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1
““Utilizing Multi-Vari and ANOVA for Billing & Utilizing Multi-Vari and ANOVA for Billing & Charge Capture Projects in a Healthcare Setting”Charge Capture Projects in a Healthcare Setting”
Mike O’NeillLean & Six Sigma and
Business Improvement in Healthcare SummitMarch 17, 2009
Insurance Carrier
Cla
im D
enia
ls
4321
Boxplot of Claim Denials By Insurance Carrier
2
AgendaAgenda
• Multi Variance Analysis – benefits and approach
• Analysis of Variance (ANOVA) & Hypothesis testing overview
• Hypothesis Tests - Healthcare insurance denial type
• Billing & Charge capture case studies1) Insurance Claim Denials Reduction
2) Hospice Billing & Charge Capture
3) Intravenous (IV) Solutions Charge Capture
Define Measure Analyze Improve Control
3
Gundersen Lutheran Health SystemGundersen Lutheran Health System
• Integrated Delivery System– Over 6,000 Employees – 325 bed Tertiary Medical Center
• Level II Regional Trauma Center• Nationally Recognized
– Top 100 Designations– Cancer Care– Cardiac Care– Health Grades Distinguished Hospital
– Western Clinical Campus for UW-Madison Medical School and School of Nursing– Medical Foundation
• Clinical Research Program• Residency & Medical Education Programs (1,199 Students)
– Variety of affiliate organizations including rural hospitals, nursing homes, etc.– 400+ physician multi-specialty group practice– Employed physician model
4
Benefits of Multi-variance Analysis
• To look at process stability over time
• To determine with high statistical confidence the capability of the outputs of a process
• To identify what is causing variation in the process
• To obtain initial components of variability. Different: Insurance Carriers; Departments; Physicians
• To provide direction and input for Improvement activities
5
y f x x x k ( , , . . . , )1 2
Initial Approach
Observation
Do
lla
rs
343128252219161310741
23000000
22000000
21000000
20000000
19000000
_X=20965700
UCL=21668217
LCL=20263183
Observation
Mo
vin
g R
an
ge
343128252219161310741
1200000
900000
600000
300000
0
__MR=264146
UCL=863042
LCL=0
1111
11
111
111
11
1
1
1111
11111111
1
Denial Backlog Balance By Week - 2007
Want to understand stability and capability of the Y - output(s)
0.200.160.120.080.04
LSL USL
LSL 0.07Target *USL 0.11Sample Mean 0.126253Sample N 30StDev(Within) 0.0152779StDev(Overall) 0.0362252
Process Data
Cp 0.44CPL 1.23CPU -0.35Cpk -0.35
Pp 0.18PPL 0.52PPU -0.15Ppk -0.15Cpm *
Overall Capability
Potential (Within) Capability
PPM < LSL 0.00PPM > USL 533333.33PPM Total 533333.33
Observed PerformancePPM < LSL 115.70PPM > USL 856301.86PPM Total 856417.56
Exp. Within PerformancePPM < LSL 60225.89PPM > USL 673167.84PPM Total 733393.74
Exp. Overall Performance
WithinOverall
New Denials Ratio
6
ClaimType 1 ClaimType 3ClaimType 2
Weekly Denials
Dept 1 Dept 2 Dept 3
Physician 1 Physician 2
Carrier 2 Carrier 3Carrier 1 Carrier 4
Procedure Procedure
Insurance Claim denials =
f (Claim Types, Carriers, Depts, Physicians, Procedure Types, …...)
.With plenty of x’s / drill downs
Find clues of key drivers:PracticallyGraphicallyStatistically
Y y1, y2, y3, y4……
7
Know what key X’s to Analyze(Initial filtering from Process Mapping)
With multiple x’s that may lead to high variability want to focus on the noise/uncontrolled type variables first
Process variation due to:
1) Similar type variables:• Insurance Carriers• Departments• Physicians• Billing/Coding Staff
2) Differences in variables over time:• Week to Week• Month to Month• Quarter to Quarter
“Discrete”variables
“Continuous”variables
8
Another look at Noise Variables• For Discrete Input VariablesDiscrete Input Variables
– Test for Variability within a piece• ExampleExample: Four procedures per patient visit
– Test for Variability within a batch• ExampleExample: Variability across procedures by physician
– Test for Variability across batches• ExampleExample: Variability across procedures within a month
• For Continuous Input VariablesContinuous Input Variables– Test for Variability within a time span
• ExampleExample: Ten insurance coverage records per day
– Test for Variability across short time spans• ExampleExample: Variability across week
– Test for Variability across longer time times• ExampleExample: Variability across months, quarters or longer
9
A Multi Vari Approach• Determine if the variables are continuous or discrete• Gather data and study key inputs impacting output
(X vs. Y)• Look at the X’s and consider which are causing
variability in the process output• Go back and look at X’s again – missing any key
inputs to study? • Look for curves, groupings and patterns in
continuous data sets• Can use the same approach whether output (Y) is
continuous or discrete• Choose and apply appropriate analysis tool
10
A Multi Vari Approach (cont…)
• Study controlled and uncontrolled (noise) inputs but..• Focus on uncontrolled inputs first:
– Variation in the Noise variables can produce dramatic mean shifts and changes in variability that lead to process instability
– These sources of variation must be attacked first before leveraging the important controlled input variables in a systematic way
• Identify similar processes and study variability differences:– Insurance Carrier to Insurance Carrier
– Clinic Department to Clinic Department
– Location to Location (Wisconsin, Minnesota, Iowa)
– Coder to Coder ; Insurance follow up to Insurance follow up staffer
• Differences in process variation over time– Week to Week
– Month to Month Complete Multi-Vari studies to
identify potential key inputs
Review Data andPrioritize Key Input Variables
11
• Discrete Variables• Boxplots• Interval Plots• Main Effects Plots• Interaction Plots• T-tests comparing two groups• ANOVA’s
• Continuous Variables• Scatterplots• Correlation• Regression• Multiple Regression
Multi vari charts can be used to investigate relationships among variables
We will review how some of these tools were used for three projects within Gundersen Lutheran Health System.
