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Expanding the Reach of Predictive Models:
Using Clinical, HRA, and Consumer Data
Dan Dunn, PhD, Senior VP of R&D, Ingenix
The National Predictive Modeling Summit
December 13, 2007 ● Washington, DC
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Agenda
 Context for Innovation
 New Sources of Data and Changing the Focus of
Measurement – a Conceptual Model
 Using Alternative Data Sources in Risk Modeling
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Context for Innovation
 Information tools to support care and health
management – current state:
 Primary focus is on disease populations or individuals of
moderate to higher risk
 Clinical information and concepts supported by administrative
medical and pharmacy claims, some use clinical data
 Outputs include measures of risk, some add gaps in care
 Many tools add reporting and some cohort modeling capabilities
 Limited use of alternative sources of data
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Context for Innovation
 Increasing interest in focusing on healthier members in
a population, or members of emerging risk
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Extend interventions to the lower end of the risk spectrum
Improve wellness, healthy behaviors and lifestyle
Improve attitudes on health
Intervene “upstream” in a more pro-active way, e.g., prediabetes, and “pre-pre”-diabetes
 Interest in creating a personal health record (PHR)
 Integrates information from a number of data sources to provide
a multi-dimensional profile of an individual’s health
 Support interventions in a more complete way – from
“end-to-end”
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Changing focus for information solutions
Increasing demand for information solutions that support
interventions for relatively healthier members or those of
emerging risk
Members without medical or
pharmacy claims
Moderate PM Risk
- chronic conditions
- some co-morbidities
- recent history, stable
Higher PM Risk
- higher cost conditions
- multiple co-morbidities
- recent acute events
0
Lower PM Risk
- smokers,
- sleep problems,
- obese, inactive
Members of
emerging clinical risk
- pre-diabetic
- onset of chronic
condition
“Sweet spot” for current state of predictive
modeling (PM) is patients of moderate to higher
risk – supporting more traditional disease and
care management
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Support “end-to-end” intervention solutions
Identification and Stratification
Medical Claims
Rx Claims
Demographics
Clinical Data
HRAs
Consumer Data
Risk Prediction
Clinical Profile
Health Behaviors
Segmentation
Match Patients to Programs
Activation
Support for Engagement and
Intervention
Intervention and
Management
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Change of Focus and Requirements
 Support analysis of healthier populations and emerging patients
 Leverage existing and new sources of data, including HRA/self
report and consumer information
 Integrate these different sources of data in innovative ways:
 Improve on existing concepts, e.g., measures of future risk
 Support new domains of measurement, including behaviors, attitudes,
and social context
 Accommodate different data scenarios – consistent data
availability unlikely across and within populations
 Create a useful context for analysis
 We are pulling together even a larger number of concepts and
variables
 Add value by developing a context – organize information for analysis,
presentation, and operations – in a flexible way
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New Information and Domains – Opportunities
 Address disease and lifestyle risk
 Whole-person approach to health management – across the full
continuum of health and risk
 Complement and expand opportunities to address further domains
of health that they may not be concentrating on
 Expand models of clinical, risk and cost with the addition of new
dimensions and sources of data
 Prediction based on a set of new concepts
 Bring behavior and attitudes to the equation
 Bring social and consumer variables to bear on risk
 Tailor interventions based on a central repository of data that has
key variables associated with outreach, intervention and outcome
 Support a Personal Health Record – informed by multiple sources of
data, describing key dimensions of health
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Using New Sources of Data and
Changing the Focus in Measurement:
A Conceptual Design
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A model of health
 What model of health can be used to structure a more
complete approach?
 Wilson Cleary model (1995) of HRQOL is helpful because it
represents a full picture of health
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Operational Model of Health: Concepts and Domains
 A more complete approach
requires methods and
outputs to measure
individuals along the
different domains that
describe health
 Domains that support
identification/stratification,
segmentation, and
activation
 Intervention Groups – a
context for integrating the
five domains
 Note – prediction and “risk”
are only one component
Clinical
Health
Behaviors
Risk and
Severity
Intervention
Groups
Health
Attitudes
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Social
Context
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Health Model Concepts and Domains
 Information/domains to support identification and stratification:
 Clinical
– A clinical description of an individual, based on diagnostic and
procedural concepts – from claims, clinical results and self report
– Examples – diabetes, pre-diabetes, CHF, depression, sleep
disorder, obesity, propensity for a clinical condition
 Risk or Severity
– Predictive model risk, condition severity, self-report health status
– Examples – relative risk, condition episode severity, health status
 Behavior (Healthy behaviors)
– HRA and claims-based measures of behavior, behaviors inferred
from consumer data
– Examples – smoking, physical activity, compliance with chronic
and preventive quality rules (gaps in care), prescription adherence
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Health Model Concepts and Domains
 Further segmentation and activation can be supported by:
 Attitudes about Health
– Readiness to change, activation and perceived social support
 Social Context (Social Score)
– Ascribed and achieved status, plus consumer-oriented variables
– Examples – Age, gender, race ethnicity, education, income, SES
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What will Intervention Groups do?
