#hasummit14 session #16: how allina health uses analytics to transform care president and chief...

#HASummit14 Session #16: How Allina Health Uses Analytics to Transform Care President and Chief Clinical Officer, Allina Health Penny Ann Wheeler, MD

ADVANCING CARE THROUGH ANALYTICS THE ALLINA HEALTH JOURNEY Penny Wheeler, M.D. President and Chief Clinical Officer September 2014

Key Questions Who is Allina Health? Why change? What are the new measures of success? Whats 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? 3

Allina is the Regions 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 5

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

If food prices had risen at medical inflation rates since the 1930s *Source: American Institute for Preventive Medicine 2009 1 dozen eggs$85.08 1 pound apples$12.97 1 pound sugar$14.53 1 roll toilet paper$25.67 1 dozen oranges$114.47 1 pound butter$108.29 1 pound bananas$17.02 1 pound bacon$129.94 1 pound beef shoulder$46.22 1 pound coffee$68.08 10 Item Total$622.27 Why Change? 7

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

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

Poll Question #1 In your opinion, which of the 4 categories of waste is the most important to address by the healthcare industry? a) Preventable Complications b) Unnecessary Treatments c) Inefficiency d) Errors

Four Measures of Success: Allina Health 2016 Strategic Outcomes 4.Organizational Vitality 1.Patient Care/Experience 2.Population Health 3.Patient Affordability 12 Better Care/ Experience Organizational Vitality Better Health Reduce per capita costs

Triple Aim Integration Initiatives Quality Roadmap GoalInitiative(s) 1) Perform under payment for quality and value models Accountable care pilots Pioneer ACO Commercial partnerships 2) Align incentives across employed and affiliated providers Allina Integrated Medical Network 3) Give providers the data and information needed to improve outcomes Advanced analytics infrastructure including a robust Enterprise Data Warehouse (EDW) 4) Provide consistently exceptional care without waste Primary care team model redesign Care management/patient engagement Clinical program optimization 5) Support transformation with new skills development Allina Advanced Training Program

Allina Health Enterprise Health Management Platform Transitioning Data to Actionable Information

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

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? 1) No use 2) Just starting or sporadic use 3) Moderate use but increasing 4) Good use 5) Very strong use 6) Unsure or not applicable

Example: Supporting Care Coordination Predicting Unnecessary Admissions and 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 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

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

20 Allina Results: Heart Failure

RARE Campaign Graph provided by ICSI 21

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 22

Predictive Model Confidence Why 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 Identication Process for Care Management Programs Using Condition-Specic Cut Points, POPULATION HEALTH MANAGEMENT, Volume 14, Number 0, 2011

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

Example: Supporting Cohort Management Providing Care to Patients with Diabetes Challenge Provide superior care for Allina Healths 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

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

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 Example: Supporting Wellness & Prevention Successfully Keeping Patients Well Mammogram Optimal Care Colon Cancer Screening Optimal Care

MD Name Supporting Wellness & Prevention Ambulatory Dashboard 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

Summary This 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

Thank You

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

Driving Improvement to Advance Care The Clinical Program Infrastructure Clinical Program Infrastructure Clinical /Operational Leadership Team Regional and system wide physician, administrative and clinical operations leaders needed to implement best practice Information Management Infrastructure Measurement System Staff support personnel and systems necessary to measure clinical, financial and satisfaction outcomes for key clinical processes Implementation Support Staff and systems necessary to develop, disseminate, support and maintain the clinical knowledge base necessary to implement best practice

Translating Concept to Action Selection 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

Pioneer ACO Selected Focus Areas Area of FocusImplemented Tactics Preventable Admissions & Emergency Department Visits Applied risk stratification to provide outreach and support to patients at risk for preventable events through Advanced Care Team or Team Care resources Outreach to patients who have not been seen, check treatment compliance and schedule visit Using After-Visit-Summary instructions during patient follow-up care Develop patient-centered goals Provide social worker support if needed Provide support for Advanced Care Planning Preventable Readmissions Applied predictive tool to identify patients most at risk for readmission Prepare integrated After-Visit-Summary and provide the patient w/a Discharge Packet Provider transitions Care transitions intervention Determine and leverage role of pharmacist Patient education Skilled nursing facility transitions Mental Health Care coordination for high-risk patients Assign a Primary Care Provider to each MH patient Eliminate delayed access Effective management of MH resources through patient prioritization Efficient patient transitions Late Life Supportive Care Redesigning care so that patients needs are documented and that caregivers including family are able to access, understand, and comply during the course of caring for the patient End Stage Renal Disease (ESRD) Currently in process of reviewing potential opportunities with nephrologists

Results: Allinas Elective Inductions < 39 Weeks (%)

#hasummit14 session #16: how allina health uses analytics to transform care president and chief...

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