Geraint Lewis Ageing Well presentation

Predictive case modelling

in social care and health

www.nuffieldtrust.org.uk

Geraint Lewis FRCP FFPH

t: 020 7631 8450

e: [email protected]

www.nuffieldtrust.org.uk

The Nuffield Trust

Case Finding

• NHS predictive models

• Models for social care

Evaluation

Remuneration

Why Predictive Modelling?

• BMJ in paper* in 2002 showed Kaiser Permanente in California seemed to provide higher quality healthcare than the NHS at a lower cost

*Getting more for their dollar: a comparison of the NHS with California's Kaiser Permanente BMJ 2002;324:135-143

• Kaiser identify high risk people in their population and manage them intensively to avoid admissions

• Inaccurate Approaches:

– Clinician referrals

– Threshold approach (e.g. all patients aged >65 with 2+ admissions)

Frequently-admitted patients

0

5

10

15

20

25

30

35

40

45

50

- 5 - 4 - 3 - 2 - 1 Intense

year+ 1 + 2 + 3 + 4

Ave

rag

e n

um

ber

of

emer

gen

cy b

ed d

ays

Regression to the mean

0

5

10

15

20

25

30

35

40

45

50

Ave

rag

e n

um

ber

of

emer

gen

cy b

ed d

ays

- 5 - 4 - 3 - 2 - 1Intense

year+ 1 + 2 + 3 + 4

0

5

10

15

20

25

30

35

40

45

50

Ave

rag

e n

um

ber

of

emer

gen

cy b

ed d

ays

- 5 - 4 - 3 - 2 - 1 Intense

year

+ 1 + 2 + 3 + 4

Emerging Risk

Kaiser Pyramid

The Pyramid

represents the

distribution of

risk across the

population

Small numbers of

people at very high

risk

Large numbers

of people at

low risk

[Size of shape is proportional to number of patients]

Inpatient

data

Inpatient

data

A&E dataA&E data GP Practice

data

GP Practice

data

Outpatient

data

Outpatient

data PARR

Patterns in routine data

Combined

Model

Census

data

Census

data

Scotland

• SPARRA

• SPARRA-MD

Wales

• PRISM model

• Welsh Predictive Risk

Service

J7KA42

J7KA42

J7KA42

J7KA42

J7KA42

J7KA42 76.4

131178 76.4

Encrypted,

linked data

Decrypted data

with risk score

attached

131178

131178

131178

131178

�� Inpatient

�� Outpatient

�� A&E

�� GP

�� Inpatient

�� Outpatient

�� A&E

�� GP

Name, Address, DOB

Name, Address, DOB

Name, Address, DOB

Name, Address, DOB

10 Million Patient-Years

of Data


of Data


of Data


of Data5 Million Patient-Years

of Data


of Data

Development Validation

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

Year 1 Year 2 Year 3

Development

Sample

�� Inpatient

�� Outpatient

�� A&E

�� GP

�� Inpatient

�� Outpatient

�� A&E

�� GP

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

Development

Sample


�� Inpatient

�� Outpatient

�� A&E

�� GP

�� Inpatient

�� Outpatient

�� A&E

�� GP

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

J7KA42

YH8TPP

G8HE9F

3LWZ67

2NX632

LG5DSD

3V9D54R

Development

Sample


�� Inpatient

�� Outpatient

�� A&E

�� GP

�� Inpatient

�� Outpatient

�� A&E

�� GP

A89KP5

833TY6

I9QA44

85H3D

6445JX

233UMB

RF02UH

A89KP5

833TY6

I9QA44

85H3D

6445JX

233UMB

RF02UH

A89KP5

833TY6

I9QA44

85H3D

6445JX

233UMB

RF02UH

Validation

Sample True

Positive

False

Positive

False

Negative

True

Negative


�� Inpatient

�� Outpatient

�� A&E

�� GP

�� Inpatient

�� Outpatient

�� A&E

�� GP

A89KP5

833TY6

I9QA44

85H3D

6445JX

233UMB

RF02UH

A89KP5

833TY6

I9QA44

85H3D

6445JX

233UMB

RF02UH

Using the Model

Last Year This Year Next Year

�� Inpatient

�� Outpatient

�� A&E

�� GP

�� Inpatient

�� Outpatient

�� A&E

�� GP

Distribution of Future Utilisation

£0

£500

£1,000

£1,500

£2,000

£2,500

£3,000

£3,500

£4,000

£4,500

0 10 20 30 40 50 60 70 80 90

Predicted Risk (centile rank)

