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wiitalaRThe lack of the correct patient discharge status code has been cited by Recovery Audit Contractors (RACs) as a problem in the claims of inpatient rehabilitation facilities (IRFs). The problem is actually twofold; improper use has resulted in overpayments in some cases and underpayments in others.

Correcting this problem is simple, says the Centers for Medicare & Medicaid Services (CMS) in the April issue of the Medicare Quarterly Provider Compliance Newsletter. Specifically, IRFs must determine the right discharge status code and enter it on claims. A basic understanding of the Medicare policy on transfers, which is described below, also will be an advantage.

Basic Billing Rules

The two-digit discharge status code identifies where the patient is transferred or discharged to at the conclusion of a healthcare facility encounter or at the end time of a billing cycle (the "through" date on the claim). Transfer cases are defined as those in which a Medicare beneficiary is transferred to another facility including the following. Note the discharge status codes following the location.

  • Another rehabilitation facility (patient status code 62);
  • Long-term care hospital (patient status code 63);
  • Inpatient hospital (patient status code 02), or
  • Nursing home that accepts payment under either the Medicare program and/or the Medicaid program (patient status codes 03, 61, or 64); AND
  • The length of stay (LOS) of the case is less than the average length of stay (ALS) for a given case-mix group (CMG).

IRFs must include the above discharge status codes in form locator 17 of the UB-04 claim or its electronic equivalent in the HIPAA-compliant 837 format. In cases when two or more patient discharge status codes apply, they must code the highest level of care known.

Omitting a code or submitting a claim with an incorrect code is a claim billing error and could result in a claim rejection or a cancellation. The bottom line: Apply the correct code in the correct place on the claim receive prompt and correct payment

Understand the Policy

The Medicare transfer policy consists of a per-diem payment amount, which is calculated by dividing the per-discharge CMG payment rate by the average LOS for the CMG.

A transfer is determined if the patient is discharged from one facility and admitted to another facility on the day of discharge. Under Medicare policy, it is not considered a transfer if this occurs on any other than the day of discharge from the first facility.

Medicare pays transfer cases a per-diem amount, and an additional half day payment for the first day. When the preceding IRF LOS is less than the average LOS for the CMG, that IRF claim should be paid a per-diem amount plus an additional half-day for the first day instead of the full IRF prospective payment system (PPS) rate. The full PPS payment represents an improper overpayment.

Here's an example of how an improper underpayment occurs. If a patient is discharged on April 3 from a facility and the claim is billed with a patient status code of 02 (transfer to an acute [short-term] hospital, but Medicare systems do not reflect another hospital claim with the admit date equal to the April 3 discharge date, the first claim was billed and reimbursed incorrectly. The problem is that the first facility claim (billed with a patient status code 02) was paid a per-diem amount when they should have received the full DRG amount.

Adopt Simple Solution

As IRF billers can see from the above, it's easy to solve a problem that's making RACs and CMS take notice. To avoid being in this situation, they should be sure they understand the basics of the Medicare transfer policy. Identifying the correct patient status code and applying it to the claim is the most important step.

CMS has published several Medicare documents on the transfer policy, and the following clarifies the topics covered above: MLN Matters SE0801 at

About the Author

Randy Wiitala, BS, MT (ASCP) is a senior healthcare consultant with Medical Learning, Inc. (MedLearn), St. Paul, MN. MedLearn is a nationally recognized expert in healthcare compliance and reimbursement. Founded in 1991, MedLearn delivers actionable answers that will equip healthcare organizations with their coding, chargemaster, reimbursement management and RAC solutions.

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RACs Target: Post-Acute Transfer Policy Could Impact Nearly One-Third of MS-DRGs

fcohen100Data mining is both an art and science. Roughly stated, the purpose is to extract useful information from data. Data mining has been used for many years and in a number of different ways, however it is only recently, with the advent of more powerful computers and more powerful software languages, that the practice has made significant gains in popularity - particularly when it comes to mining large databases.

