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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 CONFIDENTIAL & PROPRIETARY Agenda Context for Innovation New Sources of Data and Changing the Focus of Measurement – a Conceptual Model Using Alternative Data Sources in Risk Modeling CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 2 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 3 Context for Innovation Increasing interest in focusing on healthier members in a population, or members of emerging risk 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” CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 4 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 5 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 6 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 7 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 8 Using New Sources of Data and Changing the Focus in Measurement: A Conceptual Design CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 9 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 10 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 CONFIDENTIAL & PROPRIETARY Social Context © Ingenix, Inc. 11 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 12 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 13 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 14 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 15 Diabetes Intervention Group Levels 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? CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 16 Sleep Problems Intervention Group Levels 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? CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 17 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 18 New Data Sources and Domains: Challenges Consistency in the availability of information across individuals – 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 19 Using New Sources of Data in Risk Modeling CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 20 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 21 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 22 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 23 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. CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 24 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 25 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 CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 26 Questions/Comments CONFIDENTIAL & PROPRIETARY © Ingenix, Inc. 27