applications of ai to revenue cycle ... and rcm at change...denial propensity scoring in assurance...

© 2019 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved. 1© 2019 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved.

Change Healthcare

June 13, 2019

APPLICATIONS OF AI TO REVENUE CYCLE MANAGEMENT SOFTWARE AND SERVICES

© 2019 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved. 2

Agenda

• Artificial Intelligence and Its Practical Innovations in Healthcare (Adam Sullivan, , Ph.D.)

• Denial Propensity Scoring: Using AI to Effectively Prevent Claim Denials (Brian Andrews)

• Charge Capture Advisor: Using AI to improve Charge Capture (Vindali Vartak)

• Redefining RCM Excellence through AI (Greg Arnold)

• Summary and Q&A


Artificial Intelligence and its Practical

Innovations in HealthcareAdam Sullivan, Ph.D.


AI in Healthcare Summary

Early Diagnosis Health Monitoring Medical Imaging

Medical

Intelligence

Precision Medicine Personalized Treatment Plans Clinical Trials

Personalization

Smart Electronic

Health RecordsPayment Accuracy

Patient Identification

and Retention

Optimized

Healthcare

System


AI Adoption as of 2018

94%of companies believe

AI is key to competitive

advantage

1 in 20 companies has extensively

incorporated AI in offering

or processes

Source: Paul Taylor, IBM Fellow, ODSC West 2018


About 1/3 of healthcare spending is wasted

Sources: https://advocacy.consumerreports.org/research/engaging-consumers-on-health-care-cost-and-value-issues/

$10,000

$8,000

$6,000

$4,000

$2,000

$0

Average U.S. Healthcare Spending per Person in 2014

$9,700Total Wasted Spending per Person……… $2,910

Wasted

Spending

Necessary

Spending

Unnecessary ServicesExample: Duplicate Tests $800

Inefficient Care DeliveryExample: Test Results Not Shared $495

Inflated PricesExample: Excessive Profits $400

FraudExample: False Claims $285

Prevention FailuresExample: Missed Flu Shot $210

Excess Administrative CostsExample: Billing Errors $720


Change Healthcare’s initial AI focus areas

+ Lots more to come

Claims Lifecycle Management helping payers and providers create

efficiencies and reduce the cost burden in claims processing

Medical Text Understanding helping to improve and streamline

medical coding, reduce errors, fraud, waste and abuse.

Workflow Prioritization and Optimization helping make our own and our

customer’s work smarter and more efficient.

Patient Engagement helping to increase consumer/patient experience

and engagement by pairing AI with behavioral science.


Where to invest for a successful AI approach

Breath and Depth of Data

Talent & Infrastructure

Embedded into Workflow


Denial Propensity Scoring: Using AI to Effectively Prevent Claim Denials Brian Andrews

Section 2.0


AI and denials prevention for health systemsDenial Propensity Scoring in Assurance Reimbursement Management™

• Market problem

– The appeal process and final denials

can cost an average hospital up to

3% of their revenue

o Overall value: $3.6B of unnecessary

denials rework impact to payers and

providers

– Highlighting and preventing certain

types of denials before they happen

allows providers to:

o take corrective action pre-submission

o Improve first-pass adjudication rate

and cash flow

o Reduce cost of appeals


Using a robust data set, Claims Lifecycle AI identified $6.2B in forecasted allowed amounts pre-submission

OBSERVATIONINPUT RESULTS


Introducing Denial Propensity Scoring with Claims Lifecycle Artificial Intelligence

Innovation& Solution Mindset

Millions of transactions to

train models

Data Science Expertise

Industry knowledge,

expertise and in RCM workflow

today

Our trusted customers to partner with

Change

Healthcare

Assets

Predict and prevent denials – before the claim is submitted

A service to help providers not just

manage denials but prevent denials

before submission.

Uses insights from millions of

transactions to train deep

learning models.

Provides claim denial

predictions and likely reason

for denial to drive action.

Built on our Intelligent Healthcare

Platform, leveraging enterprise

API’s for real-time predictions within

the provider claim workflow.


Denial Propensity Scoring

• Assurance

Reimbursement

Management’s Denial

Propensity Scoring

solution scores

accepted claims within

current workflow for

their likelihood to deny.

• Probable reason

information is provided

to drive action steps

before the claim is

submitted to the payer

to drive acceptance

and payment and

avoid costly appeals

and follow up.

Initial Denial Categories

Authorization or Pre-certification

Avoidable Care

Bundling

Duplicate Claim or Service

Medical Coding

Documentation Requested

Medical Necessity

Missing or Invalid Claim Data

Provider Eligibility

Registration or Eligibility

Timely Filing

Other


Charge Capture Advisor:Using AI to improve Charge CaptureVindali Vartak

Section 3.0


A High Impact Job to be Done: Charge Capture

Charge capture is a critical input to provider billing processes with a direct consequence on patient

revenue.

