Utilizing MultiVari and ANOVA for Billing

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Utilizing Multi-Vari and ANOVA for Billing
Charge Capture Projects in a Healthcare Setting

• Mike ONeill
• Lean Six Sigma and
• Business Improvement in Healthcare Summit
• March 17, 2009

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Agenda

• Multi Variance Analysis benefits and approach
• Analysis of Variance (ANOVA)
  Hypothesis testing overview
• Hypothesis Tests - Healthcare insurance denial
  type
• Billing Charge capture case studies
• Insurance Claim Denials Reduction
• Hospice Billing Charge Capture
• Intravenous (IV) Solutions Charge Capture

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Gundersen 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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Initial Approach
Want to understand stability and capability of
the Y - output(s)
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Insurance Claim denials
f (Claim Types, Carriers, Depts, Physicians,
Procedure Types, ...)
Y
y1, y2, y3, y4
Weekly Denials
.With plenty of xs / drill downs
Find clues of key drivers Practically Graphically
Statistically
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Know what key Xs to Analyze(Initial filtering
from Process Mapping)

• With multiple xs that may lead to high
  variability want
• to focus on the noise/uncontrolled type variables
  first
• Process variation due to
• Similar type variables
• Insurance Carriers
• Departments
• Physicians
• Billing/Coding Staff
• 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 Variables
• Test for Variability within a piece
• Example Four procedures per patient visit
• Test for Variability within a batch
• Example Variability across procedures by
  physician
• Test for Variability across batches
• Example Variability across procedures within a
  month
• For Continuous Input Variables
• Test for Variability within a time span
• Example Ten insurance coverage records per day
• Test for Variability across short time spans
• Example Variability across week
• Test for Variability across longer time times
• Example 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 Xs and consider which are causing
  variability in the process output
• Go back and look at Xs 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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Multi vari charts can be used to investigate
relationships among variables

• Discrete Variables
• Boxplots
• Interval Plots
• Main Effects Plots
• Interaction Plots
• T-tests comparing two groups
• ANOVAs
• Continuous Variables
• Scatterplots
• Correlation
• Regression
• Multiple Regression

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 (ANOVA) and Hypothesis
Testing
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Analysis of Variance Studies (ANOVA)

• 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 lt .05 this indicates there
  is a difference
• We will look at how a study was performed for the
• above scenario but lets review Hypothesis
  testing further

For Discrete inputs (x) Continuous outputs (y)
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Hypothesis Testing Concepts Enable
You To .

• Properly handle uncertainty
• Minimize subjectivity
• Question assumptions
• Prevent the omission of important information
• Manage the risk of decision errors

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Key Terms
Ho Null Hypothesis Ha Alternative
Hypothesis P Value Probability Value
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Hypothesis Testing What is it
for Statisticians ?
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Hypothesis Testing What is it
for the Average Person ?
Ho Coding doesnt 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 dont know
• Generally, this hypothesis takes the form of
  Yf(x1,x2...xk)
• We gather data and devise a test to evaluate the
  hypothesis testing the effect of the xs 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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Hypothesis and Decision Risk
State a Null Hypothesis (Ho)
Faced with two risks of making a wrong
decision Type 1 Error Alpha Risk Type 2 Error
Beta Risk Type 1 a example Not sending a
denial balance to patient when you could Alpha
Risk Type 2 b example Sending a denial balance
to patient when you shouldnt Beta Risk
Gather evidence (a sample of reality)
DECIDEWhat does the evidence suggest? Reject
Ho? or Fail to Reject Ho?
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A Practical Hypothesis Test

