WHO Guidelines on Hand Hygiene in Health Care - Safe Care ...

<strong>WHO</strong> GUIDELINES ON HAND HYGIENE IN HEALTH CARE
general guidelines described above should consult standard
references on quality improvement methods. 2
A practical yet robust data collection plan for tracking the
percentage of workers adhering to proper hand hygiene
compliance could be set up as follows:
• select a unit of analysis to be the pilot unit or clinic;
• select a random day each week to observe hand hygiene
compliance;
• on selected days, collect a minimum of 15 observations of
hand hygiene opportunities (the denominator);
• out of these opportunities determine the number of
times hand hygiene was completed properly (this is the
numerator);
• compute the percentage of hand hygiene compliance for
that week;
• repeat this process for the next 15–20 weeks, as work goes
forward on improving compliance:
• use a run chart (see below) to assess the success of the
improvement efforts.
As measurements will be used to gauge which interventions
are successful for improving compliance, the pace of data
collection should match the pace of the improvement efforts. If
you can collect 12–15 opportunities several times a week, then
instead of collecting 1–20 weeks of data you can analyse the
data each day or several days a week rather than wait for one
data point each week. In this regime, feedback to the improvers
will occur more rapidly, and they will be able to make more
timely adjustments in their efforts. Important considerations
in the decision about how frequently to measure are (1) the
ability of the data collectors to gather data more frequently;
and (2) having sufficient opportunities to observe hand hygiene
compliance so that the denominators are appropriate.
Note that when you repeatedly gather samples over time (e.g.
daily or weekly) the sample size increases quickly. For example,
if you perform 25 hand hygiene observations each week you will
have 100 observations in a month. This provides a very robust
and stable distribution of data points for analysis.
Once the data have been obtained, statistical process control
(SPC) methods are the preferred way to analyse process
performance over time. The basic tools in this branch of applied
statistics are run charts and Shewhart control charts. These
tools can provide a degree of statistical confidence similar
to that achieved by more familiar statistical tests that use p
values and confidence intervals. Run charts, for example,
perform at roughly the 95% confidence interval, while the more
robust control chart functions at a level equivalent to the 99%
confidence intervall. 7
A run chart provides a running record of a process over time.
It offers a dynamic display of the data and can be used on
virtually any type of data (e.g. counts of events, percentages,
wait times or physiological test results). Because run charts
do not require complex statistical calculations they can easily
be understood and constructed, and can be applied by those
who lack formal statistical training. Most improvement teams
start out with run charts because they are easy to grasp, do not
require computers to develop, and provide a good foundation to
move eventually to the more robust control charts.
Interpreting run charts for significance involves the application
of a set of decision rules based on sequential patterns of
observations that refute the assumption that the measures were
drawn from a completely random system. 8 Such patterns are
based on the notion of “runs.” An example is shown in Figure
1. Note that time is displayed on the horizontal axis, while the
measure of interest is plotted on the vertical axis. The centreline
on the graph is the median. Runs are defined relative to the
median. A run consists of one or more consecutive data points
on the same side of the median. Data points falling on the
median are not counted. In Figure 1 the chart contains 4 runs as
shown by the circles drawn around the data clusters. Two data
points fall on the median.
Once the number of runs has been determined, the next step
is to apply four run chart rules to determine if the data on the
chart display random or non-random patters of variation. The
run chart rules designed to detect a non-random pattern in the
data include:
Rule 1: A shift in the process, or too many data points in a run
(6 or more consecutive points above or below the median).
Rule 2: A trend
(5 or more consecutive points, all increasing or decreasing).
Rule 3: Too many or too few runs
(use a table to determine this one).
Rule 4: An “astronomical” data point, which is a point that
visually is dramatically higher or lower that all the other data
points. This is a judgement call when using the run chart and
should be used not to determine statistical significance but
rather as a signal that more rigorous analysis with a control
chart is needed.
Figure 1 shows that the data have, in fact, shifted upwards.
This is determined by seeing that the last run contains 6
consecutive data points above the median, which is a signal of
a non-random pattern. In this particular case this is a desirable
outcome to observe, because it shows that the intervention
the team put in place between January and February of 2008
had the desired effect (i.e. the percentage of hand hygiene
compliance increased).
As improvement teams become more comfortable with data
collection and analysis, the next logical progression analytically
is to place the data on a control chart. Control charts are very
similar to the run charts with the following exceptions:
• the median is replaced with the mean;
• the upper and lower control limits (known as sigma limits)
are computed;
• more robust statistical tests are applied to the charts to
detect what Walter Shewhart (1931) called common and
special causes of variation.
248