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

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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

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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

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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……

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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

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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

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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

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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

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• 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.

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Analysis of Variance Analysis of Variance (ANOVA) and (ANOVA) and

Hypothesis Testing Hypothesis Testing

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• 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)

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• 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 ….

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Ho : Null Hypothesis

Ha: Alternative Hypothesis

P Value = Probability Value

Key Terms

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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

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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?

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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

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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

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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

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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

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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

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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”

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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

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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...

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Hypothesis Tests - Hypothesis Tests - Healthcare insurance Healthcare insurance

denial type denial type

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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?

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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

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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

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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)

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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

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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

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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

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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

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Can any of this type of analysis be applied in your areas?

• Where can you use Hypothesis testing in your job or project?

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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

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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.