Also known as predictive analytics, it is this set of methodologies that allows to recommend purchases based on what you have purchased from them (and often, from other vendors) in the past. It is predictive analytics that allows a seller to tell you that "customers who purchased this item also purchased that item."

Using data mining techniques, lenders are able to determine the probability that someone will default on a loan, allowing them to adjust interest rates based on risk. Predictive analytics also is used to conduct credit card fraud analysis in real time. I am sure that many of you at some point have received a call from your credit card company asking you to validate a "suspicious" purchase.

Issues at the Pump

Recently I went to a gas station I go to quite often to fill up, and as I usually do, I swiped my credit card at the pump. For some reason, the gas was coming out very slowly; it took more than a minute just to pump one gallon of gas, so I finished the transaction and went across the street to another gas station. This time, when I swiped my card the transaction was denied, and I had to use another credit card. Several minutes later I got a call from my credit card company asking me to verify that I had indeed attempted a purchase at that second gas station. How in the world could they have responded that quickly? Well, I conduct hundreds if not thousands of transactions a year, and when it comes to gas, when I am home they know that I normally purchase from one location.

Secondly, I rarely (if ever) purchase a gallon of gas from one location and then, within minutes, attempt to purchase gas again from another location. In this case, the credit card company used some predictive analytical algorithm to score my purchase with regard to fraud risk, and it scored high enough to invoke some action (such as putting the account on hold until I could verify the purchase). It's pretty cool stuff, actually.

The Application of Data Mining

I use data mining a lot in my work. It allows me to predict who is most likely to be an end user of my services, making my marketing efforts more efficient. In one recent case, we used data mining in order to determine the probability that a particular claim would be denied by a particular payer, giving us the opportunity to review charts in advance of billing. Regarding possible pending audits, we see potential for the use of data mining techniques, including predictive analytics, to identify "bad" claims (as defined by CMS).

Let's take a look at CERT as an example. In the most recent CERT study, it was reported that 4.5 percent of the reviewed claims were considered overpaid due to lack of medical necessity. Determination of a medical necessity denial (or overpayment) normally is defined based on documentation contained within a chart, however there are other factors that come into play. Let's say that a patient comes to the office complaining of a runny nose, itchy eyes and other symptoms that result in a diagnosis of seasonal rhinitis (ICD-9 code 477.0). Now let's say the provider codes the patient with a 99204, the second-highest level of new office visit. An audit very well may support that level of visit based on the documentation guidelines; the auditor, however, might question whether that level of E/M code (complexity of DDx) is commensurate with the level of complexity of the diagnosis. In this case, it is likely that this claim line would result in a denial due to lack of medical necessity.

Understand the issue here: there is a direct relationship between the documentation and the procedure code and the documentation and the diagnosis code, but unfortunately there is not a nexus between the procedure code and the diagnosis code - and this is where the issue of medical necessity rears its highly judgmental and elastic head.

Predicting Claims Subject to Audit

So, then, how could we possibly know ahead of time what claims have the greatest probability of being subjected to a medical necessity review? Here is where I employ predictive analytics. To start I would access the 4,500 or so claims that were determined to have been overpaid. Next I would divide the database in half. Then I would take the first group of 2,250 claims and run them through a data mining program, training a number of different algorithms. Then I would run the other 2,250 claims through these trained algorithms to see which predicted the medical necessity outcome most accurately. What I have gained now is the ability to take all of the claims from your office, run them through my data mining algorithm and spit out the claims that are most likely to be audited for medical necessity. By extension, I have created a model that uses probability to predict the likelihood that any particular claim will be subject to an audit.  Pretty cool, huh?


What occurs during this process is that the algorithm searches for  variables that contribute most to generating the accurate prediction of the outcome. For example, it may find that there are certain combinations of diagnosis codes and procedure codes that register more frequently than others. In essence, the algorithm is measuring the probability that each of these combinations would predict the outcome successfully. Maybe certain modifiers actually affect the outcome, or maybe the specialty or place of service (or type of service) even factor in some way. The goal is to figure out how much these variables contribute to the final determination, then to use this knowledge to predict which claims need to be reviewed.

