1. Area 4 SHARP Face-to-Face Conference Phenotyping Team – Centerphase Project Assessing the Value of Phenotyping Algorithms June 30, 2011

2. Topics  Centerphase Background  Project Overview  Hypothesis  Research Design  Results to-date  Next Steps

3. Centerphase: Background  CENTERPHASE SOLUTIONS, INC. is a technology-driven services company formed through a collaboration with Mayo Clinic in 2010  The goal is to leverage electronic medical records (EMRs) and clinical expertise from academic medical centers and other research sites to address a broad array of healthcare opportunities  The initial focus is to support enhanced design, planning and execution of clinical trials  Future areas include comparative effectiveness, pharmacoeconomics, compliance and epidemiological studies  Centerphase’s role on the Phenotyping Team is to evaluate the effectiveness (cost and time) of using phenotyping algorithms for identifying patient cohorts

4. It’s About Speed AND Accuracy…

5. Hypothesis The development of phenotyping algorithms and tools can reduce time and cost while maintaining or enhancing quality, associated with identifying patient cohorts for multiple secondary uses including clinical trials and care management.

6. 1.Choose a use case that can provide valuable insights into a real world application 2.Develop a phenotyping methodology (“flowchart”) to identify the patient cohort 3.Generate a random sample of patients from Mayo EMR system based on ICD9-code 4.Conduct algorithm-driven and manual processes in parallel on the sample 5.Compare the time, cost and accuracy of results from the algorithm-driven to manual process Approach

7. Type II Diabetes Mellitus (T2DM): 90-95% of all adult cases of diabetes Multi-stage phenotype representing a combined adaptation of: –The eMERGE Northwestern T2DM algorithm for clinical trial selection and –The group practice reporting options (GPRO) as defined under NCQA for population management under the Southeast Minnesota Beacon project Diagnosed and Undiagnosed Diabetes Source: 2005–2008 National Health and Nutrition Examination Survey Diabetes is a growing epidemic in this country: 25.8 million (8.3% of population) have diabetes. Last year, 1.9 million new cases alone in population of 20 years or older (CDC). Initial Use Cases

8. Use Cases Case 1: Care Management Identify all high risk patients in a pool of 500 cases Case 2: Clinical Trial Identify patients that are good candidates for a study

9. Phenotype Methodology T2DM ICD9 Code Screen 1: Age Screen 2: Medications Screen 3: Labs & Vitals Patient Cohort eMERGE Algorithm for T2DM Beacon Criteria for categorizing patient risk Identified as high risk or “RED” patient Note: All screens based on two-year measurement period 1/1/09 – 12/31/10 Blood Glucose: HbA1c > or = 9 Cholesterol: LDL > 130 Blood pressure: Systolic > 160 & Diastolic > 100 If ANY of the most recent values exceed allowable levels OR ANY of these elements has not been captured in the measurement period, patient is classified as high risk or RED Identified as T2DM patient

10. Randomly generate ONE sample set of patient records from database: Based on T2DM ICD9 codes from at least 2 visits during measurement period Sample Patient Records Screens 1 -3 Patient Result Set Patient Result Set Manual Process Algorithm-Driven Process Compare time, cost and accuracy of results Study coordinator (SC) conducts manual review of patient charts, and monitors activity time Programmer develops and runs algorithm to query records, and monitors development and run time Research Design

11. Validation and Evaluation Process “Dry Run” 20 Charts 500 Charts Step 1 Review each chart for manual and algorithm processes to identify any screening errors Confirm approaches are consistent Refine procedures as appropriate Step 2 Start with 50; review results and adjust if necessary Complete manual reviews Collect time, cost and patient result sets Conduct data queries Analyze results and evaluate / compare performance of methods Step 1 CompletedStep 2 Underway

12. And How Did We Do…

13. Initial Results 50 Charts reviewed Manual process* Identified 10 “Red” (high risk) patients Required 11.5 total hours** Algorithm-driven process* Identified 8 “Red” patients ­ All 8 were identified in manual process ­ Missed 2 patients (false negatives) Required 7.4 hours** For the purposes of this presentation, the following analysis extrapolated these results to evaluate the impact on 500 patients…. Actual findings will be reported upon completion of 500 charts * Currently evaluating accuracy of both manual and algorithm-driven processes ** Includes time for manual validation of all Red charts

14. Preliminary Analysis: Case 1 - Care Management Extrapolated to 500 Charts based upon Initial 50 Charts Algorithm 80% Less Costly Algorithm 90% Faster Note: Costs and hours reflect time for secondary manual validation of all Red charts identified through both processes

15. Preliminary Analysis: Case 2 – Clinical Trials Extrapolated to 500 Charts based upon Initial 50 Charts Manual Method Algorithm Method 80% fewer charts to review Over 30 hours saved Preliminary Comparison: Algorithm-driven to Manual process Almost 50% cost savings Note: Costs and hours reflect time for secondary manual validation of all Red charts identified through both processes

16. Preliminary Conclusions and Next Steps Initial takeaways: If extrapolated results are validated…. Applying algorithms to identify subsets of patients can save time and be cost effective Algorithms can be most effective when search for larger numbers of patients More work needs to evaluate relative accuracy Sample Patient Records Screens 1 -3 Patient Result Set Patient Result Set Manual Process Algorithm-Driven Process Next steps: Complete review of 500 charts Document results in white paper or manuscript