This document discusses using predictive models and linking different types of healthcare data to improve quality and efficiency. It provides examples of predictive models being used in the UK to identify high-risk patients and assess their future healthcare costs. The document also outlines how linking data from various sources, like medical records, hospital data, and social care, can provide a more comprehensive view of patients' care over time. Evaluating the impact of interventions using retrospective analyses with matched controls is discussed. Protecting patient privacy when linking personal data is also addressed.

1. Predictive Models and Data Linkage
Sharing international experience: Linking disease registry
information and predictive modelling to improve quality and
efficiency
September 2012
Martin Bardsley
Head of Research
The Nuffield Trust
© Nuffield Trust

2. Applications of predictive risk in the UK
• Case finding for people at high risk of admission seen as
increasingly important for people with LTCs and complex
conditions
• Examples of predicting across health and social care
• Scope to make the most of linked data sets in describing
care pathways
• Evaluation and risk adjustment
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3. Predictive risk and case finding
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4. Predictive modelling in UK
• BMJ in paper* in 2002 showed Kaiser Permanente in California
seemed to provide higher-quality healthcare than the NHS at a
lower cost. Kaiser identify high risk people in their population and
manage them intensively to avoid admissions
• • Modelling aims to identify people at risk of high costs in future
• Relies on exploiting existing information
+ve: systematic; not costly data collections; fit into existing
systems
-ve: information collected may not be predictive
• *Getting more for their dollar: a comparison of the NHS with California's Kaiser Permanente BMJ 2002;324:135-
143
© Nuffield Trust

5. Predictive Models Identify who will be
where on next year’s Kaiser Pyramid
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6. Regression to the mean:
Change in average number of emergency bed days
Predictive
models try to
identify
people here
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7. Extending models beyond healthcare
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8. Information flows
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9. Protecting individuals identities
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10. Looking at and individuals history of care
One person’s story
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11. Typical accuracy models currently used to
predict hospital admission
Model Risk threshold PPV (%) Sensitivity (%)
PARR (England) 50 65.3 54.3
70 77.4 17.8
80 84.3 8.1
SPARRA (Scotland) 50 59.4 18.0
70 76.1 3.3
S Care model 50 55 19
(Pooled £1K)
70 60 10
© Nuffield Trust

12. Range of case finding models available
SPARRA PARR (++)
SPARRA MD Combined Predictive Model
PRISM PEONY
AHI Risk adjuster LACE
ACGs (John Hopkins) MARA (Milliman Advanced Risk
Adjuster)
DxCGs (Verisk) Dr Foster Intelligence
SCOPE RISC (United Health Group)
Variants on basic admission/readmission predictions:
Short term readmissions Social care costs
Condition specific tools
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13. Wider applications of linked data
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14. Using the data available
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15. Testing for gaps in care
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16. North West e-lab
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17. Relative size of data sets collected
For one WSD area
Accident and Outpatients Inpatients Social care Community GPs
emergency 1,680,000 records 360,000 records 240,000 records matrons 60 practices
350,000 records 20,000 records 48.5 million records
March 2011 © Nuffield Trust

18. Data linkage
Social & secondary care interface
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19. Inpatient and Social Care costs per person in final year
of life by age band
over two lines
£12,000
£10,000
£8,000
£6,000
Social care
£4,000
Hospital IP care
£2,000 SC+ Hosp
£0
40 50 60 70 80 90 100
Age Band
© Nuffield Trust

20. Number of inpatient admissions (with 95% confidence intervals) per person by age
according to type of social care received
Bardsley M, Georghiou T, Chassin L, Lewis G, Steventon A, and Dixon J. Overlap of hospital use and social care in older people in England J Health © Nuffield Trust
Serv Res Policy jhsrp.2011.010171; published ahead of print 23 February 2012,

