Predictive Health Population Analytics

1. Predictive Health Population Analytics VeeraS Raghavan Executive Director & Global Practice HeadHealthcare and Life SciencesDell Services October 2015 Veeraraghavan@dell

2. Modifiable health 0 25 65 Age Illness Pre-Illness Wellness 60-80% Lifestyle Unpredictable Health Predictable (Rules-based) Health Death Adapted by DrNick from 2009 Continua Health Alliance -Brigitte Piniewski, MD

3. To put it another way…. Age 0 25 65 Illness Pre-Illness Wellness Fun Death No Fun Adapted by DrNick from 2009 Continua Health Alliance -Brigitte Piniewski, MD

4. Challenges: US example Annual avoidable readmission costs for Medicare patients $17B Annual healthcare spending with little or no effect on outcomes $840B Annual deaths as a result of “preventable harm” in hospitals 200-400K Annual deaths due to asthma. Many of which are avoidable. >3,300

5. Analytics can make a difference: US example UPMC Health Plan reduced readmission rates by identifying at-risk patients and providing personalized transition care and follow-up 37% tests and overnight stays for ER patients by using analytics and historic data to more accurately predict test outcomes and likelihood of impending cardiac events reduce ER doctors were able to reduction in surgical site infections at University of Iowa Hospitals and Clinics by providing real-time analytics during surgery 58% asthma care with email notifications to emergency rooms, case managers and asthma patients forecastingevents likely to exacerbate symptoms Optimize

6. Evolution of IT in healthcare delivery Phase 1 Phase 3 Phase 2 As healthcare delivery evolves towards collaborative care models, the ability to share data and use it to improve decision making will be a key transformative milestone Lab/eRx Hospital Physician Payer Move and exchange data EMR EMR Manage patient health Interoperability Capture and digitize records Demographics, history & utilization Personalized health interventions Electronic medical record Information driven decision making • Predictive • modeling Population health records Patient health management EMR EMR BI & analytics Analyze and manage data

7. What can you do with your big data?From reporting to search, discovery and prediction Comparative Effectiveness Research Clinical Decision Support Patient Profiling Volume Disease Mgmt/Patient Compliance Performance Mgmt Population Analytics Outcome Improvement Real-time Personalized health interventions Readmissions Demographics, history & utilization Cohort Analysis • Predictive • modeling Population health records Retrospective Data Reporting Unstructured Fraud Detection Clinical Trial Design Multiple sources Performance-based pricing Drug Discovery Health Economics &Outcome Research Consumer Segmentation Personalized Medicine Patient Satisfaction & Behavior Analytics R&D Resource Allocation Marketing Promotion/Health Campaigns Personalized Medicine Infectious Disease and Outbreak Detection Operation Mgmt Payment/Pricing R&D Public Health CRM

8. Information-driven healthcare Seamlessly integrate big data and analytics into your workflow Foresight Optimization • Prescriptive analytics • Patient flow optimization • Network leakage and design Insight Predictive analytics Inferences/ exceptions • Population risk stratification • Disease based risk models • Readmission prediction Hindsight Visualization • Gaps in care • Physician scorecards and benchmarking • Labor forecasting Reporting • Emergency dashboard • Readmission rates • HAI trends Change Mgmt. • Operational reports • Adhoc reports • Basic quality reports Model Development Master Data Management/ Governance Enterprise Data Warehouse Data Integration and Management

9. Focus on India Out of Pocket Health Expenditure (as a % of total expenditure on health) 60% 58 45 40 34 31 Delivery – highly unorganized in diff formats Delivery – highly unorganized in diff formats World average: 18 20 13 11 7 0 India China Brazil Norway US Indonesia South Africa

10. Where & how do we start? Collect targeted operational data – wait times, time & motion studies, inventory to focus on operational analytics to drive bottom-line improvements 1 ? Role of Government, Health Ministry, Industry associations in defining and enforcing data standards Regulate Industry through data Collect targeted patient experience and satisfaction data through surveys, correlate with healthcare services and physicians, monitor trends over time to drive traffic CSAT and predictive customer (patient & referring physician) behavior for top-line improvements 2 Implement a light weight EMR to collect key clinical data points smartly. Drive outcomes research and clinical quality improvements. De-identify data to enable clinical trials, open new opportunities 3

11. Hospital-IT-in-a-Box solution Dell end-to-end solution Secure orchestrated Public cloud Core 4-stack solution Hospital chain 1 Hospital chain 3 Hospital chain 2 Tele medicine Business Intelligence – Clinical Research, Executives & Operations SaaS EMR (clinical functions) ERP functions PRM • Medical record • Diagnosis • Treatment plans • Prescription • Discharge summary • CPOE • Finance & accounting • HCM & payroll • Supply chain management SaaS Administrative & finance functions (non-clinical) Enterprise image management OP & IP management Housekeeping Blood Bank Patient Billing / claims mgmt SaaS Ward management OT & CSSD Lab, Radiology & pharmacy mgmt Diet management BPM CRM Sales force automation Analyse Design Develop Camp module Referrals Enhance Deploy

12. Thank you Veera_s_raghavan@dell.com