CMS and Predictive Analytics

As many of you may know by now, CMS has announced that they are partnering with Northrop Grumman to begin real-time analysis of Medicare claims using predictive analytics. This process will be similar to how FICO uses data mining to determine credit card fraud. While I am not involved in this project, I assume that Grumman has been given access to a large number of Medicare claims that have been subject to fraud and abuse determinations and has used this data (as in our CERT example) to create an algorithm that predicts the likelihood that any given claim will be deemed improper.

If you want my opinion, I am not overly confident that this is going to be very effective. There are a lot more variables that go into filing a claim than go into making a credit card purchase - and of these variables, many simply do not exist in terms of black and white. My prediction is that this practice may cause a significant delay in payment of Medicare claims without creating a matching benefit.

Getting to the Bottom Line

The bottom line is this: the business side of healthcare is incredibly complex, and considering the diversity of players, diagnoses, therapeutic and diagnostic procedures, etc., it is very difficult to know for sure whether or not a claim is going to get paid correctly. Factor in the notion that for nearly every documentation line there is a possible combination of one of five group codes, 200 reason codes and more than 600 remark codes (that's 976 billion possible combinations), and you begin to get a picture of a very chaotic system. My experience is that no matter how good a job you do, there always is going to be some degree of disagreement that ultimately may result in audits of your claims.

As we become more sophisticated in our practices, we can begin to employ data mining techniques in order to predict which of our claims are most ripe for review, giving us an upper hand on both risk analysis and risk mitigation.

About the Author

Frank Cohen is the senior analyst for The Frank Cohen Group, LLC. He is a healthcare consultant who specializes in data mining, applied statistics, practice analytics, decision support and process improvement.

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Demystifying the Query (Audit) Process

wiitalaRIn the near future, Recovery Audit Contractors and Medicare Administrative Contractors (MACs) will take "appropriate action" on erroneously allowed claims identified in a recently completed audit (OEI-07-09-00450) by the U.S. Department of Health and Human Services' Office of Inspector General (OIG). That action will likely be RAC and MAC reviews of hospital emergency department claims for imaging services without orders or complete interpretations and reports (I&Rs).

The reason for the erroneously denied claims is one that you've heard over and over: a high incidence of "insufficient documentation." This time for claims submitted for the following diagnostic services provided in hospital EDs: computed tomography (CT), magnetic resonance imaging (MRI), and X-ray services

Audit Details

The OIG investigated the accuracy of 2008 Medicare-allowed claims for the professional component (PC), which includes payments for physicians' interpretations of images and reports on the clinical findings that are included in the hospitals' medical records. Claims submitted for interpretations without complete written reports do not meet Medicare's policy requirements and payment conditions.

The OIG pulled a sample of 220 CT and MRI claims and 220 X-ray claims. Their objectives were to determine whether:

Medical record documentation supported the services performed;

Physicians' orders were present; (This point is of concern to the American College of Radiology [ACR], which stated that "CMS's ordering physician rule does not apply to hospitals.")

Services were performed before beneficiaries left the hospital outpatient EDs (i.e., during beneficiaries' diagnoses and treatments). (Per CMS's guidance, Medicare contractors reimburse only for the interpretation performed "at the same time" as the diagnosis and treatment of the beneficiary in the ED if they receive multiple claims from, for example, the ED physician and the radiologist.)

Physicians followed ACR's documentation practice guidelines.

The Findings

In 2008, 19 percent ($29 million) of the 3 million claims for I&Rs of CT and MRI services were erroneously paid. Medicare contractors also paid close to 6.6 million claims for the I&Rs of X-ray services, and 14 percent ($9 million) of that number were paid in error. In all of these cases, the OIG discovered one or both of the following errors.

The documentation did not include physicians' orders.

The documentation did not support that I&Rs were performed.

