1. PREDICTIVE ANALYTICS IN AN ACO WORLD OSF HEALTHCARE EXPERIENCE OCTOBER 2014

2. Various Predictors Future resource utilization- DxCG, HCC Readmission

3. Resource Utilization DxCG, via Verisk, is used for identifying enrollees for Health Management programs. Health Management is a consultative service provided by a team of physicians, nurses, behavioral health specialist and coordinator.

4. Health Management Results

5. Interventions Self-management skills and motivation Care gap closure- identified in Verisk Provider coordination Social and environmental assistance Advance Care Planning Reductions in ED visits and hospitalizations Increase in office visits and pharmacy costs

6. Ambulatory Care Management Utilized similar risk scores Delivered rosters to PCPs to identify high risk and encourage referrals Direct outreach to high risk score patients Some patients engage quickly, some are optimally treated, some do not engage Risk scores are directionally sound and a good starting point

7. PREDICTIVE MODELING OSF HEALTHCARE EXPERIENCE OCTOBER 2014 30 Day Readmissions

8. Predictive Modeling What does the model predict ? How accurate it is ? OR And how do you know? C-Statistics, AUC, PPV, MAPE, R 2

9. OSF Healthcare Predictive modeling 9 Our Dilemma not about understanding the population risk But about understanding WHO is at high risk Applicable to Cost of care 30 day readmissions

10. OSF Healthcare 30 Day Readmission Predictive Model 10 Model Description Built for the One OSF population Explored more than 140 potential independent variables 70 variables included in final model Not reliant on vendor supplied risk scores Uses data currently in the EDW Can be deployed via SQL based reporting

11. Project Review 44% of Total Discharges 12% of Total Readmissions 35% of Total Discharges 34% of Total Readmissions 11% of Total Discharges 23% of Total Readmissions 6% of Total Discharges 17% of Total Readmission 4% of Total Discharges 13% of Total Readmissions 10.67% 5,957 readmissions/ 55,843 total discharges 10.67% 5,957 readmissions/ 55,843 total discharges 30.9% Obs. Risk 1,029 readmissions/ 3,334 total discharges 30.9% Obs. Risk 1,029 readmissions/ 3,334 total discharges 10.4% Obs. Risk 2,050 readmissions/ 19,737 total discharges 10.4% Obs. Risk 2,050 readmissions/ 19,737 total discharges 2.8% Obs. Risk 696 readmissions/ 24,595 total discharges 2.8% Obs. Risk 696 readmissions/ 24,595 total discharges 23% Obs. Risk 1,383 readmissions/ 6,002 total discharges 23% Obs. Risk 1,383 readmissions/ 6,002 total discharges 36.7% Obs. Risk 799 readmissions/ 2,175 total discharges 36.7% Obs. Risk 799 readmissions/ 2,175 total discharges Very Low Risk Low Risk Very High Risk High Risk Medium Risk 18.3% of discharges account for 49.8% of Readmissions

12. OSF Healthcare 30 Day Readmission Predictive Model 12

13. The Care PROCESS Future State Processes-High Level 1.Report Auto distributed via email The risk report is embedded into current state processes InPatient Case Manager/CTC 2. Readmission risks conversation with “Key Learner” and physician 3. Document high level care planning for ambulatory handoff in the “Care Coordination Note” 4.Complete Warm handoff Ambulatory Care Management 5.Ambulatory Care Manager to coordinate the mid/long term patient care plan Post Acute Care 6.Bridge acute & post acute care to work as one team for patient continuum of care

14. PREDICTIVE MODELING OSF HEALTHCARE EXPERIENCE OCTOBER 2014 Cost of Care

15. OSF Healthcare Cost of Care Predictive Model It is important to understand that the underlying risk assessment is designed to accurately explain the variation at the group level, not at the individual level, because risk adjustment is applied to large groups (AAA, 2010). As the American Academy of Actuaries notes: “... Determining average experience for a particular class of risk is not the same as predicting the experience for an individual risk in the class. It is both impossible and unnecessary to predict expenditures for individual risks. If the occurrence, timing, and magnitude of an event were known in advance, there would be no economic uncertainty and therefore no reason for insurance.” (AAA, 1980 ) 15

16. OSF Healthcare Cost of Care Predictive Model 16 Model Description

17. OSF Healthcare Cost of Care Predictive Model Commercial modelsOur model Based on previously coded encountersBuilt from the problem list Typically validated on standard populationCan be validated on individuals Limited to coded dataIncludes non-coded data from our EHR No social dataInsight into social determinants Data lagContemporaneous data Limited to data from payersPotential to risk adjust entire panel Includes pharmacyNo insight into pharmacy costs Includes data external to our EHRLimited to internal EHR data EasyDifficult 17

18. OSF Healthcare Cost of Care Predictive Model ModelR^2PPV 1%MAPE Medicare HCC9.9%11.5%NA Commercial #121.8%23.3%NA Commercial #230.15%NA88.45% OSF MODEL36.15%28.95%NA 18 R^2 – The amount of cost variation in the population explained by the model PPV 1% - Positive predictive value defined as the total number of those predicted to be in the top 1% of cost actually in the observed future 1% MAPE – Mean absolute percent error defined as the average error between expected and observed (lower is better)

19. OSF Healthcare Cost of Care Predictive Model 19 Next Steps –Validate outside Medicare and develop operational platform No single model is perfect – need to compare and contrast the two views of the patient and combine it with knowledge from the engaged Care team