1. Risk Stratification and Model Development:
Potential of “new” data and Predictive Modelling
Stephen Sutch, MAppSc, BSc.
Doctoral Student
Johns Hopkins Bloomberg School of Public Health
Baltimore, Maryland 21205 USA
ssutch@jhsph.edu
Presented at Nuffield Trust
13 June 2012

2. Themes
• Risk stratification of whole population
• Improving the use of clinical data in predictive
modelling
– Use of other data, Rx, Labs, frailty ….
• Build models for specific purposes/outcomes
• Classification and Predictive Modelling, contextual
information
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3. Working Definitions
• Case mix / risk adjustment (RA) - taking health
status / risk into consideration for health care
finance, payment, provider performance assessment
and patient outcome monitoring.
• Predictive modeling (PM) - prospective (or
concurrent) application of risk measures and
statistical technique to identify “high risk” individuals
who would likely benefit from care management
interventions.
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4. The risk measurement pyramid
Management Applications
High Case-
Disease Management
Burden Disease Needs
Management Assessment
Single High Practice
Impact Resource
Disease Management Quality
Improvement
Users Payment/
Finance
Users & Non-Users
Population Segment
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5. Using Predictive Modeling to Assign Persons
Within the Care Management Pyramid
5%
Level 3
High risk Intensive Case and Disease
with multiple Management
chronic illness
15%
Level 2
Moderate risk patients
with single chronic
Health Coaching and Lifestyle
illness or risk factors Management
80%
Level 1 Health Education and
Low risk Promotion
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6. Purposes of Predictive modeling
• Clinical prediction - Individual patient, to improve
clinical decision-making
• Population predictive models - Groups of patients,
to forecast healthcare trends and identify
candidates for healthcare interventions (e.g. DM
programs)
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7. Key non statistical considerations for model
selection if it is to be used administratively
• Transparency
– How easily can the model be understood and
explained?
• Clinical Texture
– Does the system make sense to clinicians?
• Flexibility
– Does the system support a range of applications?
• Customisable
– Adjusts to local data, new models easy to derive and
validate?
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8. Value of Predictive Modeling
Population of Persons Across Two Year Period
Prior Predicted
High Cost High Risk
Year-1 Year-2
(Prior Use) (Using Year-1
Data)
Actual
High Cost High Risk,
Year-2 Current Costs
Not High Low, Future
Risk Costs High
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9. Data
• Secondary Care
– Acute Hospitals, Inpatient, Outpatient,
– Mental Health, Rehabilitation, Community care
– Diagnoses, Procedures
• Primary Care
– Attendances, Diagnoses, Prescribing
– Labs, Examinations, Findings, Dispensing
• Patient Data
– Risk factors, lifestyle factors, Health Status, Rx
Possession, Self Care
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10. Distribution of READ Codes: Illustration
Drugs
39%
Other Findings
2% 23%
Clinical findings
8%
Administration
Procedures
11%
17%
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11. GP diagnosis
Coding and Drug prescribing
Diagnosis coding & drug PCT data US data
prescribing by GP
Prevalence Diags/Drugs Prevalence Diags/Drugs
3.60% Dx + Rx 2.67% Dx + Rx
Asthma 8.69% 0.71% Dx Only 9.77% 1.48% Dx Only
4.38% Rx Only 5.63% Rx Only
0.18% Dx + Rx 0.30% Dx + Rx
Congestive Heart Failure 2.52% 0.05% Dx Only 1.85% 0.85% Dx Only
2.29% Rx Only 0.70% Rx Only
1.36% Dx + Rx 1.28% Dx + Rx
Depression 6.23% 0.25% Dx Only 10.38% 0.66% Dx Only
4.62% Rx Only 8.43% Rx Only
0.60% Dx + Rx 2.77% Dx + Rx
Diabetes 3.91% 3.25% Dx Only 5.45% 2.23% Dx Only
0.06% Rx Only 0.44% Rx Only
1.28% Dx + Rx 5.23% Dx + Rx
Hyperlipidemia 5.32% 0.22% Dx Only 14.87% 6.85% Dx Only
3.82% Rx Only 2.78% Rx Only
4.53% Dx + Rx 8.78% Dx + Rx
Hypertension 13.09% 0.45% Dx Only 18.95% 6.05% Dx Only
8.11% Rx Only 4.12% Rx Only
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12. Stratifying Whole Populations
• Multimorbidity
– Understanding and measuring
• Classification of health need
– Stratification of disease popultions
• Multiple purposes
• Validation on whole populations
– Generalisable?
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13. Co-Morbidity is key – Multiple morbidities
encountered in UK GP practices
Average consultation in elderly involves someone with 1.9 QOF diseases
and 6.7 chronic diseases using ACG/EDC chronic disease designations
Source: Salisbury et al. From GPRD data, 488 practices 2005-2008
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14. Co-morbidities are the norm for those with
common “index” chronic conditions (US 65+)
Diabetes 9% 22% 21% 21% 27%
Heart Disease 11% 21% 25% 24% 19%
Arthritis 12% 22% 23% 22% 21%
Hypertension 17% 24% 23% 20% 16%
0% 20% 40% 60% 80% 100%
Single Condition Condition + 1 Condition + 2 Condition + 3 Condition + 4+
Source: From US Medicare (65+) data . Partnership for Solutions, Johns Hopkins University
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15. Risk Stratification – Endocrine Disorders
Source: Ashton Leigh Wigan PCT, Pilot Project
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16. Case Management and Disease Management:
Identification of individuals at risk
• Disease Management, Wellness Program Identification
– E.g. Diabetes, Hypertension Pharmacy Gaps, Poorly
Controlled Asthma, Untreated Schizophrenia
• Case Management Program Identification
– E.g High Medical Needs, Emerging Risk, High Risk for Poor
Coordination, Potential Home Health Needs
• Pharmacy Management Program Identification
– E.g. Poly-pharmacy and Medication Gaps / No Ambulatory
Care, High Rx Users
• Utilization Management Program Identification
– E.g. High Risk for Hospitalization, Emergency Room for
Primary Care, Risk for High Utilization
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17. Identify high risk members of population based
on multi-morbidity oriented “Relative Risk Score”
• Risk predicted to increase
• Total costs predicted to increase
• 7 chronic conditions
• 13 doctors
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18. Patient risk information in support of GPs,
Community Matrons
• Numerous co-morbidities
• At risk for future
hospitalization
• ER Visit with no admission
• Poly-pharmacy use
• Tobacco Use
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19. Patient View:
Comprehensive Patient Clinical Profile
Context for Forming Care Management Strategies.
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20. Current Challenges
• Recognizing Multimorbidity
– Recording of diagnoses, patterns
• Cost data
• Pharmacy data
– Prescribed v Dispensed (possession?)
• Integrated records
– GP, OP, A&E, IP, MH, Social Care
• Other data
– Functional status, Health Risk factors, Health
Status, Individual Data
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21. The Future
• Ensuring Risk Stratification is fit for purpose
• Complimenting case management
• A means to an end, not an end in itself, supporting
effective care management and equity
• Integrated care, integrated data and information
support
• Understanding individuals’ morbidity burden
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