In addition, 71 percent of the I&Rs for X-rays did not follow one or more of the ACR's suggested practice guidelines for documentation. Please Click Here

Finally, Medicare paid more than $10 million (16 percent of claims) for I&R of X-rays that were performed after patients left hospital outpatient EDs, indicating to the OIG (based on previous work) that these interpretations may not have contributed to diagnoses and treatments. The OIG gave several examples of this problem, including the following:

In one medical record, the ED notes included an interpretation of a chest x-ray, but the record indicated, "[T]he radiologist will read this later. If there is any significance to that, we will notify the patient."

The OIG stated that this documentation suggests that the treating physician was preparing to discharge the beneficiary before receiving the radiology report. Although the final I&R was included in the medical record and was dictated on the same day as the beneficiary's ED visit, OIG auditors found it "difficult to determine" whether the radiologist verbally communicated the information to the treating physician before discharge.


Recommendations and Responses

In addition to recommending that CMS take appropriate action on the erroneously allowed claims, the OIG recommended the following.

Recommendation: Educate providers on the requirement to maintain documentation on submitted claims. Remind them about the need for the medical record documentation to include (1) physicians' orders to support diagnostic radiology services performed and (2) complete I&Rs.

Response: CMS agreed will the recommendation, saying that it will issue an educational article to the provider community to emphasize that it will enforce documentation requirements. It will continue to monitor and refine its oversight of diagnostic radiology services.

Recommendation: Adopt one policy for single and multiple claims for I&Rs. Require that claimed services be contemporaneous or identify circumstances in which noncontemporaneous interpretations may contribute to the diagnosis and treatment of beneficiaries in hospital outpatient EDs.

Response: CMS did not agree that a single billed interpretation must, in all cases, be contemporaneous with the beneficiary's diagnosis and treatment. It believes that continued diagnosis and treatment can extend beyond the emergency encounter to other follow-up settings.

To this second response, the OIG stated that it agrees that diagnosis and treatment may extend to other settings subsequent to the ED encounter; however, it maintains that payment rules should uniformly require that an I&R on an ED x-ray be contemporaneous with the beneficiary's diagnosis and treatment. If not, they should satisfy some other criteria demonstrating the interpretations' contribution to patient care.

The OIG believes that CMS's current payment policy applies requirements inconsistently in different situations. Specifically, when a MAC receives multiple claims for an ED x-ray, it pays only for the I&R that directly contributed to the beneficiary's diagnosis and treatment. Any other I&R is treated as part of the hospital's quality assurance program.

In contrast, when a MAC receives one I&R claim in connection with an ED X-ray, current policy drops the requirement for contemporaneity and contribution altogether. The MAC must presume that the one service billed was a medical service to the individual and not quality assurance and pays the claim if it otherwise meets any applicable reasonable and necessary test.

The OIG says, "This inconsistency is not explained, nor do we believe the underlying rationale is obvious."

The OIG report summarized above is available at:

About the Author

Randy Wiitala, BS, MT (ASCP) is a senior healthcare consultant with Medical Learning, Inc. (MedLearn), St. Paul, MN. MedLearn is a nationally recognized expert in healthcare compliance and reimbursement. Founded in 1991, MedLearn delivers actionable answers that will equip healthcare organizations with their coding, chargemaster, reimbursement management and RAC solutions.

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Establishing a Solid Compliance Foundation: The Hospital Utilization Management Plan


Recent changes ushered in by the Patient Protection and Affordable Care Act of 2010 (PPACA) create new risks for false claims liability when auditors identify overpayments.

Section 6402(d) of PPACA amended the Social Security Act to require an entity to return any overpayments it receives and to notify the appropriate authorities. The overpayment must be reported and returned to the appropriate entity (such as CMS, OIG or the carrier) no later than 60 days from "the date on which the overpayment was identified" or "the date any corresponding cost report is due, if applicable," according to the language of the legislation. Furthermore, the PPACA makes the retention of an overpayment beyond this time frame an issue under the False Claims Act.