Involving discrete input variables.
12
Analysis of Variance Analysis of Variance (ANOVA) and (ANOVA) and
Hypothesis Testing Hypothesis Testing
13
• Anova studies used to perform statistical tests for comparing: – Means– Medians– Variances
• Performed to answer your hypothesis (Are Insurance Claim denials marked patient responsibility always true?):
Assumption: All PR (patient responsibility) denial codes generated by Insurance Carrier requires no investigation and can be transferred directly to patient
• Null Hypothesis = PR denials are the same • Alternative Hypothesis = PR denials are not the same
• The statistical test will generate a probability (p) value for your hypothesis which is based on the assumption there is no difference.
– Guideline: If P value < .05 this indicates there is a difference
We will look at how a study was performed for theabove scenario but let’s review Hypothesis testing further
Analysis of Variance Studies (ANOVA)
For Discrete inputs (x) Continuous outputs (y)
14
• Properly handle uncertainty
• Minimize subjectivity
• Question assumptions
• Prevent the omission of important information
• Manage the risk of decision errors
• Properly handle uncertainty
• Minimize subjectivity
• Question assumptions
• Prevent the omission of important information
• Manage the risk of decision errors
Hypothesis Testing Concepts Enable You To ….
15
Ho : Null Hypothesis
Ha: Alternative Hypothesis
P Value = Probability Value
Key Terms
16
Hypothesis Testing What is it for Statisticians ?
Ho: Mean Group A = Mean Group B
Ha: Mean Group A = Mean Group B
Ho: Slope of the line is 0
Ha: Slope of the line is not 0
Ho: Variance Group A = Variance Group B
Ha: Variance Group A = Variance Group B
Ho: Variable X is independent of Variable Y
Ha: Variable X is not independent of Variable Y
17
Hypothesis Testing What is it for the Average Person ?
Ho: Coding doesn’t matter for insurance claim reimbursement
Ha: Coding does matter for insurance claim reimbursement
Ho: Procedure X Avg. Cycle Time = Procedure Y Avg. Cycle Time
Ha: Procedure X Avg. Cycle Time = Procedure Y Avg. Cycle Time
For one of your projects:
Ho = What is the Null Hypothosis?
Ha = What is the Alternative Hypothesis?
18
Fundamentals of Hypothesis Testing• Based on what we know, we form a hypothesis to explain
something that we don’t know
• Generally, this hypothesis takes the form of: Y=f(x1,x2...xk)
• We gather data and devise a test to evaluate the hypothesis testing the effect of the x’s on Y
• We assume that the null hypothesis is true
• We then look for compelling evidence to reject this hypothesis
• If we reject the null hypothesis, then we accept the alternative hypothesis
• If we fail to reject the null hypothesis, then we have insufficient evidence to accept the alternative hypothesis
19
State a “Null Hypothesis” (Ho)State a “Null Hypothesis” (Ho)
Gather evidence (a sample of reality)Gather evidence (a sample of reality)
DECIDE:What does the evidence suggest?
Reject Ho? or Fail to Reject Ho?
DECIDE:What does the evidence suggest?
Reject Ho? or Fail to Reject Ho?
Hypothesis and Decision Risk
Faced with two risks of making a wrong decision
Type 1 Error = Alpha RiskType 2 Error = Beta Risk
Type 1 example:Not sending a denial balance to patient when you could = Alpha Risk
Type 2 exampleSending a denial balance to patient when you shouldn’t = Beta Risk
20
Fire Alarm Decision
A Practical Hypothesis Test
No Alarm Alarm Sounds
NO
FIRE
FIRE
STATE
OF
REALITY
CorrectDecision
Confidence 1 - alpha probability
CorrectDecision
Power or 1 - Beta probability
Type IError
alpha probability
Type IIError
Beta probability
21
How a Hypothesis Test Works – 2 Possible States of Reality
• Either we have No Fire, or a Fire Exists.
• Imagine that the smoke detector has a specified set point in terms of particles/cc
Null HypothesisTrue, No Fire
Detector Set Point
Random distribution of
particle counts innormal air
Area to right of trigger isprobability of committing
an alpha error
Area to left of trigger isconfidence or 1 - alpha
Null HypothesisFalse, Fire
Existsdistribution of particle counts in
Smoke - filled air
Area to right of trigger isthe power of the test
1 - BetaArea to left of trigger is
probability of committing a beta error
22
Hypothesis Testing:• After data is collected, statistical scores can be
calculated – In Microsoft Excel or statistical software such as Minitab
Small “P-Value” Ho is Rejected
Large “P-Value” Ho is Not Rejected
If P is low, then Ho must go!!!
