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Carilion Clinic’s Journey on the Population Health Management and Big Data Highways

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. Greetings from Western Virginia. Tonight’s Topic Health IT And Population Health.

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Carilion Clinic’s Journey on the Population Health Management and Big Data Highways

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  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. Greetings from Western Virginia

  3. Tonight’s Topic Health IT And Population Health

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

  5. 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

  6. Enterprise Data Warehouse

  7. Healthcare IT and ACOsThe 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

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

  9. 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)

  10. Key patient populations • Key Strategic Initiatives Ambulatory Case Management Patient engagement, care coordination, Extensivists, palliative care, transitions of care protocols 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 Ambulatory Quality / Pay for Performance (P4P) Cancer screening, BP, lipid, A1c, etc.; various patient engagement and contact components Low risk 45-55% Patients without medical problems; focus on prevention, wellness, and connectivity to health system Behavioral Health / Psycho-social

  11. Pay-for-performance • 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… Core measures, value-based purchasing (Hospital) HCAHPS (Hospital) HEDIS, NQF (Ambulatory) CGCAHPS (Ambulatory)

  12. Utilization Management “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.”

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

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

  15. 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

  16. 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.

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

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