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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


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


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


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%


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


Comment Reason for exclusion

Learning disability

▪ Whether an individual had a learning disability was found to be significant in other sites


Physical disability

▪ Whether an individual severe physical disability for which they received social care was found to be significant in other sites


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


£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

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


40%27%26% 8%

RISK16%37%44%3%

Very highHighModerateLowVery low

0%

0%

Risk:


39

No physicalLTCs

1+ physicalLTC

RISK

Risk distribution of people with and without physical LTCs


24%27%31%4%

RISK37%45%

14%

3%


15%0%


40

No mentalhealth LTCs

1+ mental health LTC

RISK

Risk distribution of people with and without mental health LTCs


12%24%34%2%

RISK36%44%

27%

4%


15%0%


41

Age

16-69

70+

<16

DementiaLearning 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


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


4.4 2.8 3.4 Total2.0 1.6 1.4 1.7

Data unavailable

Age DementiaLearning disability



Mostly healthy

SEGMENTATION, AGE AND CONDITIONHow do you do it to a gold standard?C

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