21. Describing patterns of social care around cancer diagnosis.
Linkage to cancer registry
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22. What was the average cost of hospital care?
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23. GP visits around cancer diagnosis
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24. Risk adjustment and Evaluation
a. Prospective Trials
b. Retrospective evaluations
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25. Using risk scores within a randomised trial
Ensuring even mix of patients Analysis by risk subgroup
March 2011 © Nuffield Trust

26. Information flows for this analysis
Local Secondary
Uses Service
Encrypted
client-event
Link to create
Combined Nuffield
operational based Model Trust
systems GP Encrypted
client-event
based Linked
Hospital Encrypted datasets
Episodes client-event
Statistics based
HES-ONS Encrypted
mortality data client-event
based
Community Encrypted
systems client-event
based
Social care Encrypted
client-event
based © Nuffield Trust

27. Distribution of Combined Model risk scores
Importance of risk adjustment
Very high risk
Top 0.5% Top 10%
High risk
Moderate risk
0.5% - 5%
Low risk
5% - 20%
10% - 45%
20% - 100%
45% - 85%
85% - 100%
General population WSD participants
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28. Exploiting admin data within an RCT- trends in
emergency hospital admissions
Start of trial
Able to chart hospital use
before recruitment
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29. Linked data in RCTS
• Enables larger sample sizes as its relatively cheap information
• Able to generate multiple outcome measures
• Track patient histories before baseline – and inform risk adjustment
• Generate intermediate points
• BUT
• Constrained by type of information collected and quality
• May exclude care from some sectors
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30. Retrospective evaluations
The Partnership for Older People Projects (POPPs)
•£60m investment by DH with aim to:
“shift resources and culture away
from institutional and hospital-
based crisis care”
•146 interventions piloted in 29 sites.
•National evaluation of whole programme
found £1.20 saving in bed days per £1 “We recommend expanding the
spent. Partnerships for Older People
Projects (POPPs) approach to
prevention across all local
authorities and PCTs.”
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31. From the 146 interventions offered under POPP, we
selected 8 for an in-depth study of hospital use
Support workers for community matrons
Intermediate care service with generic workers
Integrated health and social care teams
Out-of-hours and daytime response service
+ 4 different short term assessment and
signposting services
© Nuffield Trust

32. Our preferred option for this evaluation:
link participants to HES through a trusted third party
Collate files and
add NHS
Participating sites
numbers
Information
Collate patient lists Centre
Derive
HES ID Nuffield Trust
Patient identifiers Trial information (e.g. Non-patient identifiable keys
(e.g. NHS number) start and end date) (e.g. HES ID, pseudonymised
© Nuffield Trust
NHS number)
March 2011

33. Prevalence of health diagnoses categories in intervention
and control groups
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34. Overcoming regression to the mean using a control
group
Intervention
0.3
Start of intervention
Number of emergency hospital admissions
per head per month
0.2
0.1
0.0
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
Month
March 2011 © Nuffield Trust

35. Overcoming regression to the mean using a control
group
Intervention
0.3
Start of intervention
Number of emergency hospital admissions
per head per month
0.2
0.1
0.0
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
Month
March 2011 © Nuffield Trust

36. Overcoming regression to the mean using a control
group
Intervention
0.3
Start of intervention
Number of emergency hospital admissions
per head per month
0.2
0.1
0.0
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
Month
March 2011 © Nuffield Trust

37. Overcoming regression to the mean using a control
group
Control Intervention
0.3
Start of intervention
Number of emergency hospital admissions
per head per month
0.2
0.1
0.0
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12
Month
March 2011 © Nuffield Trust

38. Impact of eight different interventions on hospital use
© Nuffield Trust

39. Summary
• Predictive modelling practical case finding tool for
identifying high risk patients
• Possible to screen large populations using existing data
• Scope to extend linkage over time and across data sets to
give a broader view of patients’ journey
• Large data sets can be used in both prospective studies
(RCTs) and enable retrospective analyses using matched
controls
• Biggest weakness with existing administrative data is the
limited level of clinical information – yet greater use of
clinical records, audits and registries is possible
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