Metrics & Analytics for Efficient Revenue Cycle

RUBIXIS INC.

Everyone’s Goal in Revenue Cycle is to…

COLLECT 100% of the money owed

At the LOWEST

COST

The FIRST TIME

As FAST as possible

Standard Performance Metrics & Analytics• The top metrics and analytics used by providers revolve

around A/R Days, Denials & Underpayment rate, and

Cost to Collect.

• Some of the HFMA MAP Keys are listed below for

reference:

▪ Patient Access

▪ Pre-Registration, Insurance Verification, and Service

Authorization Rate

▪ Conversion Rate of Uninsured Patient to Payer Source

▪ Point-of-Service (POS) Cash Collections

▪ Pre-Billing

▪ Days in Total Discharged Not Final Billed (DNFB)

▪ Days in Final Billed Not Submitted to Payer (FBNS)

▪ Days in Total Discharged Not Submitted to Payer (DNSP)

▪ Claims

▪ UB04 (837i) Clean Claim Rate

▪ Late Charges as a Percentage of Total Charges

▪ Account Resolution

▪ Aged A/R as a Percentage of Total Billed A/R

▪ Denial Rate (Zero Pay & Partial Pay)

▪ Bad Debt, Charity Care

▪ Financial Management

▪ Cost to Collect

▪ Net Days in Accounts Receivable (A/R)

▪ Uninsured Discount, Uncompensated Care

• These metrics and analytics are good to understand

performance, and are necessary to serve the purpose of

a “report card”; they are not always helpful to

understand next steps for improving the report card.

• So we decided to take a step back and look at analytics

and metrics a level below which help identify root

causes and actions to trend them in the right direction.

• Before we get into the metrics and analytics, let’s take a

minute to understand the typical life of a claim…

Patient registration

Patient discharge

Bill finalized Bill entersClaim system

Bill releasedto payer

Bill receivedby payer

Bill processedby payer

EOB/835/Correspondence received by provider

Correctly Posted?

Post transactionIn patient accounting system

YES

1. Denial – send appeal2. Underpayment – send appeal3. Requested info – send info

Addl. Ins?

NO

Account closed out of insurance financial class

YES

Flip to next insurance in pt. acctg. System and bill next insurance

NO

START

END

Correctly Paid?

YES

NO

Fix posting issue

Life-Cycle of a Claim

Pre-Claim

Clean-Up

Follow-Up

Pt. AccountingSystem

Claim / PatientAccounting Sys. 837

(997/999, payer web site,DDE, 277, Phone call to payer)

(payer web site,DDE, 277, Phone call to payer)

(ATB, 837, 835, paper RA,Contract with payer – including Medicare, Medicaid, work comp)

Inventory Distribution

• Client 1

– Pre-Claim: delays in getting claims out

– Payer processing delays: “Call Payer for

Status” or “Claim In Process”

– Denials

– Underpaid

• Client 2

– Pre-Claim: delays in getting claims out

– Clean-up: delays in billing secondary

insurance, delays in posting correct

adjustment, posting incorrect

adjustments.

– Denials

• Client 3

– Payer processing delays: “Call Payer for

Status”

– Denials

Pain Points

Cost to CollectEfficient vs. Inefficient Queue Strategy

Increasing Productivity

Working Accounts by Probabilistic Value

Reducing Outsourcing Freebies

Cost to Collect: Efficient Queue

Strategy

StatusDays Before Account

Falls Into Queue

Maximum Days To

Resolve Status

Discharged Not Billed 5 10

Unreleased 5 15

Released - Error With Payer (Web,

277)5 10

No Claims 5 15

Pending (Web, 277, Phone) 14 Days From Claim Received Date 30

Call Payer For Status (Manual Payers) 30 Days From Claim Received Date 60

Medicare S 17 60

Medicare T 0 30

Medicare R 0 30

Medicare P 5 Days After Payment Scheduled Date 10 Days After Payment Scheduled Date

Medicare D 0 30

Web Paid (277, Web) 5 Days After Check Date 10

Web Denied (Web, 277) 5 10

Denied 0 30

Under Paid 0 45

Under Paid Line Item Denial 0 45

Correctly Paid Pending Posting 10 15

Correctly Paid Posting Issue 0 5

Move To Self-Pay 0 5

Review For Secondary Payer 0 5

1. An ideal queue configuration is based on data

elements from various data sources and not

just Patient accounting system

2. Queue configuration variables include payer,

status, balance or remaining reimbursement,

resources available, etc.

3. Account not resolved in “expected time

frame” for that status is an “exception”

4. Statuses in RED font are “Re-work” statuses

(ideally we do not want these statuses to

occur at all)

Inefficiencies in Traditional Queue Strategy

• Traditional follow-up queues are created based on “x” no. of days from bill or discharge date.

