1. 1 Carilion Clinic’s Journey on the Population Health Management and Big Data Highways June 5, 2014 Tom Denberg, MD Chief Strategy Officer Executive Vice President Carilion Clinic

2. 22 Greetings from Western Virginia

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4. 4 Tonight’s Topic Health IT And Population Health

5. Big Data and Healthcare-behind but catching up Health Catalyst

6. Big Data and Healthcare Big data is a term used for massive amounts of information that can be interpreted by analytics to provide an overview of trends or patterns. Organizations leverage big data by gathering records and information captured and then interpreting it with analytics. Common in other industries, big data has only recently begun to become a factor in healthcare. It has applications range from provider-specific business intelligence to scouring over an entire state's health records to pinpoint people who are at risk for certain ailments. Many believe that big data can help target early warning signs and improve patient safety Healthcare IT News 2014

7. 77 Enterprise Data Warehouse

8. 88 Healthcare IT and ACOs The Critical List Population identification - attribution Identification of care gaps – Decision Support Risk Stratification Cross Continuum Care management Quality and Outcomes measurement Patient engagement Telemedicine Mixing claims and clinical data Predictive modeling Clinical information exchange

9. Excess Cost Domain Estimates IOM. The Healthcare Imperative, 2010.

10. Clinician-Driven Sources of Excessive Health Care Costs (Population Health Management Focus) Preventable/avoidable hospital (re-)admission and ED visits (Case Management, Readmission Reduction) Missed prevention (Pay-for-performance) Unnecessary care (Utilization Management)

11. Key patient populations Key Strategic Initiatives Ambulatory Case Management Patient engagement, care coordination, Extensivists, palliative care, transitions of care protocols Ambulatory Quality / Pay for Performance (P4P) Cancer screening, BP, lipid, A1c, etc.; various patient engagement and contact components Sickest and/or highest- utilizing 5-10% Advanced CHF, COPD, IHD, DM, asthma, cancer, psychosocial problems Rising-risk 40-50% Patients with less severe chronic illnesses or behaviors that significant elevate morbidity or mortality risks; HTN, DM, hyperlipidemia, tobacco use, obesity Low risk 45-55% Patients without medical problems; focus on prevention, wellness, and connectivity to health system Behavioral Health / Psycho- social

12. Pay-for-performance Core measures, value-based purchasing (Hospital) HCAHPS (Hospital) HEDIS, NQF (Ambulatory) CGCAHPS (Ambulatory) Profusion of metrics Primary care emphasis Increasingly shared primary-care/specialty accountability Increasing number of specialty-specific metrics Rationale: - Reimburse for value, not just volume (maximize shared savings) - Demonstrate not skimping on care CLBSI CAUTI CHF Readmission rate… … BP control A1c control Breast CA screening…

13. “Off hand, I’d say you’re suffering from an arrow through your head, but just to play it safe, let’s get an echo.” Utilization Management

14. % CBCs ordered without apparent clinical indication during preventive exams

16. The Future- Proactive Care Identify patients at risk before they develop symptoms of heart failure Maximize treatment of underlying conditions Closer follow up Delay or prevent the onset of severe heart failure Bend the disease curve

17. CHF Onset Project Collaboration ( Carilion, IBM, Epic) 3 years data / 500,000 records reviewed NLP used to obtain unstructured data (20M) 8500 patients at risk 3500 identified with NLP Risk score generated based on clinical, social and demographic data Score available in EMR Develop treatment protocols to address at risk patients.

18. Big Data – Lessons Learned A journey, not a project Hard work Expensive New skill sets Organizational discipline Executive support Dividends can be huge