Improving Claims Processing

PayUp In$urance Company

TEAM 5

Description:

2

Project Description

• Reduce customer complaints due to delays in claims processing time.

• Reduce cost of processing claims.

Scope (In/Out)

IN SCOPE• Process/work

flow• Work schedules• Staffing

Key Benefits (including financials)

• Reduce Customer Complaints due to claims processing time by 50% by December 2016• Reduce operating cost of claims processing by 25% by December 2016

Key Interdependencies

• Claims Processors• Claims Adjustors• Claims Account Payables• IT• Finance• Customer Service

Critical Assumptions and Risks

• VP of Claims is Project Sponsor• Assume the call received quality is 95%• Assumed inventory zero at task one• Distance traveled for tasks 2-5 are zero• Quality level 100% for task 3• Adjustor inspecting and entering data the quality was

changed to 71%• Wait time of 5 minutes from receiving the call to

transferring and entering the data• 5 minutes to process the mail for delivery • The 4 week period from customer receiving the check

and cashing the check

Project Charter- Improving Claims Processing

OUT of SCOPE• Capital expenditures

3

Role Name and/or Number of Resources

Leadership Team Sponsor VP Claims

Management Team Owner Claims Processing Manager

Transformation Lead OPEX

Category Leads Data Entry, Claims Payables, Claims Adjustors

Others… Customer Service Representative, IT Support

Program Team Structure

DRAFT – FOR DISCUSSION

Core Team

Extended Team

Role Name and/or Number of Resources

IT Leads Software Application

CSR Leads Regional

Others…. Finance

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PayUp’s Current State Map

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PayUp CalculationsCalculating Lead Time

Demand = 200 Claims

Time = 7.5 Hours

= 450 Min.

Takt Time = 450/200

= 2.25 Min/Claim

= 135 Sec/Claim

Total Productive Time

Receive Call 15 Min.

Enter Data 7 Min.

Adjustor calls Customer to verify data 20 Min.

Adjustor inspects damage & enter data 65 Min.

Process & Mail Check 5 Min.

Total 112 Min.

Total Lead Time

Receive Call 0 Min.

Enter Data 2070 Min.

Adjustor calls Customer to verify data 11 Min.

Adjustor inspects damage & enter data 0 Min.

Process & Mail Check 900 Min.

Total 2981 Min.

Inventory

Receive Call 0 Cases

Enter Data 920 Cases

Adjustor calls Customer to verify data 5 Cases

Adjustor inspects damage & enter data 0 Cases

Process & Mail Check 400 Cases

Total 1325 Cases

Efficiency

Productive Time = 112 Min.

Total Time (Productive + Lead) =

3093.25 Min.

Efficiency = 0.04 Min.

Yield

Receive Call 0.95

Enter Data 0.5

Adjustor calls Customer to verify data 0.89

Adjustor inspects damage & enter data 0.71

Process & Mail Check 0.94

Yield = 0.28

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Cost/Claim Summary

• Summary view of the data by region and product to identify abnormalities in raw data

• This view tells us where to focus our efforts based on the average cost/claim and the standard deviation

• Product A cost/claim is consistent across all regions while Product B has a significant variance by region

• Product B in region 3, has the smallest cost/claim variance which means it is the most consistent

    Product    Region Data A B Grand Total

1 Average of Cost/claim 257.59 326.37 291.98  StdDev of Cost/claim2 5.88 13.97 36.332 Average of Cost/claim 259.07 327.49 293.28  StdDev of Cost/claim2 7.60 7.51 35.363 Average of Cost/claim 250.15 295.03 272.59  StdDev of Cost/claim2 6.35 3.01 23.20

Total Average of Cost/claim   255.60 316.30 285.95Total StdDev of Cost/claim2   7.65 17.72 33.35

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Graphic View of Data

• This graph showcases the cost/claim per region by product type

• Product A has an average cost/claim is consistent across all three regions

• Product B average cost/claim varies significantly by region

360340320300280260240

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

257.6 5.883 25259.1 7.603 25250.2 6.346 25326.4 13.97 25327.5 7.514 25295.0 3.010 25

Mean StDev NytisneD

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3B2B1B3A2A1A

lamroNmialc/tsoC fo margotsiH

Product B

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Cost/Claim Trend Over Time• This graph shows the

cost/claim of each product by region over the last 25 weeks

• Product B cost/claim in regions 1&2 is increasing

• Product B cost/claim in region 3 is steady

• Product A cost/claim in regions 1&2 is increasing

• Product A cost/claim in region 3 is improving

• There are opportunities for improvement in regions 1&2 for both products A&B1501351201059075604530151

350

325

300

275

250

Index

Cost

/cla

im

A 1A 2A 3B 1B 2B 3

Product Region

Time Series Plot of Cost/claim

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Potential causes for cost/claim

• This graph shows the variables that may cause an impact on the cost/claim

• We have determined that rework due to errors is a major contributor to the cost/claim

Description:

10

Project Description• Reduce customer complaints due to delays in claims

processing time.• Reduce cost of processing claims.• Focus efforts on rework due to errors as a major

contributor to the cost/claim.