• On average, 20%-40% of accounts dropping into queue in this scenario don’t need to be worked.

– Claim scheduled for payment as per website / Medicare DDE.

– Even though account is 45 days from discharge or bill date claim or appeal was recently sent.

– Payer(s) don’t pay claim that fast.

31.1%23.2%

38.8%

68.9%76.8%

61.2%

Client 1 Client 2 Client 3

Accounts falling in Queue based on “45 Days from Bill Date” Queue Strategy:

which accounts really need follow-up?

Account should NOT be in Queue Account should be in Queue

Inefficiencies in Traditional Queue Strategy

• If # of accounts falling into queue is higher than daily available touches, then that can have

– Aging implications: reps unable to work all accounts timely.

– Cost implications: hiring more FTE, outsourcing more accounts, writing off accounts due to untimely.

0%

20%

40%

60%

80%

100%

120%

Client 1 Client 2 Client 3

No. of Accounts Falling in Queue Daily* vs. Daily Follow-up Touches Available**

Queue Productivity

* This was based on estimate given to Rubixis by client

** This was calculated based on # of follow-up reps multiplied by productivity standards for the client

No. of Touches to Resolve an Account

• Ideally, we would like to get accounts resolved with “0” follow-up touches, i.e. claim gets billed, and gets paid correctly without anyone having to work the account.

• In reality, we are pretty far away from it.

43%

51%

19%

11%

9%

15%

15%

15%

15%

11%

13%

20%

20%

11%

30%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

No. of “Follow-up” Touches to Resolve an Account During Early Stage of the Project

0 touch 1 touch 2 touches 3 touches 4 or more touches

What are these Follow-up Touches?

• Avoidable:

– Claim/appeal still processing,

allow more time.

– Recently billed/sent info, allow

more time.

– Copy-pasting information from

website, remit or payer

correspondence.

– Account “under paid” or

“correctly paid” or “over paid”.

– Copy of previous follow-up

note.

• Non-avoidable:

– Insurance or patient called.

– Sent medical records or other

documentation as per

insurance request.

– Insurance or patient

information updated*.

– Needed to rebill as original

claim / appeal not received by

insurance.

* Ideally this should be caught at time of registration and should

be avoidable, but since that is not within the purview of a follow-

up rep in most scenarios, it is being classified as “non-avoidable”.

Increasing Productivity Through Status, Information & Automation

• Claims pending processing with

payer can resolved in “bulk” via

payer reports.

• Denials, Underpayments, Medicare

T statuses can be resolved in “bulk”

by grouping them via root-cause

and taking historically successful

next steps.

• By assigning status to each account,

reps know exactly what’s going on

with the account.

• “Clean-up” status accounts can be

worked at 5x productivity vs.

“Follow-up” statuses

Calculating “Collection Probability” to Prioritize#

Status

of Account

Variables Needed

to Calculate ProbabilityMethod

1 Denied

1. Denial Reason

2. Payer

3. Denial $ Overturn Rate

For each payer, calculate the overturn

success rate by denial code.

2 Underpaid

1. Primary Payer

2. Underpaid Explanation

3. Underpaid $ Recovery Rate

For each payer, calculate the underpaid

recovery success rate by explanation

3

Other “Follow-up” statuses:

e.g. Call Payer for Status,

Pending Processing etc.

1. Primary Payer

2. Expected Reimbursement

3. Total Payment

For each primary payer, calculate the

total payment received as compared to

expected reimbursement.

4

Clean-up: Move to Patient

Financial Class & Posting

Issue

1. Patient responsibility

2. Patient payment

Calculate overall ratio of patient

responsibility allowed by insurance vs.

actual patient collection

5 Review for Secondary1. Primary patient responsibility

2. Secondary payer collection

Calculate overall ratio of actual

collection from secondary payer vs.

patient responsibility as allowed by

primary payer

7 Pre Claim1. Total Charges

2. Total PaymentPCR: Payment to Charge ratio

Working Accounts by Probabilistic Value

Working and

outsourcing

accounts based on

“Remaining

Collectible $ Value”

and by “Probabilistic

$ Value” allows for

prioritizing and

accelerated cash

collection.

Reducing Outsourcing Freebies

• For aged insurance outsourcing, if the

payment comes in within 45 days from

account being assigned to vendor,

over 95% of the time it is a freebie.

• When flagging accounts for

outsourcing, some exclusions that

providers can use to reduce freebies:

– Accounts where payer already declared

“Promise to pay”.

– Primary or secondary claim was recently

sent, or an appeal was recently sent.

