geraint_london_slides25jan 2011
TRANSCRIPT
Predictive case modelling in social care and health
www.nuffieldtrust.org.uk
Geraint Lewis FRCP FFPH
Case Finding• NHS predictive models• Models for social careEvaluationRemuneration
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:o Clinician referrals o 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
Average number of emergency bed days
Regression to the mean
0
5
10
15
20
25
30
35
40
45
50
Average number of emergency bed days
- 5 - 4 - 3 - 2 - 1Intense
year + 1 + 2 + 3 + 4
0
5
10
15
20
25
30
35
40
45
50
Average number of emergency bed days
- 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
A&E data GP Practice
data
Outpatient data PARR
Patterns in routine data
Combined Model
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
Name, Address, DOB
Name, Address, DOB
Name, Address, DOB
Name, Address, DOB
10 Million Patient-Years of Data
5 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
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
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
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
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
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)
Actual
Average cost per patient
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
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 incentiveso Procedureso Disease severity
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 aspirinPatient 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 aspirinPatient 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
engageReceptivity
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 aspirinPatient 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