A probability (P) value is one statistic calculated to help determine if null hypothesis is true or false
23
Hypothesis Test Statements
A) If p is low (less than or equal to alpha) reject Ho and make the statement:
“I am (1-alpha) sure Ha is true”
B) if p is not low (greater than alpha) fail to reject Ho and make the statement:
“I have insufficient evidence to demonstrate Ha is true”
24
For most cases use .05For most cases use .05 For most cases use .05For most cases use .05
How Low Must P Be ?It Depends
• We would like there to be less than a 10% chance that these observations could have occurred randomly ( = .10)
• Five percent is much more comfortable ( = .05)
• One percent feels very good ( = .01)
• This alpha level is based on our assumption of “no difference” and a reference distribution of some sort
• But, it depends on interests and consequences
25
Too close to
call !
Proving the Null Hypothesis
• Minnesota Senate Election Results
– H: Al Franken = Norm Coleman
– Ha: Al Franken Norm Coleman• alpha = 0.05 (5% risk Factor)
• If Vote in Minnesota allows rejection of H, they Project a winner.
• If Vote does not reject H, they say...
26
Hypothesis Tests - Hypothesis Tests - Healthcare insurance Healthcare insurance
denial type denial type
27
Null Hypothesis (Ho): Patient Responsibility denials are less than $100 and therefore all PR denials received from Insurance Carriers should be auto billed to Patient
Alternate Hypothesis (Ha): Patient Responsibility denials are not less than $100 and therefore PR denials received from Insurance Carriers should not be auto billed to Patient
One Sample testAre Patient Responsibility denial
balances within $100 or less?
28
Ho: PR Denial = 100
Ha: PR Denial = 100Should we auto bill patient for all
Patient Responsibility Denials?
A P-Value !A P-Value !
Minitab - Output
One-Sample T: Patient Resp Balance
Test of mu = 100 vs not = 100
Variable N Mean StDev SE Mean 95% CI T PBalance 1828 277.4 509.4 11.9 (254.064, 300.803) 14.89 0.000
29
Other Tests / Charts willConfirm the Null Hypothesis is False
Ho: PR Denial = 100
Ha: PR Denial = 100
7200600048003600240012000
Median
Mean
300250200150100
1st Quartile 60.68Median 146.603rd Quartile 352.43Maximum 7888.55
254.06 300.80
125.16 160.70
493.45 526.51
A-Squared 249.81P-Value < 0.005
Mean 277.43StDev 509.44Variance 259530.16Skewness 7.2334Kurtosis 73.5366N 1828
Minimum 0.80
Anderson-Darling Normality Test
95% Confidence I nterval for Mean
95% Confidence I nterval for Median
95% Confidence I nterval for StDev95% Confidence I ntervals
Summary for Patient Responsibility Balance Per Denial
The $100 target to auto bill patient is not within the confidence interval range
30
Balance
Perc
ent
7500500025000
99.99
99
95
80
50
20
5
1
0.01
Mean 277.4StDev 509.4N 1828AD 249.812P-Value <0.005
Probability Plot of Patient Responsibility BalanceNormal
But…the data for our Denials study is non-normal
A rule of thumb with Hypothesis testing is to understand whether the
Data under study is from a Normal or non-normal distribution.
Once again a Small P-Value (<.05) indicates that the Null Hypothesis
In this case is false (The data = a normal distribution)
31
Normal Non-Normal
Test for Mean1 Sample T-Test1 Sample Z-Test
Example: (Ho: =100)
Z- or T-Test (if n>25)Transform to Normal and Use Z Test
Non-Parametric Tests1-Sample Wilcoxon Signed-Rank Example: (Ho: Median =100)
P Value < .05True Mean (or Median) Does Not Equal the Specified Value
Therefore try a 1 Sample Non-Normal Data Test (to test medians vs. means)
Ho: PR Denial = 100
Ha: PR Denial = 100
If P is low, reject Ho
Wilcoxon Signed Rank Test: PR Balance Test of median = 100 versus median not = 100
N for Wilcoxon Estimated N Test Statistic P MedianBalance 1828 1826 1243988.5 0.000 188.1
32
Null Hypothesis (Ho): Patient Responsibility denial reasons (1) Insurance coverage/record error; (2) secondary claim not received by Carrier; (3) denial is patient responsibility - have the same impact.
Alternate Hypothesis (Ha): Patient Responsibility denial reasons have different impact
How do the 3 top Patient Responsibility denial reasons compare?
Another PR denial scenario = Another study ! Why are we seeing PR type denials > $100
33
Test for Equal Variances
• Are the variances for the 3 Patient Responsibility denial reasons the same or are they different?
• Many statistical procedures, including analysis of variance, assume that although different samples may come from populations with different means, they have the same variance
• This is a (usually) buried assumption of an Analysis of Variance. Performing this test will prevent you from making incorrect conclusions in certain circumstances.
