Harnessing the Power of Predictive Modeling Future Trends

1. Harnessing the Power of Predictive Modeling Future Trends

2. Harnessing the Power of Predictive ModelingFuture Trends • Traditional Applications • Recent Applications • Future Trends • Motivation Index • Forecasting Disease Specific Risk • Provider Market • Forecasting Preventable Events

3. Predictive ModelsTraditional Applications • Risk Stratify the Population for care management • Manage complexly ill members (Inpatient avoidance) • Refine disease management strategies • Manage pharmacy services • Underwrite more accurately • Reimburse based on illness burden • Evaluate physician management strategies

4. Predictive ModelsChanging Focus • Traditional Application has been to Identify: • High Risk / High Cost members • Inpatient Risk • Recent Applications • Forecasting additional Cost Components • ER Visit Risk • Pharmacy Cost forecasting • Identify Intervenable or Actionable members • Future Trends • Member Motivation • Disease Specific Complications • Preventable Events for the Provider Market

5. Recent ApplicationIdentifying Actionable Members • Method A • Query population by multiple filters: • Disease • Cost Risk • Inpatient Risk • Pharmacy Risk • Mover Risk • Method B • Impact Index : Model that identifies members who have the greatest potential for outcome improvement based on guideline compliance

6. Recent ApplicationMultiple filters to identify actionable members Total Population: 216,842 members High Risk Index Top 2% High Risk + Mover 4,362 Members Forecasted Cost: $25,741 Prior Year Cost: $45,006 Savings Potential: -$84,033,930 498 Members Forecasted Cost: $20,084 Prior Year Cost: $8,832 Savings Potential: $5,603,496

7. Recent ApplicationImpact Index to identify actionable members Total Population: 925,407 Diabetes: 50,847 High-Risk Index Risk Level 4&5 High Impact Index Top 15% 14,250 Members Forecasted Cost: $14,634 Prior Year Cost: $14,527 Savings Potential: $1,524,750 13,872 Members Forecasted Cost: $8,698 Prior Year Cost: $5,089 Savings Potential: $50,064,048

8. These Drivers Disease Age/Sex Comorbidities Guideline Compliance Patterns Determine future impactability Determine potential cost savings Recent Application Impact Index

9. Future TrendsMotivation Index • Identify members • more motivated to ‘self-manage’ • comply with instructions from providers • pursue ways to improve health status • To create index use data sources • Lifestyle Data • Health Risk Assessment • Demographics • Claims Data

10. Future TrendsMotivation Index Drivers • Lifestyle Data • Net Worth • Credit History • Magazine Subscriptions • Hobbies • Clubs • Claims Data • Compliance Patterns • Preventive Care • Physician Visit Patterns Claritas US Census Bureau Media Mark

11. Future TrendsMotivation Index Variables • Claims Data • Compliance Patterns • To Guidelines • To Psych-Related Drugs • To Maintenance Drugs • Preventive Care • Use of preventive health services • Compliance to Preventive Lab Test • Compliance to standard preventive guidelines • Physician Visit Patterns • Gap/Frequency between Acute Care & Physician visits • Gap/Frequency between Physician visits per disease • Cost Ratios for Inpt / Rehab / Rx / Physician • Demographic / Misc • Age/Sex • Obesity / Smoking • Drug/Alcohol Dependency • Mental Health

12. Future TrendsMotivation Index Drivers • Patients with higher motivation scores have • Better guideline compliance • Older age • Higher preventive care use • Lower acute-care use • Shorter (Time-frames from Inpt discharge to phys-visit) • Females 40 t0 65 • Higher mammogram compliance • Asthmatics • Lower ER visits • Hypertension • Higher hypertensive drug use • Depression • Higher depression related drug use

13. Future TrendsMotivation Index Drivers Higher Antidepressant Use within Depressed Population correlates with higher motivation

14. Future TrendsMotivation Index Drivers Mammogram Use in Female Population correlates with higher motivation

15. Future TrendsMotivation Index Drivers ER Visits within Asthma Population correlates with lower motivation

16. Future TrendsForecasting Disease Specific Outcomes

17. Future TrendsProvider Market • Future Trend • Forecasting Preventable Events Pre-discharge • Preventable readmissions • Catheter-Associated UTI • Pressure Ulcers • Vascular Catheter-Associated Infection • Mediastinitis after CABG-Surgical Site Infection • Hospital-Acquired Injuries

18. Future TrendsWhy Identify Potentially Preventable Readmissions? • Comparing provider performance to enhance quality • Developing pay for performance systems • Readmission rates provide quality benchmark • Costs associated with readmissions are substantial • 30 billion in play for Medicare • Defining Preventable Readmissions • some initial discharges for which subsequent readmits excluded (e.g. LAMA, cystic fibrosis) • Readmissions are for same diagnosis • Readmissions are for related diagnosis

19. Future TrendsData for Forecasting Preventable Events • Electronic Medical Record Data • HL7 Format • Near-real time data outflow • Forecasting Model • Near-real time forecast of Readmission / Decubitus • Probability • Risk Index • Drivers • Data Needed • Vital Signs • Lab Results • Drug Dosage and Timing • Admission Discharge Transfer Data • Chief Complaint • Prior Discharge Diagnoses • Supplies

20. Future TrendsForecasting Decubitus • Drivers to forecasting Risk of Decubitus when a patient is admitted • Vital Signs • Fever • Pulse / BP / Respirations • Lab Results • White Blood Cell Counts • Blood Culture Results • Drug Dosage and Timing • Antibiotic at admission • Chief Complaint – Diarrhea • Admission Source • From SNF • Prior Discharge Diagnoses • Diabetes / CHF / Senility • Demographics • Supplies • Depends

21. Future TrendsHospital Revenue Loss with Preventable Events Source:CMS;Advisory Board Analysis

22. Future TrendsROI from Forecasting Preventable Events

23. Mbr Educ UR/UM Demand Mgmt Concurrent Case Mgmt Disease Mgmt Decision Support Personal Health Mgmt Population Risk Mgmt UR/ UM Proactive Case Management Disease Management Predictive Modeling Applications for Care Management – Paradigm Changes Predictive Modeling Applications for Care Management – Paradigm Changes Historical Current Transformed Predictive Modeling: Used to identify early members who are trending toward high-risk events Future Model Personal Health Mgmt Population Risk Mgmt UR/ UM Proactive Case Management Decision Support Disease Management Actionability Motivation Index Disease Specific

24. About the Future “Never let yesterday use up too much of today.” - Will Rogers The best way to predict the future is to create it.