- Capture all services provided

- Make charge audit efficient AND effective

*Based on post payment audit and recovery efforts

Revenue Leakage can cost providers 1%* of net revenue on missing charges for services provided and the

manual labor intensive audit work for discovery and recovery


Using AI to drive meaningful improvement:

Charge Capture Advisor

Push missing charges prediction to

the user while working a claim in

their workflow

Improve charge capture potential

proactively with impact to net

revenue of 25-75 BPS*

*Based on post payment audit and recovery efforts


Charge Capture Advisor

PREDICT & NOTIFY

AI predictions of missing

charges presented

alongside core system

where user can take

action


Engaging Users and Integrating in their workflow

Biller/Coder utilizes notification of potential missing charges as

they are working through their claims log

Biller/Coder accepts the predicted missing charge if

appropriate documentation is available and ensures all services provided are documented

Provider utilizes the predictor at a single point or at

multiple points in the workflow to ensure claim

completeness at each level

Provider assesses the impact of the predictor

and manages performance via analytics

Empathize

Define

Ideate

Build

Test

Implement


Redefining RCM Excellence through AI Greg Arnold

Section 4.0


RCM global scale, services and technical innovation

US & Off-Shore

Scale

Process

Automation

Industry

Expertise & R&D

Artificial

Intelligence

Predictive

Analytics

Compliance &

InfoSec

Patient Access & RCM

Centers of Excellence

*Focused by specific EMR (i.e. Epic, GE Centricity)

Change Healthcare RCM Operations Experience & Global Scale

Key Facts

• Revenue Cycle Outsourcing for Hospitals, Physicians & Health Systems

• $50B in annual RCM claims processing (Change Healthcare processes $1T in healthcare claims overall)

• $9.2B in annual collections

• Provides inpatient and outpatient coding and billing services for more than 25,000 providers

• ~60 office locations with 9 major offices / facilities

• 9,000 employees (on-shore and off-shore capacity)

• Early adopter of Change Healthcare technology solutions

Proven Scale and Scope of Services


We are applying machine learning solutions to redefine coding

quality, cycle time and cash performance standards

Focus Areas

• Scope: Radiology, EMS Coding

• Exploration: General Surgery, E&M, Pathology

Key Capabilities

• Provides evidence-based compliant coding

• Deep learning approach for extracting data elements from medical texts

• Novel text generation method for clinical document improvement

• A unique approach for constructing network structured information from unstructured medical charts

• Resampling method for improving the performance of machine learning tasks from healthcare texts


We are leveraging artificial intelligence to proactively manage

and prevent denials

Focus Areas

• Scope: Physician and Hospital

Key Capabilities

• RCM Services leverages the unique scoring model mentioned earlier via Claims Lifecycle AI to determine the likelihood of denial for carriers prior to submission –improving denial avoidance on behalf of our clients

• We identify key attributes that need to be provided with claim submission

• If denial is received, model scores the likelihood of

appeal success allowing focused effort on most valued claims


Also adopting accelerated AI-based document data ingestion

Focus Areas

• Scope: Physician (RAD, Path, ANE)

• Exploration: Hospital

Key Capabilities

• Collect, interpret, and ingest data from unstructured (scanned) images

• Utilize Machine Learning (ML) to pattern match critical data elements across various client formats

• Provides intelligent rationalizing to existing systems data to deliver standardized data matching

• Significantly reduce manual data entry for physical

documents


SummaryAdam Sullivan, Ph.D.

Section 5.0


Integrating practical AI applications into your RCM

1) Education: Take time to understand AI and its capabilities to broadly

improve RCM functions

2) Identification: Select areas of your revenue cycle where AI can be

deployed with maximum impact

3) Selection: Find the right specific AI application, solution, or partner to

affect change where needed in RCM

4) Implementation: Train staff, ingrate partners, ensure solution is

appropriate for your RCM workflow


Questions?

Adam Sullivan, Ph.D.Senior Director,

Artificial Intelligence

Brian Andrews

SVP Product, Hospital

Revenue Cycle Management

Vindali Vartak

Senior Director,

Advanced Analytics

Gregory Arnold

SVP & General Manager,

Physician Services

For more information, visit www.changehealthcare.com or contact us www.changehealthcare.com/contact/sales

http://www.changehealthcare.com/

http://www.changehealthcare.com/contact/sales

applications of ai to revenue cycle ... and rcm at change...denial propensity scoring in assurance...

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