Session #16: How Allina Health Uses Analytics to Transform Care

1. Session #16:How Allina Health Uses Analytics to Transform Care Penny Ann Wheeler, MD President and Chief Clinical Officer, Allina Health

2. Advancing care Through AnalyticsThe Allina Health Journey Penny Wheeler, M.D. President and Chief Clinical Officer September 2014

3. Key Questions Who is Allina Health? Why change? What are the new measures of success? What’s needed to move to higher value care? How do we use advanced analytics to drive improvement? What are our results thus far and lessons learned?

5. Allina is the Region’s Largest Health Care Organization • 13 Hospitals • 82 Clinic sites • 3 Ambulatory care centers • Pharmacy, hospice, home care, medical equipment • 26,000 employees • 5,000 physicians • 2.8 million+ clinic visits • 110,000+ inpatient hospital admissions • 1,658 staffed beds • 3.4B in revenue • 32% Twin Cities market share

6. The Imperative for Change:The Traditional Healthcare Model is Broken Representative timeline of a patient’s experiences in the U.S. health care system http://www.iom.edu/~/media/Files/Activity%20Files/Quality/LearningHealthCare/Release%20Slides.pdf

7. If food prices had risen at medical inflation rates since the 1930s*Source: American Institute for Preventive Medicine Why Change?

9. All About Creating Value… Value = Good / Cost “Quality improvement is the most powerful driver of cost containment.” - Michael Porter, PhD Economics Harvard Business School

10. What We Pay For… Now Preventable Complications Future 40% Waste Unnecessary Treatments Inefficiency All Services Add Value Errors Services That Add Value 100% Value 60% Value

11. Poll Question #1 • In your opinion, which of the 4 categories of waste is the most important to address by the healthcare industry? • Preventable Complications • Unnecessary Treatments • Inefficiency • Errors

12. Four Measures of Success:Allina Health 2016 Strategic Outcomes • Patient Care/Experience • Population Health Better Care/ Experience Better Health • Patient Affordability Reduce per capita costs Organizational Vitality Organizational Vitality

14. Triple Aim Integration InitiativesQuality Roadmap

15. Allina Health Enterprise Health Management PlatformTransitioning Data to Actionable Information

16. Bridging Historical, Current, and Predictive InformationSelected Health Intelligence & Delivery Tools at Allina Specific PPR Dashboard Readmissions Model Census Dashboard “Potentially Preventables” Modeling of Potentially Preventable Events General Reporting Workbench Enterprise Data Warehouse Predictive Retrospective Real time What happened? What is happening? What may happen?

17. Poll Question #2 • For healthcare providers, on a scale of 1-5, how well do you feel you are using predictive information to address potentially preventable events? • No use • Just starting or sporadic use • Moderate use but increasing • Good use • Very strong use • Unsure or not applicable

18. Example: Supporting Care CoordinationPredicting Unnecessary Admissions and Readmissions Results • Reduced readmissions for patients who received transition conferences (June 2013-June 2014) • High-risk patients: 15.8% decrease in readmissions • Moderate-high-risk patients: 5.4% decrease in readmissions Challenge • Substantially reduce unnecessary admissions and readmissions Solution • Predict patients at high risk for unnecessary admissions and readmissions • Develop and use census dashboard to identify and manage patients • Prioritize care coordination and clinical interventions based on risk level • Predictive model C-statistic of 0.729

19. Getting the Model to the BedsideThe Census Dashboard Identifies Transition Conference Status Identifies Patient Readmit Risk Identifies Prior IP Visits in Last Week & Month

20. Allina Results: Heart Failure

21. RARE Campaign Graph provided by ICSI

22. The Readmission Model Results:How are our patients grouped? • High Risk: • 20 – 100% Readmission Risk: 7%of population • Moderate-High Risk: • 10 – 20% Readmission Risk: 19%of population • Moderate Risk: • 5 – 10% Readmission Risk: 35% of population • Low Risk: • 0 – 5% Readmission Risk: 39%of population

