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Lean Six sigma project  on Increasing claims transactions by Advance Innovation Group
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Lean Six sigma project on Increasing claims transactions by Advance Innovation Group

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(http://advanceinnovationgroup.com) …

(http://advanceinnovationgroup.com)

This Lean Six Sigma Project done by student from Advance Innovation group which is posted to provide for benchmarking and best practices sharing purposes.


Additionally, it is advisable that you also visit and subscribe Advance Innovation Group Blog (http://advanceinnovationgroup.com/blog) for more Lean Six Sigma Project, Case Studies on Lean Six Sigma, Lean Six Sigma Videos, Lean Six Sigma Discussions, Lean Six Sigma Jobs etc.

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  • 1. Voice of the Customer-VOC2Customer Comments Critical to Quality-CTQ’sInternal Customer-Sr. ManagerOperationsSince June 2011 onwards transactioncount is not meeting defined SLA of12000 transactions for PTR (PatientResponsibility validation) per weekProduction- Patient ResponsibilityValidation (PTR) transactionExternal customer- On Shore Director- Client is profoundly dissatisfied withthe low production and has signalledthe roll-back of the process if thesituation does not improve soon.Production- Patient ResponsibilityValidation (PTR)Define
  • 2. Project CharterBusiness caseXXXX is an USA based KPO working in the domain of Revenue Cyclemanagement catering to US hospitals as clients. It has variouscentres across the US and three off-shore facilities with employeebase of 1000 FTEs. ABC is one of the major sub-processes under theumbrella of main process BP with team strength 26 FTEs. UTPteam work on three work types- Patient Responsibility (PTR)validation , Denials validation, and RCA. Data shows that fewmonths after the process was transitioned, ABC team has not beenable to meet the production SLA causing client unhappiness.TeamProcess Owner-DM:MBBBB:Members:Problem StatementOn the basis of analysis of production data (308 data points) for themonth of June 2011 to August 2011, it was found that total PTRvalidation claims processed stand at 136209 against target of140800. This not only resulted in huge revenue loss for India unitbut has also posed a threat of process roll back if situation does notimprove soon. Roll-back of ABC process will cause huge financialloss to India unit as well as may evoke collateral damage to otherprocesses and big business loss.Goal StatementTo increase the Patient Responsibility Validation (PTR) count frompresent 11350 to 12000 per week.In Scope : ABC ProcessOut Scope : All other departmentsMilestones Start Date End DateD 01-09-2011 05-09-2011M 06-09-2011 11-09-2011A 12-09-2011 17-09-2011I 18-09-2011 18-11-2011C 19-11-2011 30-11-2011Define
  • 3. ARMIKey Stakeholders ARMI WorksheetDefine Measure Analyze Improve ControlStakeholders—Director OffshoreI I I I ISponsor-Sr. Mgr. I/A I I I/A I/AMBB A & I A & I A & I A & I A & IBB I & R I & R I & R I & R I & RDy. Manager M M M M MTeam Lead/SiteLeadsR R R R RA – Approval of team decisions I.e., sponsor, business leader, MBB.R – Resource to the team, one whose expertise, skills, may be needed on an ad-hoc basis.M – Member of team – whose expertise will be needed on a regular basis.I – Interested party, one who will need to be kept informed on direction, findings.Communication PlanInformation Or Activity Target Audience Information Channel Who WhenProject Status Leadership E-mails/Meeting QQQQQ BI-WeeklyTollgate Review BB,MBB & Sr. Mgr. E-mails or Meetings As per Project PlanProject Deliverables or Activities Members Emails, Meetings WeeklyDefine
  • 4. RASICDefine
