The document describes efforts to improve outcomes for sick children at NHS Tayside. Approximately 7,000 children are referred annually, with 30% admitted. ICU admissions are about 7 per 1,000 admissions. The team aimed to reduce preventable readmissions, crash calls, and ICU/HDU admissions through improving early recognition of deterioration, appropriate escalation, and reliable management of sick children. Process measures such as PEWS, SBAR, and safety briefings showed over 98% compliance. Outcome measures like crash call rates and ICU admissions declined. The team now believes in providing high quality care and improving through a safety culture of transparency, data measurement, and continuous learning.

1. IMPROVING OUTCOMES FOR
SICK CHILDREN
NHS Tayside

2. Sick Children – Our Journey in Tayside
• 7000 acutely unwell children referred annually
– ~30% admitted
• ICU admissions ~ 7 per 1000 admissions
– Tayside accounts for 10% of all inpatient paediatric admissions
per annum in Scotland, only 5% of PICU admissions
• How many patients deteriorate in our care?
• How many ICU admissions/deaths are preventable?
• Can we improve?

3. Team ‘buy in’ - What is your Project 1?
• What really drives the team nuts – what is the biggest
waste, safety, inefficiency issue that annoys all staff
• Start there
• Consult all staff re the process and empower all staff to
test changes
• Don’t dismiss ideas until you have tried them
• Credit the team with team success

4. Tayside “Project 1”
• 5/12 old boy
• Presented at 10 am to SSAA
• Unwell for 3-4 hours with pyrexia and runny nose, still
feeding and babbling and smiling
• Known to unit – complicated neonatal course
• Thought to be well but not discharged due to parental
concern – first febrile illness since discharge
• Sudden collapse in unit and died with meningococcal
sepsis by 6pm

5. Case review
• Non recognition of the sick child
• Then late recognition and failure to act promptly
• Failure to escalate
• Once escalated senior multidisciplinary team involved in
simultaneous resuscitation
• Team invested in this patient as well known to unit
• huge division in team ensued with a blame culture
• How do we turn this around and restore faith in each other
and our team? We do our best to ensure we provide the
appropriate and timely care to all our patients.

6. Improvement Aim – ambitious or naïve
Outcome Primary Drivers Secondary Drivers (change concepts)
Early recognition (PEWS, watcher criteria)
Appropriate escalation (PEWS escalation flow chart)
Appropriate, Appropriately trained staff (life support courses, senior review, up
timely and skilling, regular updates)
reliable Testing theory in real time real place (emergency simulation)
recognition and
management of Guidelines for common emergencies updated and immediately
sick children accessible (review dates and website updating)
Zero preventable Functioning appropriate equipment (bedspace checks, resus trolley)
readmissions,
Appropriate medicines ( in date, algorithms, remove unused)
crash calls,
HDU/ PICU Timely ( teaching re timelines, process change)
admissions. SBAR – handover, escalation
In-ward deaths Effective
Safety Briefing
Multidisciplinary rounding
by June 2013 communication Daily goals
Effective discharge planning
Effective readmission planning /CYADM, anticipatory care plans
Multiagency
Infrastructure
Empower all staff to voice concerns
and culture to Safety walkrounds
promote Learning from adverse events (case note reviews, IR1, PTT)
safety Sharing all data with whole team +/- patients and carers
Capability and capacity

7. Can we predict who will deteriorate?
Can we prevent it?
% acute admissions to HDU Tayside Childrens Hospital Nov
2011 - April 2012
35
30
25
20
%
15
10
5
0
0 1 2 3 4 5 6 7
PEWS score
80% of acute admissions to HDU have a PEWS <3
Why admit to HDU?

8. What causes concern & doesn’t score in
PEWS?

9. Watchers
Gut Feelings.......
“Researchers explain that intuition represents one of the ways our brains store,
process and retrieve information........ The researchers .... concluded that intuition
- a feeling that something is right or wrong - is the brain drawing on past
experiences and current external cues to make a decision; a process so
rapid that the reaction is subconscious.”
British Journal of Psychology (April 2008)

10. How do we know a change is an
improvement?
• Outcome measures
– Crash call rate, HDU & ICU admission rates, In ward mortality
rate
– Prediction of Watchers
• Process Measures
– PEWS, SBAR, MDR, DG, safety brief, equipment checks,
guideline checks, simulations, time to first dose of antibiotics,
adherence to specific guidelines
• Balancing measures
– HDU admission rate, staff feedback (simulation), time invested in
measuring v delivering service

11. How to move towards Safety Culture of
recognising deteriorating children?

12. Safety Brief – shared mental model
MULTIDISCIPLINARY SAFETY BRIEFING WARD 29
DATE: TIME:
PLANNED
EMPTY BEDS ANTICIPATED
/ ADMISSION
(bays/SR) DISCHARGES
S
WARD ISSUES PROBLEM DETAILS (including bed
number)
YES NO
PATIENTS WITH SIMILAR NAMES
HIGH PEWS / WATCHERS
PATIENTS WITH INDIVIDUALISED
PROTOCOLS (eg CYPADM)

