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Proactively manage quality and outcomes readmissions

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  • The keys to successfully helping clinicians engage more effectively with their patients is to provide them with patient-centered insights within their workflow. Insights that are stuck on a report or dashboard are less “actionable” than insights that are at “arms reach”
  • Sources:Sections 3025 and 10309 of the Patient Protection and Affordable Care Act (PPACA) (P.L. 111-148), ….which added subsection (q) to Soc. Sec. Act sec. 1886http://cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html/http://www.ofr.gov/ofrupload/ofrdata/2012-19079_pi.pdfhttp://www.ssa.gov/OP_Home/ssact/title18/1886.htm (subsection q 901)
  • Sources:Sections 3025 and 10309 of the Patient Protection and Affordable Care Act (PPACA) (P.L. 111-148), ….which added subsection (q) to Soc. Sec. Act sec. 1886http://cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html/http://www.ofr.gov/ofrupload/ofrdata/2012-19079_pi.pdfhttp://www.ssa.gov/OP_Home/ssact/title18/1886.htm (subsection q 901)
  • An adaptive solution to tailor analytically-driven, tested clinical intervention options for the patient in order to enable better shared decisions at the point of care.
  • The analytics environment will include all the SAS technology required to ingest, integrate and prepare data for analysis  perform advanced statistical analysis, predictive modeling and scoring to derive insights into a patient’s risks, needs, and preferences  store the insights in the Patient Profile (360 view of the patient)  design and deploy point of care decision support (actions/campaigns) across all Touchpoints (channels).
  • Requires an “Information Infrastructure” to enable Quality and Safety initiatives at EVERY “touchpoint” to target, tailor, personalize and engage unique individuals.
  • Transcript

    • 1. Proactively Manage Quality & OutcomesOverview of SAS Readmission AnalyticsJanuary, 16 2013 Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 2. TOPICALSAS AGENDA  Industry Challenges & Opportunities  Vision to be Patient-Centered  Readmission Value Proposition & Use Case  SAS Analytical Platform for Readmission Analytics Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 3. INDUSTRY CHALLENGESSAS AND OPPORTUNITIES Market-driven reform Use advance analytics shifting emphasis from to surface insights and volume to value inform decisions Improve Patient experience and engagement to improve health outcomes Learn, adapt and Retain reimbursement innovate to improve revenue at risk due to clinical quality new readmission rules Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 4. AVOIDABLE READMISSIONS SAS INCREASE THE COST OF HEALTHCARE Surgical Hospitalizations 1in8 > Result in Readmission Non Surgical Hospitalizations within 30 days 1in52,000,000 Medicare Beneficiaries = $17,500,000,000 in Readmission Costs Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 5. PENALTIES FOR MEDICARE READMISSIONSSAS INCREASE OVER TIME $280M FY 2013 3% 2% 1% 2013 2014 2015 Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 6. Source: http://www.objectivehealth.com/sites/default/files/infographics/Avoidable_Admissions_1_lg.jpg Inc. Copyright © 2012, SAS Institute All rights reserved.
    • 7. READMISSION DILEMMA SAS FOR PROVIDERS What are the primary drivers What potential financial of avoidable readmissions? penalties could we be facing? What data do I need to reduceHow can I avoid or reduce readmissions? avoidable readmissions? How do I best engage patients to Where is my highest potential of prevent readmission? readmissions? Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 8. EVIDENCE-BASED ISSAS INDUSTRY BEST PRACTICE “We will analyze health and consumer data for insights into individuals’ clinical risks… …to enable the best intervention and treatment decisions at the point-of-care… …that optimize quality and cost-effective health services. ” Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 9. SAS MEET JILL About Jill: • Part-time Journalist • Babysits her grandson twice a week • 66 years old Jill’s health challenges: • Struggled to maintain a health weight • Has elevated blood pressure • Family history of heart trouble Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 10. DISCHARGE (INTERVENTION) DECISIONSSAS ARE INHERENTLY PREDICTIVE IN NATURE • What treatment regimen will most likely yield the best outcome for Jill, with her specific genetic profile, Lab results, and psychosocial patient profile? • What is Jill’s risk of readmission on the day she presents to the ED? On the day she is discharged? • What are the top three post discharge interventions that are most likely to reduce Jill’s risk of readmission? • What insidious drug interactions is Jill likely to experience based on her unique personal risk factors? • What will this specific combination of rehabilitation protocols cost Jill? Cost the system? Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 11. SAS MEET JACK Profile Predict Risk Inform Clinical Patients Score Decisions Prevent Treat Identify Personalize Execute Outreach Track, Adapt Care Gaps Care Plan Campaigns OptimizeCoordinate Engage Copyright © 2012, SAS Institute Inc. All rights reserved. Reward & Sustain
    • 12. AN ANALYTICALLY-DRIVEN APPROACHSAS FOR REDUCING AVOIDABLE READMISSIONS Prepare Integrate & Transform data for analysis Optimize Profile & Predict Measure, monitor and Build, Enhance & Refine learn through models experiments Intervene Design Decide and capture data Build Clinical Decision at the point-of-care Support “rules” Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 13. SAS SAS PLATFORM FOR READMISSION ANALYTICS Real Time Decision Support Prepare Profile & Design Intervene Optimize Integrate & Predict Build Clinical Decide and Measure, monitor,Transform data for Build, Enhance & Decision Support capture new data learn and adapt analysis Refine models “rules” at point-of-care for maximum ROI Four Modules:  Modular Approach - Readmission Prediction - Readmission Engagement  Multiple Entry Points - Readmission Decision Support - Readmission Optimization Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 14. ILLUSTRATIVESAS EXAMPLE Patient: Jill Smith Age: 66 Dx: CHF RRS: 89% RRS Cost Intervention Options Impact Impact  Rehab -16% $3,000  SNF -19% $20,000  Telemonitoring -23% $300  Home Health Visit -31% $650  RN Follow-up Call -08% $55  Rx Review -12% $175 New Risk Score: 81% $55 Dx Rx % $ Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 15. CLINICAL AND ANALYTIC SAS ENVIRONMENT Demographics Segment ID Social Patient Quality & Cost Experience Safety of Care Disease Conditions Prescriptions of Care of Care Readmission Propensity to Engage in Risk Score Home Monitoring Actions Patient Profile Analytic ModelsAnalytic Data Store SAS Analytical Environment Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 16. PATIENT PROFILE SAS SAMPLE ATTRIBUTES Sample Psychosocial Dimensions Demographics Segment ID Social Sample Descriptive Dimensions Dx Rx Disease Conditions Prescriptions Readmission Propensity to Engage Risk Score in home-monitoringSample Risk Dimensions Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 17. THE VALUE OF THE PATIENT PROFILEValue SAS “SNOWBALLS” OVER TIME Demographics Segment ID Social Disease Conditions Prescriptions Readmission Propensity to Engage Risk Score in home-monitoring Time Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 18. “NIMBLE DATA CAPTURE” SUPPORTS AGILE LEARNINGSAS IN A FLEXIBLE INFORMATION ARCHITECTURE Patient Quality & Cost Data Flow Experience of Care Safety of Care of Care Actions Patient Profile Analytic Models EDW Analytic Data Store SAS Analytical Environment Data Flow Copyright © 2012, SAS Institute Inc. All rights reserved.
    • 19. Questions?For further information contact:Avery EarwoodPrincipal Healthcare Consultant,SAS Health Analytics PracticeM: (919) 749-9098Avery.Earwood@SAS.com Copyright © 2012, SAS Institute Inc. All rights reserved.