MEDICARE ADMINISTRATIVE CONTRACTORS (MACS) AUDITS UP 700%

Between 2011 and 2012 the number of audits by Medicare Administrative Contractors (MAC) went up by 700%.   This rise in audits by 700% “do not include the reviews performed by the three legacy contractors that continue to provide claims administration services”.   So this means, the number is even greater.

BHA_Auditing_Volume_2011-12.001

Knowing the role of these different auditors is crucial:

  1. Medicare Administrative Contractors (MAC) – (1) process and pay claims; (2) conduct pre- and postpayment claims reviews; (3) recoup overpayments; (4) remediate underpayments.
  2. Zone Program Integrity Contractors (ZPIC) – perform pre- and postpayment claims reviews (looking for fraud).(*)
  3. Comprehensive Error Rate Testing (CERT) contractors – conduct postpayment claims reviews on a random sample of claims processed by the MACs (to estimate Medicare improper payment rate).
  4. Recovery Auditors (RA) – (1) conduct data analysis; (2) conduct postpayment claims reviews,

The data was collected by the GAO which argued that inconsistencies between the different programs demand that some attempt be made to harmonize the rules and processes.

 

Reference: Report to Congressional Requesters, Medicare Program Integrity: Increasing Consistency of Contractor Requirements May Improve Administrative Efficiency. GAO-13-522.  (Washington, D.C.: Government Printing Office, July 2013).

Note: (*) The Zone Program Integrity Contractor (ZPIC) companies are (1) Safeguard Services (SGS); (2) AdvanceMed; (3) Cahaba; and (4) Health Integrity.

New York State Medicaid Audits

The New York State office of the Medicaid Inspector General has an active program of auditing health care providers.   After all of the auditing and consideration process has concluded, then a “final determination” is issued.   Final determinations are defined in Title 18 of the New York Code Rules & Regulations Sec. 519.3(b): “Final determination is a final audit report or notice of agency action sanctioning a person, or requiring the repayment of overpayments or restitution.”BHA_NY_MEDICAID_AUDITS.001Source:  Barraclough NY LLC Analysis.

 

The graph above shows the number of final determinations for Medicaid audits in New York State from August 2010 until July of 2014.   During this time, there are reports of 3,626 audits.  There is an astonishing range in the number of audits for each sector.   The greatest attention is on Long Term Care, Managed Care, and Hospitals account for 49% of all audits.

Out of the 3,626 audits, only a single Nurse, a single Clinical Psychologist, and single Podiatrist received an audit.

What is the rate of auditing?  That is an auditing rate of around 77 audits per month, or 4 audits per working day in Albany.

 

 

 

Audits are a Failure and Data Mining Has Presumption of Guilt

Senator Bill Nelson (D-FL), Chairman of the Senate Special Committee on Aging has criticized the Recovery Audit Contractor (RAC) process.   His argument is, in part, that since the RACs receive between 9% – 12.5% of whatever they recover, then there is an inherent incentive to keep improper payment rates high.  Details regarding types of audits are found in a slides presentation of the RAC program.  “The Recovery Audit Program and Medicare: The Who, What, When, Where, How and Why?”  There are three types of review:  (1) Automated reviews; (2) semi-automated, which is listed as “claims review using data and potential human review of a medical record or other documentation); and (3) “complex” review, in which a medical record is required.

What does “automated review” mean?  In practice, it means the use of secretive and proprietary large data mining of records to discover patterns, leading to targeting of health care providers for audits.  It is important to keep in mind that this is not really auditing, it is merely finding targets based on patterns of data.  So, for example, if a physician or practice works overtime, and on the weekends, and therefore produces more billing that other practices that work a “normal” amount of time, then they will be targeted.

Data mining works on the assumption that if the billing records are out of the ordinary, then there is something wrong, and so the practice should be audited.  There are two sides to this.  On the one side, deviation from the norm might be a problem; on the other side, it might indicate honest health-care service providers who are working as hard as possible serving a disadvantaged market.   In that latter case, an audit based on nothing other than data mining, simply burdens the health care system, and cuts off deserving patients from the health care they are entitled to.   This is a non-incentive for the  hard workers in the health care space.

The use of big data mining to control health care costs is the world’s greatest pressure towards average performance, that it, towards mediocrity.

The use of big data mining should be abolished, or at a minimum big data mining and the issue of presumption of guilt should be investigated.