This document discusses predictive modelling to identify patients at high risk of unplanned hospital admissions. It notes that a small percentage of patients account for a large share of emergency bed days and unplanned admissions are expensive, lead to poor patient experiences, and may indicate suboptimal care. The document presents data showing predictive models can accurately identify patients at high risk of readmission and describes how the models have been used to develop interventions targeted at high-risk patients, leading to reductions in emergency admissions. It concludes by discussing expanding the use of Hospital Episode Statistics data to improve care coordination and integration across health systems.

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The value
of data to
the NHS
Dr. Geraint Lewis
Chief Data Officer

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HES: a national
treasure

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Hospital Episode Statistics (HES)

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One person’s HES record

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Predictive
Modelling

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Why predict unplanned admissions?
• Problem
• Ageing population
• Rising prevalence of chronic disease
• Financial downturn
• Rising rates of unplanned admissions
• Opportunity
• 5% of patients account for 49% of emergency bed days
• Unplanned admissions are:
• Expensive
• Poor patient experience
• May indicate suboptimal care

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0
5
10
15
20
25
30
35
40
45
50
- 5 - 4 - 3 - 2 - 1 Intense
year
+ 1 + 2 + 3 + 4
Halliday R. Department of Health analysis of Hospital Episode Statistics for England

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0
5
10
15
20
25
30
35
40
45
50
- 5 - 4 - 3 - 2 - 1
Intense
year
+ 1 + 2 + 3 + 4
Halliday R. Department of Health analysis of Hospital Episode Statistics for England

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0
5
10
15
20
25
30
35
40
45
50
- 5 - 4 - 3 - 2 - 1 Intense
year
+ 1 + 2 + 3 + 4
Halliday R. Department of Health analysis of Hospital Episode Statistics for England

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Accuracy of the PARR model
Cut-off score Positive
predictive
value
Sensitivity
50 0.65 0.54
70 0.77 0.18
80 0.84 0.08
Billings et al. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 2006;333:327
c-statistic = 0.69

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Business case modelling scenarios
Billings et al. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 2006;333:327

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Examples of other ‘Triple Fail’ Events
Source: Lewis G, Kirkham H, Duncan I, Vaithianathan R. How Health Systems Could Avert ‘Triple Fail’ Events That Are Harmful,
Are Costly, And Result In Poor Patient Satisfaction. Health Affairs 2013;32(4)

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Evaluation

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Pre/Post Comparisons
5

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-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
0.0
0.1
0.2
0.3
Month
Numberofemergencyhospitaladmissions
perheadpermonth
Intervention
Start of intervention
Prognostic score matching
Steventon A. et al. An evaluation of the impact of community-based interventions on hospital use. London: Nuffield
Trust (2011)

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-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
0.0
0.1
0.2
0.3
Month
Numberofemergencyhospitaladmissions
perheadpermonth
Intervention
Start of intervention
Prognostic score matching

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-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
0.0
0.1
0.2
0.3
Month
Numberofemergencyhospitaladmissions
perheadpermonth
Intervention
Start of intervention
Steventon A, Bardsley M, Billings J, Georghiou T, Lewis G. An evaluation of the impact of community-based interventions on hospital use. London:
Nuffield Trust (2011)

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-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
0.0
0.1
0.2
0.3
Month
Numberofemergencyhospitaladmissions
perheadpermonth
Control Intervention
Start of intervention
Steventon A, Bardsley M, Billings J, Georghiou T, Lewis G. An evaluation of the impact of community-based interventions on hospital use. London:
Nuffield Trust (2011)

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Funding
Allocation

21. • Multispecialty
Community Provider
• Primary and Acute
Care Systems
• Integrated urgent and
emergency care
services
• New options for small
hospitals
CapitatedFee for
Service
Quality
Premium
No
Quality
Premium
NHS Five Year Forward View

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What next for
HES?

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Expansion of HES

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CURRENT DEVELOPMENTS
✔ Legislative changes
• Increased scrutiny of pseudonymous data
• data will be used for the good of health and care
• “one strike and you’re out”
Phased roll-out
• 100-500 GP practices
• Independent review prior to further roll-out
Secure data facility

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NATIONAL
INFORMATION
BOARD “By June 2015, the NIB will
agree a core ‘secondary uses’
dataset that all
NHS-funded care providers
have to make available to
support commissioning,
regulation and transparency.”

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