Big Data In The Medical Industry

1. Big Data In The Medical Industry David Shein, MD dshein@veriskhealth.com Medical Director, Verisk Health

2. Agenda • Verisk Health:Who we are • What we do • Data management in the healthcare environment • Example cases • Data challenges

3. Verisk Analytics – The Science of Risk Measure, Evaluate and Navigate Risk P & C Insurance Healthcare Mortgage Lending Supply Chain

4. The Markets We Serve Understanding Healthcare Risk Payors Commercial Plans Third Party Administrators Disease & Care Management State & Managed Medicaid Medicare Advantage Providers At-risk Physician Groups Integrated Delivery Networks Provider Hospital Organizations Accountable Care Organizations Employers Self-Insured Benefit Consultants Brokers

5. The Healthcare Crisis: On the Rise ~Healthcare spending is projected to reach nearly $4.6 Trillionin the next decade Employers spent $8,300 on average per employee per year in 2010…costs are projected to rise to more than $13,400 in 2019 • Today, healthcare spending has reached $2.5 Trillion…and $3 out of every $4 spent is on chronic conditions • In 2014, when health coverage is expanded to millions of uninsured Americans, spending is estimated to increase by 9.2% • By 2019, healthcare is projected to account for nearly $1 of every $5 spent, or about 19.6% of the national GDP ~CDC ~Health Affairs: http://www.healthaffairs.org/press/2010_09_09.php Today, GM spends more on healthcare than on steel… Wheredo healthcare costs rank for you?

6. The Healthcare Crisis: A Smarter Way • According to a 2009/2010 Towers Watson Report: • Companies with effective health benefit programs not only improved employee health but also experienced superior human capital and financial outcomes, including: • Lower medical trends by 1.2% • 1.8 fewer days absent per employee • Fewer lost days due to disabilities • 11% higher revenue per employee • These organizations believe in identifying the root causes of healthcare cost increases • …and consistently analyze data integrated across programs to identify opportunities, design programs, and measure performance

7. Capability & Capacity: Understanding Risk PerformanceRisk FinancialRisk ClinicalRisk • For… • Medical Management • Identification & stratification • Predictive event modeling • Gaps-in-care measurement • Trending & reporting • For… • Provider Network Management • Quality Measurement • Employer reporting • NCQA HEDIS reporting • Program Measurement • For… • Budgeting and Cost Containment • Risk adjustment • Cost & utilization driver analysis • High-cost case identification • Fraud, waste & abuse DATA AGGREGATION – ANALYTICS – DECISION SUPPORT - REPORTING – EXPERT INTERPRETATION

8. Data Acquisition and Management is a Core Operational Strength • Examples of our Data Capabilities • Data Handling: • Acquisition: We can accept data in virtually any format • We currently accept data from over 300 different vendors with over 1,200 mapping and translation schemas • We process 50 million claims per day and 1.5 billion on a recurring monthly basis. Average turnaround time less than 10 calendar days.

9. Data Captured Enables Analysis of a Complete Picture of Health and Productivity

10. Data Elements • Medical claims • Pharmacy claims • Eligibility • Vision • Dental • HRA • (Health Risk Appraisal) • Biometric • Lab • EMR • Disability Healthcare trend: Progression from Health  Health and Wellness  Productivity

11. The Data Lifecycle at Verisk Health • D A T A • Source • Customer • Payor • Data Warehouse • Process • ETL • Scrub • Cleanse • Map • Analytics • Engine • Quality • Risk • HEDIS • Access • ASP • Service Bureau • Warehouse

12. Data Analytics Rules Engine Enterprise Analytics CDF (Common Data Format) Medical Intelligence Provider Intelligence DxCG Performance Measurement

13. Demographic Analysis • Employee • Spouse • Dependent Age/sex distribution of the population Expense distribution Population% of Spend 1% 30+% 2-5% 6-15% 16-30% 31-60% 61-100% < 1% Distribution of spend, health conditions and quality across membership status, age, sex

14. Financial Analysis • Cost trends • Norm comparison • Modeling • Predicted • Current • Future • Performance • Actual vs Predicted Focus on cost drivers Factors affecting change include: membership, utilization, pricing, intensity

15. Clinical Analytics • Disease Registry • List of top conditions (chronic and acute) • Prevalence • New diagnoses • Cost metrics • Comparison • Benchmark - Commercial norm • Period 1 / 2 • Quality and risk measures • Risks:Disease burden in population • Gaps in Care:Care delivery / quality • Clinical deep dives • Specific consultative evaluations • Comorbidity analysis Include disease prevalence, population health status, quality of care

16. Predictive Modeling • Concurrent modelsPerformance • Relative risk score • Predicts cost and utilization using prior health status and performance • Regression model fits population around normative “1.0” • Benchmarked to: - Book of Business - Norm • Predictive modelsBudgeting and forecasting • Likelihood modelsMedical managementIntervention

