How To: Analyse & Present Data

 2005 – 2017 UHBristol Clinical Audit Team – Version 4 Page 1 of 8

INTRODUCTION

The aim of this ‘How To’ guide is to provide advice on how to analyse your data and how to present it. If you

require any help with your data analysis please discuss with your divisional Clinical Audit Facilitator who will

be happy to help.

1. HOW TO ANALYSE DATA

Audit data comes in three different forms, ‘tick-box’,

numerical or free-text. Each requires different methods

of analysis, but in each case the aim is to establish which

standards are being met (% compliance) and which are

not (% non-compliance). If a standard is not being met

you need to identify why and consider how practice can

be improved to ensure that the standard is met in the

future. You may also consider if there were other,

acceptable reasons for the standard not being met, i.e.

an exception not considered during the planning stage.

A. TICK-BOX DATA

It is likely that the majority of the data that you have obtained from your data collection form will relate to

yes/no options or tick-box options from a specified list of alternatives. This is known as ‘categorical’ or

‘nominal’ data; data that can be sorted according to non-overlapping (mutually exclusive) categories, where

each subject in a sample can only fit into one category. For example:

 Staff grade: Consultant, Registrar, Specialist Nurse

 Age group: 16-20, 21-25, 26-30, etc.

 Standard met: Yes / No

In such cases, it is usual practice to add up the number of answers recorded for each option and express the

total as a raw number and as a percentage.

EXAMPLE 1:

 Sample size: 50 patients

 Audit criteria: All patients should attend a pre-operative clinic

 Question: Did the patient attend a pre-operative clinic?

 Results: Yes = 32 and No = 18.

A good way of expressing this data is:

 All patients should attend a pre-operative clinic. n=50

 Yes = 32 (64%)

 No = 18 (36%)

The ‘n=50’ indicates how many patients were in the audit sample and is used to calculate the percentages,

i.e. 32/50 = 64%.

How To: Analyse & Present Data

–

Number of patients

who meet standard

Number of

patients to

whom standard

applies

Number of

patients who

meet any listed

exceptions

100

CALCULATING COMPLIANCE WITH CLINICAL

AUDIT STANDARD

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How To: Analyse & Present Data

It is important to remember that yes/no options do not allow for ‘not applicable’ answers. Taking the

example used above, it is possible that certain patients did not meet the standard because they had an

emergency operation. In this instance the answer to the question ‘Did the patient attend a pre-operative

clinic?’ would have been ‘not applicable’. To reflect this, a variation of the percentage calculation is needed:

EXAMPLE 2:

 Audit criteria: All patients should attend a pre-op clinic

 Exception: emergency operation

 Results: Yes = 32, No = 5 and N/A (emergency) = 13

 32 patients attended a pre-op clinic

 18 did not, but 13 of these were emergencies (exceptions)

 Therefore 32/37 (86%) met the standard

 The difference between 64% in the first example, where no exceptions were taken into account, and

86% in this example is significant enough to influence our thinking about how well we are doing with

meeting this standard, so it is important to remember your exceptions!

B. NUMERICAL DATA

Some of the data items you collect are likely to be numerical values, e.g.

 Temperature: 34°, 35°, 36°, 37°, 38°, etc.

 Days post-op: 1, 2, 3, 4, 5, 6, 7, 8, etc.

 Age: 16, 17, 18, 19, 20, 21, etc.

Lists of numbers like this can be summarised using measures of central tendency and dispersion:

 Measures of central tendency look at the middle/common values in a list of data items: the mean,

median and mode.

 Measures of dispersion look at how spread the data is: the range.

MEASURES OF CENTRAL TENDENCY

The mean is the average value, calculated

as:

Sum of all the values ÷ Number of values

The table to the right shows data about

length of stay (LOS) on three wards.

For Ward 1, the mean is:

(1x4) + (2x8) + (3x12) + (4x18) + (5x20) + (6x18) + (7x12) + (8x8) + (9x3) = 511 = 4.96

4 + 8 + 12 + 18 + 20 + 18 + 12 + 8 + 3 103

The mean LOS on ward 1 is therefore 5 days (rounded to nearest whole day).

If the same formula were used to calculate the means for wards 2 and 3, you will find that for each ward,

the mean LOS is 5 days. However, the mean is not always the best measure of central tendency.

Length of Stay

(days)

Number of patients discharged

Ward 1

Ward 2

Ward 3

–

100

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How To: Analyse & Present Data

The LOS for all three wards is illustrated in Chart 1 below. The mean suggests that the data is the same for

all three wards, however the chart indicates that this is not the case. The problem with the mean is what it

does not tell us.

