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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