13. Results: process measures
Reaching >98%
compliance with process
measures summer 2011

14. Ap
r
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
Ma -09
y
Ju -09
n-0
Ju 9
Au l-09
g
Se -09
p-0
Oc 9
t
No -09
v-0
De 9
c-
Ja 09
n
Fe -10
b-1
Ma 0
r
Ap -10
r-1
Ma 0
y
Ju -10
n-1
Ju 0
Au l-10
g
Se -10
p-1
Oc 0
t
No -10
v-1
De 0
c-
Ja 10
n
Fe -11
b-1
Ma 1
r
Ap -11
r-1
Ma 1
y
HDU Admission Rate
Ju -11
n-1
Ward 29, Ninewells Hospital
Ju 1
Au l-11
g
HDU Admission Rate
Se -11
p-1
Oc 1
t
Ward 29, Ninewells Hospital
No -11
v-1
De 1
c-
Ja 11
n
Fe -12
b-1
Ma 2
r
Ap -12
r-1
Ma 2
y
Ju -12
n-1
Ju 2
Au l-12
g
Se -12
p-
Oc 12
t
No -12
v-1
De 2
c-1
2
Balancing measure: HDU admission rate

15. Ap
r
0
5
10
15
20
Ma -09
y-0
Ju 9
n-0
Ju 9
l
Au -09
g-0
Se 9
p-
Oc 09
t
No -09
v
De -09
c-0
Ja 9
n
Fe -10
b-
Ma 10
r
Ap -10
r-1
Ma 0
y
Ju -10
n-1
Ju 0
l
Au -10
g-1
Se 0
p-
Oc 10
t
No -10
v-1
De 0
c-
Ja 10
n
Fe -11
b-
Ma 11
r
Ap -11
r-1
Ma 1
y
Admission
Ju -11
Ward 29, 29, Ninewells Hospital
n-1
Ju 1
l
Au -11
g-1
Se 1
PICUICU Admission Rate Rate
p-
Oc 11
t
No -11
Ward Ninewells Hospital
v-1
De 1
c-
Ja 11
n
Fe -12
b-
Ma 12
r
Ap -12
r-1
Ma 2
y
Ju -12
n-1
Ju 2
l
Au -12
g-1
Se 2
p-
Oc 12
t
No -12
v-1
De 2
c-1
2
Outcome measure – PICU admission rate

16. Ja
n-1
0
10
20
Fe 0
b-
10
Ma
r-1
0
Ap
r-1
Ma 0
y-1
Ju 0
n-1
0
Ju
l-1
Au 0
g-1
Se 0
p-1
0
Oc
t -1
No 0
v-1
De 0
c-1
0
Ja
n-1
Fe 1
b-
11
Ma
r-1
1
Ap
r-1
Ma 1
y-1
Ju 1
n-1
1
Ju
l-1
Au 1
g-1
Se 1
p-1
Ninewells
1
Oc
t -1
Crash Call Rate
No 1
Ward 29,HDU, SSAA Ninewells Hospital
v-1
De 1
c-1
1
Ja
n-1
Ward 29, Crash Call Rate Hospital
Fe 2
b-
12
Ma
r-1
2
Ap
r-1
Ma 2
y-1
Ju 2
n-1
2
Ju
l-1
Au 2
Outcome measure: Crash Calls
g-1
Se 2
p-1
2
Oc
t -1
No 2
v-1
De 2
c-1
2

17. Ap
r-
0
10
Ma 09
y-0
Ju 9
n-0
Ju 9
l-
Au 09
g-0
Se 9
p-0
Oc 9
t-
No 09
v-0
De 9
c-0
Ja 9
n-
Fe 10
b-
1
Ma 0
r-1
Ap 0
r-
Ma 10
y-1
Ju 0
n-1
Ju 0
l-
Au 10
g
Se -10
p-1
Oc 0
t-
No 10
v-1
De 0
c-1
Ja 0
n-
Fe 11
b-
Ma 11
r-
Ap 11
r-
Ma 11
In Ward Mortality Rate
y-1
Ju 1
Ward 29, Ninewells Hospital
n-1
Ju 1
l-
Au 11
g-1
In-Ward Mortality Rate
Se 1
p-1
Oc 1
t-
Ward 29, Ninewells Hospital
No 11
v-1
De 1
c-1
Ja 1
n-
Fe 12
b-
Ma 12
r-
Ap 12
r-
Ma 12
y-1
Ju 2
n-1
Ju 2
l-
Au 12
g-1
Se 2
p-1
Oc 2
Outcome measure: In-Ward Mortality
t-
No 12
v
D e -1 2
c-1
2

18. Outcome measure: combined outcome
Potential for national Serious Harm Index?
Ward 29 Ninewells Hospital total significant events rate
(total mortality + crash calls + ICU admissions)
40
35
30
25
20
15
10
5
0
Apr-10
Aug-10
Apr-11
Aug-11
Apr-12
Feb-10
Sep-10
Jun-10
Oct-10
Nov-10
Dec-10
Feb-11
Sep-11
Jun-11
Oct-11
Nov-11
Dec-11
Feb-12
Jan-10
Mar-10
May-10
Jul-10
Jan-11
Mar-11
May-11
Jul-11
Jan-12
Mar-12
May-12
Simulation New PEWS charts and reliability
started for multiple process measures
across whole unit

19. Who is the sickest patient on the ward?
• 16 different responses
May • Little overlap
• No agreement with attending
2011 Consultant
• Agreement
May • Theory: Early recognition and
shared mental model increases
number of reviews, decreases
2012 time to treatment and prevents
deterioration

20. Tayside “Project 1” outcomes
• Tayside team believe in themselves as individuals and
as a team
• We know we are providing high quality care (and have
the data to show it)
• We may be improving outcomes for children but it is
early days
• We know we have improved staff morale (and have data
to prove it!)
• We now we have a team who “knows how to improve”
• We are now on project 40+

21. Learning / Challenges – developing a
Safety Culture
• Data is everything:
– Baseline
– And accurate, appropriate measurement
• Person dependence & improvement fatigue
• Capability and capacity
• Culture – transparency about “bad data”
• Running before we could walk – especially simulation
• “spread control”
• What do we not know? Should we be worried about it?