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Supporting Better Care Fund resubmissions
Webinar
28 August 2014
CONFIDENTIAL AND PROPRIETARY
Risk Stratification and information governance
1
Several webinars will be held across 3 topics over the next 3 weeks; Today’s webinar will focus on Risk stratification and IG related to it
Section 751 28, 29 Aug
3, 5 Sep12.00-13:30
Topic Dates Facilitator
▪ David Owens
▪ Olwen Dutton
Risk stratification and
information governance2 28 Aug, 9.00-10:30
Additional dates TBC▪ Oleg Bestsennyy
▪ Debbie Terry
Financial analysis3 TBC▪ Oleg Bestsennyy
Overview of webinars
2
Today’s content
Risk stratification1 40 mins + 10 mins Q&A
Information governance2 30 mins + 10 mins Q&A
3
Risk stratification contents
How risk stratification helps?A
How do you do it to a gold standard?B
What can be achieved in 2 weeks?C
1
4
McKinsey research shows that there are 3 building blocks to a successful integrated care system
Support with Enablers
Payment Governance Information Leadership Support
Success in coordinated care
Organise Delivery
Care
Coordi-
nation
Self-
empowerment
and education
Individual
care plans
Multi-disciplinary
teams
Understand Needs 21
3
SOURCE: Carter, Chalouhi, Richardson – What it takes to make integrated care work (McKinsey Health
International, 2011); Amended and updated in 2014
How risk stratification helps?A
5
A robust segmentation/stratification is the foundation for ensuringpatient-centred planning
In depth understanding of population needs with segmentation/ stratification
Evidence-based
planning
Outcomes and impact modeling
Financial analysis
1 2
4
3
Create evidence based plans by
understanding the
right evidence-
backed
interventions for
segments of the
populations with
expected impact,
timing and cost
Outcomes should be selected to
crystalise the goals the HWBB sets for
the population; they should be stretching
but achievable based on impact modeling
informed by the evidence based and
understanding of the population needs
Financial analysis should
set out the overall impact of
initiatives (in terms of activity,
commissioner spend and
investment) by segment and
the costing and assumptions
of specific initiatives over the
next year, but should link to
the five year plan
Use best available
data to understand population needs
quantitatively as well
as qualitatively, making
use of risk stratification
and segmentation
1
3
2
How risk stratification helps?A
6
Two approaches to understanding patient needs: risk stratification and patient segmentation
▪ Better clinical decision-making:
prioritisation of efforts and focus
▪ Identification of intensity of care support
required
▪ Prioritisation of resources
Risk stratification: Grouping population
based on how likely people are to use
services
Patient segmentation: Grouping population
based on common characteristics (e.g., age,
condition, demographics)
▪ Better clinical decision-making: innovative
care delivery models
▪ Realignment of resources with patient
needs
▪ Payment innovation for various segments
based on need
15
Age
16-69
70+
<16
DementiaLearning
disabilitySEMI
More than
one LTCCancerOne LTC
Severe
Physical Disability
Mostly
healthy
0.1k
0.9k 2.7k
3.2k
0k
0.8k
0.1k
0.1k
5.3k
7.0k
0.1k
4.5k
17.1k
1.6k
3.7k
49.2k
18.4k
Patient segmentation: Distribution of population of a certain CCG into 18 various segments
SOURCE: Analysis of anonymised person-level linked data from 1 CCG – 2012/13
71,252 23,213 ~ 115,000Total12,382 1,198 897 5,932
Data unavailable
They are not mutually exclusive! Best in-class examples do them both in concert
1 2
How risk stratification helps?A
7
Risk stratification: 20% of population with the highest risk of an acute admission in one locality drive 70%+ of health and social care expenditure…
SOURCE: McKinsey team analysis, HES 2011/12, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs
Total
Very high risk
High risk
Moderate
risk
Low risk
Very low
risk 444,916
266,950
133,473
40,044
4,450
£134.6m
£190.6m
£347.0m
£320.6m
£118.3m
£303
£714
£2,600
£8,007
£26,587
88x
889,883 £1,249 £1,111.2m
Average cost per
capita, £
Total
spend, £mPopulation Per cent of
budget, %
17%
12%
31%
29%
11%
20%
RISK STRATIFICATIONB How do you do it to a gold standard?
