Measuring and reducing errors in Medicaid and CHIP is important, but the current program and the proposed regulatory provisions are flawed and misleading. Reducing errors should involve not only reducing payments that are issued in error, but reducing the rate at which eligible applicants are erroneously denied Medicaid coverage. CMS should give develop a better, more valid approach to error determination when there are cases of missing or insufficient provider or eligibility data and issue a new proposed rule that offers a new approach or approaches

Ku, Leighton C.

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George Washington University: Health Sciences Research Commons (HSRC)

Himmelfarb Health Sciences Library, The George Washington UniversityHealth Sciences Research CommonsHealth Policy and Management Issue Briefs Health Policy and Management8-4-2009Do Medicaid and CHIP Measure Errors Correctly?Leighton C. KuGeorge Washington UniversityFollow this and additional works at: http://hsrc.himmelfarb.gwu.edu/sphhs_policy_briefsPart of the Health Policy CommonsThis Report is brought to you for free and open access by the Health Policy and Management at Health Sciences Research Commons. It has beenaccepted for inclusion in Health Policy and Management Issue Briefs by an authorized administrator of Health Sciences Research Commons. For moreinformation, please contact hsrc@gwu.edu.Recommended CitationKu, L. (2009). Do Medicaid and CHIP measure errors correctly? Washington, D.C.: Department of Health Policy, School of PublicHealth and Health Services, The George Washington University.                Issue Brief  Do Medicaid and CHIP Measure Errors Correctly?    By  Leighton Ku, PhD, MPH Director, Center for Health Policy Research    The George Washington University School of Public Health and Health Services Department of Health Policy    August 4, 2009            1 Do Medicaid and CHIP Measure Errors Correctly?   The President, Congress and the public demand that government programs are run accountably, with as little fraud or waste as possible.  In recent years, Medicaid and the Children’s Health Insurance Program (CHIP) have developed a multi-million dollar system to measure the extent to which program dollars are spent erroneously.  It is useful to note from the outset that “errors,” which may be unintentional, are not the same as “fraud,” which requires deliberate intent to cheat.  Moreover, fraud and error are problems that afflict private health insurance, as well as public programs.1  Improving Medicaid and CHIP program accountability requires assessing the level of payment errors and developing better policies and procedures to reduce them.  The current procedures used for Medicaid and CHIP do not measure errors accurately and significantly overstate the actual level of payment errors, however.  This inaccuracy can lead to very misleading error rates and a misunderstanding of how to improve program integrity.     Under the 2002 Improper Payment Information Act, the Centers for Medicare and Medicaid Services (CMS) developed a Payment Error Rate Measurement System (PERM) for Medicaid and CHIP to measure errors either caused by inappropriate payments to health care providers or managed care organizations or errors made on behalf of people who are not actually eligible for benefits.  The PERM system became fully operational in 2007 and has been relatively controversial since its initial development.  In February 2009, the Children’s Health Insurance Program Reauthorization Act (CHIPRA) made some important modifications to the legal requirements for PERM.2  In response, CMS issued a proposed rule on July 15, 2009 to amend PERM requirements for Medicaid and CHIP, with public comments due by August 14.3   One requirement of CHIPRA was that new regulations include “clearly defined criteria for errors for both States and providers.”4  Under PERM, a federal contractor is responsible for reviewing errors made in paying fee-for-service claims for health care providers or making capitation payments for managed care plans.  As part of the review, the contractor asks providers to submit copies of patient medical records to document that the claims were valid.  The contractor then checks whether a claim was paid more than it should have been or whether the performed procedure was medically appropriate.  States are responsible for eligibility reviews.  They check eligibility records to determine whether a program enrollee met the state’s Medicaid or CHIP eligibility rules.    Following the existing CMS PERM policies, the proposed regulations define provider errors as including “an improper payment made due to lack of or insufficient                                                  1 Rosenbaum, S., Lopez, N., Stifler, S. "Health Insurance Fraud: An Overview." Robert Wood Johnson Foundation and the George Washington University School of Public Health and Health Policy, Jun 23, 2009. 