Chart 1

The data collected for Ward 1 is almost perfectly symmetrical, with the graph illustrating that the data

follows the shape of a ‘bell curve’. Data that conforms to this shape is known as ‘parametric’ data. In this

instance the mean is an appropriate measure of central tendency.

The data for Wards 2 and 3 is non-parametric; their graphs do not form a symmetrical curve. Describing

their notable features, Ward 2 has a significant proportion of patients with a LOS of 3 days together with a

number of patients staying 9 or 10 days. Ward 3 has a peak LOS of 6 days. It can be seen that using the mean

alone with non-parametric data is not very informative. The median and mode can help to convey the

missing information.

The mode is the most commonly occurring value. For Ward 2 this is 3 days and for Ward 3 it is 6 days. This

should be obvious from both the raw data and the graph. If the highest occurrence is shared by more than

one value you could either state them all as modal values or none. For example, if for Ward 3 there were 10

patients discharged on both day 5 and day 6 you could either say there were 2 modal values of 5 and 6, or

that there was no mode.

The median is the mid-point of all the values. For Ward 2, we have data on 67 patients. If we made a list of

LOS, placed in order from the lowest to the highest, the mid-point would be the 34

value, i.e. there are 33

values below and above this. The 34

value relates to a patient who was discharged after 5 days, so this is

the median. For Ward 3, we have data on 42 patients, i.e. there is no single mid-point. In this case, take the

average of the 21

and 22

value (there are 20 values below and above these two values). The 21

value

relates to a patient who was discharged after 5 days and the 22

value relates to a patient who was

discharged after 6 days, so the median is 5.5 days (5+6 divided by 2).

Unless you are well versed in statistics, we would advise that you use all three measures of central tendency

or show the information using a graph. In general, quote median rather than mean for non-parametric data.

Not all lists of numerical data should be analysed in this way and obtaining figures for compliance with your

standards is still the principal aim of analysing clinical audit data. For example, if your standard is “The

patient will be considered medically fit for surgery if temperature <38°C” and you collect a list of

temperature data, it would not be meaningful to present the mean, median and mode temperature. What

you are interested in here is the percentage of surgical cases with temperature <38°C.

Length of Stay

1 2 3 4 5 6 7 8 9 10

Day

Number of patients discharged

Ward 1

Ward 2

Ward 3

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How To: Analyse & Present Data

MEASURES OF DISPERSION

As well as stating the mean, median and mode, it is also good practice to provide some indication of how

spread the data is. The range states the lowest and highest values. In our example:

Ward 1 has a range of 1-9 days

Ward 2 has a range of 1-10 days

Ward 3 has a range of 1-8 days

A more subtle way of expressing dispersion is to use quartile range. This involves listing your values from

lowest to highest, as per calculating the median, and then dividing the values into four equal parts or sub-

ranges. The range you are interested in lies between the second and third quarter (or ‘quartile’).

So, for example, some more LOS data:

Ward C: 1 2 3 5 | 5 6 6 7 | 7 7 7 9 | 9 11 20 38

In this case the range is 1-38 days, but the quartile range is 5-9 days.

The quartile range is useful in taking out outlying data (data some distance away from the median), as in the

case of Ward C above and Ward 2 in our first example. Ward 2 has the largest range but a comparable

quartile range to Wards 1 and 3:

Ward 1 - range 1-9 days; quartile range 4-6 days

Ward 2 - range 1-10 days; quartile range 3-7 days

Ward 3 - range 1-8 days; quartile range 4-7 days

ANALYSING DATA AGAINST STANDARDS

If your standard statement was ‘Patients should be discharged by the end of their 5th day following surgery’,

using LOS data for ward 2, you find that 45 out of the 67 discharged patients had a LOS of 5 days or less.

You would write this as 45/67 (67%) patients met the standard.

C. FREE-TEXT DATA

If you include an open question in your data collection form, you will obtain free-text data. In order to

analyse this data you should group comments into themes or categories, i.e. as if you were creating

categorical tick-box options for the data collection form. You might also want to consider reproducing some

comments verbatim in your report if they are particularly pertinent.

