Dr Guy Glover
Consultant in Critical Care
Guys and St Thomas’ NHS Foundation Trust
Improving the Response to Acute
Deterioration in Covid-19 and Beyond

Declarations
• Consulting / speaker fees from Sedana Medical / BD
• No conflicts relevant to this talk

Background – the majority of acute deterioration in
hospital may be preventable
• 70% of patients have abnormal physiology before cardiac arrest or unplanned ICU admission
• There is an opportunity to identify earlier and intervene to prevent deterioration
Time
Stable
Kause et al Resuscitation 2004; 62: 275–282
Deteriorating
Critical

Population level
• All hospital inpatients
• ‘Track and Trigger’ system to screen for acute
deterioration
Subgroup of at-risk patients
• Defined by high NEWS / clinical concern
• Protocolised escalation to local team +/- CCOT
Individual patient
• Higher intensity monitoring and interventions
• Apply care bundles
• Stratify risk
• Critical Care admission if required
However, identifying the at-risk patient is challenging

NEWS2 – a clinical decision support tool which
correlates with adverse outcomes in hospital
Smith GB et al. Resuscitation 2013; 84:465-70

Acutely Ill Patient in Hospital Steering Group Report
Compliance to observation standards
NB final ward of hospital
admission
Dec
17
Jan
18
Feb
18
Mar
18
Apr
18
May
18
Jun
18
Jul
18
Aug
18
Sep
18
Oct
18
Nov
18
Total
Dec
17
Jan
18
Feb
18
Mar
18
Apr
18
May
18
Jun
18
Jul
18
Aug
18
Alan Apley AMS 81% 71% 69% 74% 78% 81% 81% 81% 82% 75% 80% 80% 78% 63 69 68 64 61 75 78 75 70
GI Page (STH) 87% 76% 68% 72% 74% 73% 73% 73% 82% 76% 80% 81% 76% 85 97 105 99 97 93 96 93 100
GI Northumberland (STH) 86% 78% 72% 73% 74% 74% 74% 74% 84% 77% 79% 82% 77% 100 97 94 107 103 112 116 112 111
Nightingale 84% 72% 66% 71% 68% 74% 74% 74% 73% 68% 73% 78% 73% 59 83 86 89 81 84 87 84 76
Aston Key 87% 78% 75% 81% 78% 77% 77% 77% 81% 76% 80% 80% 79% 91 95 106 91 69 63 65 63 79
Florence Florence 84% 77% 72% 72% 75% 77% 77% 77% 87% 82% 80% 86% 79% 72 82 92 84 65 71 73 71 70
Patience Patience 85% 73% 71% 73% 77% 75% 75% 75% 76% 78% 79% 77% 76% 67 64 56 71 71 71 73 71 73
Richard Bright Richard Bright 90% 82% 77% 82% 83% 85% 85% 85% 86% 82% 86% 89% 84% 64 93 93 95 83 75 77 75 82
Acute Assessment Unit 97% 84% 75% 75% 75% 66% 69% 72% 75% 78% 0 4 5 4 6 7 7 7 6
Acute Admissions Acute Admissions 88% 77% 71% 78% 78% 78% 78% 78% 85% 78% 82% 87% 80% 91 97 111 101 93 96 99 96 95
Albert Albert 85% 75% 70% 77% 74% 77% 77% 77% 78% 76% 79% 82% 77% 129 126 124 130 115 125 129 125 119
Alexandra Alexandra 83% 76% 70% 76% 76% 80% 80% 80% 81% 73% 72% 77% 77% 122 138 133 130 123 111 115 111 84
