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The Path to Shared Savings With Population Health Management Applications

The Path to Shared Savings With Population Health Management Applications. Eric Just, VP Technology Kathy Merkley, RN, VP Clinical Engagement April 9, 2014. Accountable Care Organizations & Shared Savings.

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The Path to Shared Savings With Population Health Management Applications

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  1. The Path to Shared Savings With Population Health Management Applications Eric Just, VP Technology Kathy Merkley, RN, VP Clinical Engagement April 9, 2014

  2. Accountable Care Organizations & Shared Savings • Healthcare provider organizations responsible for providing coordinated care for their patients • Contract with payers through some form of shared risk payment model • Most payment models include downside risk to the healthcare providers • Payment models reward high-quality, low-cost care with shared savings

  3. Population Health Management (PHM)The Key to Shared Savings Four Building Blocks of Population Health Management 1 2 Provider Network Population 3 4 developing the asset Quality Outcomes Cost Outcomes

  4. PHM and Accountable Care (AC) Accountable Care Financing and Administration packaging and marketing the asset developing the asset Population Health Management

  5. What Does Health Catalyst Do? • Enterprise Data Warehouse “single source of truth” • Library of data acquisition adapters • Metadata repository • Auditing and access control • Supports a variety of analytic applications • Health Catalyst • Client developed Platform

  6. What Does Health Catalyst Do? • Reports & Dashboards • Ad-hoc query • Registries • Quality measures • Population health • Data mining • Clinical improvement • Workflow analysis • Modeling and predictive analytics Applications Platform

  7. What Does Health Catalyst Do? • Installation • Configuration • Data Architecture • Improvement • Project Management • Clinical Improvement • “Lean” Process Improvement Services Applications Platform

  8. Application Families Foundational Applications Discovery Applications Advanced Applications Encourage broad use of the data warehouse by presenting dashboards, reports, and basic registries across clinical and departmental areas. Allow users to discover patterns and trends within the data that inform prioritization, inspire new hypotheses, and define populations for management. Provide deep insights into evidence-based metrics that drive improvement in quality and cost reduction through managing populations, workflows, and patient injury prevention.

  9. Demos Foundational Applications Discovery Applications Advanced Applications` EDIT—Executive Dashboard Integration Tool (Key Performance Indicator editable collage from all app categories) CAFE—Comparative Analytics Framework and Exchange—across Healthcare Systems and National Benchmarks Key Process Analysis (KPA) Population Suitese.g., Ischemic Heart Disease Population Explorer Cohort Builder Patient Satisfaction Explorer Population Modules e.g., CABG, Stent, AMI Comorbidity Analyzer General Ledger Explorer Regulatory Explorer Readmission Explorer Workflow / Operational Suites e.g., Acute Medical Attribution Modeler Practice Management Explorer Suite Workflow/Operational Modules e.g., ICU, MedSurg, Emergency ACO Explorer Suite Patient Flow Explorer Readmission Predictor Financial Management Explorer Patient Injury Prevention Suites e.g., Infection Prevention Payment Model Analyzer Labor Management Explorer Metric Correlation Analyzer Patient Injury Prevention Modules e.g., CAUTI, CLABSI, SSI Patient Flight Plan Predictor Rev Cycle Explorer

  10. Demos: How Analytics Drive Shared Savings Demo 1: Key Process Analysis (KPA).Identify areas of greatest opportunity for quality improvement and savings Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  11. CARDIOVASCULAR KPA: Clinical Hierarchy Clinical Program Heart Rhythm Disorders Ischemic Heart Disease Care Process Families Vascular Disorders Heart Failure Care Processes ACS AMI PCI CABG

  12. KPA: Measuring Opportunity Using provider variation to calculate the potential financial impact of improving and standardizing care processes Mean Cost per Case = $10,000 Dr. J. 15 Cases $15,000 Avg. Cost Per Case Total Opportunity = $75,000 Total Opportunity = $1,200,000 Total Opportunity = $175,000 Total Opportunity = $500,000 $4,000 x 25 cases = $100,000 opportunity $5,000 x 15 cases = $75,000 opportunity Cost Per Case, Vascular Procedures

  13. Demos: How Analytics Drive Shared Savings Demo 1: Key Process Analysis (KPA).Identify areas of greatest opportunity for quality improvement and savings Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  14. Heart Failure Statistics • Heart failure (HF) is one of the most rapidly increasing cardiovascular disorders. • Leading cause of hospitalization in individuals over 65 years of age.¹ • Third leading cause of hospitalization in the U.S. in all age groups.² HF is the most common cause of readmission.3 Rates approach 30% within 60-90 days of discharge.4 1Krumholz HM, Chen YT, Wang Y et al. Am Heart J. 2000;139(1 Pt 1):72–7.. 2Heart Disease and Stroke Statistics—2012 Update. Circulation. 2012;125:e2-220.3Jencks SF, Williams MV, Coleman EA. N Engl J Med. 2009;360:1418-28.4Gheorghiade M, Vaduganathan M, Fonarow GC et al. J Am CollCardiol. 2013;61:391-403.

  15. CMS and Medicare Readmission Penalties • Nearly 25% of all patients hospitalized for heart failureare readmitted within 30 days. • CMS has labeled HF as an area of excessive readmission. • CMS penalties will ensue to reduce readmission rates 2% Loss 1% Loss 3% Loss http://www.ama-assn.org/amednews/2012/08/27/gvsb0827.htm. American Medical Association. Accessed online 12/28/2012.