Prior to passage of the Fraud Enforcement and Recovery Act of 2009 (FERA), the False Claims Act extended liability to  "any person who ... knowingly makes, uses or causes to be made or used a false record or statement to conceal, avoid or decrease any obligation to pay or transmit property to the government." FERA focused on retention of overpayments ("reverse false claims"), rather than the affirmative submission of a false record or statement. FERA expanded false claims liability to include a person who "knowingly conceals or knowingly and improperly avoids or decreases an obligation to pay or transmit money or property to the government."  The term "knowing" is defined to include "deliberate ignorance" or "reckless disregard." Thus, FERA eliminated the requirement of an affirmative act of concealment, expanding false claims liability to include knowing failure to repay an overpayment.

Impact on Overpayments

The changes implemented under PPACA and FERA directly impact overpayments identified by audit contractors and raise important issues for providers weathering a RAC or Medicare audit. For instance, when does an overpayment become "identified," triggering the 60-day deadline for reporting and returning the overpayment? The Office of the Inspector General (OIG) has yet to clarify at what point these obligations are initiated in the audit context.

The Medicare appeals process may shelter providers appealing claim denials from an immediate obligation to repay during the appeal, but the obligation may apply once the matter is resolved. The appeals process also creates a limitation on recoupment of current Medicare payments during the first two stages of appeal, and this may satisfy the overpayment reporting requirement. At present, the application of the 60-day time frame for reporting and returning remains unclear, as it applies to the various aspects of the appeals process.

Minimizing Risk of Liability

The interplay between PPACA and FERA also raises questions about the responsibilities of providers who realize during an audit review that they received an overpayment - and the potential penalties for providers with an inability to pay back an overpayment. While these issues do not have clear answers at this time, providers can expect to gain clarity as to their liabilities and responsibilities pertaining to the retention of overpayments as these statutory provisions are applied in practice. In the meantime, it is important for entities involved with RAC and Medicare audit processes to consider carefully whether, at any stage in the appeals process, the facts demonstrate an existing overpayment. If an overpayment is discovered, healthcare providers are advised to discuss their obligation to repay with legal counsel in order to help minimize the risk of liability under the False Claims Act.

Liability under the False Claims Act is significant. The retention of an identified overpayment can result in civil monetary penalties, which can include $10,000 for each item or service and an assessment of three times the amount claimed for each item or service. Moreover, failure to report and return an overpayment may result in an exclusion from participation in federal and state healthcare programs.

New Recoupment Authority

In addition to the aforementioned liability under the False Claims Act, any additional potential liability is enhanced by Medicare's new recoupment authority under PPACA. Section 6401(a) of PPACA grants CMS the authority to adjust payments to related providers and suppliers on the basis of their federal tax identification numbers. The new rules allow CMS to hold liable for the debts of "related parties" providers and suppliers with the same tax identification number, regardless of those entities' billing numbers or NPI numbers.) Previously, CMS only could recover unpaid Medicare overpayments from related entities sharing the same provider number. The previous system limited Medicare's recoupment ability, since most entities with multiple sites enroll under different provider numbers. CMS now may withhold funds due to related providers and suppliers as long as they share a federal tax identification number. This change to permit "cross-provider" recoveries enhances Medicare's ability to collect overpayments from entities with multiple locations and provider numbers.


As accelerated audit activities performed by a variety of audit contractors continue to pick up steam, providers are advised to become aware of the potential liabilities related to the identification of Medicare overpayments, and to develop a comprehensive plan for a successful audit appeal.

About the Authors

Andrew B. Wachler is the principal of Wachler & Associates, P.C.  He graduated Cum Laude from the University of Michigan in 1974 and was the recipient of the William J. Branstom Award. He graduated Cum Laude from Wayne State University Law School in 1978. Mr. Wachler has been practicing healthcare and business law for over 25 years and has been defending Medicare and other third party payor audits since 1980.  Mr. Wachler counsels healthcare providers and organizations nationwide in a variety of legal matters.  He writes and speaks nationally to professional organizations and other entities on a variety of healthcare legal topics.