Anne Anne 83% 76% 71% 73% 73% 77% 77% 77% 79% 72% 75% 77% 76% 123 126 134 135 139 129 133 129 133
Evan Jones (EMU) inc Frailty
Evan Jones (EMU) inc Frailty 93% 84% 79% 86% 83% 84% 84% 84% 85% 84% 36 46 50 49 43 43 45 43 6
Clinical Decision Unit 86% 86% 87% 94% 88% 30
Henry Henry 82% 75% 68% 71% 72% 74% 74% 74% 77% 67% 71% 76% 74% 125 132 124 132 125 118 122 118 127
Hillyers Hillyers 78% 76% 70% 76% 74% 76% 76% 76% 83% 79% 80% 81% 77% 84 86 76 76 74 84 87 84 78
Mark SU Mark SU 88% 80% 72% 79% 79% 79% 79% 79% 76% 71% 77% 79% 78% 110 116 134 130 125 118 122 118 120
William Gull (STH) William Gull (STH) 75% 67% 63% 70% 68% 70% 70% 70% 76% 70% 75% 77% 71% 135 129 137 133 121 117 121 117 119
Becket Becket 83% 77% 67% 74% 76% 75% 75% 75% 78% 74% 75% 81% 76% 76 68 88 88 88 90 94 90 73
Coronary Care Unit Coronary Care Unit 88% #### 79% - 75% 71% 71% 71% 53% 84% 79% 74% 2 0 1 2 2 1 1 1 2
Doulton Doulton 82% 75% 69% 78% 73% 80% 80% 80% 83% 79% 82% 83% 79% 114 124 100 92 98 96 99 96 117
Luke (STH) Luke (STH) 90% 80% 78% 86% 84% 83% 83% 83% 89% 84% 83% 83% 84% 45 46 55 60 51 53 55 53 51
Luke HDU Luke HDU 94% 82% 59% 79% 82% 75% 75% 75% 92% 90% 78% 82% 80% 2 7 4 1 1 2 2 2 1
Sarah Swift - Vascular Sarah Swift - Vascular 89% 80% 75% 81% 80% 81% 81% 81% 85% 80% 81% 85% 82% 119 128 121 122 120 130 134 130 127
Stephen Stephen 84% 74% 72% 76% 77% 79% 79% 79% 78% 75% 81% 79% 78% 125 118 128 135 136 137 142 137 124
Blundell Ward (ENT) Guys
Blundell Ward (ENT) Guys 85% 78% 73% 79% 74% 79% 79% 79% 81% 77% 78% 79% 78% 77 82 82 85 74 77 79 77 75
Dorcas Dorcas 69% 69% 63% 78% 63% 83% 83% 83% 90% 83% 84% 89% 84% 7 3 3 4 3 5 6 5 13
Esther Esther 91% 80% 74% 78% 76% 79% 79% 79% 81% 73% 77% 81% 79% 59 71 82 90 83 89 92 89 74
Hedley Atkins Hedley Atkins 84% 77% 70% 70% 72% 76% 76% 76% 75% 72% 72% 75% 74% 114 118 135 115 116 129 133 129 137
Samaritan Samaritan 82% 72% 68% 72% 70% 75% 75% 75% 76% 75% 76% 80% 75% 106 102 100 100 107 113 117 113 101
George Perkins George Perkins 87% 76% 70% 74% 73% 73% 73% 73% 75% 69% 75% 70% 74% 93 84 99 96 95 105 109 105 98
Queen Queen 88% 81% 77% 78% 85% 86% 86% 86% 87% 83% 87% 90% 85% 57 49 49 52 47 50 51 50 50
Somerset 87% 79% 71% 76% 75% 77% 77% 77% 80% 77% 79% 79% 78% 54 63 53 60 59 57 59 57 50
Sarah Sarah 89% 82% 73% 77% 82% 80% 80% 80% 83% 78% 81% 86% 81% 48 64 66 60 50 67 69 67 77
Gynaecology Ward Gynaecology Ward 96% 84% 79% 85% 84% 84% 84% 84% 85% 85% 88% 92% 86% 81 94 111 97 85 91 94 91 92
Total 85% 77% 71% 76% 76% 78% 78% 78% 80% 76% 79% 81% 78% #### #### #### #### #### #### #### #### ####
