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Navin icd 10 us_healthcare fraud waste and abuse_v_2013



Healthcare fraud hypotheses, ICD 10, Healthcare Integrity programs, ICD 9,

Healthcare fraud hypotheses, ICD 10, Healthcare Integrity programs, ICD 9,



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Navin icd 10 us_healthcare fraud waste and abuse_v_2013 Navin icd 10 us_healthcare fraud waste and abuse_v_2013 Presentation Transcript

  • ICD 10 USA Healthcare FraudWaste & Abuse (FWA: 2013)Prevention | InnovationBy: Navin Kumar SinhaDouble Check Consulting Presentationsinhanavin@hotmail.com
  • Introduction• Significant changes in Healthcare Insurance industry poseboth threat and opportunity to government programs such asCHIPS, Medicare and Medicaid• The cost of health-related fraud, waste and abuse is projectedup to 30% of 4.8 trillion $ healthcare economy in 2014. Withhealth care reform, there is increased regulation and financialpressures on Payors and federal health entities. Need toprevent large FWA $ towards unnecessary and harmfultreatment reduction goals because they aggravate thedisease conditions, very costly in future. A few target exampleare:-– One major healthcare insurance company paid 1 Billion $ in 2006 forPlastic Surgery Fraud.– $100 Million FWA payment in Only three months by an insurancecompany from Southern USA (2011). View slide
  • Fraud, Waste and Abuse (FWA)Provider Fraud• Billing for services not actually performed• Falsifying a patients diagnosis to justifytests, surgeries or other procedures thatarent medically necessary• Misrepresenting procedures performed toobtain payment for non-covered services,such as cosmetic surgery• Up-coding – billing for a more costly servicethan the one actually performed.• Unbundling – billing each stage of aprocedure as if it were a separate procedure• Accepting kickbacks for patient referrals• Waiving patient co-pays or deductibles andover-billing the insurance carrier or benefitplan• Billing a patient more than the co-payamount for services that were prepaid orpaid in full by the benefit plan under theterms of a managed care contract.Consumer Fraud• Filing claims for services or medications notreceived• Forging or altering bills or receipts• Using someone elses coverage or insurancecard View slide
  • Evaluate ICD-9 to ICD 10 Conversion:-One icd-9 to Many icd-10 diagnoses-One icd-9 to one icd-10 diagnoses-Many icd-9 to one icd-10 diagnoses-Analytics based on fraud conditions and edits from ICD-9 diagnoses codes that areexpected NOT to change/deleted as ICD-10 (1 to 1 relationship) will stop Billions offraud loss Now.- Increased granularity from one ICD 9 to multiple ICD 10 takes away provider excusesthat there was nothing else related to code and bill for. Although this group of ICD 9may be in minority, the Advance Analytics endeavor will save Billions of $ fraud!- Hypothesis: “Provider fraud is especially experimenting with Many ICD-9 to1 ICD-10 relationship now to resist the shrinking revenue from 2015”.Specific Fraud Prevention Goal
  • Prevention Objectives and Innovation• Leverage Healthcare Fraud Business Analytics to save MINIMUM$ 500K EVERY WEEK of a month for each year.• Utilize Fraud Prevention Analytics Methods and Conditionsprospectively to monitor ICD-9 that are historically known to befraudulent and hence translate fraudulently as ICD-10.• Providers practice variations ICD-10 Hypotheses evaluation:– “As the ICD 10 diagnoses codes wander withinprocedures (CPT), it helps accelerate the chargedollar (payment)”– “Perfectly coded disease (ICD 10) are too good to betrue; services billed but never rendered and hencefraudulent”.
  • Recommendation & Next Steps• Data Mining approach to identify fraud provider population– Data mining with multivariate statistical analysis to detect outliers.– Advance Analytics methods include regression, decision trees, and neural networks etc, each ofwhich has subcategories underneath.• Attack historically known ICD-9 diagnoses to Industry for FWA activities with Predictive and AdvanceAnalytics. They’re especially important for stopping ICD-10 fraud! Because of clinical code confusion(ICD9 to ICD 10 transition), some providers would take advantage 200% beyond 2015!– Opportunity of save multi Billion $ from fraud in government health programs.• Healthcare Industry has struggled with inpatient fraud especially! High threshold $ is evaluated, forexample, claims below 10K-20K is Not reviewed due to the fear from high false positives…– Top 5 Health Insurance company paid $100 Million in 3 months…– Requires highest level of clinical knowledge; 65K rows of data after cleaning turned out to be 1500rows only in a F100 company; analytics efforts were marred by often steep learning curves…– High enthusiasm on conceptualized fraud conditions by management evaporated during datapreprocessing step in a F100 company; clean data was unable to support all that…– Imperative to pick healthcare fraud from very low number of rows and variables. For example, in2006, Navin Sinha looked at 2 rows over 20 variables and made clinical decision to research aprovider. Claims data found that provider billed fraudulently >10 Million $ in past few months!Navin made similar observations in 2011 also (Big data is also Big $ in Small healthcare fraud data!)– The current (2013) innovation is finding outlier events under uncertainty. Algorithms detect TrueFraud also from minimum rows and variables when lots of false positives included purposely. TheAlgorithms depicting false positive Inpatient data as fraud are being documented and rejected.
  • BENEFITS• Multi-layered approach todetection and prevention.• Shift away from ‘pay andchase’ model toward a moreproactive approach toidentify and mitigate FWAthrough business workflow.• Management capabilitiesthat assemble alerts frommultiple monitoring systemsto enable prioritization ofsuspicious claims.• Service Lowers claim losses,Larger cash flow and Higherreturn on investment (ROI)than that of clinicalmethods only or black boxanalytics methods only.
  • Benefits: ROI Possibility From TOP 5 US Healthcare InsuranceCompany Website (public information): 2005-06 achievementROI for FWA prevention solution implemented byleading Healthcare provider:•Cumulative 2-year net benefit of approx. $125 Mand ROI of 755%, driven by reductions in payouts dueto fraudulent, erroneous, and abusive claims•Improved fraud prevention results by 370% in asingle 18-month period• An insurer saved $11 million in its first year, andclinical investigator productivity climbed 30percent as activities that once took hours nowtake minutes.• One state prevented $14 million in Medicaidfraud and detected an additional $27 million infraudulent claims, leading to indictments.• A national insurer’s fraud investigators nowquickly stop payment on complex fraud cases.The solutions frees up experienced investigatorsto work on highly complex cases.
  • Navin SinhaUS Citizen.Fremont, CA 94536.952-905-6636.sinhanavin@hotmail.comDouble Check Consulting Presentation.Thank You!