls that cause injury and even
death still occur, the focus is shifting from reacting to an event toward
developing prevention programs. Another reason to adopt such a focus is that
the majority of today’s hospital patient population is at high risk for falls
because they are increasingly elderly, living longer, experiencing multiple
diseases, and taking many medications. Even those organizations that have
developed a falls prevention program have a high rate of falls because the
assessment and program can be so routinized that it becomes

a paper
exercise to illustrate to the accreditation agencies that the organization is
in compliance with assessing patients.

There are organizations that believe
if there are no falls being reported, there are no falls occurring. Patent
nonsense. The New York State Commissioner of Health has taken an hard-line
approach to the reporting of errors and is critical of hospitals that
underreport. She is quite right to take this position, because without
information, improvements cannot be intelligently implemented.

In our health care system it took
almost eight months to develop a definition of “fall” that was acceptable to
all caregivers. What might seem to a layperson a straightforward concept can be
quite complicated?

For example,
does a “fall” have to result in the patient being on the floor? Can a patient
“fall” if that patient is being assisted onto a chair by a caregiver? Does a
“fall” have to be observed by another to distinguish it from a collapse or a faint?
Measurements cannot be standardized unless everyone involved in data collection
understands what data they are collecting.

It’s obvious that if the reasons for
the falls are understood and if appropriate improvements can be developed and implemented,
that would decrease the incidence of falls. This decrease would produce many
advantages: the organization’s safety objectives would be met, the potential
for malpractice claims against the hospital would be reduced, patient
satisfaction would be increased, the budget would no longer be adversely
affected by costs of falls, LOS would be reduced, and most important, patient
safety would be preserved.

With data, professionals can understand
the scope of the problem they have and determine whether resources should be
used for improvements. If you have 10 falls per 1,000 patients (1 percent),
over the course of six months, perhaps you would determine that your
improvement efforts should be focused elsewhere. But if you discover that your
unit or hospital has 10 falls per 50 patients, or 20 percent every week, you
know you have a far more serious problem to address.

You need a
sense of the dimensions of the problem, that is, data that reveal how many
incidents (the numerator of the measure) were related to how many possibilities
(the denominator), and also a time frame to delimit that data, to help you
measure, or quantify, the incidence of falls, or any other variable. The
numerator of a measure is defined by the question being considered, such as do
elderly patients with diabetes have an increased likelihood of a fall? With
data, such questions can be answered accurately.

Data can be gathered on patient age,
patient diagnosis, and the time when (on what shift) the patient falls. In addition,
information is readily available on the patient-staff ratio at the time of the
fall, on the unit of the patient who falls, and on the cause of the fall. There
can be many

variables to assess.
Was the call bell not answered in a timely way? Was there an obstruction on the
floor? Were the lights not working properly? Did medication play a part? What
happened to the patient is also documented: was there an injury, what kind of
injury was it, what was

the cost in
terms of LOS, and what were the unanticipated services (return to the OR) or
clinical outcomes, such as infection or malpractice suits? All these pieces of
data are associated with measures. Taken together the information enables an
administrator to grasp the situation

in a complex
way (rather than to assume the nurse was not doing her or his job) and
implement improvements. Good administrators have valid data underlying their
decisions. Data collection and analyses should also be the responsibility of
clinical supervisors, such as the head nurses and the chairs of clinical
departments.

Regulatory agencies require hospitals
and health care organizations to correlate human resource indicators, such as
staffing ratios, with quality indicators, such as falls. A common suggestion
that makes a kind of intuitive sense is that patient falls are related to the
number of

nurses and
other health care staff available for bedside care on the unit, if deployed
appropriately. However, in our system, when we collected information that
tracked staffing turnover with the rate of falls (see Figure 2.4), it appeared
there was no correlation between them.

Our
conclusion was that a single indicator (that is, staffing) was insufficient

to explain as
complex a phenomenon as falls. For example, case mix index, that is the degree
of illness associated with specific diagnoses, in combination with staffing
ratios, may be more informative about patients at risk for falls. Without these
data, leadership might have been tempted to increase staff, with the associated
expense, to reduce falls—without success.

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