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Session #16: How Allina Health Uses Analytics to Transform Care. Penny Ann Wheeler, MD. President and Chief Clinical Officer, Allina Health. Advancing care Through Analytics The Allina Health Journey. Penny Wheeler, M.D. President and Chief Clinical Officer September 2014. Key Questions. - PowerPoint PPT Presentation
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Session #16:How Allina Health Uses Analytics to Transform Care
President and Chief Clinical Officer, Allina HealthPenny Ann Wheeler, MD
ADVANCING CARE THROUGH ANALYTICSTHE 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?
• 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
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
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 patient’s 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
20091 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?
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All About Creating Value…
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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
ServicesThat Add
Value
40%Waste
60%Value
All ServicesAdd
Value
100%Value
Future
Now
What We Pay For…
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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 Complicationsb) Unnecessary Treatmentsc) Inefficiencyd) Errors
Four Measures of Success:Allina Health 2016 Strategic Outcomes
4. Organizational Vitality
1. Patient Care/Experience
2. Population Health
3. Patient Affordability
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Better Care/
Experience
Organizational Vitality
Better Health
Reduce percapita costs
Triple Aim Integration InitiativesQuality 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
Allina Health Enterprise Health Management PlatformTransitioning Data to Actionable Information
Bridging Historical, Current, and Predictive InformationSelected Health Intelligence & Delivery Tools at Allina
“Potentially Preventables”
Census Dashboard
Enterprise Data Warehouse
Reporting Workbench
PredictiveRetrospective Real time
What is happening?What happened? What may happen?
PPR Dashboard
Spe
cific
Gen
eral
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 use2) Just starting or sporadic use3) Moderate use but increasing4) Good use 5) Very strong use6) 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 BedsideThe Census Dashboard
Identifies Patient Readmit Risk
Identifies Prior IP Visits in Last Week & Month
Identifies Transition Conference Status
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Allina Results: Heart Failure
2011 Q1
2011 Q2
2011 Q3
2011 Q4
2012 Q1
2012 Q2
2012 Q3
2012 Q4
2013 Q1
2013 Q2
2013 Q3
2013 Q4
0%
5%
10%
15%
20%
25%
Combined Metro
Combined Metro Linear (Combined Metro)
RARE Campaign
Graph provided by ICSI
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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
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0% to 5% 5% to 10% 10% to 15%
15% to 20%
20% to 25%
25% to 35%
35% to 80%
0% to 5% 5% to 10% 10% to 15%
15% to 20%
20% to 25%
25% to 35%
35% to 80%
Percent of Total Patients
0.389098235272172
0.348655001305824
0.131720329813827
0.061392381449837
9
0.030556281013319
4
0.025332985113606
7
0.013244786031414
4
Percent of total Readmissions
0.135885570046277
0.305847707193943
0.215187210769878
0.130416491375684
0.087084560370214
7
0.072780816154817
1
0.052797644089188
2
3%
8%
13%
18%
23%
28%
33%
38%
43%
3%
8%
13%
18%
23%
28%
33%
Model estimated percent probability of readmission
Percen
t of Total P
atien
ts
Percen
t of Total R
ead
mission
s
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
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
$9,000
-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 EventHealthways Data for Diabetics with heart Failure(blue line)
Example: Basic Cost Curve for Individual with a Major Hospitalization
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Point of traditional payer-based care management
Point of predictive intervention
Green: potential cost curve with predictive intervention
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
Supporting Cohort ManagementDriving 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
Jan-
11
Mar
-11
May
-11
Jul-1
1
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-11
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-12
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74.0%
76.0%
78.0%
80.0%
82.0%
84.0%
86.0%
88.0%
Mammogram Optimal CareGoal = 85%
Example: Supporting Wellness & Prevention
Successfully Keeping Patients Well
56.0%
61.0%
66.0%
71.0%
76.0%
Colon Cancer Screening Optimal Care
Goal = 73%
Mammogram Optimal Care
Colon Cancer Screening Optimal Care
MD Name
Supporting Wellness & PreventionAmbulatory 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
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
Thank You
Transition from Volume to ValuePlanning 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
requirementsToday Transition Tomorrow
Phase Objectives
• Evolve priorities based on:• Contracts• Populations• Regulatory changes
Driving Improvement to Advance CareThe 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 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
Pioneer ACOSelected 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
Results: Allina’s Elective Inductions < 39 Weeks (%)
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06-5%
0%
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10%
15%
20%
25%
30%
35%
Allina Allina 2009 Baseline Allina 2013 Goal