Integrating Predictive Analytics Into Clinical Practice For Improved Outcomes & Financial Performance

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Integrating Predictive Analytics Into Clinical

Practice For Improved Outcomes & Financial

Performance

June 11

th

_{, 2015}

Transforming the HHS Experience Improving the relationship between payers, providers and consumers

Presenters:

Mike Lardieri, AVP Strategic Program Development, North Shore LIJ Health System Ravi Ganesan, President Core Solutions, Inc.

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Agenda

• An overview of various predictive analytics tools • The role of EHRs in predictive analytics

• Practical real world examples for improving clinical practice • Using predictive analytics in population health management

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Pyramid for Analytical Need

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• Level 1 Questions/Curiosity (Physiological)

- More Questions Than Answers

• Level 2 Data (Safety)

- You Need Data to Make Decisions

• Level 3 People (Love/Belonging)

- You Need People who Love Data and are Adaptable to the Changing Nature of Technology & Analytics

• Level 4 Socialization (Esteem)

- Share the Data – Make Sure it Can be Understood by Senior Leaders as Well as the Rank & File

• Level 5 Artificial Intelligence/Smart Systems

- (Self Actualization)

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Large Vendors

Challengers

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• Availability of Software as a Service Model

• Prices are dropping

• Improved usability for business users

• Increasing specialization on verticals –

healthcare, customer service etc.

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Role of EHRs in Predictive

Analytics

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The Health Information Technology Pyramid

Electronic Health Record

Specialized Clinical &

Financial Tools

Apps and Point of

Care Solutions

Devices

Big Data

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The Role of The EHR

• Data Collection - Standardized, Validated Data

• Demographics

• Clinical Quality Measures (MU)

• Behavioral Health Quality Measures

• Analytics - Integration

• Seamless integration with Predictive tools through APIs

• Intelligence - Real-time Feedback

• Configurable business rules and workflows to inform and educate users and direct care.

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EHR Checklist

 Meaningful Use Certified

 Stage 2

 Plans for Stage 3

 Clinical Tools

 Integration of standardized behavioral health tools

 Single Integrated Database

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Gen 1 vs. Gen 2 EHRs    _       _{ } _{ }       _{ }    

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Paradigm Shift

EHR

+

_Intelligence

Predictive

Reactive Care Proactive Care

Healthcare Cost Savings Consumer Cost Savings

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Real world Examples For

Improving Clinical Practice

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Predictive Modeling Process What is the problem we are trying to solve? Gather as much data as available Evaluate Models Run Predictive Analytics

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Suicide Predictive Model

Challenge:

• 41,149 suicides reported in 2013 (CDC);

• 10th leading cause of death for Americans;

• After cancer and heart disease, suicide accounts for more years of life lost than any other cause of death;

• Over 19% suicide rate among people 45 to 64 years old; • 77.9% were male and 22.1% were female.

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Value Based Purchasing

• CMS program that rewards quality over Quantity. A good indicator of

what the future of reimbursements for behavioral health is going to look like.

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Scheduling Efficiency

• Improving access to behavioral health services requires improvements in scheduling efficiencies.

• Same Day Access – Step in the right direction.

• “Third next available” (TNA) appointment and “office visit cycle time” are validated measures, but not widely used. • 2013 study of the Massachusetts private sector reported

wait times of 50 and 39 days for internal medicine and family practice respectively.

• Scheduling has a direct impact on customer experience. http://www.iom.edu/~/media/Files/Perspectives-Files/2015/SchedulingBestPractices.pdf

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Revenue Maximization

Opportunities:

• Impact of ACO on payer mix

• Identify unit costs and impact of various cost

components

• Self Pay/Bad Debt Management

• Transition from fee for service to value based

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Using Predictive Analytics in

Population Health

Management

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Key Principles for Population Management

1. Population-Based Care: Focus on caring for the whole

population you are serving, not just the individuals actively seeking care.

2. Data-Driven Care: Utilize data and analytics in order to make informed decisions to serve those in your population who most need care.

3. Evidence-Based Care: Make use of the best available evidence to guide treatment decisions and delivery of care.

4. Care Management: Engage in actionable care management for the population you serve.

Source: http://www.integration.samhsa.gov/integrated-care-models/14_Population_Management_v3.pdf

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Steps For Implementing

Population Management

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Population Management For Blood Pressure Build a List Identify Care Gaps – No Rx Coordinate Care Check

Compliance MonitoringRemote

Identify other gaps

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Predicting Sickness – Blue Cross Goal: Identify Patients Likely to be hospitalized in the next 3 months Algorithms based on claims, lab, Rx, height, weight, family history etc. to

score risk.

Assign Health Coach to coordinate care

and reduce readmissions.

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Projects At North Shore LIJ

Health System

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Opportunities

•Preventable Readmissions •Length of Stay

•Hospital Acquired Conditions •Chronic Care Management

•Predictive Illness / Disease Progressions •Identification of High Cost Cases

•Wellness Program Management •Micro-Segmentation & Plan Design

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Q&A

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THANK YOU!

Question and Answers