34
In Minitab select: STAT>ANOVA>TEST FOR EQUAL VARIANCESIn Minitab select: STAT>ANOVA>TEST FOR EQUAL VARIANCES
“No Claim”2ndary claim not received
by Carrier
Coverage Error
Bill Patient
B for Normal
L for non-normal
35
Other Graphical Representations• Let’s also look at the Main
Effects Plot and the Interval Plot
• These two plots provide different graphical representation of the differences between the three factors– Main Effects provides
just the means– Interval provides means
and different views of the confidence of those means
• Let’s take a look at each...
36
Denial Reason
Me
an
of
Ba
lan
ce
BillPatientNoClaimCoverageError
2000
1500
1000
500
Main Effects Plot (data means) for Denial Balance
The main effects plot highlights that higher PR denial balances result from a secondary claim not being received by the carrier.
Root cause investigation required with primary and secondary carriers
Main Effects Plot
37
Denial Reason
Bala
nce
BillPatientNoClaimCoverageError
3500
3000
2500
2000
1500
1000
500
0
95% CI for the MeanInterval Plot of Balance vs Denial Reason
Interval Plots
An interval plot highlights the mean measurement and variability of the data
Root cause investigation required with primary and secondary carriers
Mean
Confidence Interval – 95% certainty that this istrue value of population mean
38
Boxplots• The Boxplot is another graph/method for looking at the data
that may be easier to see differences in the distributions
• Boxplots show the spread (variability) and center of the data
Bala
nce
250
200
150
100
50
0
Boxplot of Bill Patient Balance Sample
39
Bala
nce
250
200
150
100
50
0
Boxplot of Bill Patient Balance Sample
0% *
100% *
* Not including any Outliers
1st Quartile
4th Quartile
2nd Quartile
3rd Quartile
25%
75%
50% (Median, not the Mean)
Outlier
Quartiles rank order the data from lowest to largest value
40
Boxplot OutliersBoxplot Outlier - Any data value that exceeds either the Upper Limit or Lower Limit as calculated below:
UL = 3rd Quartile + 1.5 x ( 3rd Quartile - 1st Quartile )
LL = 1st Quartile - 1.5 x ( 3rd Quartile - 1st Quartile )Descriptive Statistics: Balance Looking at Descriptive Statistics for Balance dataVariable N Mean Median StDev SE MeanBalance 1507 56.992 55.9 30.798 0.793
Variable Minimum Maximum Q1 Q3Balance 17.010 257.000 30.000 86.200
UL = 86.200 + 1.5 x ( 86.200 – 30.000 ) = 170.5 Anything greater = OutlierLL = 30.000 - 1.5 x ( 86.200 – 30.000 ) = -54.3 Any smaller value = Outlier
Compare to Maximum & Minimum values to see if Outliers exist.
Maximum of 257.000 is greater than UL of 170.5 which is why there is an outlier identified at the top of the previous slide
41
Back to the Top Patient Responsibility Denial Reason
• Prior analysis revealed “No Claim” denial reason was a key variability driver
– No Claim = primary carrier partial payment received but balance did not transmit to secondary carrier
• This was a surprise to the Billing & Insurance follow-up analysts leading to investigation of secondary carriers with “No Activity Since Filing” Denial Types
Type
No A
ctiv
ity C
ount
ContractedGovernment
350
300
250
200
150
100
50
0
No Activity Since Filing - Secondary Insurance Carrier Types Investigation & Action:
Medicare secondary claims not being received by Medicaid. Provider identifier and taxonomy code issues
Action involved manual rebilling of claims to Medicaid
42
Can any of this type of analysis be applied in your areas?
• Where can you use Hypothesis testing in your job or project?
43
Determine plan before Analysis and Execution
Multi-variance pre planning provides for:
• Statement of Objective• List of Key Process Input Variables (KPIV’s) and Key
Process Output Variables (KPOV’s) to be studied• Ensure Measurement Systems are capable• Sampling plan approach• Method of data collection• Team member involvement• Clear responsibilities assigned• Outline of data analysis to be performed
44
Key Multi-Vari Analysis and Execution Steps
1. Collect data
2. Analyze data:– Is the process stable, in control?– Which are the key noise variables affecting the output
variable?– Which are the key controlled variables that influence the
output variable?
3. Investigate root cause and develop action plan
4. Implement improvement actions
5. Measure progress
6. Identify & prioritize key variables for Control Plan
45
Billing & Charge capture Billing & Charge capture case studiescase studies
1)1) Insurance Claim Denials ReductionInsurance Claim Denials Reduction
2)2) Hospice Billing & Charge CaptureHospice Billing & Charge Capture
3)3) Intravenous (IV) Solutions Charge CaptureIntravenous (IV) Solutions Charge Capture
46
Case Study # 1 Insurance Claim Denials Reduction
Project Purpose & Background (April – December, 2008):
• The purpose of this project is to focus on reducing the number and amount of insurance carrier denials for claims submitted by Gundersen Lutheran Clinic for reimbursement.
• Objective is two fold: 1) Reduce the incoming rate of “new” claim denials2) Reduce the backlog of unresolved denials
Project Justification and Benefits:
• The number and amount of Insurance claims denials have increased by more than 70% during 2007. Baseline dollar amount as of March, 2008 = $23 Million
• Insurance claim denials result in bottom line financial impact with unresolved denials resulting into write-offs
47
Team Member Involvement and Data Gathering Approach
Team agreement to focus on gathering data to helpanswer some key questions:
1) What are the sources of denials? Which insurance carriers? Which clinic departments? Which physicians?
