How Best Performing Benefits Plans Use Predictive Modeling to Achieve Cost Reductions

August 31, 2021 Webinar

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Meet Poindexter Webinar:

Welcome!

How Best Performing Benefit Plans Use AI…To Achieve Double Digit Cost Reductions

National Reach Clients 3.5 Million Members Deep Proven Experience

Founded 2004

© Virgin Pulse 2021. All Rights Reserved.

Agenda

• The Business of Data Analytics

• Healthcare Intelligence: Predictive Modeling

• Covid-19 Update

• Applying Data Analysis and Predictive Modeling

• Social Determinants of Health

The Business of Data Analytics

Characteristics of Legacy Healthcare Analytics Systems

6

ü Query based systems that required the user to have an intimate knowledge of claims formatting.

ü Static reporting with limited drill down capabilities

ü Primarily focused on retrospective financial reviews

ü Rigid input and output structures

ü Non-graphical user interfaces

ü Client server (premise based)

ü Limited data storage and lengthy load times

Advanced Features / Differentiators of a Healthcare Data Analytics Systems

ü Integrated and Configurable dashboards to highlight areas of most interest

ü Automated Reporting for Executive level presentations

ü Prescriptive Analytics that provide recommendations for improvement

ü Potentially Preventable Events (Hospital and ER)

ü Integrated medical and Rx reporting

ü Advanced analytics platforms will include tools to measure the

performance of initiatives that health plans may choose to put in place

ü MUST TELL THE STORY IN LAYMANS TERMS

7

Healthcare Intelligence

Predictive Modeling

What information feeds into the data model?

Lab & Biometrics

Medical &

Pharma

Claims

Vision &

Dental

HRA

EMR /

EHR

Eligibility

Poindexter Platform

Analytics / Predictive Modeling

Results Tracking

360o View

OpportunityIdentification Support for

Action Steps

Our model relies on the historical data within it –that goes back nearly 15 years for some clients.

23 Trillion bytes of data!

The need for historical data for risk score processing is why we require multiple years of data for predictive purposes.

What Are the Predictive Modeling Inter-Workings?

• APH’s clinical leaders Barbara Rutkowski, EdD, MSN, CCM, VP, Clinical Operations and Susan Mutto, RN, MA, Director of Clinical Development lead in-house efforts to research any relevant clinical information and code set updates for the model.

• As input to the algorithm predictors of risk, they rely on sources such as, MIPS, Milliman, Medical Boards, Associations such as the American Diabetes Association, American Cancer Society, Global Initiative for Chronic Obstructive Lung Disease (GOLD), CPT code sets, ICD-10-CM code sets, HCPCS code sets, medical and clinical literature, well-established, commonly referenced national clinical guidelines, and more.

• Their efforts are overseen and guided by the APH Medical Director, Edwin Matthews, MD., who is Board Certified by the American Board of Internal Medicine.

Predictive Modeler – The Healthcare Factor

• Age

• Gender

• Location

• Diagnosis

• Conditions

• Procedures

• Socioeconomic Consideration

• Cost

• Event Type (inpatient, outpatient, emergency)

• Drug Identity

• Dosage

• Lab

• Time Period

Model Includes:

What Are the Predictive Modeling Inter-Workings?

Our first step in building a predictive model is deciding which features of the data to use, and how to represent those

features.

Feature Engineering

Then, how we categorize and utilize patient member data has an enormous impact on how our model will function.

Intelligently shaping our input data is a practice called feature engineering, the practice of manipulating raw data into a form that helps our model perform better.

What Are APH’s Predictive Modeling Inter-Workings?

An example of categorization for Feature Engineering:

• Diagnosis codes can be used to categorize records into more general groups and comorbidities.

• Lab results can help to show trends over time, or can be used as binary thresholds.

• Some diagnoses, such as a broken leg, should expire after a reasonable interval, while others, such as hemophilia, are permanent conditions.

Feature Engineering (continued)

Example of a Feature

• Poindexter is combing the power of advanced Predictive Modeling with Covid-19 claims to determine future risk for your population

• As a part of the review process we will be highlighting focus areas for potential risk, including

• Under-utilization of ambulatory services

• Increased Rx Brand utilization

• Cost and Utilization of members diagnosed with the virus

• Members who are at-risk for complications due to underlying health issues

Review and Analysis

“Phenotyping” means that the algorithm uses member-specific information to customize risk at the individual level.

In addition to healthcare data, a member’s age, gender, claims data, behaviors and compliance are all used to calculate their risk by looking for similar members in the APH database and calculating a similar cost / outcome.

