#HASummit14

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?

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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

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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 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

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2011 Q4

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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

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0.030556281013319

4

0.025332985113606

7

0.013244786031414

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Percent of total Readmissions

0.135885570046277

0.305847707193943

0.215187210769878

0.130416491375684

0.087084560370214

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0.072780816154817

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0.052797644089188

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3%

8%

13%

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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

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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

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74.0%

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Mammogram Optimal CareGoal = 85%

Example: Supporting Wellness & Prevention

Successfully Keeping Patients Well

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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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Allina Allina 2009 Baseline Allina 2013 Goal