8
… But only 36% of primary care
80%
14%
13%
27%
64%
29%
36%
Top 3 strata
Rest of the
population
Total spend 71%
Primary care spend
Community care
spend73%
Social care spend 87%
Total hospital spend 86%
Emergency hospital
spend97% 3%
Population 20%
SOURCE: McKinsey team analysis, HES 2011/12, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs
Spend profile
Is there a
need to
transform care
delivery model
to ensure
more
preventive
primary care focus is given
to those in the
top 20% of the
population
most at risk of
an admission?
Per cent of total spend
RISK STRATIFICATION
B How do you do it to a gold standard?
9
Patient segmentation: Independent variables included in regression analysis
1 Psychosis, schizophrenia and bipolar disorders
SOURCE: Nuffield trust research, clinical input
Diagnosis
AsthmaHeart failure and LVD
Cancer
CHD Stroke
CKD
COPD
Dementia
Depression
Severe and enduring mental illness (SEMI)1
Number of LTCs
Diabetes
Hyperten-sion
Not included
Death and end of life care
▪ There is evidence to suggest end of life care is a significant driver of care spend
▪ Not available in data
Unpredict-able episodic require-ments
▪ Main determinant of care demand among those who do not have chronic conditions
▪ No indicator that is independent of spend outputs (so inclusion would be circular)
▪ No forward predictive power – an episode of care in one year is not a good predictor for the next
Depriva-tion and social exclusion
▪ Socially excluded groups, like the homeless may have distinct care needs and be a significant driver of demand
▪ Not available in data
Comment Reason for exclusion
Learning disability
▪ Whether an individual had a learning disability was found to be significant in other sites
▪ Not available in data
Physical disability
▪ Whether an individual severe physical disability for which they received social care was found to be significant in other sites
▪ Not available in data
Epilepsy
Age
Other
Total spend
SEGMENTATION, AGE AND CONDITIONB How do you do it to a gold standard?
10
16-69
70+
<16
Example: Average patient spend (£k) varies dramatically between various segments in one UK locality
£5.5k
£2.9k
£3.3k
£2.7k
£1.4k
£1.0k
£2.5k
£0.7k
£0.5k
£3.6k
£6.1k
£18.2k
£19.4k
£23.2k
£15.3k
£9.7k
£3.8k
SOURCE: Analysis of anonymised person-level linked data from 1 CCG – 2012/13
£781 £1,612 £ 1,758 Total£4,050 £9,542 £19,681 £5,000
Data unavailable
Age DementiaLearning disability
SEMIMore than one LTC
CancerOne LTCSevere Physical Disability
Mostly healthy
SEGMENTATION, AGE AND CONDITIONB How do you do it to a gold standard?
Avg.
11
What can you do in the next 2 weeks?What can be achieved in 2 weeks?C
Using your most recent HES data, JSNA or
QOF registry data:
▪ Identify proportion of the population that is
elderly (75+) OR has a long-term condition
– Use QOF or JSNA to assess the
prevalence of major long-term conditions
– Alternatively, look for specific diagnoses
codes associated with major long term
conditions in your HES data
▪ Working with your CSU or your analytics
team, analyse HES data to assess how
many non-elective (NEL) admissions,
outpatient appointments and A&E visits
were associated with the elderly or people
with major LTCs and what proportion of the
total number of NEL/OP/A&E activity this
represents
▪ Monitor will be releasing a tool,
the “Ready Reckoner”, that can
be used to facilitate a basic
segmentation analysis
▪ It can help your locality
estimate the average per-capita
spend for various segments
based on the type of locality,
total population size and total
Firms-reported budgeted
▪ Watch out for link to this tool on
the BCF website
Monitor’s “Ready Reckoner”“Quick and dirty” segmentation 21
12
2
3
4
5
1
6
7
Create core team, define vision
Secure the right delivery resources
Get, link, test and validate the data
Manageand evolvethe datasets
Build clinical buy in and address IGissues
Design the right technical solution
Establish governance and leadership
0-1 months 1-2 months 2-3 months 1-2 months 0-1 months 2-3 months On going
Growing momentum and
and increasing number of staff involved across settings over time
Typically needs small full time dedicated project team (1-3 FTEs)
Typically needs full time involvement from IT and data teams (1-3 FTEs) and investment in IT depending on complexity of technology solution
Developing a best-in-class solution: a 7 step process that could take up to 12 months
B How do you do it to a gold standard?