This allows for a very dynamic and individualized risk scoring method.

Foundation of the Predictive Models: Phenotyping

Predictive Modeling Output

• Predicts the likelihood of conditions (i.e. myocardial infarction, stroke, bone fracture, kidney disease, etc.) and events (i.e. admissions, re-admissions, ER utilization, etc.) six to 12 months in the future.

• Each member is assigned a High, Moderate or Low Risk Score.

• Risk Score is a weighted combination of multiple factors including predicted cost, predicted risk of events, and care gaps.

• Risk Levels are determined by the percentile a member’s risk score falls into.

• Predicts plan risk and expense 12 months in the future for both medical and pharmaceutical spend.

APH’s Predictive Modeling

We offer transparency into what drove risk scoring to support

more informed decision-making and

member-specific planning

Transparency into drivers of risk

APH’s Predictive Modeling360o longitudinal view of the entire

employee / member supports more informed decisions and actions• 49 year old male

• High blood pressure

• Type 2 Diabetes

• Unusual pain script activity (6 prescribing providers, 3 pharmacies)

• Gaps in Care

• High predicted risk score

• Predicted risk for Admission w/in next 12 months

• Predicted risk for Myocardial Infarction

It’s important to comprehensivelyunderstand each

individual in order to map out the best plan

to support their healthcare

requirements

What visibility comes from the model?

CLI

NICAL

PREDICTI

VE

NETWORK

WELLNESS

FI

NANCI

AL

• Medical• Pharmacy• Longitudinal

record – 360o

view

• Cost• Risk• Utilization

• Physician Performance• Quality• Utilization• Cost

• Identification• Engagement• Tracking

• Trending• Spending

Analysis• Benchmarking

Applying Data Analytics and Predictive Modeling

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• Identify out of line trend patterns

• Assess plan opportunities

• Build action plans and recommendations

• Plan adjustments

• Resource Gaps such as disease management or wellbeing programs

Evidence Based Action Plans

Applying Data Analytics and Predictive Modeling

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• Predictive financial analysis informs Re-insurance renewal to reduce or avoid costly lasers and increases

• Predictive clinical analytics identify conditions with high risk to assist with decision making for Wellbeing and Disease management programs

• Care Gaps provide visibility into how well members are managing risk with the current resources available

Business and Clinical Needs

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Better TogetherTwo platforms combined with the same goal: Changing Lives for Good

• The power of claims powered analytics combined with best a best in market wellbeing program

• Better understand members that aren’t actively engaging in programs

• 16 predictive models proactively identify at-risk members to action individuals that wouldn’t have been on the radar before

• Targeted members hand delivered to a coach for review and engagement

• Here’s how it can work

Outcome

• Heart attack avoided and $$$$ saved

Action

• VP platform triggers member to connect with a coach or program

Notify

• Coach notified for review and engagement

Identify

• Member predicted with a high risk of heart attack

Measurement Year 1 Year 2 Year 3 Year 4

Total Savings $978,049 $1,303,798 $1,289,004 $4,464,685

IP Utilization Trend -7% -70% -71% -76%

ER Utilization Trend -40% -15% -14% -33%

Preventive Visit Trend

+61% +2% +9% +8%

$450.24

$1,101.03

$236.46

$879.74

$153.59

$446.88

$793.78

$193.20$350.35

$979…

$153.09

$391.66

$150.18$233.19

$352.37

$161.66

$0.00

$200.00

$400.00

$600.00

$800.00

$1,000.00

$1,200.00

Location A Location B Location C Location D Location E Location F Location G Location H

PMPM - 12 Months Before and After

12 Months Before 12 Months After

Measurable ResultsTargeted Nurse Outreach and Coaching Program

How do Insights Drive Client Action?Predictive Analysis helps clients drive more informed

targeted, decisions and actions

• Delivering current state and predictive analytics visibility

• Identifying the right targets for support, influence and education

• Supporting recommendation of the right outreach program by the right resources for each identified cohort group and individual

APH’s Neighborhood Environmental Index

Social Determinants on Health Focused for Your Population

• Key Drivers

• Neighborhood Score comprised of wealth/income analysis to determine socioeconomic advantage

• Housing index to identify median housing value

• Income Index to set the median household income

• Education analysis to determine the number of individuals with high school and college degrees

• Occupation Index to identify the percentage of persons over 16 with executive, managerial or professional specialty occupations

APH and Poindexter have developed a clearer path forward to better understanding future risk and how to engage members for the best possible outcome

Thank You!