  • 5. SIPOCSupplier Input Process Output CustomerDatabase TeamClaim informationuploaded in ClaimsRepository/DataLoaderClaims uploading in AHTOContractAHTO contract puts the claiminformation into end-userunderstandable format intorespective hospital database US SME/PMUS SME/PMClaims uploaded intoAHTO contract Claims uploading Claims in Pre-filter bucketUS SME/ PM/India UTPTeamUS SME/PMClaims in UTC in Pre-filter bucket Segregation of work-typePTR and Denials claims aremoved into their respectivefolder by using multiplecriteriaUS SME/ PM/India UTPTeamUS SME/PMPTR and Denialsclaims in respectivefolder in UTC Prioritization of workSegregation of work intoyear-wise bucketsUS SME/ PM/India UTPTeamIndia Site leadsClaims segregatedinto year-wisebuckets in UTC Assignment of workBuckets assigned to eachassociate Associates/Site leadsUTC claimsfolders/bucketsClaims, PAS, EOBs,Payor websites etc. Claim validationReview of PTR & Denialaccounts by UTP teamAssociates/Site leads/USSME/PMAssociates/Site leadsClaimfindings/analysis Claim sign-offEach account is codedappropriately with notesAssociates/Site leads/USSME/PMDefine
  • 6. CTQ TreeTransaction count forPatient Responsibility(PTR) ValidationTransaction count forPatient Responsibility(PTR) ValidationOperational Definition: Total number of PTRtransactions processed in a week.Operational Definition: Total number of PTRtransactions processed in a week.PTR transactions < 12000 in a week isdefect.PTR transactions < 12000 in a week isdefect.Performance Std.: 12000 PatientResponsibility (PTR) validation transactionsin a weekPerformance Std.: 12000 PatientResponsibility (PTR) validation transactionsin a weekDefineLSL: 12000 PTR transactions minimum perweekLSL: 12000 PTR transactions minimum perweek
  • 7. Data Collection PlanKPI Operational Definition Defect Def Performance StdSpecification LimitOpportunityLSL USLProductivityImprovement forPatient Responsibility(PTR) TransactionsTotal # of PTR transactions processed in aweekPTR transactions< 12000 in a weekis defect.12000 PTR transactionsin a week12000 PTRtransactionsminimum/weekNA WeeklyKPI Data TypeData ItemsNeededFormula to beusedUnitPlan to collect DataPlan tosampleWhat Databaseor Container willbe used torecord this data?Is this anexistingdatabaseor new?If new, Whenwill thedatabase beready for use?When is theplanned startdate for datacollection?ProductivityImprovement forPatientResponsibility (PTR)TransactionsDiscreteTotal # oftransactionsprocessed withnumber ofworking daysTotal # oftransactionsprocessed/#of associatesfor a weekCount Excel 2007 Yes NA NA100 % dataJune 2011 toAugust 2011Measure
  • 8. Effectiveness For PTR Transactions9MeasureEffectiveness (% of agreement)= total # of agreed data pointstotal # of data points= (298/308)*100= 96.75%In the above validation task, numbers of PTR transactions processed on weekly basis were cross-checked by two persons- Team Lead and Project Lead. After validation process it was found thatthere were 10 data points where agreement score was zero.Total # of data points 308Agreed data points 298
  • 9. Current Capability - Process Sigma LevelMeasureTotal # of data points 308# of defects 223DPO 0.724025974DPMO 724025.974Out of total sample data points (308) there are 223 data points that have not been ableto meet the production target for PTR validation.
  • 10. Graphical Summary For PTR validationAnalyze Normality: P value < 0.005 Shape: Non-Normal Measure of centraltendency : data is nonnormal measure ofcentral tendency will bemedian = 480
  • 11. Normality test PTR validationAnalyze Normality: P value <0.005. It means data isnot normal.
  • 12. Fishbone Diagram-organizing potential causesFactors identified through brainstormingAnalyze
  • 13. Hypothesis Tests To Be PerformedYD X Data Type TestsNumber of PTR Transactions& Denials Transactions(Productivity)Location Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Age Continuous Binary Logistic Regression (BLR)Qualification Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Marital Status Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Gender Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Work Exp Continuous Binary Logistic Regression (BLR)Typing Speed Discrete (Category)Chi Square (1 Way OR goodness ofFit )Process Knowledge Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Dy. Manager Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Site Lead cum Trainer Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Manager Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Shift Discrete (Category)Chi Square (1 Way OR Goodness ofFit)Analyze
