Forget ESP! Using Data to PREDICT the Future of Your...

Forget ESP! Using Data to PREDICT the Future of Your Revenue Cycle

Front-End Revenue Cycle Intelligence

Introduction

Jase DuRardChief Revenue Officer, AccuReg

Agenda

• What is Analytics

• Current Issues

• Strategies• Departmental Changes

• Denial Prevention

• Patient Liability

• Application to Staff

• Measurement

What are Analytics?

“Analytics” refers to the systematic use of technologies, methods, and data to derive insights and enable fact-based decision-making for planning, management, operations, measurement, and learning.

Importance of Analytics to Your Future

Importance of Analytics to Your Future

Limitations

11%

17%

20%

22%

25%

38%

0% 20% 40%

Other

Our reports are too complex for people to use

Our reports are not actionable

We have too many solutions in place

It is too disparate

We do not have access to all of the data we need

What are the short-comings of your current revenue cycle management analytics solution? (Please select all that apply.)

n = 136Responses = 154

Current Issues

What You Are MeasuringVs.

What You Should Be Measuring

Problems with Traditional RCM

Metrics• Net A/R days — Net A/R dollars divided by 3-month net average daily revenue

• Allowance for doubtful accounts — Bad-debt reserve

• Bad debt and charity as a fraction of gross revenue — Bad-debt dollars plus charity dollars divided by gross revenue

• Denials as a fraction of gross revenue — Technical and clinical denials dollars divided by gross revenue

• Cash as a fraction of cash goal — Total A/R cash divided by cash collection goal

• Point-of-service collections as a fraction of goal — Point-of-service cash divided by point-of-service cash goal

Problems with Traditional RCM

Metrics• Cash collections — Total cash deposited in the bank from all A/R sources: government, non-government, self-pay

and bad-debt recovery

• Gross and net receivables by component — Total receivables from all A/R components: in-house, discharged-not-final-billed and final-billed

• Net accounts receivable — Total receivables, net of allowances from: government, managed care, bad-debt reserve and other contract payers

• In-house and discharged-not-final-billed receivables — Total non-discharged and discharged-not-final-billed A/R

• Third-party aging percentage greater than 90 days from discharge — Third-party A/R greater than 90 days divided by total third-party A/R

• Cash as a fraction of net revenue — Total A/R cash divided by total net revenue

Predictive Analytics

Instead of simply presenting information about past events to a user, predictive analytics estimate the likelihood of a future outcome based on patterns in the historical data…. Provider and payer organizations can apply predictive analytics tools to their financial, administrative, and data security challenges, as well, and see significant gains in efficiency and consumer satisfaction.- Healthcare IT Analytics Sept. 4, 2018

Drivers of Analytics Investment

Do You Have a Strategy?

Strategy

Departmental Changes

Pre-Service Revenue Opportunities

Key Performance Indicators for “Best” Patient Registration ProcessesRecommended by NAHAM AccessKeys™

© 2017 AccuReg - All rights reserved. No part of this document may be reproduced, distributed, or transmitted in any form whatsoever without the expressed written permission of DSI/AccuReg.

Front-End RCM Transformation Blueprint™

Scheduled Patient Rate

Completed Order Rate

Pre-Registration Rate

Completed Pre-Reg Rate

Estimate to Registration Rate

Collection Opportunity Rate

Pre-Denial Resolution Rate

RCM OUTCOMES 2% Collections to NPR

<2% FEFP Denials*

50% of scheduled patients with electronic orders have complete data; demographic, insurance and CPT/ICD

80% of expected registrations are scheduled >48hrs prior to arrival

95% of Scheduled Patients Pre-Registered at Min Tier 1

90% of pre-denials identified are resolved prior to service

50% of Registrations with Estimates Generated

60% of Estimated Dollars Collected Pre-Service

95% of Pre-Registered Patients Completed at Tier 4

2% Collections to NPR <2% FEFP Denials*

90% of pre-denials identified are resolved prior to service

60% of Estimated Dollars Collected Pre-Service

50% of Registrations with Estimates Generated

95% of Pre-Registered Patients Completed at Tier 4

95% of Scheduled Patients Pre-Registered at Min Tier 1

50% of scheduled patients with electronic orders have complete data; demographic, insurance and CPT/ICD

80% of expected registrations are scheduled >48hrs prior to arrival

Scheduled Patient Rate

Completed Order Rate

Pre-Registration Rate

Completed Pre-Reg Rate

Estimate to Registration Rate

Collection Opportunity Rate

Pre-Denial Resolution Rate

RCM OUTCOMES

Front-End RCM Transformation Blueprint™

Key Performance Indicators for “Best” Patient Registration ProcessesRecommended by NAHAM AccessKeys™

© 2017 AccuReg - All rights reserved. No part of this document may be reproduced, distributed, or transmitted in any form whatsoever without the expressed written permission of DSI/AccuReg.

80% of expected registrations are scheduled >48hrs prior to arrival

50% of scheduled patients with electronic orders have complete data; demographic, insurance and CPT/ICD

95% of Scheduled Patients Pre-Registered at Min Tier 1

95% of Pre-Registered Patients Completed at Tier 4

50% of Registrations with Estimates Generated

60% of Estimated Dollars Collected Pre-Service

90% of pre-denials identified are resolved

prior to service

2% Collections to NPR

<2% FEFP Denials*

RCM Performance Outcomes:Pre-Service Cash, Denials Avoidance, Patient Access Experience

Electronic Orders: Fax to Portal,Enforce Payer Requirements, Process Redesign, Training

Physician Engagement:Revenue Impact Education, Process Redesign, Training

Denial Prevention Solutions: QA, Eligibility, Estimation, Necessity, Authorization, Financial Assistance, Identity, Patient Arrival Tracking, Education, Denials Analysis, Collections Training, Process & Policy Redesign Consulting Services

Expand Pre-Reg Operations: Services, Technology & Process

Tier 1 – Basic Pre-RegistrationTier 2 – Insurance ClearanceTier 3 – Estimation & CollectionTier 4 – Financial Assistance Screening

POS Collections Solutions: Estimation, Payment Processing, Collections Training, Process & Policy Consulting

Strategy

Patient Liability

2019 Survey by Cedar

Patient Estimation and Collection

Parameters for example below: Scope = All collections, even if there was no estimate; Include Eligibility Stats = Yes; Grouped By = Month

Patient Estimation and Collection

Parameters for example below: Scope = All collections, even if there was no estimate; Include Eligibility Stats = Yes; Grouped By = Month

Who to Collect From?

Using predictive analytics to identify patients likely to skip an appointment without advanced notice can improve provider satisfaction, cut down on revenue losses, and give organizations the opportunity to offer open slots to other patients, thereby increasing speedy access to care……EHR data can reveal individuals who are most likely to no-show, according to a study from Duke University. A team found that predictive models using clinic-level data could capture an additional 4800 patient no-shows per year with higher accuracy than previous attempts to forecast patient patterns.

Strategy

Denial Prevention

Denials By Opportunity

Opportunities by Adjustment Code

Denials By Adjustment Code

Denials by Adjustment Code

Patient Liability By Payer

Strategy

Application to Staff

Accountability and Modeling Staff

Measuring and CQI

Strategy

Measurement

Measuring Outcomes

www.accuregsoftware.com

Chief Revenue Officer

Jase DuRard

(615) 438-5273

jdurard@accuregsoftware.com