– Primary has already correctly paid and

secondary claim needs to be sent out.

65%

81%

71%

35%

19%

29%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

What % of fee was from accounts where payment transaction happened within 45

days of account being outsourced?

> 45 days 0-45 days

Based on Rubixis’ experience working with clients, on average,

20-40% of vendor fee are freebies and can be prevented.

Reducing Aging & A/R DaysUnderstand which areas of claim life-cycle are major contributing factors

Implement targeted strategy

Patient registration

Patient discharge

Bill finalized Bill entersClaim system

Bill releasedto payer

Bill receivedby payer

Bill processedby payer

EOB/835/Correspondence received by provider

Correctly Posted?

Post transactionIn patient accounting system

YES

1. Denial – send appeal2. Underpayment – send appeal3. Requested info – send info

Addl. Ins?

NO

Account closed out of insurance financial class

YES

Flip to next insurance in pt. acctg. System and bill next insurance

NO

START

END

Correctly Paid?

YES

NO

Fix posting issue

Life-Cycle of a Claim

Pre-Claim

Clean-Up

Follow-Up

Pt. AccountingSystem

Claim / PatientAccounting Sys. 837

(997/999, payer web site,DDE, 277, Phone call to payer)

(payer web site,DDE, 277, Phone call to payer)

(ATB, 837, 835, paper RA,Contract with payer – including Medicare, Medicaid, work comp)

Aging Factors: Pre-claim

61.7%

33.0%

45.2%

21.5%

22.3%

21.7%

11.0%

6.9%

8.2%

16.5%

9.8%

17.1%

16.4%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

Discharge Date to First Bill Drop Date

0-5 days 6-10 days 11-15 days 16-25 days 31+ days

99.9%

83.9%

91.7%

4.6%

3.3%

11.5%

5.1%

75% 80% 85% 90% 95% 100%

Client 3

Client 2

Client 1

Bill Drop Date to First Claim Date

0-5 days 6-10 days 11+ days

Please note: The values are % of total balance

Aging Factors: Billing the Incorrect Payer

90.6%

97.1%95.5%

9.5%

3.0%4.5%

84.0%

86.0%

88.0%

90.0%

92.0%

94.0%

96.0%

98.0%

100.0%

102.0%

Client 1 Client 2 Client 3

% of Time Primary Payer Changed After First Claim Sent Date, by Count of

Accounts

No Change Primary Payer Changed

31%

19%

12%

23%

15%

19%

27%

30%

33%

14%

29%

29%

5%

8%

7%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

Primary Payer Change Aging from First Claim Sent Date

1-5 days 6-45 days 46-90 days 91-180 days 181-360 days

Aging Factors: Clean-up

• Clean-up includes accounts where:

– Primary payer has correctly paid, pending billing the 2ndary payer.

– Correctly paid by insurance, pending to move account to patient financial class.

– Account has been correctly paid but pending to post correct adjustment or payment or both.

78%

28%

71%

22%

72%

29%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

What % of “Clean Up” inventory, by count of accounts, is being worked in an

acceptable time frame?

Account is being worked in acceptable manner

Account is behind acceptable resolution timeframe

Aging Factors: Payer Processing Delay

24%

17%

20%

39%

Client 1

39%

36%

17%

8%

Client 2

47%

26%

16%

11%

Client 3

For a period of time, we looked at how long did it take on average for payers to respond to claims sent.

Aging Factors: Payer Processing Delay

• A major payer for client was consistently

delaying claims processing, creating

domino effects on aging, cash collection

and rep productivity.

• By generating automated status-request

reports with claim# and other relevant

data fields every week, managers started

dealing with payer directly; this led to

faster resolution and also allowed reps to

channel their efforts towards more

productive queues.0% 0.90% 1.41%3.46%

11.67%13.52%

27.11%

41.69%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Claim Processing Time Trend for aMajor Payer for Client 1

> 45 days from Claim sent date

Aging Factors: Follow-up Response Delay

• Once queues have been

optimized so that accounts

are not unnecessarily falling in

them, it is important that if

accounts do fall in a queue,

they are worked promptly.

• Delay in responding to

information request from

payers contributes to aging.