Directorate Ward
Gastrointestinal
Medicine and
Surgery
Transplant,Renal
and Urology
Womens
Avg daily number of electronic NEWS
% with all 6 parameters completed and within appropriate timeframe
Surgery
Cardiovascular
Services
Oncology and
Haematology
Acute Medicine
The GSTT QI program - a process and data driven
approach
Acutely Ill Patient in Hospital Steering Group Report Nov-18
Days with high NEWS - total score >=5 or single parameter = 3
NB final ward of
admission
Dec
17
Jan
18
Feb
18
Mar
18
Apr
18
May
18
Jun
18
Jul
18
Aug
18
Sep
18
Oct
18
Nov
18
Avg
Dec
17
Jan
18
Feb
18
Mar
18
Apr
18
May
18
Jun
18
Jul
18
Aug
18
Sep
18
Oct
18
Nov
18
Total
Alan Apley AMS 3.0 3.8 3.6 2.9 1.5 1.8 2.0 2.6 2.4 2.8 2.4 2.2 2.6 92 117 100 91 44 57 59 82 74 85 74 67 942
GI Page (STH) 2.1 2.7 3.7 3.1 2.1 2.8 2.2 2.7 2.7 2.4 3.5 4.0 2.8 66 84 104 95 64 86 65 83 85 73 107 121 1033
GI Northumberland (STH) 2.8 2.1 2.1 2.4 2.0 2.4 2.4 1.4 2.4 3.9 5.6 4.8 2.9 86 66 59 74 60 75 73 42 75 117 173 145 1045
Nightingale 1.1 3.2 3.1 3.3 1.3 2.2 1.8 2.5 1.7 4.3 3.2 2.7 2.5 33 98 87 101 40 68 53 79 54 128 99 80 920
Aston Key 1.8 1.7 1.8 1.9 1.3 0.9 1.4 1.3 1.4 2.0 1.6 2.6 1.6 55 53 51 59 38 29 43 40 42 60 50 77 597
Florence Florence 1.8 1.7 2.7 3.4 1.6 1.6 1.7 1.2 1.5 0.6 1.5 2.1 1.8 55 52 76 104 47 50 52 38 47 18 45 63 647
Patience Patience 2.5 2.5 2.1 3.2 2.2 3.2 2.9 3.5 3.7 2.1 2.5 2.8 2.8 76 78 60 98 67 99 86 108 115 64 79 84 1014
Richard Bright Richard Bright 1.3 2.9 2.2 2.4 1.8 0.8 1.6 1.5 0.8 2.5 1.9 2.2 1.8 40 91 61 74 53 24 47 47 25 74 58 66 660
Acute Assessment Unit 0.0 0.2 0.3 0.0 0.2 0.3 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0 6 9 1 5 9 5 7 7 3 6 7 65
Acute Admissions Acute Admissions 5.0 5.1 5.2 4.5 3.4 5.2 3.3 4.6 4.0 4.8 3.9 3.9 4.4 154 158 146 138 103 162 99 143 124 143 122 118 1610
Albert Albert 5.5 3.9 5.3 4.3 3.8 4.0 5.4 4.6 4.8 3.2 3.1 3.0 4.2 171 122 148 134 114 123 161 143 148 96 95 89 1544
Alexandra Alexandra 3.5 3.1 3.4 4.2 3.4 2.5 2.2 2.4 1.6 3.8 4.4 4.2 3.2 110 97 96 129 101 78 66 74 51 115 136 125 1178
Anne Anne 4.9 4.3 5.1 5.8 3.9 4.2 4.1 4.6 4.6 3.9 4.6 5.3 4.6 153 133 144 180 117 130 122 143 144 116 143 160 1685
Evan Jones (EMU) inc Frailty
Evan Jones (EMU) inc Frailty 0.6 1.0 1.2 0.7 0.7 0.7 0.9 0.4 0.2 0.0 0.0 0.0 0.5 19 31 33 21 21 22 26 13 5 0 0 0 191
Clinical Decision Unit 0.6 0.8 0.1 18 25 43
Henry Henry 3.6 3.8 3.8 4.8 4.3 3.8 2.6 2.1 2.4 3.7 5.1 4.3 3.7 113 119 105 150 129 119 78 66 75 111 159 130 1354
Hillyers Hillyers 3.7 3.6 3.0 2.6 2.6 2.9 3.3 3.0 1.5 1.3 3.0 2.7 2.8 114 111 83 81 77 90 100 94 46 38 92 80 1006