13
Further reading
▪ North West London “Whole Systems” toolkit: Chapter 4 (http://integration.healthiernorthwestlondon.nhs.uk/chapter/what-population-groups-do-we-want-to-include-)
▪ “Understanding Patients’ Needs and Risk: A Key to a Better NHS”, McKinsey 2013 (http://bit.ly/20prcnt)
▪ Combined Predictive Model, King’s Fund 2006 (http://www.kingsfund.org.uk/sites/files/kf/field/field_document/PARR-combined-predictive-model-final-report-dec06.pdf)
▪ “Choosing a predictive risk model: a guide for commissioners in England”, Nuffield 2011 (http://www.nuffieldtrust.org.uk/publications/choosing-predictive-risk-model-guide-commissioners-england)
14
Questions?
We will move on to the information governance module in 10 minutes
15
Today’s content
Risk stratification1 40 mins + 10 mins Q&A
Information governance2 30 mins + 10 mins Q&A
Better Care Fund
Risk Stratification and Information Governance
Risk stratification IG Checklist
• Available in the “How to” guide - Appendix
• Based on NHS England Risk Stratification and Information Governance Advice - and
• Confidentiality Advisory Group (CAG) conditions for operating under s251 approval
• What needs to be done to ensure compliance with Data Protection principles
Risk stratification – data flows
• Collection of data from general practice
• Collection of data from Secondary Uses Services
within HSCIC (DSCROs)
• Processing of data in Accredited Safe Havens (ASHs) or
contracted third parties
• Provision of data to commissioners
• Provision of data to general practice.
3 types of data
Data Conditions for Use
Anonymised or aggregated data Few restraints – for publication,
reporting, strategic planning, joint
strategic needs assessment, support
H&WB Boards
Personal confidential data or identifiable
data
Only available to health or social care
professionals responsible with a
“legitimate relationship” for direct care
of the individual OR with explicit consent
De-identified data for limited access
(includes “pseudonymised data” and
“weakly pseudonymised data”)
Not for publication – risk of re-
identification. Access strictly limited to
specific roles for specific purposes with
tight controls AND legal basis.
Cannot leave safe haven unless
anonymised
Lawful options
Data processing for risk stratification should be conducted fairly and lawfully by:
• using technology that allows data to be extracted from its source, pseudonymised, stratified automatically and returned in a non-identifiable format without it being seen by a human throughout the process (“Black box”); or
• Explicit consent; or
• under the conditions set out in the Section 251 Regulations, which limit access and use of data; and in both cases
• using controls to ensure personal confidential data is only accessible to those health and social care professionals responsible for the provision of direct care and treatment.
Section 251
• S251 only has the lawful power to set aside the Common Law Duty of Confidence
• All principles of the Data Protection Act 1998 can be satisfied, especially the principle 1 for processing to be fair
• The following are required to satisfy DPA 1998, schedule 1, part II, 12
• An NHS Contract under NHS Act 2006 s9 satisfies the DPA requirement
• A Deed of Contract satisfies the DPA requirement
Checklist – steps to ensure IG controls
• Develop a risk stratification policy
– Stakeholders
– Identify data controller and data processor roles
– preventative interventions
• Select a suitable predictive model
– Register of approved risk stratification providers
– Automated or human decision making
Ensure the is a legal basis
• Privacy laws
• Right to opt-out/dealing with dissent
• Fair processing – essential
• S251 and exit plans
• Matching data using NHS number
• Data flows through ASH and DSCRO
• Point of Pseudonymisation – Black box technology
Fair processing
• Communications plan
• Develop fair processing materials
• Active communication
• Historic data
Agree a defined data set
• Adequate, relevant, not excessive
• Historical data
• Excluded data
• Opt-outs
• Retention and disposal plans
• GP data extracts – GPES or system supplier?
