IM Symposium: VBCM
Doug Thompson PhD
Tom Cavin ASA, MAAA
August 2012
Value Based Care Models
• Delivering quality timely care by aligning member and provider
incentives where focus is shifted from ‘quantity’ of service to
‘value’ of service
• Two models considered here
– Intensive and Extended Medical Home
– Bridges to Excellence Physician Recognition
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Patient Centered Medical Home
• Contractual arrangements with providers, designed to incent
decreases in cost and increases in quality via improved coordination
of care
• The models vary by the breadth of the managed member population
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Bridges to Excellence
• Requires no contracting, only recognition through third party
benchmarking of member biometric data
• Incentives for physicians of $100 per member per annum
• Specialized by condition ‘module’
– Diabetes Care Recognition
– Cardiac Care Recognition
BRIDGES TO EXCELLENCE
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Two Models Two Approaches
• EMH/IMH
– IMH focuses on member identification and risk stratification techniques
• BTE
– Focuses on finding a suitable group for a matched control
 Analysis centered on applying a generalized linear model and setting
classification level contingent on key explanatory variables
 Considerations for propensity scoring and applicability in analysis
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Population Identification:
Treatment and Control
• Key determinants in measuring value center on finding the
appropriate comparison population
– The number of parameters necessary to define the study and control
populations are inversely proportional to the sample size
– Some input from outside sources such as the Care Continuum
Alliance’s: Outcome Guidelines for certain exclusionary criteria
• Consistent definition of your population
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Condition identification
Age bounds
Geography
Benefit Design
• Exclusionary Criteria
– High severity, low frequency exclusions
– Discontinuous Enrollment
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Population Matching
• Is it appropriate in this setting?
– Is your sample treatment a sufficiently randomized grouping or
concentrated in geography, morbidity or some other categorical variable?
– Are there unobserved covariates of ‘x’ not represented in your model?
• Propensity Scoring
– A function created to describe the probability (between 0 and 1) of the
considered outcome
– Intuitively basic categories such as age/gender, similar geographies, and
morbidities would eliminate much differentiation between groups
– Goal with propensity scoring is to isolate the non-intuitive relationships
existing in the observed ‘x’ values and allow for a method by which each
treatment member is matched to their nearest control counterpart
– Assists in eliminating the relative representation bias of ‘x’ dependencies for
the control and treatment group
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Regression Considerations
• The explanatory variables you choose should begin to ‘carve out’
their effects on the dependent variable of choice
– Looking for explanatory variables with low correlation
– Avoid over-fitting your model
• Explanatory variables considered in BTE
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Age/Gender
Geography
DCG Prospective Risk Score
BTE Recognition Flag
• Sampling for validity and fit
– Stratified sampling applied for fitting and testing model
– Measuring actual to residuals at a ‘class’ level
• Measuring Influence
– Cook’s Distance
– Fisher Scores
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Bootstrapping to Find Confidence
Intervals
• Your model data set is larger and you are interested in bounding
your model results without assuming a normal distribution
– Sample with replacement to match your original population counts
– Stratify on your treatment/control indicator
– Re-run regression model and predictor outputs for each bootstrapping
sample created
– Percentile Bootstrap, take the desired percentiles (5th and 95th, for a 90%
confidence intervals) as the bounds for your predictor variable
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Member Identification
• This is a key component of the IMH model (less central to EMH)
– A goal of IMH is to focus management on the members who are expected
to benefit most (as opposed to a broader population)
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HCSC’s Approach to Member
Identification: Tactical
• In the initial “tactical” phase, HCSC’s plans had license to use
whatever member identification algorithm they deemed appropriate
• Approaches:
1.
2.
3.
Select members with high expected future costs based on Verisk
models
Select members with high likelihood of benefitting from medical
management based on internal algorithm
Select members with high prior-year costs
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HCSC’s Approach to Member
Identification: Strategic
• Next (Strategic) phase: Use a single, Enterprise-wide, optimized
member identification approach
1.
2.
3.
Predict which members are most likely to have high costs next year
that could be impacted by PCPs
Consider a wide variety of leading indicators (“predictors”) of future
costs
Use statistical modeling techniques to determine 1) what information
is useful for prediction, 2) how to weight different pieces of information
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Identification Algorithm
• Regression-based algorithm
• Target that the algorithm predicts: Next-year member spend, after
excluding categories not expected to be impactible by PCPs
(based on expert clinical judgment)
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Identification Algorithm Inputs
• Inputs considered include:
– Verisk models (e.g., 18, 26, 56, 51, 71)
– HCSC’s proprietary algorithms for selecting members for medical
management programs
– Clinical Intelligence Rules
– Cost (prior year allowed amount)
– Selected utilization measures (admits, ER, selected procedures and Dxs)
– Rx data
– Behavioral health key measurements
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Identification Algorithm vs. Other
Risk Prediction Models
• This is similar to other prospective risk prediction models (e.g.,
CMS-HCC, CDPS, Verisk), except:
1.
2.
3.
It has a different target (next-year PCP-impactible spend vs. total spend)
It considers a wider variety on inputs, enabling greater prediction
accuracy
It is tailored to HCSC’s own member population
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Key Takeaways
• HCSC has implemented several subtypes of Value Based Care
Models (VBCMs), and now is in the process of evaluating and
improving them
• HCSC has been using advanced analytical techniques to optimize
its VBCM programs, as illustrated in this presentation
– Advanced analytics are essential for accurate program evaluation and
optimal member identification
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