#HASummit14 Session #16: How Allina Health Uses Analytics to
Transform Care President and Chief Clinical Officer, Allina Health
Penny Ann Wheeler, MD
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ADVANCING CARE THROUGH ANALYTICS THE ALLINA HEALTH JOURNEY
Penny Wheeler, M.D. President and Chief Clinical Officer September
2014
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Allina Health Enterprise Health Management Platform
Transitioning Data to Actionable Information
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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
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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
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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
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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
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20 Allina Results: Heart Failure
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RARE Campaign Graph provided by ICSI 21
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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
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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
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(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
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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
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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
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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
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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
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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
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Thank You
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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
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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
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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
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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