  • 14. 15Denials Transactions v/s AgeThe BLR test shows that age does not have significant impact on team productivity.AnalyzeBinary Logistic Regression: Target Met (Yes/No) versus AgeLink Function: LogitResponse InformationVariable Value CountTarget Met (Yes/No) Yes 85 (Event)No 223Total 308Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -1.33778 1.17174 -1.14 0.254Age 0.0151635 0.0472723 0.32 0.748 1.02 0.93 1.11Log-Likelihood = -181.395Test that all slopes are zero: G = 0.103, DF = 1, P-Value = 0.749
  • 15. 16Denials Transactions v/s Work ExpThe BLR test shows that work exp has significant impact on team productivity.AnalyzeBinary Logistic Regression: Target Met (Yes/ versus Work Exp (in month)Link Function: LogitResponse InformationVariable Value CountTarget Met (Yes/No)_1 Yes 102 (Event)No 206Total 308Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -1.23225 0.283625 -4.34 0.000Work Exp (in month) 0.115465 0.0547040 2.11 0.035 1.12 1.01 1.25Log-Likelihood = -193.321Test that all slopes are zero: G = 4.520, DF = 1, P-Value = 0.034
  • 16. Denials Transactions v/s Qualification17AnalyzeThe Chi-square (1 Way) test shows that qualification hassignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: QualificationTest ContributionCategory Observed Proportion Expected to Chi-SqB.Com 154 0.25 77 77.0000B.Sc 98 0.25 77 5.7273M.A 14 0.25 77 51.5455MBA 42 0.25 77 15.9091N N* DF Chi-Sq P-Value308 0 3 150.182 0.000
  • 17. Denials Transactions v/s Marital Status18AnalyzeThe Chi-square (1 Way) test shows that Marital Status hassignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: Marital StatusTest ContributionCategory Observed Proportion Expected to Chi-SqMarried 42 0.5 154 81.4545Unmarried 266 0.5 154 81.4545N N* DF Chi-Sq P-Value308 0 1 162.909 0.000
  • 18. Denials Transactions v/s Gender19AnalyzeThe Chi-square (1 Way) test shows that Gender has nosignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: GenderTest ContributionCategory Observed Proportion Expected to Chi-SqFemale 154 0.5 154 0Male 154 0.5 154 0N N* DF Chi-Sq P-Value308 0 1 0 1.000
  • 19. Denials Transactions v/s Typing Speed20AnalyzeThe Chi-square (1 Way) test shows that Typing Speed hassignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: StatusTest ContributionCategory Observed Proportion Expected to Chi-SqFail 223 0.5 154 30.9156Pass 85 0.5 154 30.9156N N* DF Chi-Sq P-Value308 0 1 61.8312 0.000
  • 20. Denials Transactions v/s Process Knowledge21AnalyzeThe Chi-square (1 Way) test shows that ProcessKnowledge has significant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: Status_1Test ContributionCategory Observed Proportion Expected to Chi-SqFail 242 0.5 154 50.2857Pass 66 0.5 154 50.2857N N* DF Chi-Sq P-Value308 0 1 100.571 0.000
  • 21. Denials Transactions v/s Site Lead cum Trainer22AnalyzeThe Chi-square (1 Way) test shows that Site Lead cumTrainer has no significant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: Site Lead cum TrainerTest ContributionCategory Observed Proportion Expected to Chi-SqAmit Kalra 15 0.25 16.5 0.136364Gyan Deep 18 0.25 16.5 0.136364Manu Vasdev 18 0.25 16.5 0.136364Neetu Gaur 15 0.25 16.5 0.136364N N* DF Chi-Sq P-Value66 0 3 0.545455 0.909
  • 22. 23Binary Logistic Regression: Target Met (Yes/No) versus AgeLink Function: LogitResponse InformationVariable Value CountTarget Met (Yes/No) Yes 85 (Event)No 223Total 308Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -1.33778 1.17174 -1.14 0.254Age 0.0151635 0.0472723 0.32 0.748 1.02 0.93 1.11Log-Likelihood = -181.395Test that all slopes are zero: G = 0.103, DF = 1, P-Value = 0.749PTR Transactions v/s AgeThe BLR test shows that age has no significant impact on team productivity.Analyze
  • 23. 24PTR Transactions v/s Work ExpThe BLR test shows that work exp has no significant impact on team productivity.AnalyzeBinary Logistic Regression: Target Met (Yes/ versus Work Exp (in month)Link Function: LogitResponse InformationVariable Value CountTarget Met (Yes/No) Yes 85 (Event)No 223Total 308Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -1.13418 0.289538 -3.92 0.000Work Exp (in month) 0.0374581 0.0568588 0.66 0.510 1.04 0.93 1.16Log-Likelihood = -181.229Test that all slopes are zero: G = 0.435, DF = 1, P-Value = 0.510
  • 24. PTR Transactions v/s Qualification25AnalyzeThe Chi-square (1 Way) test shows that qualification hassignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: QualificationTest ContributionCategory Observed Proportion Expected to Chi-SqB.Com 154 0.25 77 77.0000B.Sc 98 0.25 77 5.7273M.A 14 0.25 77 51.5455MBA 42 0.25 77 15.9091N N* DF Chi-Sq P-Value308 0 3 150.182 0.000
