Using data to improve patient safety is not only required by governmental agencies but has become a recent focus of media attention.

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

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