2 Section 601 of CHIPRA (P.L. 111-3).   3 Federal Register, 74(134): 34468-87, July 15, 2009. 4 Section 601(c)(1)(A) of CHIPRA.     2 documentation.”5 Eligibility errors include “a lack of or insufficient documentation in the case record to make a definitive determination of eligibility or ineligibility.”6  That is, when the evidence to determine whether a payment or eligibility determination was made properly is inconclusive because of missing or incomplete records, the case is automatically counted as an error.     This is inappropriate, both from statistical and common sense perspectives.  It also runs counter to a standard principle of American justice, that someone is innocent until proven guilty.7     For Medicaid medical fee-for-service claims, which had the highest measured error rate, HHS reported that the “majority of the FY 2007 errors (90%) were a result of non-response or insufficient documentation.”8  Error rates related to managed care payments and eligibility were less common than fee-for-service claim errors, but missing or insufficient documentation were common reasons for errors in those areas as well.   HHS reported an overall 10.5 percent error rate in Medicaid for 2007, based on PERM.  But since the majority of errors were related to missing and insufficient documentation, errors could actually be determined for a much smaller fraction of Medicaid payments, probably less than 3 or 4 percent.  (The HHS report does not provide enough information to provide a clear estimate.)  In comparison, estimates of total health care fraud, which primarily involves private health care spending and is more narrowly defined than error, have run between 3 and 10 percent.9     Another important finding reported by HHS is that in 2007 errors in which eligible people were incorrectly denied Medicaid coverage (called negative errors) were about twice as common as errors in which ineligible people were incorrectly granted coverage.10  It was more likely that an eligible person was denied Medicaid coverage than that an ineligible person received benefits improperly.                                                     Why are medical records data missing or incomplete?  Insights are available from a 2004 report by the HHS Office of the Inspector General (OIG), which examined missing and incomplete data in the Comprehensive Error Rate Testing (CERT) program, which is a predecessor to PERM that measures provider errors in Medicare.11  In 2003  5 Proposed Sec. 431.958 6 Proposed Sec. 431.960(d)(1)(iv) 7 There are other problems with PERM, as currently operated and as proposed.  For the sake of brevity, this paper focuses only on the issue of missing and insufficient documentation. 8 Dept. of Health and Human Services, FY 2008 Agency Financial Report, Sec. III: Other Accompanying Information, Sec. 11.42, Nov. 17, 2008.   9 National Health Care Anti-Fraud Association.  “The Problem of Health Care Fraud.” Available at:  http://www.nhcaa.org/eweb/DynamicPage.aspx?webcode=anti_fraud_resource_centr&wpscode=TheProblemOfHCFraud.  Federal Bureau of Investigation, Financial Crimes Report to the Public, Fiscal Year 2007. Available at: http://www.fbi.gov/publications/financial/fcs_report2007/financial_crime_2007.htm 10 Dept. of Health and Human Services, op cit. 11 HHS Office of the Inspector General, memo to the CMS Administrator, “Review of Providers’ Responsiveness to Requests for Medical Records Under the Comprehensive Error Rate Testing Program,”   3 about half of the 9.8 percent initial error rate reported for Medicare was due to missing or incomplete data.  After making adjustment based on OIG experience about the incidence of actual errors, this initial error rate was reduced to 5.8 percent because OIG’s experience indicated that most of the cases with missing data were not actual errors at all; they were valid cases for which data were simply missing.  In the September 2004 report, OIG examined the problem of missing data more closely and reported what providers said about why data were often missing.  For the 505 claims considered as nonresponses, providers said they:  • Did not receive the requests for data from the error rate contractor (46% of the missing claims), • Had already provided the information (16%), • Did not have access to medical records that were maintained at another location (9%), • Had other reasons (30%), including the provider was deceased or no longer in business, the provider’s headquarters office had to review the request for medical records, there were concerns about confidentiality of patient records under the Health Insurance Portability and Accountability Act, the claim had been cancelled, etc.  It is plausible that, in some cases, providers who have intentionally defrauded Medicaid or CHIP fail to respond or submit incomplete data in order to hide their fraud.  (Of course, providers who intend to commit fraud could also submit falsified records for review, which would be less obvious than failing to submit any documents at all.)  The majority of cases are likely to be valid and proper medical transactions in which the data are missing for innocent reasons, such as those described above.    In many cases the data are missing for reasons beyond the control of the provider.  For example, if the request for medical records is sent to the wrong address or the physician has left the area, passed away or retired, the provider may be innocent of any wrongdoing, but the lack of response would be interpreted as an error under existing rules.  In some cases, the records may have been properly submitted, but the contractor’s office may have lost it.  Finally, in some cases the provider may submit the documents, but not within the timeframe demanded by CMS and the case still counts as an error.   In the field of statistics, the problem of “missing data” is well known.12  There are a variety of methods that statisticians use to attempt to cope with this problem, but almost no statistician would agree that all missing data should be counted as errors by default.13                                                                                                                                                     Sept. 29, 2004.  Similar descriptions of the reasons for missing data were reported in reports for pilot projects for PERM. 12 See, for example, Paul Allison (2001).  Missing Data.  Newbury Park, CA: Sage Publications, Kalton, G. and Kasprzyk, D. (1986) “The Treatment of Missing Survey Data,” Survey Methodology. 12(1): 1-16, or Little, R. and Rubin, D. (1987) Statistical Analysis with Missing Data, New York: Wiley Interscience. 13 A few years ago, I sought advice on this topic from Dr. Fritz Scheuren, a noted expert on missing data who was, at the time, the President of the American Statistical Association.  He agreed that it was wrong to consider missing and incomplete data as automatic errors.  We tried to arrange a meeting with CMS staff to discuss missing data and other ways to resolve the problem, but CMS declined to meet.   4                                                  One option is to drop the cases with missing or incomplete data from the sample and to only analyze cases where complete data are present.  Another is to drop the missing cases and draw additional cases as replacements.14  While these are not ideal solutions, they are relatively easy to implement and produces a more valid estimate of errors than the automatic error approach now used by CMS.  A more sophisticated, but more difficult, approach is to intensively seek data from a sample of the missing responses, identify the actual error rates from this subsample, and use this subsample to estimate the fraction of true errors.  This is akin the to method OIG used when it made an estimate of the percentage of missing and insufficient documentation cases that were true errors for the CERT program and used this estimate to compute an adjusted error rate.  CMS no longer uses this approach, however.  Some may believe that determining missing data cases as errors and sanctioning the providers is desirable because it creates a strong incentive for providers to comply and eventually leads to better reporting.  But there are other ways to improve response rates.  And even if CMS chooses to sanction those who do not respond, it need not assume that all missing data are errors.  The administrative procedures needed to improve response rates can and should be separated from the process of determining which cases are errors.     Measuring and reducing errors in Medicaid and CHIP is important, but the current program and the proposed regulatory provisions are flawed and misleading.  Reducing errors should involve not only reducing payments that are issued in error, but reducing the rate at which eligible applicants are erroneously denied Medicaid coverage.  CMS should give develop a better, more valid approach to error determination when there are cases of missing or insufficient provider or eligibility data and issue a new proposed rule that offers a new approach or approaches.     14 These methods have been approved by CMS does in the one situation where it has acknowledged that missing data might not be an error, when a provider can prove that medical records have been destroyed in a fire, flood or disaster that has been declared by the Federal Emergency Management Agency (FEMA).  In the case of FEMA disasters, CMS permits the case to be discarded and will replace it a new case.  CMS, “Policy for Handling Lost or Destroyed Documentation,” undated policy statement.  http://www.cms.hhs.gov/PERM/Downloads/PERMLostDocPolicy.pdf 

Do Medicaid and CHIP Measure Errors Correctly?

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Do Medicaid and CHIP Measure Errors Correctly?

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