Sooner, Better, Faster: Harnessing the Power of Big Data in Healthcare. Somesh Nigam, Ph.D. SVP and Chief Analytics & Data Officer

Transcription

1 1 Sooner, Better, Faster: Harnessing the Power of Big Data in Healthcare Somesh Nigam, Ph.D. SVP and Chief Analytics & Data Officer

2 2 Uniquely positioned to be a catalyst for change Connected to all key players Broad access to data and information Ability to give stakeholders a reason to care $

3 3 Where should change take us? Enhance collaboration between insurers and doctors/hospitals, enabling better care Move to a value-based payment system and more accountability Increase cost and quality transparency Improve patient engagement

4 4 Analytics in Action Executive Management IT Executive Dashboard Opportunity Identification Strategic Decision Provider Network Reimbursement Physician Profiling Facility Profiling Fee Increase Analysis Disease Management Member Stratification Gap of Care Evaluation Member Outreach Provider 360 Hospital 360 Member 360 Provider Evaluation Peer Comparison Contract Negotiation Business Operation Marketing & Customer Service Member Profiling Marketing Initiatives Member Education Healthcare Analytics Provider Community Data Integrity Client Reporting Program Evaluation Predictive Modeling

5 5 How Analytics Drive our Business UNDERSTANDING OUR MEMBERS (OR PURCHASERS) Employers Insured (Subscribers, Dependents) Illness Burden (chronic, healthy, rare diseases, obesity, etc.) Consumer Insights: (income, relationships, employment status, behavior) Their demographics (age, sex, race, ethnicity, location) UNDERSTANDING THE HEALTHCARE DELIVERY SYSTEM Hospitals, Doctors, Labs, Diagnostic Centers Practice Patterns Quality of Care Delivered Billing Patterns and Prices Referral Patterns

6 Future Current 6 Available Data Claims Electronic Health Record (EMR) Lab Result Risk Score Episode Grouper Member Eligibility Customer Service Data Consumer Behavior Social Media Member Survey Caps in Care Cost/Quality Measures Preventable Services Gene Bank Mobile Health?

7 Shifting focus towards driving innovation 7 As the Enterprise Data Warehouse and supporting infrastructure mature, the shift to data analysis and innovation will intensify.

8 Data Excellence Key Objectives 8 A culture of analytic innovation by incorporating big data and advanced architectures and solutions A modern, scalable platform that allows for exploration and rapid, iterative deployment of new analytics A single source of truth, ensuring common standards and definitions across the enterprise Provide the foundational platform to drive the consumption strategy and associated business value

9 Data Excellence Program - Platform Data Sources Integration Layer Integrated Data Layer Access Layer Consumption Layer Facets EDI Cloud / On Premise Query Users CRM Cactus Portico FAMS Lab Jiva L a n d i n g ETL Pub/Sub CDC HL7 EAI Big Data Lake Reports API / Web Service Extracts Alerts Visualization / Dashboards Mobile Application (Jiva, Facets, etc.) Member Portal External Data MDM EDW Analytic Tools Group Portal Other Big Data Connector Pub/Sub Provider Portal IT develops & supports data in & out of Op systems Hand-off between IT & EIM No persistent data in this layer (except MDM)* Governed, mastered, structured, unstructured, and persistent data No persistent data in this layer* Data in this layer is both derived from EDW and transactional from Op systems Future State *Persistent data is data that is stored for the long term (i.e., years) for reporting and analysis 9

10 Prospective View of Top 15% Risk Members Disease Comorbidity Bubble size is total allow. Bubble color is # of members. Label: ETG Family, % of total allow Source: Episode Treatment Grouper (ETG), Impact Intelligence 10

11 What is Predictive Modeling? 11 Definition: Predictive modeling is a process that uses data mining and probability to forecast future outcomes. Multiple Attributes: Each model is made up of a number of predictors, which are variables that are likely to influence future results. Equation: Once data has been collected for relevant predictors, a statistical model is formulated. All Kinds of Models: The model may employ a simple linear equation or it may be a complex neural network, mapped out by sophisticated software. Model Needs to be Validated: As additional data become available, the predictive model needs to be validated or revised.

12 Improving Prediction of Inpatient Events A predictive model was developed on members with selected chronic conditions (CAD,CHF,DIAB,HYPTN,ASTHM,COPD) to predict the likelihood of inpatient hospitalization in the next six months. Improved timeliness of recent adverse events Condition-specific models Enhancements lab results, social determinants of health, timelier data, etc model achieved 87% improvement over commercially available models in predicting hospitalizations 12

13 13 Measuring the Quality of Care The right Treatment, at the right Time in the right Setting Avoiding Complications Avoiding Admission and Re-Admissions Avoiding ER visits Closing Gaps in Care Wellness and Prevention Compliance w/ Medications w/ Therapy

14 Driving Value 14 Business Problem Business Outcomes Improve ACO payment models to incentivize providers for high-quality, valuebased care Reduce avoidable admissions/ avoidable ER visits via timely, data-driven clinical interventions Improve Population Health programs by better targeting and better management on chronic members Improve Customer Service by analyzing customer service data and predicting complaints

15 Questions? 15