Post on 19-May-2015
transcript
Predictive case modelling
in social care and health
www.nuffieldtrust.org.uk
Geraint Lewis FRCP FFPH
t: 020 7631 8450
e: info@nuffieldtrust.org.uk
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
10 Million Patient-Years
of Data
5 Million Patient-Years
of Data
5 Million Patient-Years
of Data5 Million Patient-Years
of Data
5 Million Patient-Years
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
Year 1 Year 2 Year 3
���� 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
Year 1 Year 2 Year 3
���� 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
Year 1 Year 2 Year 3
���� 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)
Overcoming regression to the mean using a control group (2)
Start of intervention
Overcoming regression to the mean using a control group (3)
Start of intervention
Start of intervention
Overcoming regression to the mean using a control group (4)
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
geraint.lewis@nuffieldtrust.org.uk
t: 020 7631 8450
e: info@nuffieldtrust.org.uk
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
Details Model predicts
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
Details Model predicts
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
Details Model predicts
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
Details Model predicts
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