DRAWING CONCLUSIONS

The end stage of your analysis is concluding how well the standards were met and, if applicable, identifying

reasons why the standard was not met in all cases. These reasons might be agreed to be acceptable (and

could potentially therefore be added to the exception criteria for the standard in future) or will show what

needs to be your focus for improvement. In theory, any case where the standard (criteria or exceptions) was

not met in 100% of cases suggests a potential for improvement in care. In practice, where standard results

are close to 100%, it may be agreed that any further improvement will be difficult to obtain and that other

standards with lower results should be the priority targets for action. This decision will depend on the topic

area; in some ‘life or death’ type cases it will be important to achieve 100%, while in other areas a lower

(but still high) percentage might be considered acceptable.

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How To: Analyse & Present Data

2. DISPLAYING DATA

USING TABLES

The simplest way to present data is in a table and this is the best way to show compliance with your audit

standards (see Table 1); in fact, our audit report template will ask you to provide this regardless of what else

you do in the way of analysing and displaying your data.

Table 1: compliance with audit standard Table 2: category of caesarean sections in audit

Standard

Target

Result

Category of urgency

Frequency

Percentage

All prescriptions should

include frequency of dose

100%

80% (40/50)

Category 1

21%

Category 2

47%

Category 4 (elective)

32%

USING CHARTS

The purpose of a chart is the visualisation of data, i.e. providing useful information in a graphical form. It is

simple enough to enter figures into a spreadsheet and hit the “insert chart” button, but in order for charts

to be meaningful and useful to your audience a little more thought and preparation is often necessary.

The key principles to think about are:

- What message am I trying to convey?

- Do I need to represent the data graphically to get the message across?

- What kind of chart will deliver my message in the clearest way?

- What information could I include in order to anticipate and answer my audience’s questions?

 Bar/column charts – For categorical data –

Generally used to show frequency, e.g.

number of patients meeting the standard /

not meeting the standard, or the number of

patients seen by different staff groups.

For example, if your standard stated that all

patients in A&E who meet certain criteria

should be seen by a consultant, you might

want to show your audience what grade of

staff saw the patients if it was not the

consultant (see Chart 2, right).

Chart 2

 Versions of bar charts (stacked or

comparative) – You can show more than

one standard and/or more than one

audit per chart, either by displaying bars

with different values next to each other

(Chart 3) or by stacking bars on top of

each other (Chart 4).

Chart 3

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How To: Analyse & Present Data

Chart 4 Chart 5

 Pie charts - For categorical data – Used to show proportion of parts compared to a whole, e.g.

percentage compliance with a standard, or the types of device fitted in a sample of patients whose

devices failed (see Chart 5). As a general rule, keep the number of slices low (advice differs on the

maximum, but above 6 slices your chart may become hard to interpret) and avoid using this type of

chart if the values are all very similar, as small differences in the size of slices are difficult to see.

Another occasion to avoid using a pie chart is when the parts do not add up to a meaningful whole; for

example, if you wanted to illustrate ‘type of treatment’ in cases where patients are likely to have had

more than one treatment each. In this instance the pieces of the pie would add up to the total number

of ‘treatments’ rather than the total number of patients, which could be misleading and is probably not

very meaningful in itself. A bar chart could be used instead.

 Line charts – Can be used to show change

over time, e.g. ongoing compliance with a

regular monthly audit of hand hygiene or

equipment checks

Chart 6

CREATING GOOD CHARTS

Good charts should focus on getting your message across rather than creating fancy and distracting images.

Clutter should be avoided and the charts clearly labelled.

EXAMPLE CHART 1:

A chart might be considered unnecessary

to illustrate this result. Consider whether

people need to see a graphical

representation of your data; in this case

simply expressing the data as ‘37/40

members of staff (93%) took personal

protective equipment on domiciliary

visits’ should be sufficient. In a project

with a lot of standards producing a chart

for every single one may confuse rather

than clarify the results. People may not

remember which image related to which

standard.

Did member of staff take personal protective

equipment with them?

Yes

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How To: Analyse & Present Data

EXAMPLE CHART 2:

What’s wrong?

 The 3D effect makes it difficult to read how

many “yes” or “no” answers there are.

 The title needs more detail.

 The axes should be labelled. In this case the x

axis might not need labelling according to

what other titling is put on chart, but the y

axis needs to be labelled to be meaningful.

 In this instance the legend is not needed, as

there is only one data series (unlike in the

stacked or comparative bar charts shown in

charts 3 and 4)

 There is a lot of white space on the page. It

looks unprofessional.

Revised version

 It has been changed to a 2D chart.

 Titles have been added.

 There is better use of the space available.

 The scale has been extended a little way past

the highest bar.

 The sample size number has been added for

quick reference.