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IM Symposium: VBCM

Doug Thompson PhD Tom Cavin ASA, MAAA August 2012. IM Symposium: VBCM. 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

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IM Symposium: VBCM

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  1. Doug Thompson PhD Tom Cavin ASA, MAAA August 2012 IM Symposium: VBCM

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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 • Condition identification • Age bounds • Geography • Benefit Design • Exclusionary Criteria • High severity, low frequency exclusions • Discontinuous Enrollment

  7. 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

  8. 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 • 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

  9. 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

  10. 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)

  11. 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: • 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

  12. HCSC’s Approach to Member Identification: Strategic • Next (Strategic) phase: Use a single, Enterprise-wide, optimized member identification approach • 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

  13. 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)

  14. 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

  15. Identification Algorithm vs. Other Risk Prediction Models • This is similar to other prospective risk prediction models (e.g., CMS-HCC, CDPS, Verisk), except: • 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

  16. 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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