Act

ual

Ave

rag

e co

st p

er p

atie

nt

NHS Combined Model

Clinical Profiles

Tackling the Inverse Care Law

Developing Business Cases

How the output of predictive

models are used• Case Management

• Intensive Disease Management

• Less Intensive Disease Management

• Wellness Programmes

Potential Misuses

� Dumping

� Cream-skimming

� Skimping

Health Needs

• Diagnoses

• Prescriptions

• Record of Health

Contacts

Social Care Needs

• Client group

• Disabilities

• Record of care

history

Health Service Use

• GP visits

• Community care

• Hospital care

Social Care Use

• Residential care

• Intensive home

care

• Direct payments

Predictive

Model

PAST

FUTURE

Evaluation of Preventive Care

5

Start of intervention

Overcoming regression to the mean using a control group (1)







Person-Based Resource Allocation

• Historically, GP practice budgets set on area-

based variables

• New approach is person-based

• Exclude certain variables to avoid perverse

incentives

– Procedures

– Disease severity

[email protected]

t: 020 7631 8450

e: [email protected]

Model

predicts:

Details

Examples

Trend

Model

predicts:Cost

Details Model predicts

which patients

will become

high-cost over

next 6 or 12

months

Examples Low-cost

patient this

year will

become high-

cost next year

Trend

Model

predicts:Cost Event


which patients

will become

high-cost over

next 6 or 12

months

Model predicts

which patients

will have an

event that can

be avoided

Examples Low-cost

patient this

year will

become high-

cost next year

Patient will be

hospitalized

Patient will

have diabetic

ketoacidosis

Trend

Model

predicts:Cost Event Actionability


which patients

will become

high-cost over

next 6 or 12

months

Model predicts

which patients

will have an

event that can

be avoided

Model predicts

which patients

have features

that can readily

be changed

Examples Low-cost

patient this

year will

become high-

cost next year

Patient will be

hospitalized

Patient will

have diabetic

ketoacidosis

Patient has

angina but is

not taking

aspirin

Patient does

not have

pancreatic

cancer

(Ambulatory

Care Sensitive)

Trend

Model

predicts:Cost Event Actionability Readiness to

engage


which patients

will become

high-cost over

next 6 or 12

months

Model predicts

which patients

will have an

event that can

be avoided

Model predicts

which patients

have features

that can readily

be changed

Model predicts

which patients

are most likely

to engage in

upstream care

Examples Low-cost

patient this

year will

become high-

cost next year

Patient will be

hospitalized

Patient will

have diabetic

ketoacidosis

Patient has

angina but is

not taking

aspirin

Patient does

not have

pancreatic

cancer

(Ambulatory

Care Sensitive)

Patient does

not abuse

alcohol

Patient has no

mental illness

Patient

previously

compliant

Trend

Model

predicts:Cost Event Actionability Readiness to

engage

Receptivity


which patients

will become

high-cost over

next 6 or 12

months

Model predicts

which patients

will have an

event that can

be avoided

Model predicts

which patients

have features

that can readily

be changed

Model predicts

which patients

are most likely

to engage in

upstream care

Model predicts

what mode and

form of

intervention

will be most

successful for

each patient

Examples Low-cost

patient this

year will

become high-

cost next year

Patient will be

hospitalized

Patient will

have diabetic

ketoacidosis

Patient has

angina but is

not taking

aspirin

Patient does

not have

pancreatic

cancer

(Ambulatory

Care Sensitive)

Patient does

not abuse

alcohol

Patient has no

mental illness

Patient

previously

compliant

Patient prefers

email rather

than telephone

Patient prefers

male voice

rather than

female

Readiness to

change

Trend

Documents

Geraint Lewis Ageing Well presentation