2) What is the total impact of denials on Accounts Receivable? What is the denial rate (both incoming and backlog)? How much AR is tied up in denied accounts? How much cash/margin is lost due to denial write-offs?
3) What is the resolution rate on denied accounts? How quickly are denials resolved? Which denial types are easily resolved?
48
Patient Scheduling
Patient Visit
Inputs
- Patient - Physician
- GL Patient Liaison - Nurse, PA
- Insurance Info
- Insurance set-up
Patient Care
- Diagnosis: Patient chart captures details of services performed for claim generation
- Regular Billing
- Special Billing / Workman’s Comp
- Hospital (Inpatient) or Clinic (Outpatient)
- Insurance or patient responsible financial class
System that Captures charges with error or
Hold codes
Patient Charge Entryand Posting
HoldingTank
Coding
- Worklist
- Coders perform error correction and input valid service codes
Insurance Claim is generated and data captured
in the Billing/AR system
Payment Posting
- Worklist
- 22 Electronic Payers
- Manual Payments
- Posting of Insurance Payments, adjustments, disallowances
- Posting of Claim Denials (Remark Codes)
Billing and Insurance Follow Up
- Worklist
- Insurance Aging Balances
- Explanation of Benefits received (detailed information from the Insurance Carrier listing payment or denial information)
- Issue resolution and appeals process with Insurance Carrier to handle denial
Customer Service,Credit and Collections
- Worklist
- Balance Transfer to have charge moved to a patient responsible class
- Statement to patient after insurance follow-up efforts exhausted
- Phone call to patient
- Payment from Guarantor/Patient
- May involve referral to outside collection agency
Charge is Closed(Zero Balance)
- Insurance Payment
- Patient Payment
- Charge write-off
Insurance may route claim to Coders for correction
Error correcting a charge reverses the process steps
System thatcaptures charge
inputs byCharge Entry analyst
Patient Registration
Practice Plus
CLINIC BUSINESS FLOW
Claims “Scrubber”
Practice Plus Billing
- Compliance Advisor
- Claims Administrator
Batch Loader &
other ancillary system feeds
Outputs
49
Outputs:(1) Count and Dollar value of unresolved denials backlog(2) Count and Dollar value of new incoming denials
Measurement Methods: Control Charts, Pareto Charts
(1) Insurance Claims submitted(2) Insurance coverage records
(1) Insurance Carriers(2) Clinic Departments and Locations(3) Physicians(4) Insurance Billing & Follow up staff
Measurement Methods: Sample claim records with denial codes (100 minimum)Denial reason volume by uncontrolled inputs
Charts: Box Plots, Main Effect Plots, Interval Plots, Pareto Charts
Denials Management Sampling Plan
Purpose of the sampling: To understand process stability of insurance claim denials and identify key variables impacting the reasons for denials
Controlled Inputs:
Uncontrolled Inputs:
50
Initial Data Challenges1. Historical data unreliable – lack of tracking
2. Difficult to identify denials which were
• Resolved by resolution efforts versus • Written off
3. No measurement system in place to track new denials
4. Poor categorization of denial reason codes
But practically it was clear that insurance claim denials
activity was impacting business performance
Observation
Do
lla
rs
343128252219161310741
23000000
22000000
21000000
20000000
19000000
_X=20965700
UCL=21668217
LCL=20263183
Observation
Mo
vin
g R
an
ge
343128252219161310741
1200000
900000
600000
300000
0
__MR=264146
UCL=863042
LCL=0
1111
11
111
111
11
1
1
1111
11111111
1
Denial Backlog Balance By Week - 2007
Gundersen Lutheran Clinic: Annual Gross Revenue $800 MAccounts Receivable $120 M
51
Pareto ChartInitial attempt for denials prioritization indicated data source was adequate but needed refining.
Had to spend time mapping multiple denial codes (“remark codes”) to a single denial reason in order to properly identify “20% of the problems causing 80% of the denial performance”
Count 4558788165288166323914871021227782786081740367723604618545535605Percent 2.8 49.810.1 9.1 7.5 4.8 4.5 4.4 3.8 3.3
Cum % 50.2 100.010.1 19.2 26.7 31.5 36.0 40.4 44.2 47.4
Dolla
rs
Perc
ent
Remark CodeOther
CO24CO109
OA52COB7
CO50CO97CO18PROV#CO16
18000000
16000000
14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
100
80
60
40
20
0
Pareto Chart of Claim Denial Dollars By Remark CodeAmerican National Standards Institute
(ANSI)
Claim Adjustment Reason Codes
Over 250 different industry denial codes plus other types used by Insurance Carriers
52
Denials Data Source Refinements
CARRIER #CARRIER NAMECLAIM NUMBERDATE FILEDGUARANTOR #PATIENT #PATIENT NAMEREMARK CODEDEPARTMENTPHYSICIANFINANCIAL CLASSPROCEDURE CODESPROCEDURE MODIFIERSDATE OF SERVICELOCATION CODE (IA, MN, WI)
Indicates new data fields
Once properly capturing relevant databegan to Pareto top denial reasons by:
Insurance Carrier TypesInsurance CarriersDepartmentsPhysiciansProcedure TypesBilling & Insurance Follow-up Staff
53
Remark CodeCO11CO4CO5CO50CO96CO97COB15COB18CODINGPI4
Procedure code and modifier were invalid on the date of serviceDenial Remark Code changed to CODINGProcedure code inconsistent with modifer used or mod is missing
Not deemed a medical necessityNon-Covered chargesPayment for service included in anotherProcedure requires a qualifying service
DescriptionDiagnosis inconsistent with procedureProcedure code inconsistent with modifer used or mod is missingProcedure code inconsistent with the place of service
Mapping of Multiple Denial CodesDenial Reason = Coding Error
Same Approach for:• Provider Billing # missing• Lack of Prior Authorization/Pre-certification • Registration Errors• Billing Errors
54
Categorizing & Mapping the denial code data
helped prioritize
Prioritize by Denial Dollars and….…..