Mark SU Mark SU 2.8 2.5 2.6 2.0 1.4 1.8 2.4 3.2 2.9 3.8 2.5 2.0 2.5 86 77 72 63 43 55 73 99 90 115 76 60 909
William Gull (STH) William Gull (STH) 9.9 8.2 8.3 7.9 9.4 6.5 6.9 5.6 5.7 4.9 4.7 6.0 7.0 307 255 231 244 282 202 206 174 178 147 145 181 2552
Becket Becket 3.0 2.1 3.4 3.6 3.6 4.4 3.1 3.3 3.3 3.1 3.5 2.8 3.3 92 65 94 111 108 135 93 103 103 92 110 84 1190
Coronary Care Unit Coronary Care Unit 0.2 0.0 0.1 0.3 0.1 0.1 0.0 0.5 0.4 0.0 0.2 0.0 0.2 7 1 3 8 3 3 1 14 12 1 5 0 58
Doulton Doulton 3.2 3.6 3.6 2.7 3.1 1.8 2.5 3.0 4.0 2.2 2.3 2.8 2.9 100 111 100 85 93 55 76 93 124 65 71 83 1056
Luke (STH) Luke (STH) 0.6 1.6 1.0 1.3 1.0 1.5 1.5 0.9 0.6 0.7 0.5 0.7 1.0 19 50 28 40 29 46 44 28 18 21 14 22 359
Luke HDU Luke HDU 0.0 0.0 0.3 0.1 0.1 0.0 0.1 0.0 0.1 0.0 0.1 0.1 0.1 1 1 7 2 3 0 3 0 2 1 2 4 26
Sarah Swift - Vascular Sarah Swift - Vascular 3.2 2.9 2.8 2.6 2.3 1.3 1.5 2.4 2.0 1.6 2.0 2.1 2.2 100 90 79 82 68 40 45 75 62 49 63 62 815
Stephen Stephen 5.3 5.5 6.0 5.2 5.4 5.6 3.9 6.3 6.6 5.2 4.0 5.5 5.4 163 172 169 162 162 174 117 194 205 157 123 166 1964
Blundell Ward (ENT) Guys
Blundell Ward (ENT) Guys 1.2 1.1 1.4 0.9 2.2 1.7 2.5 2.1 1.9 1.4 2.6 1.7 1.7 37 34 40 29 66 52 75 64 58 43 81 52 631
Dorcas Dorcas 0.6 0.4 0.3 0.1 0.4 0.5 0.4 0.5 0.6 3.4 4.5 3.7 1.3 18 11 7 4 12 14 13 15 19 103 141 112 469
Esther Esther 0.6 1.9 1.9 2.4 3.1 2.1 3.0 1.8 1.9 2.3 2.2 2.6 2.1 18 59 53 74 94 65 90 56 59 69 69 78 784
Hedley Atkins Hedley Atkins 5.3 5.9 6.9 7.1 6.5 6.6 6.3 5.7 6.9 6.0 6.1 6.4 6.3 164 182 192 221 194 206 190 176 215 179 190 193 2302
Samaritan Samaritan 4.1 5.4 6.1 4.4 3.9 4.2 6.1 4.6 3.6 3.8 4.8 4.8 4.6 127 168 172 135 118 130 183 144 112 115 149 143 1696
George Perkins George Perkins 1.4 1.6 1.8 1.7 1.6 2.1 1.4 1.7 1.9 2.5 1.6 3.2 1.9 42 49 50 53 49 65 41 52 59 75 51 95 681
Queen Queen 0.6 0.5 0.5 0.8 0.8 0.3 0.5 0.4 0.6 0.2 0.5 0.3 0.5 18 17 14 25 23 10 15 13 20 7 14 10 186
Somerset 1.2 2.0 1.3 1.7 1.5 1.3 1.3 1.6 1.1 1.1 1.4 1.3 1.4 36 62 35 54 45 40 39 51 34 34 43 38 511
Sarah Sarah 0.7 1.7 1.8 1.2 1.0 1.1 1.3 1.3 1.8 1.2 1.0 1.4 1.3 23 52 50 38 30 35 38 39 55 37 31 41 469
Gynaecology Ward Gynaecology Ward 1.6 1.7 3.1 1.6 1.3 1.7 1.8 1.6 2.2 2.4 2.2 2.2 1.9 51 52 88 49 38 53 54 50 67 73 67 65 707
Total 89 94 102 97 85 84 84 85 84 87 94 98 90 2746 2924 2846 3009 2540 2601 2531 2642 2609 2624 2901 2926 32899
Surgery
Womens
Number of high NEWS days
Gastrointestinal
Medicine and
Surgery
Transplant,Renal
and Urology
Acute Medicine
Cardiovascular
Services
Mean patients per day with high NEWS
Directorate Ward
Oncology and
Haematology