  16. Improvement Methodology • A goal is a desired result the workgroup envisions, plans and commits to achieve an organizational desired end-point by a specified deadline. • AIM statements are written, measurable, and time-sensitive objectives that move the team toward achieving the goal .

  17. CV Heart Failure • Goal: Decrease 30 day readmission rates of heart failure patients AIM #1 Establish a baseline of all cause 30 day readmission rates for HF patients, create and validate 30 day and 90 day readmission rates for all HF patients. AIM #2 AIM #3

  18. CV Heart Failure • Goal: Decrease 30 day readmission rates of heart failure patients AIM #1 Identify high risk heart failure patients and extend the identification of these patients to a Risk Stratification Model to predict the likelihood of all cause 30-day readmission rates. AIM #2 AIM #3

  19. CV Heart Failure • Goal: Decrease 30 day readmission rates of heart failure patients AIM #1 Schedule a follow-up appointment for all HF patients within 24 hours of discharge with a focus on high risk patients being seen within 48-72 hours after discharge. AIM #2 AIM #3

  20. CV Heart Failure • Goal: Decrease 30 day readmission rates of heart failure patients AIM #1 Establish a medication reconciliation baseline and track compliance in order to achieve 75% compliance by X date. AIM #2 AIM #3 AIM #4

  21. CV Heart Failure • Goal: Decrease 30 day readmission rates of heart failure patients AIM #2 A follow-up phone call from a nurse post-discharge to assess whether the patient has obtained his/her medication and has no barriers to making their follow-up appointment. AIM #3 AIM #4 AIM #5

  22. Organizational TeamsIt’s not just about technology = Subject Matter Expert = Data Capture = Data Provisioning & Visualization Cardiovascular Clinical Program Guidance Team = Data Analysis Heart Failure MD Lead RN SME Ischemic MD Lead RN SME Heart Rhythm MD Lead RN SME Vascular MD Lead RN SME Guidance Team MD lead (e.g., Heart Failure MD Lead) RN, Clin Ops Director Knowledge Manager DataArchitect Application Administrator • Permanent Teams • Integrated Clinical and Technical members • Supports Multiple Care Process Families

  23. Demos: How Analytics Drive Shared Savings Demo 1: Key Process Analysis (KPA).Identify areas of greatest opportunity for quality improvement and savings Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  24. Appendix

  25. Advanced Applications • Pediatrics • Appendectomy • Asthma Acute • Asthma Chronic* • Cardiovascular • Atrial fibrillation* • Conduction disorders* • Ischemic Heart Disease* • Heart Failure • Community Care • Diabetes* • Asthma* • Primary care • General Medicine • Diabetes* • DKA (diabetic ketoacidosis) • Deep vein thrombosis* • Peripheral vascular disease* • Pulmonary • Pneumonia Community acquired • Pulmonary embolism* • Infectious Diseases • Cellulitis* • Urinary Tract Infection* • Meningitis* • Sepsis • Gastrointestinal • Anal/rectal disorders* • Appendectomy • Inflammatory diseases* • Lower GI procedures* • Obstruction* • Neurosciences • Stroke* • - Hemorrhagic* • - Vascular* • - Transient ischemic attack* • Oncology • Breast • Gastrointestinal • Thoracic • Orthopedics • Fractures • - Hip/pelvis* • - Lower extremity* • - Upper extremity* • Spine • Total hip* • Total knee* • Surgery - Vascular • Aortic aneurism* • Other venous disorders* • Varicose veins* • Women and Newborns • Antenatal Steroid • C-section Delivery • Elective Inductions • NTSV cesarean • Newborn • Departmental • EC (Emergency Care)* • Laboratory* • OR Workflow* • Radiology* • Nursing* • Other • Coordinated Care • Labor & Productivity • Medication Management • OPPE (Ongoing Professional Practice Evaluation) • Physician Credentialing • Primary Care • Professional Billing • ACO • Patient Injury Prevention • VT/PE prevention* • CAUTI • CLABSIControlled substance diversion prevention * In Development

  26. Architecture Overview Data Marts and Applications Common Definitions and Standardization Population Definitions, Comorbidities, Attribution, Patients, Labs, Encounters, Diagnoses, Medications Source Marts EMR Financial Patient Sat. HR Administrative Claims EMR Financial Patient Sat. HR Administrative Claims e.g. EPSi, Peoplesoft, Lawson e.g. Lawson, Peoplesoft, Ultipro e.g. Epic, Cerner e.g. Press Ganey, NRC Picker e.g. API Time Tracking e.g. Medicare

  27. Demo 1: Key Process Analysis.Identify areas of greatest opportunity for quality improvement and savings Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  28. Demo 1: Key Process Analysis.Identify areas of greatest opportunity for savings and quality improvement Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  29. Demo 1: Key Process Analysis.Identify areas of greatest opportunity for savings and quality improvement Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  30. Demo 1: Key Process Analysis.Identify areas of greatest opportunity for savings and quality improvement Demo 2: Population Explorer. Identify potential risk by understanding relative size of disease populations and risk profiles Demo 3: Heart Failure.Achieving quality improvement and cost reductions by directing targeted interventions to high-risk patients Demo 4: Community Care.Monitoring high-risk patients in primary care to prevent expensive acute treatment

  31. Thank You Next Educational Webinar • By Failing to Prepare, You Are Preparing to Fail • Laying the Foundation for Sustainable Change and SuccessDate: Wednesday, April 16th • Time: 1:00-2:00 PM ET • Presenter: John Haughom, MD, Senior Advisor, Health Catalyst • Register at http://healthcatalyst.com/

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