The document discusses key rules and considerations for using patient safety data and indicators. It notes that qualitative data is equally or more important than quantitative data. It emphasizes that quantitative data has pitfalls and that indicators need to be chosen appropriately for the situation and purpose. The document also cautions about comparing outcomes without accounting for differences in patient characteristics and advises that larger sample sizes are needed to reliably measure changes in many safety outcomes.
2. www.england.nhs.uk
1st rule of #statisticsclub
Qualitative data are at least equally important, and probably
much more important, than quantitative data…..
2
….. but it’s quantitative data that have the pitfalls & perils,
so that is my focus today
8. www.england.nhs.uk 8www.england.nhs.uk
“The results at that stage showed a slight numerical
advantage for those who had been treated at home. It was of
course completely insignificant statistically.
“I rather wickedly compiled two reports, one reversing the
numbers of deaths on the two sides of the trial. As we were
going into committee, in the anteroom, I showed some
cardiologists the results……..
9. www.england.nhs.uk 9
“……they were vociferous in their abuse: `Archie’, they said,
`we always thought you were unethical. You must stop the
trial at once…’
“I let them have their say for some time and then apologised
and gave them the true results, challenging them to say, as
vehemently, that coronary care units should be stopped
immediately.
“There was dead silence and I felt rather sick because they
were, after all, my medical colleagues.”
Professor Archibald Cochrane & Max Blythe One Man's Medicine (1989) p.211
10. www.england.nhs.uk
Cognitive dissonance
10
• We have a strong need for our personal beliefs and
our personal actions to chime
• The discomfort we feel when they don’t is ‘cognitive
dissonance’
http://britishgeriatricssociety.
wordpress.com/2013/05/16/
all-down-to-numbers/
• If we believe we are part of
effective, motivated, caring
teams, it is very hard to also
simultaneously believe:
o We haven’t achieved real
improvements in safety
o We might be less safe than
peers
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Which is not to say we shouldn’t
use data for motivation
“The consistent delivery of well-executed safe care under typically
difficult circumstances tends to go unrecognised"
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3rd rule of #statisticsclub
One size does not fit all - there is no such thing as a good
indicator, or a good data source, just one that is good in
particular situation for a particular purpose
14
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IncidentsSubmitted
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CULTURE indicators
STRUCTURE indicators
PROCESS indicators
planning process indicators
delivery process indicators
OUTCOME indicators Are we
safe
today?
Types of indicator
17. www.england.nhs.uk
What type of indicator?
17
1. CULTURE indicator
2. STRUCTURE indicator
3. PROCESS - planning process indicator
4. PROCESS - delivery process indicator
5. OUTCOME indicator
97% of patients who need a pressure reliving
mattress received it within four hours
18. www.england.nhs.uk
What type of indicator?
18
1. CULTURE indicator
2. STRUCTURE indicator
3. PROCESS - planning process indicator
4. PROCESS - delivery process indicator
5. OUTCOME indicator
86% of nurses agree that most pressure ulcers
can be prevented
19. www.england.nhs.uk
What type of indicator?
19
1. CULTURE indicator
2. STRUCTURE indicator
3. PROCESS - planning process indicator
4. PROCESS - delivery process indicator
5. OUTCOME indicator
We have 42 pressure relieving mattresses per
100 beds
20. www.england.nhs.uk
4th rule of #statisticsclub
We don’t do structural measurement
nearly often enough
20
30%
9%
26%
35%
on all wards
on most
wards
on one or
some wards
not on any
wards
“This [MH unit for older
people] has no physio input.
