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Session #16: 
How Allina Health Uses Analytics to Transform Care 
Penny Ann Wheeler, MD 
President and Chief Clinical Offi...
ADVANCING CARE THROUGH ANALYTICS 
THE ALLINA HEALTH JOURNEY 
Penny Wheeler, M.D. 
President and Chief Clinical Officer 
Se...
Key Questions 
• Who is Allina Health? 
• Why change? 
• What are the new measures of success? 
• What’s needed to move to...
4
Allina is the Region’s Largest 
Health Care Organization 
• 13 Hospitals 
• 82 Clinic sites 
• 3 Ambulatory care centers 
...
The Imperative for Change: 
The Traditional Healthcare Model is Broken 
Representative timeline of a patient’s experiences...
Why Change? 
If food prices 
had risen at 
medical inflation rates 
since the 1930s 
*Source: American Institute for Preve...
All About Creating Value… 
9 
Value = Good / Cost 
“Quality improvement is the most powerful driver of 
cost containment.”...
Preventable Complications 
Unnecessary Treatments 
Inefficiency 
Errors 
Services 
That 
Add 
Value 
40% 
Waste 
60% 
Valu...
Poll Question #1 
In your opinion, which of the 4 categories of 
waste is the most important to address by the 
healthcare...
Four Measures of Success: 
Allina Health 2016 Strategic Outcomes 
1. Patient Care/Experience 
2. Population Health 
3. Pat...
Triple Aim Integration Initiatives 
Quality Roadmap 
Goal Initiative(s) 
1) Perform under payment for quality and 
value m...
Allina Health Enterprise Health Management Platform 
Transitioning Data to Actionable Information
Bridging Historical, Current, and Predictive Information 
Selected Health Intelligence & Delivery Tools at Allina 
PPR Das...
Poll Question #2 
For healthcare providers, on a scale of 1-5, 
how well do you feel you are using predictive 
information...
Example: Supporting Care Coordination 
Predicting Unnecessary Admissions and 
Readmissions 
Challenge 
– Substantially red...
Getting the Model to the Bedside 
The Census Dashboard 
Identifies Patient 
Readmit Risk 
Identifies Transition 
Conferenc...
20 
Allina Results: Heart Failure 
25% 
20% 
15% 
10% 
5% 
0% 
Combined Metro 
Combined Metro Linear (Combined Metro) 
201...
RARE Campaign 
Graph provided by ICSI 
21
The Readmission Model Results: 
How are our patients grouped? 
• High Risk: 
– 20 – 100% Readmission Risk: 7% of populatio...
Predictive Model Confidence 
Why do we believe the Readmission Model? 
Comparing existing models with standard C-Statistic...
Example: Basic Cost Curve for Individual 
$9,000 
$8,000 
$7,000 
$6,000 
$5,000 
$4,000 
$3,000 
$2,000 
$1,000 
$0 
with...
Example: Supporting Cohort Management 
Providing Care to Patients with Diabetes 
Challenge 
– Provide superior care for Al...
Supporting Cohort Management 
Driving Improvement through Access to Information 
Select by patient, 
clinic, provider or 
...
Example: Supporting Wellness & Prevention 
Successfully Keeping Patients Well 
Challenge 
– Avoiding future illness is cor...
Supporting Wellness & Prevention 
Ambulatory Dashboard 
MD Name 
Ability to focus on a 
specific provider or 
patient popu...
Summary 
This is only just the start… 
Lessons Learned 
– Pareto analysis of population data key for determining 
opportun...
Thank You
Transition from Volume to Value 
Planning for the inflection point 
Payment Type 
Penetration 
FFS 
Global payment 
Other ...
Driving Improvement to Advance Care 
The Clinical Program Infrastructure 
Clinical Program Infrastructure 
Clinical /Opera...
Translating Concept to Action 
Selection of Key Allina Health Initiatives 
Allina Integrated Medical (AIM) Network 
– Alig...
Pioneer ACO 
Selected Focus Areas 
Area of Focus Implemented Tactics 
Preventable 
Admissions & 
Emergency Department 
Vis...
Results: Allina’s Elective Inductions 
< 39 Weeks (%) 
35% 
30% 
25% 
20% 
15% 
10% 
5% 
0% 
2009-01 2009-04 2009-07 2009-...
How Allina Health Uses Analytics to Transform Care - HAS Session 16
How Allina Health Uses Analytics to Transform Care - HAS Session 16
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How Allina Health Uses Analytics to Transform Care - HAS Session 16

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PENNY ANN WHEELER, MD
President and Chief Clinical Officer, Allina Health

Published in: Healthcare
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How Allina Health Uses Analytics to Transform Care - HAS Session 16

