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

APPENDICES
Appendix 4.
Monitoring hand hygiene by direct methods
The power calculations detailed in Part III, Section 1.1 of the <strong>WHO</strong> <strong>Guidelines</strong> for Hand Hygiene in Health Care
are critical for obtaining reliable estimates of the percentage of hand hygiene compliance at the organization level
at a single point in time. The objective of these calculations is to determine the sample size necessary to produce
results that can be generalized to larger populations and can meet the defined degree of confidence and margin
of error. These considerations are similar to those involved in conducting point-in-time research. Examples of this
approach can be found in political polling, market research, and educational testing. When measurements are
made in the context of an improvement initiative, however, the research questions and approaches to sampling
are different. An improvement team is typically interested in answering the following questions: (1) are we making
progress toward a goal of increased hand hygiene compliance? and (2) how will we know when we have reached
the goal?
Studies aimed at improvement, known as analytical studies, 1
seek only enough data, collected repeatedly at suitable
intervals, to detect and track the effectiveness or efficiency
of improvement efforts over time. The requirements for data
collection and inference under such circumstances are different
from those required by clinical or population research aimed at
answering questions about efficacy. 2 For instance, you do not
need a valid scale to monitor weight loss, only a consistent one.
It does not matter if the scale reads a few pounds too light or
too heavy; as long as the readings are reasonably consistent:
you can successfully track your progress over time, and you will
know when you have lost that extra 10 pounds because your
daily readings will hover around the desired level. Of course, if
your goal is to weigh exactly 150 lb, you will need a scale that is
valid as well as reliable.
In the case of improving hand hygiene, the improvement goal
typically is to bring compliance (i.e. the percentage of fulfilled
hand hygiene opportunities) above 95% by introducing systems
improvements, behavioural incentives, education, and other
interventions described elsewhere in these guidelines. The
challenge for improvers, therefore, is to determine if progress is
being made towards the target, and when it has been reached.
In order to judge the effects of the interventions, baseline
measures should be taken on the units where improvement
work is under way; then performance over time can be
compared with the baseline and the desired target or goal.
Sampling strategies for tracking improvement initiatives draw
from both probability and non-probability sampling techniques.
For ministries of health or other agencies that are interested
in gauging the impact of an initiative in a region, a province or
a health system, it may be desirable or necessary to start the
work and track progress in a small sample of institutions or
settings. For example, imagine that you have 12 clinics spread
out across a region. Rather than collecting detailed data at all
12 clinics every day you might want to select one clinic to pilot
test a new strategy for hand hygiene compliance. You could
select a clinic to be the pilot, based on your knowledge of the
clinics (e.g. Clinic 4 has experience with improvement work and
would be more receptive to trying a new project related to hand
hygiene compliance). This is what Deming characterized as
judgement sampling. 3 Another approach would be to randomly
select one of the clinics to be the pilot. To do this you would
write the numbers 1–12 on separate pieces of paper (it is best
to use the same size of paper) place them in a bowl and stir
them around. Without looking at the pieces of paper, reach into
the bowl and select one piece of paper. If the number 7 was on
this piece of paper then Clinic 7 would be the one that you have
randomly selected to be the pilot clinic for our hand hygiene
test. Once a unit of analysis has been selected, you will need to
make decisions on two key concepts related to improvement
studies: (1) the number of data points needed to represent
accurately the variation in the process and (2) the number
of observations included in each data point. Both of these
concepts are briefly described below.
Whether you are using judgement sampling based on your
knowledge of the unit(s) of analysis or simple random sampling
where all units of analysis have an equal probability of being
selected, you should try to obtain around 20 data points (or
subgroups) before analysing the variation in the process. The
general assumption behind this guidance is that a relatively
stable distribution of the results starts to form when you have
15–25 data points. 4-6 When you have fewer than 15 data points
the variation in the process has a tendency to be quite volatile
and the probability of improperly representing the current
variation due to a type I or type II error increases. 7 Obtaining
around 20 data points, therefore, taken within the unit of
analysis where improvement efforts are under way, can provide
a robust enough estimate to gauge whether improvement is
occurring.
When tracking hand hygiene compliance, the preferred measure
is typically a percentage where the numerator is the total
number of times an HCW was observed to have appropriately
washed his or her hands before and after a patient encounter.
The denominator is the total number of observations made.
When analysing data based on percentages it is advisable to
have denominators that are at least in the double digits. The
general guidance is that a minimum of 12–15 observations
should be in the denominator before a percentage is calculated.
For example, if you have only 4 observations in the denominator
and 2 of the HCWs (the numerator) properly washed their hands
this produces a 50% compliance number (2/4 = 50%). But this
is not as robust a 50% calculation as one with a denominator
of 18 with 9 HCWs as the numerator. Data collection for
improvement not only needs to be based on sound statistical
methods but it also needs to be practical and reasonably easy
for the data collectors. Those interested in gaining more insight
on more precise sampling estimates than those offered in the
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