Dollars 400210381348335985357125634580772551672195787114686851172585863908535735Percent 2.4 2.3 2.021.4 20.7 15.3 11.7 8.8 7.0 5.2 3.2Cum % 95.7 98.0 100.021.4 42.1 57.4 69.1 77.9 84.9 90.1 93.3
Dolla
rs
Perc
ent
6500000
5500000
4500000
3500000
2500000
1500000
500000
40
30
20
10
0
Pareto Chart of Remark Code - Baseline 2008 Denial Dollars
$3.6M $3.5M
$2.6M
$2.0M
$1.5M$1.2M
$0.9M$0.5M
$0.4M $0.4M $0.3M
55
….and by Denial Counts
Count 946 677 48317028 12781 9742 4813 4433 3646 2099 1917Percent 1.6 1.2 0.829.1 21.8 16.6 8.2 7.6 6.2 3.6 3.3Cum % 98.0 99.2 100.029.1 50.9 67.5 75.8 83.3 89.5 93.1 96.4
Count
Perc
ent
20000
15000
10000
5000
0
30
25
20
15
10
5
0
Pareto Chart of Remark Code - Baseline 2008 Denial Count
56
Denials Backlog Problem
Insurance Carrier #
Am
ount
65002620103201025002220101500212200120101200011000
700
600
500
400
300
200
100
0
-100
-200
95% CI for the MeanInterval Plot of Denial Balance By Insurance Carrier
Insurance Carrier #
Denia
l Count
2201015002
500
400
300
200
100
0
95% CI for the MeanInterval Plot of Denial Count vs Carrier Type
Commercial Primary
Gov’t Secondary Gov’t Secondary
Commercial Primary
Investigation revealed high dollar balances for primary carriers – lower count volume
But can’t ignore lower dollar balances
Lower dollar balances for secondary carriers – significant DAILY count volume
57
Denials Backlog Study
Denials backlog defined for Gundersen Lutheran
• Claims partially paid or fully rejected by Insurance Carrier
• Claims with No Activity Since Filing (no response from Insurance Carrier since 45 days of claim filing)
• Denials requiring resolution by Billing & Insurance Follow-up staff
– Carrier resolution (phone call, written appeal, rebills)
– Transfer to patient responsibility
– Denial is a valid write-off
• For several reasons the backlog grew to unmanageable levels (process change did not follow organization / system changes)
58
Denials Backlog Study
An initial study looked at denials volume by staff “worklists”
Me
an
of
Co
un
t
8000
6000
4000
2000
0
8000
6000
4000
2000
0
Insurance Analyst Hours/day Supervisor
Carrier Type Coding Provider Bill Number Reqd
Main Effects Plot (data means) for Denial Counts on Worklists
Notice any differences?