17. Additional Analytics • Network utilization • Geography • Pricing / discount differentials • Tests • Lab • Imaging • Vascular • Utilization • ER • Admissions • Acute • Subacute • Office • Specialty services • Med specialties • DME • Non-PBM drugs* Understand patterns of utilization and healthcare spending

18. Pharmacy Analytics • Prescribing patterns • Brand • Generic • Opportunities for generic conversion • Medication possession ratio (MPR) • Measure of Rx compliance • Ratio of: # Days filled • # Days Rx • Pricing • Brand • Generic • Fill location • Mail order • Non-PBM drug • Costs by location • e.g. office, outpatient hospital Utilizing PBM and claims data Examples of analytic capabilities:

19. Case #1: Employer • Challenges: • Manage health care costs • Provide insight into vendor performance • Budgeting and forecasting • Large multi-state employer • High rates of chronic disease • Benefit strategy includes • High deductible health insurance and HMO options • Disease management through outside vendors

20. Case #1: Cost Drivers • Answers questions including: • What is the overall performance across all carriers? • How do specific subgroups perform? • What is driving changes in medical spend? • Are there areas to focus for controlling cost?

21. Case #1: Cost Drivers • Aggregate carrier information to common format for overall analysis • Analyze cost trends and determine drivers • Report on demographic and membership patterns across all carriers • Analyze patterns of health spend by cuts across membership • Influences include health policy, general economy • Impacts on membership changes (e.g. hiring or layoffs) • Monitor changes

22. What Are Key Cost Drivers? Change in Member Months (Thousands) Change in Medical PMPM $ -5% +5% Total Expenses $ Millions 352 335 +1% 300 285 129 128 P1 P2 P1 P2 Change in Total PMPM $ P1 P2 +7% Change in Pharmacy PMPM $ 390 364 +9% P1 P2 P1 P2 Sep 08 – Aug 10: Calculations are based on Total expenses and membership

23. Medical Cost Drivers Change in Unit Pricing $ / Event +2% 564 552 Change in Medical PMPM $ +5% P1 P2 300 285 Change in Utilization Events / Member Months +4% 0.59 0.56 P1 P2 P1 P2 Full Cycle - Demo

24. Case #1: Health Analytics • Answers questions including: • What are the key conditions driving health care spend in the population? • What is the quality of the care delivered to the population? • Overall • Subgroup analysis • What approaches can be taken to address the health issues? • How to track interventions?

25. Case #1: Health Analytics • Disease Registry • Identifies key conditions • Prevalence and change in prevalence • Cost by member (PMPM/PMPY) • Comparison to norm • Future capabilities will include industry-specific norm (NAICS) • View by subgroup • Business unit • Individual carriers • Insight into significant population health conditions and patterns of change • Provides a focus for disease management and wellness

26. 2 Disease registry key findings: PMPY cost • PMPY for the top 4 prevalent conditions is higher than the VH Norm. • Members with Coronary Artery Disease and Diabetes contribute to highest costs. • Opportunities for wellness and disease management programs $11,799 $10,401 $15,111 $10,781 $21,602 $17,393 Co #1 VH Norm Analysis Period. Based on current members

27. Case #1: Health Analytics Cont’d • Quality and Risk Measures • Detail on health status of overall population • Compared to BOB and norm • Quality measures – gaps in care • Baseline evaluation and comparison • Evaluate range of quality across multiple conditions • Enable monitoring over time for changes • Evaluation can be done for overall population or subgroup analysis • Carrier • Business unit (location, division) • Demographic cut (membership, geographic)

28. 4 Quality of care comparison: Coronary Artery Disease 29.0% Patients diagnosed with CAD and without a lipid profile test in the last 12 months 26.8% • Plan performance is similar • No apparent issues with access to care • Disease management toimprove lipid treatment may lower future CAD costs 38.9% 1.6% Patients diagnosed with CAD and without an office visit in the last 12 months 3% 2.2% 36.8% Patients diagnosed with CAD and without antihyperlipidemic drugs in the analysis period 42.5% 29.2% 7.7% Patients diagnosed with CAD and HTN without antihypertensive drugs in the analysis period 8.1% 13.6% VH Norm Plan A Plan B

29. Case #1: Risk Modeling • Answers questions including • How does the health risk for population of interest compare to other populations? • How do specific areas compare? • How to budget for next year’s health costs?