Analysis - compliance with the observation standard
decreases as acuity increases

0%
20%
40%
60%
80%
100%
0
30
60
90
120
150
180
210
240
270
300
330
360
390
420
450
480
510
540
570
600
630
660
690
720
750
780
810
840
870
+
cumulative % original NEWS
time between consecutive measurements (mins)
original NEWS score = 5 or 6
0000hrs - 0800hrs 0800hrs - 1200hrs 1200hrs - 2000hrs 2000hrs - 0000hrs
Standard = 1 hr
(+ 15 mins
tolerance)
0%
20%
40%
60%
80%
100%
0
30
60
90
120
150
180
210
240
270
300
330
360
390
420
450
480
510
540
570
600
630
660
690
720
750
780
810
840
870
+
cumulative % original NEWS
time between consecutive measurements (mins)
original NEWS score between 1 and 4
0000hrs - 0800hrs 0800hrs - 1200hrs 1200hrs - 2000hrs 2000hrs - 0000hrs
Standard = 6hrs
(+ 30 mins
tolerance)
Low acuity patients
receive routine
observations with
gaps in the afternoon
and overnight
High acuity patients
receive increased
observations but it is
difficult to deliver
1hourly on a ward

Mapping patient pathways revealed delays
in patient pathways
-4
rs
4-5
hrs
5-6
hrs
6-12
hrs
12-24
hrs
24+
hrs
NEWS to CRT visit
-4
rs
4-5
hrs
5-6
hrs
6-12
hrs
12-24
hrs
24+
hrs
dmit to CC admission
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 hr 2 hrs 3 hrs 4 hrs 5 hrs 6 hrs 12 hrs 24hrs >24
hrs
Proportion of patients who waited less than
high NEWS to admit CC
high NEWS to CRT
CRT to decision to admit
decision to admit to CC admission

Whebell S et al. Critical Care 2021

The Acute Deterioration dashboard
now allows us to track process and
outcome metrics in near real time…

Triangulating data with qualitative observations
AIP Performance
Report metrics
Learning from
Adverse Events
Incident
reporting
Behavioural
Insights

Understanding process, behaviours and barriers, then co-creating
new and improved ways of working

Pivoting our acute deterioration analytics to optimise
the escalation pathway in Covid-19

Case by case review of unplanned ICU admissions
0
1
2
3
4
5
6
7
8
9
14-Jan
13-Mar
17-Mar
21-Mar
25-Mar
29-Mar
02-Apr
06-Apr
10-Apr
14-Apr
18-Apr
23-Apr
02-May
28-May
06-Jun
Level1 Ward
ED
0
5
10
15
20
00 02 04 06 08 10 12 14 16 18 20 22
Level1 Ward
ED

NEWS2 – significantly weighted towards disturbance in RR,
HR and SBP, with only a binary grading for oxygen
Smith GB et al. Resuscitation 2013; 84:465-70

Royal College of Physicians, 2020

The ROX index
• ROX index (SpO2 / FiO2) > 4.8 at 12hrs predicts need for intubation
RR
• We had previously shown that the delta-ROX could predict the need for critical care
admission for patients on high flow oxygen during an CCOT pilot
Roca O, Journal of Critical Care 2016; 35: 200–205
Price E, Intensive Care Society, 2018

Hypothesis: The ROX index may help identify patients at
risk of deterioration earlier than NEWS2
• In Covid-19 there may be relatively little cardiovascular disturbance and tachypnoea may
be a late sign. In NEWS2 supplemental oxygen is only a binary grading system. Therefore,
NEWS2 could be low despite significant hypoxaemic respiratory failure.
• RR 20
• SpO2 92%
• Non-rebreath mask O2
• SBP 120mmHg
• HR 90bpm
• Alert
NEWS2 = 3
but
ROX = 5.9