Scope (In/Out)IN SCOPE• Process/work flow• Work schedules• Staffing

OUT of SCOPE• Capital expenditures

Key Benefits (including financials)• Reduce Customer Complaints due to claims processing time by 50% by December 2016• Reduce operating cost of claims processing by 25% by December 2016

Key Interdependencies• Claims Processors• Claims Adjustors• Claims Account Payables• IT• Finance• Customer Service

Critical Assumptions and Risks• VP of Claims is Project Sponsor• Assume the call received quality is 95%• Assumed inventory zero at task one• Distance traveled for tasks 2-5 are zero• Quality level 100% for task 3• Adjustor inspecting and entering data the quality was changed to 71%• Wait time of 5 minutes from receiving the call to transferring and

entering the data• 5 minutes to process the mail for delivery • The 4 week period from customer receiving the check and cashing the

check

Project Charter- Improving Claims Processing

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Descriptive Statistics on Cost/Claim

  Ratio Experience #Claim Types Training Gender # Claims

Mean 25.37% 4.84 3.76 0.52 0.48 295.20

StdDev 14.29% 3.00 2.70 0.51 0.51 126.82

Min 6.00% 0.00 1.00 0.00 0.00 121.00

Max 54.29% 10.00 9.00 1.00 1.00 486.00

• This graph shows the variables that may cause an impact on the cost/claim

• As shown above the more experience you have the more claim types you have access to fix, and the total number of claims you resolve will increase as well

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Measurement Errors • Data entry error

– Observation 17 had an extra digit that skewed the data • Time/Cost to correct error has no data• Training data is subjective and limited estimated to the last six months

– No specific data on the type of training received • Data on experience is given on only in full year increments

– No indication on where the experience was gained• Complexity of claim types unknown• The claim completion percentage data is unknown

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Benchmarking• Internal Benchmarking

– Product B region 3 is our most consistent in cost/claim – Product A, on average, is lower cost/claim than Product B– Determine if we have other products and or regions that can be used

for benchmarking – We will utilize these values to revaluate our project goals

• External Benchmarking– Compare our product cost/claim by type and region to competitors – Employee survey on best practices from previous employers – Hire managerial consulting team to evaluate best practices across

similar industries

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1086420

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00%

Experience

Ratio

Scatterplot of Ratio vs Experience

• The more experience the employee has the fewer errors they make• The Correlation Coefficient is -0.018• This means that with each year of experience, the error ratio will decrease by

2%

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60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00%

_Claim Types

Ratio

Scatterplot of Ratio vs _Claim Types

• The more types of claims the employee processes the fewer errors they make• The Correlation Coefficient is -0.022• With the greater variety of claim types, the error ratio will decrease by 2%

• This may be due to the fact that the more experienced employees are given the more difficult and varied claim types

• Additional data is required to investigate further

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500400300200100

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00%

_ Claims_1

Ratio

Scatterplot of Ratio vs _ Claims_1

• The more claims that the employee handles, the fewer errors they make• The Correlation Coefficient is -0.00025• With the greater number of claim types, the error ratio will decrease by 0.025%

• This may be due to the fact that the more experienced employees are given more claims

• The correlation coefficient is near zero therefore, the relationship is not as statistically significant as the other variables.

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72.00%54.00%36.00%1800%0.00%-18.00%

5

4

3

2

1

0.

0.2942 0.2024 60.2781 0.1803 60.2177 0.1018 70.2307 0.08358 6

Mean StDev N

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FYMYFN

MN

gniniarT dna redneG yb % rorrE

• This chart depicts the relationship between error ratio, gender and training.• From the chart, gender has minimal impact on error ratio. • Training has an ≈6% reduction in error ratio. • Training reduces variability ≈10%.• Density equals number of employees per grouping.

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SUMMARY OUTPUT

Regression StatisticsMultiple R 0.972796116R Square 0.946332283

Adjusted R Square 0.93559874Standard Error 0.036267397Observations 25

ANOVA  df SS MS F Significance F

Regression 4 0.463866817 0.115966704 88.16587837 2.07396E-12Residual 20 0.026306482 0.001315324Total 24 0.490173299     

  Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% predictive y

Intercept 0.53922 0.020425519 26.39940911 5.09468E-17 0.496614735 0.581828505 0.496614735 0.581828505 y 0.53922

Experience -0.01773 0.004487416 -3.950826798 0.000789231 -0.027089587 -0.008368417 -0.027089587 -0.008368417 10 -0.17729

#Claim Types -0.02208 0.005799133 -3.807191021 0.001103845 -0.034175188 -0.009981628 -0.034175188 -0.009981628 5 -0.11039

Training -0.08027 0.01581556 -5.075241387 5.78282E-05 -0.113258466 -0.047277105 -0.113258466 -0.047277105 1 -0.08027

# Claims -0.00025 0.000123593 -2.055265672 0.053147542 -0.000511826 3.79402E-06 -0.000511826 3.79402E-06 400 -0.10161

  0.06967

• Equation Y= 0.54 – 0.02 x Years of Experience – 0.02 x No. claim types – 0.08 x training – 0.00025 x No. of claims

Recommendation:• Years of experience is a significant contributor to error reduction, therefore, hire

experienced employees.• Provide training to all employees who have not received training.

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Lean Six Sigma Elevator Pitch

“Not only is this something that we should do, I think it is something that we must do, if we are going to stay competitive and maximize our profitability and consistently deliver the best quality products and services to our customers. The concept of Lean and Six Sigma are applicable to all areas of the organization, not just manufacturing. The tools and techniques I am learning will help us to not only identify costly waste but will also help us to deliver actual sustained improvements by utilizing the DMAIC (Define, Measure, Analyze, Improve, Control) methodology and lean/six sigma tools.”

- Team 5

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Team Five Members• Rory Donnerstag• Julio Rivas• Richard Singletary• Michael Kenyon• Farahnaz Sayani• Cheng-yu Ouyang• Amari Hanes