 Provide a context to organize and focus information – in a way
that is consistent from both a clinical perspective and also
from an operational perspective
 Describe both clinical and wellness concepts – e.g., diabetes,
smoking, sleep disorder
 Have defined levels – that map to potential cohorts for
intervention – e.g., level of acuity; categories of smoking
status; level of physical activity
 Have rules and algorithms that assign an individual to an
Intervention Group – and further to a level
 Incorporate methods to accommodate different data
availability scenarios for each individual
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Examples of Intervention Groups
Disease Management
Wellness
Asthma/COPD
Smoking/Tobacco
CAD
Physical Activity
CHF
Nutrition
Diabetes
Safety
Back, Joint, Surgical Option Problems
Stress
Mental Health (Depression)
Safety
Obesity
Alcohol Abuse
Sleep Problems
Sexual Risk Activity
Pain Syndromes
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Diabetes
 Intervention Group Levels
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Severe Diabetes
Moderate Diabetes
Mild Diabetes
Pre-Diabetes
“Pre-Pre-Diabetes”
No Diabetes
 Information used to identify and stratify
 Medical and Rx: diagnoses, drug therapies
 Predictive model risk
 HRA and consumer: self-report, obesity, behaviors consistent with
propensity for diabetes
 Map relevant clinical and family history to further define levels
 Ask ourselves: If I run a diabetes management program, what would
I want to understand about my members?
 Severity of diabetes, propensity
 Associated health behaviors, co-morbid conditions and attitudes
 What factors are associated with engaging members?
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Sleep Problems
 Intervention Group Levels
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Severe sleep problems
Moderate sleep problems
Mild sleep problems
No sleep problems
 Information used to identify and stratify
 Medical and pharmacy: diagnoses, drug therapies for treatment,
diagnostic tests
 Predictive model risk
 HRA: self-report, sleep problem questions, medication self report
 Ask ourselves: If I run a program for sleep problems what would I
want to understand about my members?
 Severity of sleep problem
 Other behaviors, conditions and attitudes associated
 What factors are associated with engaging members?
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Outputs
 Summary and detail results for an individual along each
of the domains
 Information, reports and views centered around the
concept of an Intervention Group
 with links between a patient, a group, their level
 detailed information supporting:
–
–
–
–
Intervention Group assignment
appropriate segmentation
activation for intervention
the intervention itself
 Risk scores and other summary measures
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New Data Sources and Domains: Challenges
 Consistency in the availability of information across
individuals –
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most will have claims
some will have HRAs and/or consumer data
clinical lab results may be available
timeliness of the information
 Opportunities for risk models – leveraging different
types of information
 Creating a flexible context for using this information – it
translates in different ways depending on the
appropriate focus for a patient and the domains
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Using New Sources of Data in Risk Modeling
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Measuring Health Risk – Overview
Markers of Risk
· Demographics
· Medical Claims
· Rx Claims
· HRA
· Lab Results
· Consumer
Data Inputs
Outputs
Disease
Prevalence, CoMorbidities,
Complications
Grouping of Inputs to
support Disease
Identification and
Disease Severity (e.g.,
Episodes of Care)
Complete Member
Risk Profile
Combine Profile and
Risk Results to
Complete Member
Profile
Condition-Based
Risk Markers
Grouping of Diseases
and Conditions into
Clinically
Homogeneous Risk
Marker Categories
Weighting of
Profile to Compute
Risk
Apply Weights
Measuring
Contribution of each
Marker to Overall
Risk
Service-Based
Risk Markers
- High Acuity Events
- Moderate/Lower
Risk Markers
- Rx Markers
Member Clinical
Profiles
Array Markers for
each Member to
Create a Clinical Risk
Profile
Translating Markers into Risk Measures
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New sources of data in risk modeling*
(*in addition to administrative claims and enrollment)
 HRA surveys
 What it adds
– Clinical indicators – e.g., self report of a condition not observed
in claims
– Overall assessment of health status
– Behaviors that indicate propensity for a higher risk clinical
condition
 Modeling approach
–
–
–
–
New indications for disease risk markers
Propensity-based markers of risk – e.g., likelihood of diabetes
Behaviors, other – smoking, obesity
Estimate risk weights for new markers – use to adjust risk score
 Challenges
– Data availability and timeliness
– Reconciling conflicting information
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New sources of data in risk modeling*
(*in addition to administrative claims and enrollment)
 Clinical lab results
 What it adds
– Condition severity – e.g., organ function tests and cancer
tumor/stage diagnostics
– Trends in levels
 Modeling approach
– Add lab-result based risk markers to a model
– Estimate risk weights for new markers – use to adjust risk score
 Challenges
– Data availability
– Timing
– Benefits a relatively small percentage of population – although
impact can be significant for these patients
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Using lab results in risk modeling
Lab Results and Prediction
Prediction Difference
($ PMPM)
Added risk indicated by lab result markedly outside of normal range
1,800
1,600
1,400
1,200
1,000
800
600
400
200
0
-200
Albumin
ALP
CRP
Chol Ratio
CA-125
HbA1c
Lab performed in last 90 days. Comparison of predicted (Impact Pro without Lab Model)
and actual PMPM and relationship of prediction error with lab results ranges (“Difference”).
Only most extreme lab result findings included on slide.
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New sources of data in risk modeling*
(*in addition to administrative claims and enrollment)
 Consumer data
 What it adds
– Social Context – income, education
– Consumer habits – purchases, auto registration
– Categories – groupings of individuals to
 Modeling approach
– Categories and derived variables
– Test risk weights for new markers – use to adjust risk score?
 Challenges
– Data availability
– Timing
– TBD on general contribution to predictive accuracy on top of
claims – likely most helpful for lower risk
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Summary
 Information tools to support care and health management –
current state:
 Primary focus: disease populations, moderate to higher risk
 Limited use of alternative sources of data
 Mostly support ID & stratification
 Use of alternative data sources both provides new opportunities
and requires a new conceptual idea about “predictive modeling”
 More complete view of the patient
 Supporting the full cycle of care and health management, including
segmentation, activation and the intervention itself
 Focus on healthier individuals and wellness programs is not best
supported by a risk “score” – but by a multi-domain description of
that individual
 Challenges – consistent availability of data and creation of a
context that supports operational realities
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Questions/Comments
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