54%

21%

43%

32%

45%

39%

7%

13%

9%

10%

6%

5%

11%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

Rep Response Time: Time taken by rep after account fell in “Follow-up” queue to work it

1-15 days

16-30 days

No response: less than 30 days since account in queue

31+ days

No Response: over 30 days since account in queue

A/R Days Impact by Claim Life-Cycle Category

Account Category Client 1 Client 2 Client 3Project

Rating

PRE-CLAIM Balance A/R Days Balance A/R Days Balance A/R Days

Unreleased $7.31M 2.4 Days $7.38M 2.1 Days $1.42M 0.9 Days Easy

Discharged Not Billed $2.47M 0.8 Days $3.17M 0.8 Days $1.26M 0.8 Days Easy

CLEAN-UP

Correctly Paid Posting Issue $0.34M 0.1 Days $6.84M 1.9 Days $0.16M 0.1 Days Easy

Review for Secondary Payer $0.05M 0.01 Days $0.88M 0.2 Days $0.02M 0.01 Days Easy

Move to Self-pay $0.03M 0.01 Days $0.17M 0.05 Days $0.16M 0.1 Days Easy

FOLLOW-UP

Call Payer for Status / Pending Processing $15.10M 5.1 Days $6.49M 1.9 Days $2.84M 1.8 Days Medium

Denied $21.27M 7.2 Days $22.22M 6.5 Days $6.19M 3.7 Days Difficult

Underpaid $7.99M 2.7 Days $5.85M 1.7 Days $2.14M 1.1 Days Difficult

Web Denied & Medicare-T $2.07M 0.7 Days $3.15M 0.9 Days $3.61M 2.1 Days Medium

$57.02M 19 A/R Days $64.88M 16 A/R Days $17.79M 11 A/R Days

Denial ManagementTransforming denial data into actionable data

Using data to identify “prevention” and not just “management” opportunities

Help staff prioritize via predictive analytics

Denial Management

• Denial management has always been a key area of revenue opportunity: however, even though healthcare providers have enormous amounts of denials related data, they have not always been able to successfully derive actionable insights from them.

• For denial management, we are interested in:– Understanding the true $ value that has been denied

– Looking at accurate and actionable denial reason, as well as looking at denials from all available sources (remits, website/DDE, payer correspondence, rep phone conversation etc.)

– Looking at denials by department, DRG, procedure code, revenue code, diagnosis code, service area etc., to understand trends for bulk resolution.

– Prioritizing reps’ time on first working denials where provider has a history of successfully overturning it.

Understanding True $ Value of Denials

Client counting as denial but not Rubixis

$2M

Rubixis counting as

denial but not client

$0.7M

$4.9M

$0.8M• Denials on line item where

expected is $0

$0.7M• Inconsequential Denials: account

already correctly paid

$0.5M• Denial on secondary claims where

max collectible value is $55k

$0.7M• Correspondence based denials• Paper EOB based denials

Denial Management: Denial Data

75%

79%

67%

4%

9%

12%

21%

9%

11%

9%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Client 3

Client 2

Client 1

Breakdown of Count of Denied Accounts by Source of Denial

Electronic 835 Rep modified Paper EOB Correspondence

51%59%

48%

49%41%

52%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Client 1 Client 2 Client 3

Breakdown of Count of Denial Reasons by “Generic” vs. “Specific”

Specific Denial Reason Generic Denial Reason

Denial Management: Prioritizing Denials by Overturn Rate

By calculating success rate

of overturning denials by

denial reason and payer,

client was able to prioritize

what accounts needed to

be put in front of staff for

resolution, versus

outsourcing accounts with

lower success rates to

external vendors or writing

off lower balance accounts

completely.

Denial Reason

Denial Code description# of

AccountsRemaining

Reimb.*Probability

of CollectionEstimated Collection

16Claim/service lacks information

283 $1,805,914 34% $614,011

31Patient cannot be identified as our insured.

143 $677,247 86% $582,432

252An attachment/other documentation is required

81 $614,233 77% $472,959

125 Submission/billing error 102 $592,076 51% $301,959

227Information requested from the patient/insured/responsible party was not

178 $509,187 9% $45,827

Others Others 2,587 $6,015,369 53% $3,188,146

Grand Total 3,361 $10,214,025 51% $5,209,153

Summary• Cost to Collect

– Efficient vs. Inefficient Queue Strategy– Increasing Productivity– Working Accounts by Probabilistic Value– Reducing Outsourcing Freebies

• Reducing Aging and A/R Days– Understand which areas of claim life-cycle are major contributing

factors– Implement targeted strategy

• Denial Management– Transforming denial data into actionable data– Using data to identify “prevention” and not just “management”

opportunities– Help staff prioritize via predictive analytics

OutcomeACROSS THE THREE CLIENTS

COST TO COLLECT(Please note: relevant costs for our study were

business office staffing costs & outsourcing costs)

Down by 15-30%

A/R DAYS Down by 10-20%

DENIAL RATE Down by 15-30%

CASH TO NET REVENUES Up by 2-5%

Thank youRUBIXIS INC.

Manoj Sharma | msharma@rubixis.com

Sid Tewari | sid.tewari@rubixis.com