Device % O2 Flow l/min Assumed % O2 if no %
O2 selected
Nasal cannula 1 24
Nasal cannula 2 28
Nasal cannula 3 32
Nasal cannula 4 36
Simple mask 1 24
Simple mask 2 28
Simple mask 3 32
Simple mask 4 36
Simple mask 5 40
Simple mask 6 44
Simple mask 7 48
Simple mask 8 52
Simple mask 9 56
Simple mask >=10 60
Venturi 24 2
Venturi 28 4
Venturi 35 8
Venturi 40 10
Venturi 60 12
NIV OR CPAP 1 24
NIV OR CPAP 2 26
NIV OR CPAP 3 28
NIV OR CPAP 4 30
NIV OR CPAP 5 32
NIV OR CPAP 6 34
NIV OR CPAP 7 36
NIV OR CPAP 8 38
NIV OR CPAP 9 40
NIV OR CPAP >=10 44
Humidified 28
Humidified 35
Humidified 40
Humidified 60
Tracheostomy mask Treat as per simple face mask
HFNC Continuous % O2
values from 21 -
100
Continuous flow rate
values from 0 - 60
100% Reservoir Mask 15l/min 15 80
FiO2 is a key determinant, yet can
only be estimated on low flow
oxygen

Last recorded ROX ([SpO2/FiO2]/RR) prior to unplanned CC admission
[96/0.4]/20
[90/0.6]/22

0
10
20
30
40
50
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
29
31
33
LowestROX vs outcome
no unplanned event
unplanned event
0
20
40
60
80
100
120
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17
HighestNEWS vs outcome
no unplanned event
unplanned event

Physiological antecedents to
cardiac arrest, crit care
admission or death on ward (n
= 186)
• 24 hours preceding the
unplanned event
• FiO2 inflects approx. 10 hours
before
• RR does not increase until
approx. 5 hours before
• Little CVS disturbance or
delirium
Ø Can use to predict how EWS
will function

Pimentel M et al. Resuscitation
2020;156:99-106
Covid-19 Other viral
pneumonias

The ROX index has greater predictive validity than NEWS2
for deterioration and may trigger up to 4 hours earlier
NEWS2
AUROC
0.815
ROX
AUROC
0.848
Prower et al, eClinical Medicine 2020

Now incorporated as a parallel early warning score at
GSTT, pending further validation

Sum litres of oxygen
OxygenLiterVAL OxygenLiterVAL
ward re-matched HFNC 0.5 1 2 3 4 5 6 11 15 Grand Total
Acute Admissions 5 2 4 6 17
Albert 4 4
Anne 1 2 3
Aston Key 2 4 6
Becket 1 1
Dorcas 2 2 3 7
Doulton 1 1
Edward Ward 2 2 3 8 11 26
Esther 1 2 3
Gastro-Intestinal Lab 8 8
George Perkins 1 8 9
GI Northumberland 8 3 4 15
GI Page 4 15 19
Gynaecology Ward 4 4
Hedley Atkins 1 4 5
Henry 0.5 0.5
Howard 2 2
Mark SU 2 5 7
Nightingale 4 4
Patience 2 2
Samaritan 2 3 4 9
Sarah Swift (Vascular) 6 11 15 32
Somerset (Surgery) 1 1
Stephen 2 2
Victoria High Dependency 1 1
William Gull (STH) 1 4 5
Grand Total 0.5 20 66 12 32 5 6 22 30 193.5
Snap shot at 00:00 22/11/20
Finally, we used the data to heat-map oxygen utilisation, guiding
patient allocation and stewardship of resources

Next steps
• Integration of the benefits of a graded FiO2 system, with the benefits of a
standardised NEWS
• Validation in other cohorts of patients with hypoxic respiratory failure and
in multi-centre studies, followed by prospective evaluation of benefit
• Post-pandemic returning to our original mission to improve consistency
and quality in the acute deterioration pathway, including sepsis and AKI

Lessons learnt
• Digital healthcare has created the substrate for data-driven clinical care though we remain at an early
stage
• The ability to measure is a pre-requisite for both understanding, and for improvement, though change
does not necessarily automatically follow
• Improving the response to acute deterioration requires an optimal detection of deterioration, based on a
clear understanding of the disease trajectory - Covid-19 has been a unique opportunity to manage a
homogenous disease and requires its own approach
• A simple index of respiratory variables may outperform existing early warning scores, though further
validation is required
• Collaborative networks with common data and data sharing are required to rapidly develop and test
future predictive models

Thank you
guy.glover@gstt.nhs.uk
Thank you
guy.glover@gstt.nhs.uk
+