April 2013 Equifax Confidential & Proprietary Information Copyright © 2006, Equifax Inc., Atlanta, Georgia. All rights reserved. Improving Revenue Cycle

•April 2013

Equifax Confidential & Proprietary InformationCopyright © 2006, Equifax Inc., Atlanta, Georgia. All rights reserved.

Improving Revenue Cycle PerformanceThrough Financial Management

Solutions

Equifax Confidential & Proprietary Information

2

Market Challenges• Slower Economy

– 1.5 million NC residents or 17% of population are uninsured (Source – www.statehealthfacts.org)

– NC Medicaid/Medicare is 2.7 million or 30% of population

– NC Unemployment rate at 9.1%; 5th highest in US– Collection of self-pay dollars is even more difficultE

– Escalating healthcare costs are forcing employees to share more of the burden

• Government Regulations

– Changes in Medicare/Medicaid reimbursement policies reduces income

– New revenue models

– Uncertainty and rapid acceleration of change


Information Solutions

ID Verification, Fraud Tools,

Authentication

Financial Solutions Value Add

Patient ID Verification

Credit InformationFull Service Data

Breach

ComprehensiveAuthentication

Solutions

Healthcare Payment /

Income Prediction

Data/DemographicEnhancement

Provider Authentication

Custom Analytics

Data/Demographic Enhancement

Employment & Income Verification

Skip Locate/Mail Return

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Real Time/Online

Patient Access

PointOf

Service

Business

OfficeOr EBO

TPCBadDebt

Ab

ilit

y t

o im

ple

men

t custom

mod

els

ID Verif.-

GenericScoring

ID Verif.-

GenericScoring

Same as POS +

-IncomeVerif.

-Custom Scoring

Gen. Scoring

-CustomScoring

-Employmen

t-

Locate

/’;

Cust

om P

resu

mpti

ve C

harit

y M

odel

-Verify Identity-Determine probability to Pay-Pro-Active Financial Counseling

-Verify Identity-Determine Probability to Pay-Pro-Active Financial Counseling

-Prioritize Workflow-Update Identification-Verify Income-Presumptive Charity

-Prioritize Workflow-Obtain Employment-Skip Locate

Optimize the Revenue Cycle

Batch/Offline

0-90

/120

Day

s

120+

Day

s

DataBreach

Connexus

PatientPortals

Authentication


Healthcare PaymentAnd Income Prediction


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Credit in Healthcare• Permissible Purpose

– Typical billing procedures used in healthcare qualify as an extension of credit under the FCRA and ECOA

• Use of credit information does not impact the patient– Inquiries only viewable by the patient– Not taken into account in credit scoring

• Strictly regulated to ensure patient privacy– Tough security procedures for membership


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Credit in Healthcare

• Using traditional credit reports or financial credit scores may not be predictive of a patient’s probability to pay for healthcare services

• High number of patients with little or no credit history

• Tools must be unique, effective and built specifically for healthcare industry

• Traditional financial credit scores focus on propensity to repay financial debt

• Payment behavior patterns for medical debts are more complex


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Scoring in Healthcare

• Credit scoring is an analytical methodology used to objectively assist in evaluating patient behavior. It utilizes past behavior to predict the performance of future behavior

• Objective in healthcare:– Identify of patients likely or unlikely to pay their health

care debt aka Probability to Pay – Identify patients’ Capacity to Pay– Consistent evaluation for Charity/Medicaid candidates– Collection/Account Management prioritization– Likelihood to file bankruptcy


Equifax Confidential, Page- 9

Score / Model Development Process

Attributes/Variables

Number of public records

Utilization ratios

Number of unpaid collections

Balance of unpaid collections

Balance of patient account

Previous history with hospital

Financial class of patient

Income estimation

Economic Means Criteria

Cred

it

Cu

sto

m

Out of over 600, the most predictable

attributes are isolated to analyze and develop the model

Models are developed froma sample data set that

represents confirmed behaviorof what it is meant to predict.


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Equifax patient

Credit Database

Information + Historical Credit

Score Appended

Observation Performance or “Present Time”

12-24Month

Performance Outcome Period

DevelopmentDevelopmentSampleSample

Performance

Information

(Patient paid amounts)

Patients Admitted 7/10-12/10

Self-Pay Patient Accounts outcome - 9/11

How do you know scores are predictive?

• Validations are done to determine how well a score is performing at segmenting accounts receivables to differentiate account management strategies.


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Total Population: Probability to Pay ScoreRisk Score TOTAL POPULATION Direct Pmt Insurance Pmt AmtRank Range Int % Cum % Int % Cum % Int % Cum%Low 979+ 3,105 5.0% 3,105 5% 2,033 9.35% 2,033 9.35% $456,524 10.20% 10.20%

2 972-979 3,105 5.0% 6,210 10% 1,975 9.08% 4,008 18.43% $473,954 10.59% 20.79%3 965-972 3,106 5.0% 9,316 15% 1,953 8.98% 5,961 27.41% $494,767 11.06% 31.85%4 956-965 3,105 5.0% 12,421 20% 1,951 8.97% 7,912 36.38% $451,184 10.08% 41.93%5 944-956 3,106 5.0% 15,527 25% 1,861 8.56% 9,773 44.94% $430,920 9.63% 51.56%6 929-944 3,105 5.0% 18,632 30% 1,881 8.65% 11,654 53.59% $470,955 10.52% 62.08%7 908-929 3,106 5.0% 21,738 35% 1,699 7.81% 13,353 61.40% $350,982 7.84% 69.93%8 880-908 3,105 5.0% 24,843 40% 1,487 6.84% 14,840 68.24% $303,045 6.77% 76.70%9 845-880 3,106 5.0% 27,949 45% 1,362 6.26% 16,202 74.51% $218,357 4.88% 81.58%