Establish contracts
• Need to identify data controller & data
processor
• The following are required to satisfy DPA 1998,
schedule 1, part II, 12
• An NHS Contract under NHS Act 2006 s9
satisfies the DPA requirement
• A Deed of Contract satisfies the DPA
requirement
Contracts and Agreements
Contracts and Agreements
Contracts and Agreements
Contracts and Agreements
Procedures to control access to
identifiable data
• Only clinicians directly responsible for patient
care can see patient identifiable risk scores
• Caution accessing additional information –
consent
• An opportunity to get explicit consent for
subsequent use of data e.g. monitoring
effectiveness
AOB and completion of revised plans
• List of risk stratification approved suppliers
• Risk Stratification Assurance Statement (CAG 7-
04(a)/2013 compliance for CCGs
http://www.england.nhs.uk/ourwork/tsd/ig/risk-
stratification/
33
BACK-UP
34
5 key enablers are crucial to change behaviour, with information being the first building block
SOURCE: Carter, Chalouhi, Richardson – What it takes to make integrated care work (McKinsey Health International, 2011); Latkovic - The trillion dollar prize (Health International 2013) and Fountaine, Richardson and Wilson - Changing behaviour in primary care (Health International 2013)
TightGovernance
ClinicalLeadership Support at scale
Payment innovation
▪ Significant
(30%+)
▪ At scale
(30%+)
▪ Sustained
(3-5 years)
▪ Align risk and
reward across
system
Right Information
▪ Solve IG▪ Support
– Unders-tandingneeds
– Citizen records
– Clinical decision making
– Peer pressure
– Payment
▪ CEOs &
Boards
commitment
of resources
▪ Bind in
payors,
hospitals,
primary care
and local
government
▪ Hold to
account
▪ Role model
behaviour
▪ Deliver
consistently
▪ Hold peers to
account
▪ Work within
team
▪ New ways of
doing things
requires
support to
learn how
▪ Pivotal new
roles for care
coordination
▪ Management
resources to
support
clinicians
35
Business case
1-3
mo
nth
s
Establish leader-
ship coalition
Va
rie
sOperational
Blueprint
2-6
mo
nth
s Implemen-tation and delivery O
ng
oin
g
Scale up
On
go
ing
Key
partners
aligned
5 year plan
with
▪Savings,
▪ Investment
▪Expected
payback
Detailed design
▪ Interventions
▪ Payments
▪ Governance
▪ Information
▪ Delivery plan
▪Enroll
individual
providers
▪Train staff
▪Enroll
patients
▪Extract
data
▪Hold new
meetings
Roadmap
for
expansion
and
program
expanded
to new
areas
5 steps in the typical journey to create integrated care systems – where are you today?
SOURCE: McKinsey & Company
36
Patient attribution & characteristics:Who is the user /
patient? Their name,
conditions...
Patient attribution & characteristics:Who is the user /
patient? Their name,
conditions...
Cost calculation:How much does the
care cost the tax
payer?
Cost calculation:How much does the
care cost the tax
payer?
Outcome details:What is the final
outcome? Result,
quality?
Outcome details:What is the final
outcome? Result,
quality?
i
iiProvider & activity details: What care
is provided? Where
is care provided? By
whom?
Provider & activity details: What care
is provided? Where
is care provided? By
whom?