1. Number of denials by analyst
2. Hours spent per day on worklist
3. Supervisor or staff list
4. Insurance Carrier Type
5. Claim require coding
6. Billing number needed for Provider
1 2 3
4 5 6
59
Denials Backlog Study
Then needed to understand the incoming volume of new denials
Incoming Denial Dollars
Insurance Analyst
Denia
l Dolla
rs
30000
25000
20000
15000
10000
5000
0
95% CI for the MeanInterval Plot of Daily Denial Dollars vs Insurance Analyst
CodingCoding Provider
Billing #
60
Denials Backlog Study
Insurance Analyst
Denia
l Count
400
300
200
100
0
95% CI for the MeanInterval Plot of Daily Denial Count vs Insurance Analyst
Incoming Denial Counts
Investigation revealed small dollar balances were filtered to a general supervisor worklist – quickly accumulated secondary claims and/or small balances not economical to pursue
61
Top Actions from Initial Study
• Identified additional Lean project involving Provider billing numbers: Credentialing to Billing sub-processes
– Transitioned responsibility from Credentialing to Billing group
– Streamlined front end requirements to gather necessary provider billing documentation from Human Resources & Clinic Departments
• Realigned Coding staff responsibility for denials resolution
• Action plans developed with top Commercial and Government carriers
– Significant gap identified with cross over claims from Primary to Secondary carrier (Medicare to Medicaid system edit failure)
– Top Commercial carrier transmitting high volume of general denial codes (Claim lacks information)
• Implemented new measurement system for Denials activity tracking
62
Week #
Week DateClaim Denial
Backlog Count
Claim Denial Backlog Amount
New Claim Denial Count
New Claim Denial
Amount
Denials Write-off
Count
Denials Write-off Dollars
Denials Resolved - To Patient Count
Denials Resolved - To Patient Dollars
Denials Resolved - with Carrier Count
Denials Resolved - with Carrier
Dollars
Total Denials Resolved
Count
Total Denials Resolved Dollars
Denials Backlog
Ending Count
Denials Backlog Ending Balance
Date
Co
un
t
500
400
300
200
100
0
_X=363.5
UCL=452.3
LCL=274.7
Provider # - Incoming Denial Count
Date
Do
llars
120000
100000
80000
60000
40000
20000
0
_X=105601
UCL=119441
LCL=91761
Provider # - Incoming Denial Dollars
Denials Management Tracking Model
There was no baseline or tracking of incoming denials activity
63
Denial Reason: No Provider Billing NumberPrioritization – Step 1
Count 62182 60829790566 209424 183166 134939 115054 87152 74112 64386Percent 3.5 3.444.4 11.8 10.3 7.6 6.5 4.9 4.2 3.6Cum % 96.6 100.044.4 56.1 66.4 74.0 80.4 85.3 89.5 93.1
Do
llars
Pe
rce
nt
Department Other341130010123001011900241130030213001013210211130010130001013310
2000000
1500000
1000000
500000
0
100
80
60
40
20
0
Pareto Chart of Denials for No Provider Billing # - By Department
64
Count 76804 539 231 214 159 95 84 63Percent 3.435.5 23.8 10.2 9.4 7.0 4.2 3.7 2.8
Cum % 100.035.5 59.3 69.5 78.9 86.0 90.2 93.9 96.6
Count
Perc
ent
Physician # Other618961541466214625462532146172
2500
2000
1500
1000
500
0
100
80
60
40
20
0
Pareto Chart of Provider Billing # Denials: By Physician for Dept. 1013310
Denial Reason: No Provider Billing NumberPrioritization – Step 2
65
Provider Billing # Scenario
De
nia
l Co
un
t
NewLocationNewHireDeactivation
35
30
25
20
15
10
5
0
Boxplot of Provider Billing # Denials by Type
Provider Billing Number Issue
To implement process improvements for obtaining provider billing #’s had to understand the various reasons:
Which reason is the primary cause for this type of Denial ?
Billing # expired and needs renewal
Billing # not obtained when
hiring new provider
Additional billing # not obtained when provider goes to new
location
66
Carrier
We
ekl
y D
en
ial C
ou
nt
4321
80
70
60
50
40
30
20
10
0
Provider Billing # Denial Count by Carrier
Physician
We
ekl
y C
ou
nt
62536172721720
70
60
50
40
30
20
10
0
Boxplot of Provider Billing # Denial Count by Physician
Provider Billing Number
Can root cause of not having provider billing # in place be due toDifferent Insurance Carriers?
Different Physicians?
67
Provider Billing Number Learnings
• Emphasized need for improving front end processes versus all work on back-end resolution
• Focusing on other upstream processes which link to a denial
– Registration, insurance set-up errors
– Prior Authorization / Pre-certification
– Physician referrals
– Physician dictation and billing packet documentation
– Coding of claims
68
Date
Dolla
rs
24000000
23000000
22000000
21000000
20000000
19000000
_X=23213304
UCL=23840771
LCL=22585837
Clinic Insurance Claim Denials - Dollar Backlog
Date
Count
75000
70000
65000
60000
55000
_X=74977UCL=76000
LCL=73953
Clinic Insurance Claim Denials - Count Backlog
Progress in 2008
Continue Control Plan monitoring in 2009 and
Phase 2 actions
Project start at April, 2008
69
Case Study # 2Hospice Billing & Charge Capture
Project Purpose & Background (May – October, 2008):
• The purpose of this project is to improve the process of billing for services rendered and properly capturing charges for nursing home type visits reimbursable by Medicare
Project Justification and Benefits:
• The amount of unbilled services has grown to over $1 Million
• An undetermined amount of charges for nursing home visits have not been entered into the clinical/financial system
70
Enter Encore Note Sync LaptopDownload to
Encore FinancialAudit Clinical to
FinancialPost to billing Bill runs - Encore
Billing Scrubber Update/Merge AR Work on Errors “Networks” Sent to Payors
Clinician Clinician AutomaticTammy
5-10% Error RateTammy Deb K.
Automatic Deb K.
Deb K.Deb B.Amy B.