30. Case #1: Risk Modeling • Normative benchmark for overall population risk assessment • Normalized to BOB for division analysis • Carrier • Business unit (location, division) • Demographic cut (membership, geographic) • Concurrent models • Predictive models

31. Quality comparison:Risk-adjusted spend and utilization by carrier Well managed sick pop. Carrier Lives RRS PMPM (unadjusted) PMPM (RRS-adjusted) Admissions Index ER Index Imaging Index 1 3,180 1.45 285.12 196.63 1.02 0.98 0.92 2 8,327 0.71 85.97 121.08 0.72 0.80 0.95 3 16,784 1.00 200.41 200.41 1.01 0.95 1.05 4 1,903 2.12 532.86 251.35 1.32 1.41 1.21 5 1,201 0.86 189.22 220.02 1.1 1.22 0.85 Poorly managed “healthy” population pop. Analysis for demonstration purposes

32. Case #2: Provider Organization • Challenge: Rising costs and medical expense management • Tools for financial analysis, modeling and forecasting • Monitor network utilization and cost factors • Practice pattern variation: Provider dashboard • Quality • Efficiency • Utilization patterns • Large single-state provider organization • Accepts risk from insurance contracts • Looking to perform at the forefront of healthcare with innovative reimbursement approaches • Alternative Quality Contract • Experience with local clinical information (EMR) • Own data warehouse

33. Case #2: Clinic and PhysicianManager and Dashboard • How does performance compare across physicians and clinic sites? • “My patients are sicker than yours” • Who are the top performing providers and clinics? • Efficiency • Quality of care • Where to focus for performance improvement? • How to evaluate utilization patterns… • Drug • ER • Specialists • DME • … and take action?

35. Case #2: Clinic and PhysicianManager and Dashboard • Metrics for comparison based on quality, volume, risk and efficiency • Quality (gaps in care) • Relative risk models • Generic utilization rates • Drill down functionality across drug class to dose • Readmission rates • Efficiency scores: actual utilization concurrent predicted • Total admissions • Potentially avoidable admissions • ER visits • Imaging • Drill down capability to view individual physician or clinic • Comparison to BOB or norm

36. Case #2: Outside Utilization • How to evaluate referral patterns: • Where are outside referrals going? • What diagnoses are being treated outside? • What procedures are being done outside?

37. Case #2: Outside Utilization • Define “events” • Typical views by claim or unit vs episode group • Includes ancillary claims • Capture true event cost • Evaluate utilization patterns • Cost and quality • View events by: • Provider: Professional • Location: Facility • Specialty • Group (similar events) • Code level

38. Case #2: Outside Utilization • Evaluation of referral patterns:

39. Case #3 • Challenge • Create HRA report • Understand implications of self-reported health outcomes • Large benefit management company • Needs to provide information on wellness and augment disease management for clients • Chronic disease • Clients are capturing HRA (health risk appraisal)

40. Augmenting Member Level Data for Analysis: HRA • Self-reported data • History of disease • Lab results (glucose) • Biometrics (height, weight) Claims data • Example conditions with claims and HRA data • Diabetes Tobacco use Obesity • Health Risk Appraisal

41. Diabetes* by Claims and HRA • Individuals with claims for Diabetes have a relatively low rate of diagnosis reporting on HRA • Adding HRA will increase the population of diabetic individuals by about 5% (30%) *Diabetes claims: Medical Intelligence disease registry criteria *Diabetes HRA: Blood glucose value, blood glucose range, or diabetes history

42. Compliance Rates in Diabetes: Identification by Claims vsHRA Highest compliance Medium compliance Lowest compliance • Individuals identified by HRA only have a lower compliance rate • Individuals identified by claims and report having diabetes in HRA (overlap) have the highest compliance rate

43. Primary Site Physically hosted at Class A Datacenter, SAS 70 Type II certified Industry-leading managed services and enterprise infrastructure provider Located in downtown Boston with alternate sites throughout the US Direct connection to Tier 1 Sprint backbone, MCI, Verizon backup Raised floor, redundant grid power, climate control, smoke detection, fire suppression systems Data Hosting Platform - ASP • Secondary Site • Duplicate attributes of Primary Site • Located 30 miles northwest of Boston • Direct connections to multiple Tier 1 backbones (Level 3, Global Crossing) • End User Data Warehouse migrated to secondary site during Final QC to Production Post phase • Transactional updates migrated nightly in batch

44. Security • FTP • HTTPS • Password change 90 days • Idle time out • Lock out after 3 unsuccessful login attempts • Client admin for all users • User-level rights access • PHI • Data level • Clinical vs financial

45. Challenges • Processing • Normative data set • 10 mil lives • Clinical quality measures • 500+ • Cloud • Private vs public • Maintaining accuracy of imported data • Security • Managing PHI

46. Challenges • Meeting the needs of a changing industry • PPACA • ACOs • Changing paradigms for care, reimbursement and reporting • Clinical progress • New drugs and diagnoses • Changing clinical guidelines • Coding evolution (ICD-10) • New reimbursement policies • Readmissions • Episodes of care • New and evolving reporting needs • ACO

47. Questions ?