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Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
Health Plan Auditing
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Health Plan Auditing

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Auditing 100% of claims vs. random sample audits

Auditing 100% of claims vs. random sample audits

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  • 1. James Herrington Chief Marketing Officer Healthcare Data Management, Inc. A presentation by: April 20, 2010 George P. Sillup, Ph.D., M.S. Saint Joseph’s University Haub School of Business
  • 2.
  • 3. <ul><li># of Employees Cost Per Employee/Yr. Annual Plan Cost </li></ul>20,000 $10,000 $200,000,000 Note: An October 2009 study by Towers Perrin projected that the average annual health benefit spend will exceed $10,000 per employee for the first time in 2010. 10,000 $10,000 $100,000,000
  • 4. <ul><li>Annual Plan Cost Percent Plan Waste Total Dollar Waste </li></ul>$100,000,000 8% $8,000,000 $200,000,000 8% $16,000,000 Note: HDM audits normally find from 5% – 8% wasted expense.
  • 5. <ul><li>Stop the bleeding. </li></ul><ul><li>Hold Third-Party Administrators (TPAs) and Pharmacy Benefit Managers (PBMs) accountable for claim errors and fraud. </li></ul><ul><li>Bring about plan changes to reduce waste. </li></ul>
  • 6. <ul><li>Audit based on 100-percent-of-claims analysis. </li></ul><ul><li>Focused audit of 100-percent-of-claims. </li></ul><ul><ul><li>Scans 100 percent of the claims for errors that fit a pre-determined profile. </li></ul></ul><ul><li>Random Sampling. </li></ul><ul><ul><li>Usually around 200 to 400 claims. </li></ul></ul>
  • 7.
  • 8. Ronald K. Klimberg, Ph.D. Saint Joseph’s University Haub School of Business Conducted by: The authors thank Healthcare Data Management, Inc. for funding this study. George P. Sillup, Ph.D., M.S. Saint Joseph’s University Haub School of Business
  • 9. <ul><li>To examine the implications of two approaches to health plan auditing. </li></ul><ul><ul><li>Auditing based on a 100%-of-claims analysis, utilizing the HDM five-step protocol. </li></ul></ul><ul><ul><li>Random Sampling (300 and 400-claim samples). </li></ul></ul>
  • 10. <ul><li>Steps 1 & 2 – Data Warehouse </li></ul><ul><ul><li>HDM downloads all of the data from steps 1 and 2 to data warehouse. </li></ul></ul><ul><ul><li>1. Paid claims data requested from administrator(s). </li></ul></ul><ul><ul><li>2. Client provides eligibility data, benefits covered and not covered, place of service, co-pays, etc. </li></ul></ul>
  • 11. <ul><li>Step 3 – Benchmarks </li></ul><ul><ul><li>HDM integrates comparative logic created from ASO, SPD, formulary, industry standards (e.g. CMS, AMA, FDA rules, etc.) and proprietary logic to catch duplicates/fraud. </li></ul></ul><ul><li>Step 4 – Exception Analysis </li></ul><ul><ul><li>Analysis commences on 100% of claims and eligibility data compared to best business practices and administrator's procedures. Exception reports are generated with potential errors and issues identified. </li></ul></ul>
  • 12. <ul><li>Step 5 – Claims Auditing </li></ul><ul><ul><li>After analyzing 100% of claims according to the ASO, SPD and industry standards, an onsite claims audit validates the logic and findings of the 100% analysis. </li></ul></ul><ul><li>Notes : </li></ul><ul><li>HDM refers to data that have not yet been subjected to an onsite audit as “pre-audit </li></ul><ul><li>data” and to data that have undergone the onsite logic check as “post-audit data.” </li></ul><ul><li>Exceptions = errors. </li></ul>
  • 13. <ul><li>HDM provided medical claims data from two Fortune 100 corporations – company A and company B. </li></ul>
  • 14. Entire Population of Claims Company Type $ Amount of Claims Paid # of Claim Records A Pre-Audit 2006 12,803,426 54,192 Pre-Audit 2007 12,544,893 54,371 Post- Audit 2006 12,803,426 54,192 Post-Audit 2007 12,544,893 54,371 B Post-Audit 118,368,625 463,919
  • 15. $$ Exception claims—range from 4.6% to 24.7% ## Exception claims—range from 4.4% to 15.5% Entire Population of Claims Exception Claims Company Type $ Amount of Claims Paid # of Claim records $ Amount of Exception Claims # of Exception Claim Records % of $ Errors % # of Error Claims A Pre-Audit 2006 12,803,426 54192 $2,234,051 8299 17.4% 15.3%   Pre-Audit 2007 12,544,893 54371 $3,092,431 8428 24.7% 15.5%   Post-Audit 2006 12,803,426 54192 $1,327,346 3850 10.4% 7.1%   Post-Audit 2007 12,544,893 54371 $1,792,882 5465 14.3% 10.1% B Post-Audit 118,368,625 463919 $5,467,944 20395 4.6% 4.4%