0-5% Error Rate
MedicareSelf Pay
MedicadeCommercial
Same Day as visit End of day 1:30 – 2:00 AM 2X Week 15th of Month
Hospice Billing Flow
a
a
Initial process review indicated gaps betweenclinical teams and the Billing group
- New system- Roles & responsibility changes
71
Investigation Approach
• Limited baseline history of unbilled amounts but Clinical Director raised initial concern based on department financial reviews
• Even with limited history for unbilled amounts data confirmed enough of a trend to signal an issue
Month
Dolla
rs
654321
1140000
1120000
1100000
1080000
1060000
1040000
1020000
1000000
_X=1060382
UCL=1127491
LCL=993274
Unbilled Hospice Charges
72
Investigation Approach
• Initial team meetings between clinical and billing teams quickly identified gaps with some simple Six Sigma Lean tools
– Process Mapping
– Responsibility Matrix
– Cause & Effect matrix prioritization, FMEA
• Practical discussion revealed inputs into the system by clinical teams were being hung up in the system and not passed or visible to billing
– Incorrect service types being selected by clinicians for nursing home visits
– Lack of connectivity between Clinical and Billing teams
73
Department
Un
bill
ed
Do
llars
HomeHealthHospice
1000000
800000
600000
400000
200000
0
Boxplot of Unbilled Charges by Department
It was quickly determined what
department to focus on for
unbilled charges
What is the primary source of unbilled charges?
74
Process error – Incorrect service codes selected for nursing home visits
Mean o
f Unbill
ed C
ount
700
600
500
400
300
200
100
0
Proper Service Code Service Type
Main Effects Plot (data means) for Unbilled
75
Month
Dolla
rs
1413121110987654321
1200000
1100000
1000000
900000
800000
700000
600000
500000
400000
_X=1060382
UCL=1127491
LCL=993274
Unbilled Hospice Charges
Progress Report
Oh, oh ?Actually result of process cleanup. Along with unbilled amounts discovered charges not yet posted (incremental revenue for charges booked at month end)
Increase in unbilled amounts caused delay in charge entry
76
Hospice Billing & Charge CaptureKey Learnings
• Co-location of charge entry/billing analyst with Clinical team
• Proper system security access for clinical and financial teams
• Education to clinicians on service code entry and mistake proof of system to flag for incorrect codes
77
Case Study # 3IV Solutions Charge Capture
Project Purpose & Background (June – October, 2008):
• The purpose of this project is to implement a new process to properly support the charging of Intravenous (IV) Solutions as dispensed from Pyxis med stations
Project Justification and Benefits:
• Hospital operations are transitioning to the EPIC platform for the inpatient record and inpatient order entry portion of Gundersen Lutheran’s overall electronic health record in November, 2008
• A process for charging IV Solutions needs to be implemented in advance as part of EPIC readiness deployment and to ensure revenues are captured during this interim period
78
Primary Process Change
Nurses administering IV solutions must capturethe record in the Pyxis med station for charging
IV ChargingProcessIV inventory usage
Remote stock of IVsNursePyxis Med stationPink Sheets
Charge capture
IV billing
Compliance rate of usage vs. billing
Inputs Outputs
79
Data Challenges
Creation of reports from different source systems to implement a measurement system for tracking IV charge compliance
– Inventory usage report
– Pyxis med station billing report
– Invision billing report (for operating units not utilizing Pyxis)
– Manual tracking of “Pink Sheets” for operating units without Pyxis or Invision
80
Dept
Com
plia
nce
Perc
ent
151413121110987654321
90
80
70
60
50
40
30
20
95% CI for the MeanCompliance Ratio Over 8 Weeks by Dept. Monitored initial weeks of
implementation and targeted additional education and support needed for operating units / departments Any differences by Department?
Compliance Minimum Target Rate = 50%
81
Me
an
of
CR
NoYes
0.8
0.7
0.6
0.5NoYes
NoYes
0.8
0.7
0.6
0.5
Pink Sheets Nurse Ed
Stamp
Main Effects Plot (data means) for CR
Targeting some Key Input Variables
Compliance RatiosCould be improvedWith some effort:
1) For departments without Pyxis med stations improve manual method of capturing charges on “pink sheets”
2) Use Nurse Education to assist specific departments
3) Some departments used a stamp method as reminder
82
Progress Report
Objective:
Increase compliance rate prior to EPIC deployment
Observation
Com
plia
nce
Rati
o
21191715131197531
0.9
0.8
0.7
0.6
0.5
0.4
0.3
_X=0.4852
UCL=0.6240
LCL=0.3463
Compliance Ratio Trend By Week
83
IV Usage Compliance Ratio - Billed to Usage Quantity
0%
20%
40%
60%
80%
100%
120%
IV Usage Compliance Ratio - Billed to Usage Quantity
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Week 1 compliance
rates by Department
Final Week compliance
rates by Department
84
Mike O’NeillEfficiency Improvement LeaderGundersen Lutheran Health System
Mike O’Neill is a Master Black Belt, Efficiency Improvement Leader for the Gundersen Lutheran Health System in La Crosse, Wisconsin. Mike joined Gundersen in March, 2008 after spending 23 years in industrial manufacturing with Trane, an Ingersoll-Rand Company.
Mike became a certified Black Belt and Master Black Belt during his tenure with Trane. He was the Six Sigma Leader for the commercial global finance team and led multiple transactional projects involving the order to cash cycle. His last assignment at Trane was Global Customer Quality Leader having responsibility for all warranty processes and policies, collecting customer quality information, establishing customer focused metrics, and timely claim resolution.
Since joining the Healthcare industry Mike has been leading projects and mentoring project leaders in the application of Six Sigma in areas of revenue charge capture and billing process improvement.
Mike has a bachelor’s degree in business administration and economics from the University of Wisconsin-Stevens Point and a master’s degree in business administration from University of Wisconsin-La Crosse.