iii
iv v
ScopeSettings covered: For as many
providers as possible
Patients covered: For as many
individuals as possible
ScopeSettings covered: For as many
providers as possible
Patients covered: For as many
individuals as possible
Frequency: As soon after the interaction
as possible
Frequency: As soon after the interaction
as possible
Time period covered: For as long a time
period as appropriate and necessary
Time period covered: For as long a time
period as appropriate and necessary
Safety and IG Compliance: In an IG compliant mannerSafety and IG Compliance: In an IG compliant manner
Creating patient-level linked datasets involves capturing this information for all interactions and linking them at a person level
Every time care is delivered to an individual various kinds of information is generated
Technology solution: Using an appropriate technology
solution
Technology solution: Using an appropriate technology
solution
vi
vii
viii
ix
x
To get started, a “gold standard” stratification/segmentation requires a patient-level linked database
How do you do it to a gold standard?C
37
Various types of non-proprietary risk stratification models exist in the UK
SOURCE: ‘Combined Predictive Model Final Report’, DH, Kings Fund, NYU, December 2006; ‘Forecasting emergency admissions in Devon - the
Devon predictive model’, Todd Chenore, June 2012; ‘Overlap of hospital use and social care in older people in England’, Bardsley,
Georghiou, Chassin, Lewis, Steventon and Dixon, 2011
▪ Significant escalation in social care interventions are relatively rare compared to hospital admissions and therefore harder to predict. This means social care risk assessment is less effective
▪ CPM captures most high risk patients/users who are likely to be admitted to hospital in next year
▪ As 71% of social care users over 75 have secondary care admission in past three years CPM will also highlight most of high risk individuals for health and social care
Risk model
Predictive accuracy
Data sourcesFocus Comment
PARR
▪ Inpatient
▪ Outpatient
▪ A&E
▪ Hospital
admissions
▪ Basic predictive
accuracy
Combined Predictive Model (CPM)
▪ Inpatient
▪ Outpatient
▪ A&E
▪ Primary care
▪ Hospital
admissions
▪ Similar to
PARR but
includes GP
data
Torbay
▪ Inpatient
▪ Outpatient
▪ A&E
▪ Primary care
▪ Local risk
factors
▪ Hospital
admissions
▪ Similar to CPM
but adapted for
local risk
factors
Social care model
▪ Social care
assessments
▪ Social care
activity
▪ Age
▪ Health
factors
included from
PARR
▪ Resident
care
admission
▪ £5,000
increase in
care
package
cost
▪ Less effective
than CPM as
trying to predict
rare events
▪ Social care
data challenges
reduce
accuracy
How do you do it to a gold standard?C RISK STRATIFICATION
38
Non-elderly
Elderly RISK
Almost half of elderly (75+) fall into high or very high risk categories, compared to only 3% of non-elderly
SOURCE: McKinsey team analysis, HES 2010/11, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs
40%27%26% 8%
RISK16%37%44%3%
Very highHighModerateLowVery low
0%
0%
Risk:
How do you do it to a gold standard?C
39
No physicalLTCs
1+ physicalLTC
RISK
Risk distribution of people with and without physical LTCs
SOURCE: McKinsey team analysis, HES 2010/11, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs
24%27%31%4%
RISK37%45%
14%
3%
Very highHighModerateLowVery low
15%0%
How do you do it to a gold standard?C
40
No mentalhealth LTCs
1+ mental health LTC
RISK
Risk distribution of people with and without mental health LTCs
SOURCE: McKinsey team analysis, HES 2010/11, FIMS, Q research/NHS Information centre, PSSEX; NHS Reference Costs
12%24%34%2%
RISK36%44%
27%
4%
Very highHighModerateLowVery low
15%0%
How do you do it to a gold standard?C
41
Age
16-69
70+
<16
DementiaLearning disability
SEMIMore than one LTC
CancerOne LTCSevere Physical Disability
Mostly healthy
0.1k
0.9k 2.7k
3.2k
0k
0.8k
0.1k
0.1k
5.3k
7.0k
0.1k
4.5k
17.1k
1.6k
3.7k
49.2k
18.4k
Patient segmentation: Distribution of population of a certain CCG into 18 various segments
SOURCE: Analysis of anonymised person-level linked data from 1 CCG – 2012/13
71,252 23,213 ~ 115,000Total12,382 1,198 897 5,932
Data unavailable
How do you do it to a gold standard?C SEGMENTATION, AGE AND CONDITION
42
Example: coefficient of variance (variability) within each segment
16-69
70+
<16
1.6
1.9
1.8
1.3
2.2
1.2
1.7
2.4
1.6
2.4
1.8
2.3
2.9
3.3
2.0
5.0
3.4
SOURCE: Analysis of anonymised person-level linked data from 1 CCG – 2012/13
4.4 2.8 3.4 Total2.0 1.6 1.4 1.7
Data unavailable
Age DementiaLearning disability
SEMIMore than one LTC
CancerOne LTCSevere Physical Disability
Mostly healthy
SEGMENTATION, AGE AND CONDITIONHow do you do it to a gold standard?C
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