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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%