10 808-845 3,105 5.0% 31,054 50% 1,165 5.36% 17,367 79.86% $181,930 4.07% 85.64%11 769-808 3,106 5.0% 34,160 55% 1,000 4.60% 18,367 84.46% $160,839 3.59% 89.24%12 725-768 3,105 5.0% 37,265 60% 820 3.77% 19,187 88.23% $115,276 2.58% 91.81%13 682-725 3,106 5.0% 40,371 65% 636 2.92% 19,823 91.16% $96,526 2.16% 93.97%14 636-682 3,105 5.0% 43,476 70% 514 2.36% 20,337 93.52% $76,102 1.70% 95.67%15 591-636 3,106 5.0% 46,582 75% 393 1.81% 20,730 95.33% $52,524 1.17% 96.85%16 548-591 3,105 5.0% 49,687 80% 282 1.30% 21,012 96.62% $35,584 0.80% 97.64%17 503-548 3,106 5.0% 52,793 85% 260 1.20% 21,272 97.82% $39,346 0.88% 98.52%18 454-502 3,105 5.0% 55,898 90% 199 0.92% 21,471 98.74% $34,851 0.78% 99.30%19 396-454 3,106 5.0% 59,004 95% 147 0.68% 21,618 99.41% $19,223 0.43% 99.73%

High low-396 3,105 5.0% 62,109 100% 128 0.59% 21,746 100.00% $12,178 0.27% 100.00%TOTAL 62,109 100% 21,746 100% $4,475,067 100%

Goods (Payers)

Validations Show the Story


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The Scoring Story in Healthcare: Payment Risk•Scoring shows providers how to segment

their workflow or POS activity

“Goods” – top 35% of the total population includes 61% payers and 70% of the direct payments

“Extra Effort” – includes 25% of the total population, 27% payers, and 22% of the direct payments

“Minimal Effort” – bottom 40% of the total population only has 12% payers and only 8% of the direct payments

Risk ScoreRank RangeLow 979+

2 972-9793 965-9724 956-9655 944-9566 929-9447 908-9298 880-9089 845-88010 808-84511 769-80812 725-76813 682-72514 636-68215 591-63616 548-59117 503-54818 454-50219 396-454

High low-396


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Collect on Healthcare DebtPatient AHigher Probability to Pay and Higher Ability to Pay

Prediction: Likely to pay; likely to be able to payAction: Make maximum collection effort if at point-of-service and bill remaining balance

Reduce Collection EffortsPatient DLower Probability to Pay but Higher Ability to Pay

Prediction: Less likely to pay; likely to be able to payAction: Make minimum collection effort – if not successful, refer patient to Financial Counseling for possible settlement/discount or attempt to get commitment on payment plan

Attempt to Collect/Verify Medicaid and CharityPatient BHigher Probability to Pay, but Lower Ability to Pay

Prediction: Likely to pay, but are unable to payAction: Refer patient to Financial Counseling for potential discount and Medicaid/financial assistance evaluation

Verify Medicaid / Charity Care ApplicationPatient CLower Probability to Pay and Lower Ability to Pay

Prediction: Unlikely to pay; less likely to be able to payAction: Refer patient to Financial Counseling – very likely to be eligible for Medicaid / financial assistance

Low

High Payment Predictor Low

Inco

me

Pre

dic

tor

Take it a Step Further: Add Capacity to Pay


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Goals in Revenue Cycle Process• Discover potential financial problems at point

of service – to more effectively manage accounts early

• Predict the patient’s probability and capacity to pay – to reduce days in accounts receivable and improve cash flow

• Prioritize workloads of accounts and balances – to get the most from your resources and reduce collections cost

• Proactively identify potential Medicaid and financial assistance candidates up front – to better determine where to focus your resources



ID Verification,Fraud, and Authentication


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Goals in Revenue Cycle Process

•Identify potential fraudulent demographic information – to decrease fraud risk and costly mail returns

•Verify patient addresses – to decrease returned mail, postage costs and lost dollars from returned statements that become uncollectible

•Accounts Receivable “last check” at 120 days or prior to Third Party Collection for updated contact information


ID Verification and Fraud Detection•Identity Fraud Scores with or without credit data determine the likelihood of a patient application presenting a fraudulent identity to reduce fraud losses, streamline processes and increase revenue.

•Can delivers specific warning alerts about the patient’s identity like and updated information

– Inquiry SSN associated with person reported as deceased

– Inquiry SSN is invalid– Inquiry SSN may be a tax ID number Equifax Confidential, Page- 17

These tools are not something simply for organizations with advanced technology or expansive staffs. Every operation can realize improvements by working smarter

and maximizing productivity of each employee and asset. The key is to make sure to choose the right solution so you

put the right effort into the right account.



Questions?


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Thank You


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Equifax Predictive Sciences

•Formed over 20 years ago (1992)

•The modeling and analytic division of Equifax

•Solutions for clients in a variety of industries– Financial Services– Telco– Insurance– Healthcare

•Over 100 custom models or analytical projects completed annually

•3-4 generic products created every year



Equifax Predictive Sciences•Equifax Predictive Sciences

– Formed over 20 years ago (1992)– Solutions for clients in a variety of industries– 10+ years working with healthcare data and

providers– Generic healthcare payment prediction scoring

•Medlytix Custom Scoring– Founded by former Equifax employees to focus

further on healthcare– Partnered exclusively with Equifax– 3 years working with healthcare data and providers– Proven custom scoring installations

Documents

April 2013 Equifax Confidential & Proprietary Information Copyright © 2006, Equifax Inc., Atlanta, Georgia. All rights reserved. Improving Revenue Cycle