  1. 1. Session #16: How Allina Health Uses Analytics to Transform Care Penny Ann Wheeler, MD President and Chief Clinical Officer, Allina Health
  2. 2. ADVANCING CARE THROUGH ANALYTICS THE ALLINA HEALTH JOURNEY Penny Wheeler, M.D. President and Chief Clinical Officer September 2014
  3. 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? 3
  4. 4. 4
  5. 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 5
  6. 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. 7. Why Change? 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 7
  8. 8. 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
  9. 9. 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
  10. 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
  11. 11. Four Measures of Success: Allina Health 2016 Strategic Outcomes 1. Patient Care/Experience 2. Population Health 3. Patient Affordability 4. Organizational Vitality 12 Better Care/ Experience Better Health Reduce per capita costs Organizational Vitality
  12. 12. Triple Aim Integration Initiatives Quality Roadmap Goal Initiative(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
  13. 13. Allina Health Enterprise Health Management Platform Transitioning Data to Actionable Information
  14. 14. Bridging Historical, Current, and Predictive Information Selected Health Intelligence & Delivery Tools at Allina PPR Dashboard “Potentially Preventables” Census Dashboard Enterprise Data Warehouse Reporting Workbench Retrospective Real time Predictive What happened? What is happening? What may happen? General Specific Readmissions Model Modeling of Potentially Preventable Events
  15. 15. 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
  16. 16. 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
  17. 17. Getting the Model to the Bedside The Census Dashboard Identifies Patient Readmit Risk Identifies Transition Conference Status Identifies Prior IP Visits in Last Week & Month
  18. 18. 20 Allina Results: Heart Failure 25% 20% 15% 10% 5% 0% Combined Metro Combined Metro Linear (Combined Metro) 2011 Q12011 Q22011 Q32011 Q42012 Q12012 Q22012 Q32012 Q42013 Q12013 Q22013 Q32013 Q4
  19. 19. RARE Campaign Graph provided by ICSI 21
  20. 20. 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 0% to 5% 5% to 10% 10% to 15% 15% to 20% 20% to 25% 25% to 35% 35% to 80% 45% 40% 35% 30% 25% 20% 15% 10% 5% Percent of Total Patients 39% 35% 13% 6% 3% 3% 1% Percent of total Readmissions 14% 31% 22% 13% 9% 7% 5% 35% 30% 25% 20% 15% 10% 5% 0% 0% Percent of Total Readmissions Percent of Total Patients Model estimated percent probability of readmission
  21. 21. 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 Identification Process for Care Management Programs Using Condition-Specific Cut Points”, POPULATION HEALTH MANAGEMENT, Volume 14, Number 0, 2011
  22. 22. Example: Basic Cost Curve for Individual $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 $0 with a Major Hospitalization -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Months Before and After High Cost Event Healthways Data for Diabetics with heart Failure(blue line) 24 Point of traditional payer-based care management Point of predictive intervention Green: potential cost curve with predictive intervention
  23. 23. Example: Supporting Cohort Management Providing 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
  24. 24. Supporting Cohort Management Driving Improvement through Access to Information Select by patient, clinic, provider or any combination Filter by Pioneer Shows performance of composite measure components ACO Patients
  25. 25. Example: Supporting Wellness & Prevention Successfully 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 76.0% 71.0% 66.0% 61.0% 88.0% 86.0% 84.0% 82.0% 80.0% 78.0% 76.0% 74.0% Mammogram Optimal Care Goal = 85% Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 56.0% Colon Cancer Screening Optimal Care Goal = 73% Mammogram Optimal Care
  26. 26. Supporting Wellness & Prevention Ambulatory 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
  27. 27. 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
  28. 28. Thank You
  29. 29. Transition from Volume to Value Planning for the inflection point Payment Type Penetration FFS Global payment Other Time 100% 50% 5% • Retain patients (keepage) • Regulatory requirements • Manage risk progression • Payment reform • Increase volume • Maximize payment • Minimize cost • Meet regulatory requirements Today Transition Tomorrow Phase Objectives • Evolve priorities based on: • Contracts • Populations • Regulatory changes
  30. 30. 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
  31. 31. 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
  32. 32. Pioneer ACO Selected Focus Areas Area of Focus Implemented 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 patient’s 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
  33. 33. Results: Allina’s Elective Inductions < 39 Weeks (%) 35% 30% 25% 20% 15% 10% 5% 0% 2009-01 2009-04 2009-07 2009-10 2010-01 2010-04 2010-07 2010-10 2011-01 2011-04 2011-07 2011-10 2012-01 2012-04 2012-07 2012-10 2013-01 2013-04 Allina Allina 2009 Baseline Allina 2013 Goal

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