• Fire Alarm Decision

S T A T E O F R E A L I T Y
No Alarm
Alarm Sounds
N O F I R E
Correct Decision Confidence 1 - alpha
probability
Type I Error alpha probability
Correct Decision Power or 1 - Beta probability
Type II Error Beta probability
F I R E
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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 Hypothesis True, No Fire
Area to right of trigger is probability of
committing an alpha error
Area to left of trigger is confidence or 1 -
alpha
Random distribution of particle counts in normal
air
Detector Set Point
Area to right of trigger is the power of the
test 1 - Beta
Null Hypothesis False, Fire Exists
Area to left of trigger is probability of
committing a beta error
distribution of particle counts in Smoke -
filled air
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Hypothesis Testing

• After data is collected, statistical scores can
  be calculated In Microsoft Excel or
  statistical software such as Minitab

A probability (P) value is one statistic
calculated to help determine if null hypothesis
is true or false
If P is low, then Ho must go!!!

• Small P-Value
• Ho is Rejected

• Large P-Value
• Ho is Not Rejected

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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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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

For most cases use .05
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Proving the Null Hypothesis

• Minnesota Senate Election Results
• Ho Al Franken Norm Coleman
• Ha Al Franken ? Norm Coleman
• alpha 0.05 (5 risk Factor)
• If Vote in Minnesota allows rejection of Ho, they
  Project a winner.
• If Vote does not reject Ho, they say...

Too close to call !
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Hypothesis Tests - Healthcare insurance denial
type
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One Sample testAre Patient Responsibility denial
balances within 100 or less?

• 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

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Minitab - Output
One-Sample T Patient Resp Balance Test of mu
100 vs not 100 Variable N Mean StDev
SE Mean 95 CI T
P Balance 1828 277.4 509.4 11.9
(254.064, 300.803) 14.89 0.000
A P-Value !
Ho PR Denial 100 Ha PR Denial 100
Should we auto bill patient for all Patient
Responsibility Denials?
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Other Tests / Charts willConfirm the Null
Hypothesis is False
Ho PR Denial 100 Ha PR Denial 100
The 100 target to auto bill patient is not
within the confidence interval range
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Butthe 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 (lt.05) indicates that the Null
Hypothesis In this case is false (The data a
normal distribution)
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Therefore try a 1 Sample Non-Normal Data Test
(to test
medians vs. means)
Wilcoxon Signed Rank Test PR Balance Test of
median 100 versus median not 100
N
for Wilcoxon Estimated
N Test Statistic P Median Balance
1828 1826 1243988.5 0.000 188.1
Ho PR Denial 100 Ha PR Denial 100
If P is low, reject Ho
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How do the 3 top Patient Responsibility denial
reasons compare?
Another PR denial scenario Another study ! Why
are we seeing PR type denials gt 100

• 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

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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.

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In Minitab select STATgtANOVAgtTEST FOR EQUAL
VARIANCES
Coverage Error
B for Normal L for non-normal
No Claim 2ndary claim not received by Carrier
Bill Patient
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Other Graphical Representations

• Lets 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
• Lets take a look at each...

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Main Effects Plot
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
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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
is true value of population mean
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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

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100
75
4th Quartile
3rd Quartile
50 (Median, not the Mean)
2nd Quartile
1st Quartile
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Quartiles rank order the data from lowest to
largest value
0
Not including any Outliers
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Boxplot Outliers
Boxplot 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 )
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

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 (KPIVs) and
  Key Process Output Variables (KPOVs) 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

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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

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Billing Charge capture case studies

• Insurance Claim Denials Reduction
• Hospice Billing Charge Capture
• Intravenous (IV) Solutions Charge Capture

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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
• Reduce the incoming rate of new claim denials
• 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

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Team Member Involvement
and Data Gathering Approach

• Team agreement to focus on gathering data to help
• answer some key questions
• What are the sources of denials?
• Which insurance carriers?
• Which clinic departments?
• Which physicians?
• 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?
• What is the resolution rate on denied accounts?
• How quickly are denials resolved?
• Which denial types are easily resolved?

