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How real data will transform predictive models


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How real data will transform predictive models

  1. 1. How Real Data Will Transform Predictive Models Individualized, Automated Care Management<br />Charles DeShazer, MD<br />VP, Quality, Medical Informatics & Transformation<br />Dean Health System<br />Madison, WI<br />
  2. 2. Key Industry Assumptions<br />Premise: Advanced Predictive Modeling leveraging EHRs will be essential to evolving care management to the next level<br />Drivers of transformation<br />Current cost inflation curve is unsustainable<br />Payers (esp CMS) are moving towards paying for value rather than volume<br />EHR will become a standard tool <br />Quality will become not only the “ticket to play” but also a key basis of competition (value = quality/cost)<br />Primary care will be the engine for quality<br />
  3. 3. Challenges<br />
  4. 4. Complex Case Management<br />1000 Lives<br />25%<br />Disease/Demand Management<br />14,000 Lives<br />50%<br />15,000 Lives<br />15%<br />Health<br />Mgmt<br />70,000 Lives<br />10%<br />Population vs. Costs vs. Interventions<br />Example of 100,000 People in a Population<br /> % of <br />Cost<br />% of<br />Population<br />1%<br />14%<br />15%<br />70%<br />
  5. 5. 24 hours in the life of a PCP<br />“The Impending Collapse of Primary Care Medicine and Its Implications for the State of the Nation’s Health Care,” a report from the American College of Physicians, 2006<br />Yarnall KS, et al. Primary care: is there enough time for prevention? Am J Public Health 2003; 93:635 <br />Ostbye T, et al. Is there time for management of patients with chronic diseases in primary care? Ann Fam Med 2005; 3:209<br />
  6. 6. Facts per Decision<br />
  7. 7. Next generation care management will require managing costs (risk) and improving quality by managing chronic and complex conditions (and the data about those conditions)<br /><ul><li>Predictive modeling is about identifying these patients early through data analysis and proactively providing resources and guidance to reduce/eliminate risk and manage conditions
  8. 8. PM + EHR data will enable complex, proactive clinical decision making otherwise not feasible
  9. 9. You will have to fix the PCP’s day in the process</li></li></ul><li>Ingredients for Transformation<br />
  10. 10. If we assume the hard work will be done…<br />Cultural change<br />Shift from paying for volume to paying for value<br />Physician compensation<br />Physician leadership<br />Patient engagement and activation<br />Effective accountability and governance structure<br />PCMH + ACO<br />Then the stage is set for transformation…<br />
  11. 11. Meaningful Use <br />Creating infrastructure for transformation of healthcare over next 3-5 years<br />Stage 1: Data capture in structured format<br />Stage 2: Advanced clinical processing<br />Stage 3: Improved outcomes<br />Important aspects for Predictive Modeling<br />More electronic clinical data, more structured data<br />Problem List Management<br />Clinical Decision Support<br />Patient Health Record & sharing data with patients/family<br />
  12. 12. ICD-10<br />One of the most comprehensive regulatory changes in the history of healthcare in the US<br />Unlike MU, it is an unfunded regulatory event<br />Replaces 30 year old ICD-9-CM, which is outdated and lacks clinical granularity<br />Important aspects for Predictive Modeling<br />Provides granularity to diagnostic information that should greatly enhance predictive models<br />Improved ability to specify and measure healthcare services<br />Enable better integration of predictive modeling and clinical decision support<br />Richer data structures for research<br />
  13. 13. ICD-10 Asthma Codes<br />More granular clinical information will enhance predictive models as well as enable real-time program referrals especially when followed serially and combined with other data.<br />
  14. 14. EHR Data for Predictive Modeling<br />Predictive models will be enhanced in several ways by leveraging comprehensive, real-time data for better models and then tightly coupling and embedding this knowledge in care processes<br />Eliminate claims lag<br />Real-time identification of changes in status<br />Service utilization enhancement of models<br />More accurate and comprehensive coding (ICD-10) due to clinical use of data (MU) will enhance models<br />Feedback of care process (validation of models)<br />HealthCare Partners Medical Group use of PM for high need patients<br />Created comprehensive care center and homecare team informed by PM for highest need patients<br />This occurred after optimizing transitions of care and use of hospitalists<br />Reduced hospital use by 20% and saved the system $2 million per year for every 1,000 patients<br />Source: Health Affairs 30, No.3 (2011): 416-418<br />
  15. 15. Next Generation: Individualized Guidelines<br />Individualized guidelines outperform general guidelines<br />Patients with complex clinical profiles may not fit easily into well-defined guideline categories<br />Individualized guidelines take into account all of an individual’s information (e.g., lab values, biomarkers, demographics, history, medications, etc.) as well as the continuous nature of risk factors and expected benefit from treatments.<br />David M. Eddy, MD, PhD, from Archimedes, San Francisco, California, conducted a person-specific, longitudinal analysis of participants in the Atherosclerosis Risk in Communities study, which included 15,792 patients between the ages of 45 and 64 years.<br />Compared with patients simulated to receive random care, the researchers found that individual guidelines based on the CV Guidelines Calculator could reduce MIs and strokes at the same rate as JNC 7 guidelines, but at a 67% cost savings, or for the same medical costs, individualized guidelines could prevent 43% more MIs and strokes than JNC 7 guidelines.<br />Source: Ann Intern Med May 3, 2011 154:627-634<br />Model is being tested at Kaiser Permanente in conjunction with their EHR & panel management system.<br />Model is only feasible with a full EHR. This would enable automated, proactive and near real-time individualized care management.<br />Addition of genomic data will enhance models tremendously.<br />