Proactively manage quality and outcomes readmissions

Copyright © 2012, SAS Institute Inc. All rights reserved.

Proactively Manage Quality & Outcomes

Overview of SAS Readmission AnalyticsJanuary, 16 2013


Industry Challenges & Opportunities

Vision to be Patient-Centered

Readmission Value Proposition & Use Case

SAS Analytical Platform for Readmission Analytics

SAS TOPICAL AGENDA


SAS INDUSTRY CHALLENGES AND OPPORTUNITIES

Market-driven reform shifting emphasis from volume to value

Retain reimbursement revenue at risk due to new readmission rules

Learn, adapt and innovate to improve clinical quality

Improve Patient experience and engagement to improve health outcomes

Use advance analytics to surface insights and inform decisions


1in8Result in

Readmission within 30 days

1in5

Surgical Hospitalizations

Non Surgical Hospitalizations

2,000,000 Medicare Beneficiaries = $17,500,000,000 in Readmission Costs

SAS AVOIDABLE READMISSIONS INCREASE THE COST OF HEALTHCARE

>


$280M FY 2013

2013 2014 2015

1%2%

3%

SAS PENALTIES FOR MEDICARE READMISSIONS INCREASE OVER TIME

Copyright © 2012, SAS Institute Inc. All rights reserved.Source: http://www.objectivehealth.com/sites/default/files/infographics/Avoidable_Admissions_1_lg.jpg

http://www.objectivehealth.com/sites/default/files/infographics/Avoidable_Admissions_1_lg.jpg




What are the primary drivers of avoidable

readmissions?

Where is my highest potential of readmissions?

How do I best engage patients to prevent

readmission?

How can I avoid or reduce avoidable readmissions?

What potential financial penalties could we be

facing?

What data do I need to reduce readmissions?

SAS READMISSION DILEMMAFOR PROVIDERS


SAS EVIDENCE-BASED ISINDUSTRY BEST PRACTICE

“We will analyze health and consumer data for insights into individuals’ clinical risks…

…to enable the best intervention and treatment decisions at the point-of-care…

…that optimize quality and cost-effective health services.”


About Jill:• Part-time Journalist • Babysits her grandson twice a week • 66 years old

Jill’s health challenges: • Struggled to maintain a health weight • Has elevated blood pressure • Family history of heart trouble

SAS MEET JILL


• What treatment regimen will most likely yield the best outcome for Jill, with her specific genetic profile, Lab results, and psychosocial patient profile?

• What is Jill’s risk of readmission on the day she presents to the ED? On the day she is discharged?

SAS DISCHARGE (INTERVENTION) DECISIONS ARE INHERENTLY PREDICTIVE IN NATURE

• What are the top three post discharge interventions that are most likely to reduce Jill’s risk of readmission?

• What insidious drug interactions is Jill likely to experience based on her unique personal risk factors?

• What will this specific combination of rehabilitation protocols cost Jill? Cost the system?


Treat

Coordinate Engage

Predict Risk Score

Inform Clinical Decisions

IdentifyCare Gaps

PersonalizeCare Plan

Execute Outreach Campaigns

Track, AdaptOptimize

ProfilePatients

Prevent

Reward & Sustain

SAS MEET JACK


PrepareIntegrate & Transform

data for analysis

Profile & PredictBuild, Enhance & Refine

models

Design Build Clinical Decision

Support “rules”

InterveneDecide and capture data

at the point-of-care

Optimize Measure, monitor and

learn through experiments

SAS AN ANALYTICALLY-DRIVEN APPROACH FOR REDUCING AVOIDABLE READMISSIONS


PrepareIntegrate &

Transform data for analysis

Profile & Predict

Build, Enhance & Refine models

Design Build Clinical

Decision Support “rules”

InterveneDecide and

capture new data at point-of-care

Optimize Measure, monitor, learn and adapt

for maximum ROI

Modular Approach

Multiple Entry Points

Four Modules:- Readmission Prediction- Readmission Engagement- Readmission Decision Support- Readmission Optimization

Real Time Decision Support

SAS SAS PLATFORM FOR READMISSION ANALYTICS


SAS ILLUSTRATIVE EXAMPLE Patient: Jill Smith

Age: 66Dx: CHF RRS: 89%

Dx Rx % $

Intervention OptionsRRS

ImpactCost

Impact

Rehab -16% $3,000

SNF -19% $20,000

Telemonitoring -23% $300

Home Health Visit -31% $650

RN Follow-up Call -08% $55

Rx Review -12% $175

New Risk Score: 81% $55


SAS CLINICAL AND ANALYTIC ENVIRONMENT

Analytic Data Store

Analytic Models

Patient Profile

Actions

SAS Analytical Environment

Cost of Care

Quality & Safetyof Care

Patient Experience

of Care

Disease Conditions

Readmission Risk Score

Prescriptions

Segment ID

Propensity to Engage inHome Monitoring

SocialDemographics


Dx

Disease Conditions

ReadmissionRisk Score

Rx

Prescriptions

Segment ID

Propensity to Engage in home-monitoring

SocialDemographics

SAS PATIENT PROFILESAMPLE ATTRIBUTES

Sample PsychosocialDimensions

Sample DescriptiveDimensions

Sample Risk Dimensions


SAS THE VALUE OF THE PATIENT PROFILE“SNOWBALLS” OVER TIMEV

alu

e

Time

Disease Conditions

ReadmissionRisk Score

Prescriptions

Segment ID

Propensity to Engage in home-monitoring

SocialDemographics


SAS “NIMBLE DATA CAPTURE” SUPPORTS AGILE LEARNINGIN A FLEXIBLE INFORMATION ARCHITECTURE

EDW

Data Flow

Data Flow

Analytic Data Store

Analytic Models

Patient Profile

Actions

SAS Analytical Environment

Cost of Care

Quality & Safetyof Care

Patient Experience

of Care


Questions?For further information contact:

Avery EarwoodPrincipal Healthcare Consultant, SAS Health Analytics PracticeM: (919) [email protected]

Health & Medicine

Proactively manage quality and outcomes readmissions