23. Predictive Model ConfidenceWhy do we believe the Readmission Model? Comparing existing models with standard C-Statistic (Area under ROC Curve) measure of performance • Random coin toss selection: 0.5 • State-of-art techniques(ACG): (0.70 to 0.77)[1] • Current Allina technique: 0.861 Allina Model was found to have a precision* of ~ 0.9 *Precision is the fraction of Predicted patients that actually have a PPE. In this case, on a dataset in which it was tested about 90% of patients predicted by the model had a PPE. Note, this is different from sensitivity, which is the fraction of actual PPE instances that are predicted. 1 Shannon M.E. Murphy, MA, Heather K. Castro, MS, and Martha Sylvia, PhD, MBA, RN, “Predictive Modeling in Practice: Improving the Participant Identification Process for Care Management Programs Using Condition-Specific Cut Points”, POPULATION HEALTH MANAGEMENT, Volume 14, Number 0, 2011

24. Example: Basic Cost Curve for Individual with a Major Hospitalization Point of traditional payer-based care management Point of predictive intervention Green: potential cost curve with predictive intervention (blue line)

25. Example: Supporting Cohort ManagementProviding Care to Patients with Diabetes Challenge • Provide superior care for Allina Health’s diabetic population Solution • Identified and stratified diabetes cohorts using registries • Identified gaps in care for diabetes patients (e.g. A1c, blood pressure management) • Provided workflow capability for care teams to manage the population through ambulatory quality dashboard Results • Highest national score for Diabetes Care Quality Measure in 2012 of all CMS Pioneer ACOs • U.S. leader in management of diabetes patients and Diabetes Optimal Care results

26. Supporting Cohort ManagementDriving Improvement through Access to Information Select by patient, clinic, provider or any combination Filter by Pioneer ACO Patients Shows performance of composite measure components

27. Example: Supporting Wellness & PreventionSuccessfully Keeping Patients Well Challenge • Avoiding future illness is core to superior population health management Solution • Established and reported on optimal care scores for individuals • Identified gaps in care and accurately connected them to care teams to close gaps in care Results • Eliminated significant gaps in wellness screening and preventative care • Allina Health has achieved some of the best ambulatory optimal care scores in the nation through a focused clinician engagement strategy using the EHMP Colon Cancer Screening Optimal Care Mammogram Optimal Care

28. Supporting Wellness & PreventionAmbulatory Dashboard MD Name Ability to focus on a specific provider or patient population Shows performance on optimal care and component measures with patient detail, provider name and clinic

29. SummaryThis is only just the start… Lessons Learned • Pareto analysis of population data key for determining opportunity and focus • Consistent quality drives lower cost of care • Focus on waste / “unhelpful care variation” • Use predictive modeling to focus care management resources • Strengthen the patient/primary care team relationship • Keep the patient at the center of all decisions

30. Thank You

31. Transition from Volume to ValuePlanning for the inflection point • Increase volume • Maximize payment • Minimize cost • Meet regulatory requirements • Evolve priorities based on: • Contracts • Populations • Regulatory changes • Retain patients (keepage) • Regulatory requirements • Manage risk progression • Payment reform Phase Objectives 100% Payment Type Penetration FFS 50% Global payment Other 5% Time

32. Driving Improvement to Advance CareThe Clinical Program Infrastructure

33. Translating Concept to ActionSelection of Key Allina Health Initiatives Allina Integrated Medical (AIM) Network • Aligns 900+ independent physicians and 1,200 Allina Health employed physicians to deliver market-leading quality and efficiency in patient care • Clinical Service Lines (CSLs) • Provide consistently exceptional and coordinated care across the continuum of care and across sites of care. CSLs are physician-led, professionally-managed and patient centered. Medicare Pioneer ACO • Member of CMS Pioneer Pilot Demonstration • Above average performance for 25 of 33 quality performance measures, including the highest performer for 3 of the measures • Held the Pioneer ACO Population to 0.8% cost growth for 2012 Northwest Metro Alliance • A multi-year collaboration between HealthPartners & Allina Health in the Northwest Twin Cities suburbs focused on the Triple Aim and a learning lab for ACOs • Since the Alliance model was implemented, medical cost increases have been below the metro average for the past two years and cost increases were less than one percent for two years in a row • Expanded access to stress tests for ED patients with chest pain and prevented 480 low-risk chest pain inpatient admissions, saving an estimated $2.16 Million in 2012

34. Pioneer ACOSelected Focus Areas

35. Results: Allina’s Elective Inductions < 39 Weeks (%)