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• Initial Data Challenges
• Historical data unreliable lack of tracking
• Difficult to identify denials which were
• Resolved by resolution efforts versus
• Written off
• No measurement system in place to track new
  denials
• Poor categorization of denial reason codes
• But practically it was clear that insurance
  claim denials activity was impacting business
  performance

Gundersen Lutheran Clinic Annual Gross Revenue
800 M Accounts Receivable 120 M
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Pareto Chart
Initial 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
American National Standards Institute
(ANSI) Claim Adjustment Reason Codes Over 250
different industry denial codes plus other types
used by Insurance Carriers
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Denials Data Source Refinements
Once properly capturing relevant data began to
Pareto top denial reasons by Insurance Carrier
Types Insurance Carriers Departments Physicians Pr
ocedure Types Billing Insurance Follow-up
Staff
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Mapping of Multiple Denial Codes
Denial Reason Coding Error

• Same Approach for
• Provider Billing missing
• Lack of Prior Authorization/Pre-certification
• Registration Errors
• Billing Errors

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Categorizing Mapping the denial code data
helped prioritize
Prioritize by Denial Dollars and...
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.and by Denial Counts
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Denials Backlog Problem
Commercial Primary
Govt Secondary
Govt Secondary
Commercial Primary
Investigation revealed high dollar balances for
primary carriers lower count volume But cant
ignore lower dollar balances Lower dollar
balances for secondary carriers significant
DAILY count volume
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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)

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Denials Backlog Study
An initial study looked at denials volume by
staff worklists

• Notice any differences?
• Number of denials by analyst
• Hours spent per day on worklist
• Supervisor or staff list
• Insurance Carrier Type
• Claim require coding
• Billing number needed for Provider

1
2
3
4
5
6
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Denials Backlog Study
Then needed to understand the incoming volume of
new denials
Coding
Provider Billing
Incoming Denial Dollars
Coding
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Denials Backlog Study
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
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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

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Denials Management Tracking Model
There was no baseline or tracking of incoming
denials activity
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Denial Reason No Provider Billing
NumberPrioritization Step 1
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Denial Reason No Provider Billing
NumberPrioritization Step 2
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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 not obtained when hiring new provider
Billing expired and needs renewal
Additional billing not obtained when provider
goes to new location
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Provider Billing Number
Can root cause of not having provider billing
in place be due to Different Insurance
Carriers? Different Physicians?
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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

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Project start at April, 2008
Progress in 2008 Continue Control Plan
monitoring in 2009 and Phase 2 actions
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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

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Initial process review indicated gaps
between clinical teams and the Billing group

• New system
• Roles responsibility changes

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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

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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

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What is the primary source of unbilled charges?
It was quickly determined what department to
focus on for unbilled charges
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Process error Incorrect service codes selected
for nursing home visits
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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
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Hospice Billing Charge Capture Key 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 Lutherans
  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

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Primary Process Change
Nurses administering IV solutions must
capture the record in the Pyxis med station for
charging
Inputs
Outputs
IV Charging Process
Charge capture IV billing Compliance rate of
usage vs. billing
IV inventory usage Remote stock of
IVs Nurse Pyxis Med station Pink Sheets
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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

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Compliance Minimum Target Rate 50
Monitored initial weeks of implementation and
targeted additional education and support needed
for operating units / departments Any
differences by Department?
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Targeting some Key Input Variables

• Compliance Ratios
• Could be improved
• With some effort
• For departments without Pyxis med stations
  improve manual method of capturing charges on
  pink sheets
• Use Nurse Education to assist specific
  departments
• Some departments used a stamp method as reminder
  

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Progress Report
Objective Increase compliance rate prior to
EPIC deployment
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Week 1 compliance rates by Department
Final Week compliance rates by Department
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Mike ONeill Efficiency Improvement
Leader Gundersen Lutheran Health System Mike
ONeill 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 bachelors degree in business
administration and economics from the University
of Wisconsin-Stevens Point and a masters degree
in business administration from University of
Wisconsin-La Crosse.