  • 16. Company Type $ Amount of Exception Claims Paid $ Amount of Over- Payment Records $ Amount of Under- Payment Claims $ Total Over- and Underpayment Claims A Pre-Audit 2006 $2,234,051 $229,012 $87,716 $316,728   Pre-Audit 2007 $3,092,431 $458,049 $62,498 $520,547   Post-Audit 2006 $1,327,346 $162,649 $26,654 $189,302   Post-Audit 2007 $1,792,882 $160,050 $25,459 $185,508 B Post-Audit $5,467,944 $702,556 $59,417 $761,973
  • 17. Company A’s – Claims Histogram What claims are responsible for this expenditure?
  • 18. Company A’s – Over- and Underpayment Claims Histogram
  • 19. What claims are responsible for this expenditure? Company B’s – Claims Histogram
  • 20. Company B’s – Over- and Underpayment Claims Histogram
  • 21. <ul><li>The resulting set of medical claims record exceptions (errors) was used as the benchmark for comparison with random sampling results. </li></ul><ul><li>That is, this data set was assumed to be the total population of exceptions. </li></ul>
  • 22. Population of Claims Random Sample Population of Exception Claims Random Sample
  • 23. <ul><li>For each data set... </li></ul><ul><ul><li>Generated 100 random samples of sample size 300. </li></ul></ul><ul><ul><li>Generated 100 random samples of sample size 400. </li></ul></ul>
  • 24. 300 Sample Size Amount Missed Company Type % of $ Amount of Exception Claims Paid % of $ Amount of Over- Payment Records % of $ Amount of Under- Payment Claims $ Amount of Claims Paid $ Amount of Overpayment Records $ Amount of Under- Payment Claims A Pre-Audit 2006 3.66% 3.97% 3.64% $2,152,257 $219,926 $84,522   Pre-Audit 2007 3.57% 3.72% 3.59% $2,982,092 $441,017 $60,253   Post-Audit 2006 7.87% 8.22% 7.73% $1,222,858 $149,282 $24,593   Post-Audit 2007 5.46% 5.56% 5.35% $1,695,056 $151,145 $24,096 B Post-Audit 1.48% 1.42% 1.45% $5,386,944 $692,615 $58,554
  • 25. 400 Sample Size Amount Missed Company Type % of $ Amount of Claims Paid % if $ Amount of Over-Payment Records % of $ Amount of Under-Payment Claims $ Amount of Claims Paid $ Amount of Overpayment Records $ Amount of Under- Payment Claims A Pre-Audit 2006 4.85% 5.12% 4.85% $2,125,686 $217,294 $83,459   Pre-Audit 2007 4.85% 4.84% 4.78% $2,942,504 $435,859 $59,510   Post-Audit 2006 10.55% 10.78% 10.30% $1,187,249 $145,123 $23,908   Post-Audit 2007 7.44% 7.33% 7.24% $1,659,439 $148,326 $23,616 B Post-Audit 1.96% 2.02% 1.89% $5,360,753 $688,396 $58,297
  • 26. <ul><li>Even the larger sample size missed over 90% of the claim errors identified by the 100% methodology. </li></ul><ul><ul><li>In terms of dollars, random sampling missed from about $200,000 to over $750,000 in over- and underpayment exceptions. </li></ul></ul><ul><li>100% auditing depicts plan utilization more accurately. </li></ul><ul><li>Random sampling missed the opportunity to identify the root causes of claim errors. </li></ul>
  • 27. <ul><li>State of Wyoming: </li></ul><ul><li>A retrospective analysis of approximately two year’s worth of medical claims uncovered... </li></ul><ul><ul><li>$3,186,135 in exceptions among total paid claims of nearly $223,000,000. </li></ul></ul><ul><ul><li>More than one-half of the exceptions were due to billing and coding edits. </li></ul></ul>
  • 28. <ul><li>State of Wyoming </li></ul><ul><li>Distribution of $3,186,135 in Exceptions </li></ul><ul><li>Random sampling would have failed to identify... </li></ul><ul><ul><li>A minimum of 90 percent – $2,867,522 – of the exceptions. </li></ul></ul><ul><ul><li>Order of magnitude per exception category. </li></ul></ul>
  • 29. <ul><li>City of Fort Worth : </li></ul><ul><li>A retrospective audit of approximately two year’s worth of medical claims uncovered nearly $2,434,499 in errors among total paid claims of nearly $87,000,000. </li></ul><ul><li>Most of the errors were overpayments due to claims being paid in duplicate. </li></ul>
  • 30. <ul><li>City of Fort Worth </li></ul><ul><li>Distribution of $2,434,499 in Exceptions </li></ul><ul><li>Random sampling would have failed to identify... </li></ul><ul><ul><li>A minimum of 90 percent – $2,191,049 – of the exceptions. </li></ul></ul><ul><ul><li>Order of magnitude per exception category. </li></ul></ul>
  • 31. Population of Claims Population of Exception Claims -$$ Random Sample
  • 32. <ul><li>Results indicate that random sampling missed at least 90 percent of the exceptions the “100 percent” methodology caught. </li></ul><ul><li>Identify a need for a paradigm shift from random sampling to goal of “zero defects” using 100% audit. </li></ul><ul><li>Begin to understand reason for claims and thereby eliminate them. </li></ul>

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