  • 25. PTR Transactions v/s Marital Status26AnalyzeThe Chi-square (1 Way) test shows that marital status hassignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: Marital StatusTest ContributionCategory Observed Proportion Expected to Chi-SqMarried 42 0.5 154 81.4545Unmarried 266 0.5 154 81.4545N N* DF Chi-Sq P-Value308 0 1 162.909 0.000
  • 26. PTR Transactions v/s Gender27AnalyzeThe Chi-square (1 Way) test shows that gender has nosignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: GenderTest ContributionCategory Observed Proportion Expected to Chi-SqFemale 154 0.5 154 0Male 154 0.5 154 0N N* DF Chi-Sq P-Value308 0 1 0 1.000
  • 27. PTR Transactions v/s Typing Speed28AnalyzeThe Chi-square (1 Way) test shows that typing speed hassignificant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: StatusTest ContributionCategory Observed Proportion Expected to Chi-SqFail 223 0.5 154 30.9156Pass 85 0.5 154 30.9156N N* DF Chi-Sq P-Value308 0 1 61.8312 0.000
  • 28. PTR Transactions v/s Process Knowledge29AnalyzeThe Chi-square (1 Way) test shows that process knowledgehas significant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: Status_1Test ContributionCategory Observed Proportion Expected to Chi-SqFail 242 0.5 154 50.2857Pass 66 0.5 154 50.2857N N* DF Chi-Sq P-Value308 0 1 100.571 0.000
  • 29. PTR Transactions v/s Site Lead cum Trainer30AnalyzeThe Chi-square (1 Way) test shows that site lead cumtrainer has no significant impact on team productivity.Chi-Square Goodness-of-Fit Test for CategoricalVariable: Site Lead cum TrainerTest ContributionCategory Observed Proportion Expected toChi-SqAmit Kalra (U.P.) 84 0.25 77 0.63636Gyan Deep Dixit 84 0.25 77 0.63636Manu Vasdev 84 0.25 77 0.63636Neetu Gaur 56 0.25 77 5.72727N N* DF Chi-Sq P-Value308 0 3 7.63636 0.054
  • 30. Results of Hypothesis Tests31AnalyzeX P- value For PTR P- value For Denials ImpactAge0.749 0.749 Per BLR test, there is no significant relationship between age andteam productivity.Work Exp0.510 0.034 Per BLR test, there is direct & significant relationship between workexp and team productivity.Qualification 0.000 0.000Qualification has significant impact on process performance. Bylooking at the graph, it is evident that associates withcommerce/science background have over-exceeded the expectedperformance.Marital Status 0.000 0.000Marital Status has significant impact on process performance. Bylooking at the graph, it is evident that unmarried associates haveover-exceeded the expected performance.Gender 1.000 1.000 Gender difference has no impact on process performance, evidentfrom the graph.Typing Speed 0.000 0.000 Typing speed has direct & significant impact on processperformance, evident from the graph.ProcessKnowledge0.000 0.000 Process Knowledge has significant impact on process performance,evident from the graph.Site Lead cumTrainer0.054 0.909 Site Lead cum Trainer has no significant impact on processperformance, evident from the graph.
  • 31. Quality Functional Deployment32ImproveCompleteness Matrix tells that Work exp, Typing Speed, Process Knowledge and Qualification need to be taken care ofwith urgency.
  • 32. Action Plans33ImproveThe above Action Plans have been shortlisted on the basis of DM score >= 10 and need to be worked on priority basis.
  • 33. PTR Transaction Count- before Improvement34Improve
  • 34. PTR Transaction Count- after Improvement35Improve
  • 35. Pre & Post PTR Productivity Improvement Projection36Improve
  • 36. Failure Mode & Effect Analysis (FMEA)37Improve
  • 37. U Chart For PTR Transaction-Before Improvement38ControlBy looking at the above control chart it is apparent that our process is within control limits and there isno special cause variation. However, the mean is far away from the target. Our goal is to reduce themean of defects and bringing it closer to the target.
  • 38. C Chart For PTR Transaction-After Improvement39ControlAs per the above control chart, our process is within control limits and after implementing theimprovement plan and actionable items rigorously we have been able to reduce the number ofdefects and brought the mean closer to the target.
  • 39. 40Thank you