Balance and strength
assessments never get
done”
“We cannot put walking
frames within reach as there
is no room left once you
have a chair beside the bed”
Royal College of Physicians 2012
Report of the 2011 inpatient falls pilot
audit www.rcplondon.ac.uk
Weekend access to mobility
aids for new patients
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Purposes of safety measurement
21
One often used model:
measurement for research
measurement for judgement
measurement for improvement
Alternative less prone to misunderstanding
measurement to understand priorities
measurement to see how we compare to others
measurement to see if we’re getting better (or worse)
22. www.england.nhs.uk
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85-89
90-94
95-99
100+
% of all
reported
acute falls
Age group
Breakdown by age of falls in acute
clusters
0%
1%
2%
3%
4%
5%
6%
00 (12 AM -
Midnight)
01 (1 AM)02 (2 AM)03 (3 AM)04 (4 AM)05 (5 AM)06 (6 AM)07 (7 AM)08 (8 AM)09 (9 AM)10 (10 AM)11 (11 AM)12 (12 PM -
Midday)
13 (1 PM)14 (2 PM)15 (3 PM)16 (4 PM)17 (5 PM)18 (6 PM)19 (7 PM)20 (8 PM)21 (9 PM)22 (10 PM)23 (11 PM)
% of all reported
acute falls
Hour
Falls incidents by hour of occurrence, for acute
clusters
Understanding priority areas
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acute hospitals Community hospitals Mental health units
Location of incident
Percentofsample
Apparently unwitnessed by staff
Witnessed by staff
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acute hospitals Community hospitals Mental health units
Location of incident
Percentofsample
Apparently unwitnessed by staff
Witnessed by staff
0%
5%
10%
15%
20%
25%
30%
35%
Acute hospitals Community hospitals Mental health units
0%
5%
10%
15%
20%
25%
30%
35%
Acute hospitals Community hospitals Mental health units
23. www.england.nhs.uk
5th rule of #statisticsclub
Measurement to see how we compare to
others: when it comes to comparing
outcomes, case mix really matters
23
(but case mix shouldn't be
a problem for well-designed
process measures)
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Safety outcomes & case mix
0%
5%
10%
15%
20%
25%
0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 100+
Age of patient (years)
0%
5%
10%
15%
20%
25%
0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 100+
Age of patient (years)
85 years +
Deandra S et al. Arch Gerontol Geriatr 56 (2013) 407–415
NPSA Slips trips and falls in hospital data update NPSA 2010
Risk factors for hospital falls Odds Ratio
History of falls 2.85 (1.14–7.15)
Cognitive impairment 1.52 (1.18–1.94)
25. www.england.nhs.uk
Older people are not evenly distributed
25
Therefore unadjusted higher/lower rates of falls compared to
other trusts with very different age profiles are highly unlikely to
be useful indicators of relative safety
All ages falls rate
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
R
ETIR
EM
ENT
TO
W
N
B
R
ETIR
EM
ENT
TO
W
N
D
R
ETIR
EM
ENT
TO
W
N
A
R
ETIR
EM
ENT
TO
W
N
C
U
RBAN
TEACH
IN
G
B
U
RBAN
TEACH
IN
G
C
U
RBAN
TEACH
IN
G
D
U
RBAN
TEACH
IN
G
A
fallsper1,000beddays
26. www.england.nhs.uk
50-fold differences between wards
26
Royal College of Physicians 2011 The FallSafe Quality Improvement project: report for the Health Foundation
Therefore higher/lower rates of falls compared to other wards
in the same trust highly unlikely to be useful local indicators
of safety
29. www.england.nhs.uk
7th rule of #statisticsclub
When it comes to measuring if we have got
better (or worse) size matters
(and we usually have plentiful processes to
measure but far fewer outcomes)
31. www.england.nhs.uk
Sample safety outcome indicators:
scaled to ward-level*
IF these safety outcomes were distributed evenly across
acute wards, an average ward would have around:
• One case of c difficile per year
• One MRSA bloodstream infection per decade
• One new pressure ulcer per quarter
• One fall with minor injury per month
• One fall with hip fracture every five years
* Approximations based on c. 5,000 acute/rehabilitation hospital wards in England, PHE trust
attributed/trust-assigned HCAI data, NRLS reported falls, assumption that acute ‘new’ p ulcer prevalence
as measured by ST represents about 4 x acute p ulcer incidence
32. www.england.nhs.uk
What scale and time would give you
a reasonable chance of being able to
distinguish a 25% improvement from
natural variation ?
32
For falls with injury
1. One ward two years
2. Ten wards two years
3. One medium sized hospital two years
4. Five hospitals two years
5. Fifty hospitals two years
33. www.england.nhs.uk
What scale and time would give you
a reasonable chance of being able to
distinguish a 25% improvement from
natural variation ?
33
For hospital-associated MRSA?
1. One ward two years
2. Ten wards two years
3. One medium sized hospital two years
4. Five hospitals two years
5. Fifty hospitals two years
35. www.england.nhs.uk
8th rule of #statisticsclub
Your data don’t have to be perfect to be good
enough – but you do need to know how
imperfect they are
35
37. www.england.nhs.uk
9th rule of #statisticsclub
If it looks too good to be true, it probably is!
37
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