Jennifer Colagiovanni is an attorney at Wachler & Associates, P.C.  Ms. Colagiovanni graduated with Distinction from the University of Michigan and Cum Laude from Wayne State University Law School.  Upon graduation, Ms. Colagiovanni was nominated to the Order of the Coif. Ms. Colagiovanni devotes a substantial portion of her practice to defending Medicare and other third party payer audits on behalf of providers and suppliers.  She is a member of the State Bar of Michigan Health Care Law Section.

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Data Mining as an Audit Tool

wiitalaRDo the rules that Recovery Audit Contractors (RACs) follow when they audit hospitals for the technical component (TC) of radiology services apply to ambulatory surgery centers (ASCs)?

That's a question that one of our readers following my article entitled "RACs Find Errors on Professional Claims for Technical Component of Radiology"

The answer is "it depends" on knowing the answers to a couple of questions.

Who Are You Billing?

When does an ASC bill Medicare Part B and when does it bill Medicare Part A? The answers aren't hard to find when you look in the right place, which in this case is Trailblazer's (one Medicare contractor) ASC billing manual. Trailblazer provides an informative rundown of payment information. (See References.)

Whether an ASC bills the Part B carrier (with the CMS-1500 form) or the Part A fiscal intermediary (with the CMS-1450/UB-04 form) depends upon the type of ASC. In general, for Medicare purposes, an ASC is a distinct entity that operates exclusively for the purpose of furnishing outpatient surgical services to patients. The ASC must enter into a "participating provider" agreement with CMS.

The surgical center may be independent, which means it isn't part of a provider of services or any other facility. It also may be operated by a hospital, which means it is under that facility's common ownership, licensure or control.

When hospital-operated, it has the option either of being covered under Medicare as an ASC or as a hospital-affiliated outpatient surgery department. The following guidelines apply to ASCs affiliated with hospitals. The ASC:

  • Must elect this affiliation and continue to be so covered unless the Centers for Medicare & Medicaid Services (CMS) determines there is good cause to do otherwise;
  • Be a separately identifiable entity (physically, administratively and financially independent and distinct from other hospital operations) with ASC costs treated as a non-reimbursable cost center on the hospital's cost report;
  • Meet all the requirements with regard to health and safety; and
  • Agree to the assignment, coverage and payment rules applied to independent ASCs.

The ASC also is surveyed and approved as complying with their specific conditions for coverage. (See References.) If a facility meets the above requirements, it bills the Medicare contractor on form CMS-1500 or the related electronic data set and is paid the ASC payment amount.

If a hospital-based facility decides not to become a certified ASC, it bills the FI on the CMS-1450 form or the related electronic data information (EDI) code set and is subject to hospital outpatient billing and payment rules as well as certification and participation requirements.

Specific Radiology Payment

Medicare will pay separately for certain radiology services that are provided integral to covered surgical procedures in ASCs. In this case, the word "integral" means that the services were provided immediately before, during or after a covered surgical procedure.

Medicare will pay ASCs for ancillary radiology services at the lesser of the ASC rate or the amount of the non-facility practice expense under the Medicare physician fee schedule (MPFS). The ASC may receive separate payment for the TC of the covered ancillary radiology procedure by using modifier TC with the code reported on the claim.

Radiology services that have a PC (professional component) or TC indicator on the MPFS database must be submitted with the TC modifier to indicate payment for the technical component of the procedure.

In addition, effective January 1, 2009, the ordering/referring physician must be reported on claims for diagnostic services submitted by ASCs. This information should be reported in Items 17 and 17b or the electronic equivalent.

If you are responsible for managing professional and technical component billing for radiology suppliers, physicians, and non-physician practitioners, keep the following in mind:

  • Under the prospective payment system (PPS) for acute-care hospitals, suppliers that render non-physician Part B services during inpatient stays are required to bill the hospitals, not the Medicare carriers, for those services.


  • Medicare claims-processing contractors cannot pay for the TC of radiology services furnished to patients in inpatient or outpatient settings. The TC payment for services performed for beneficiaries in a hospital inpatient stay are part of the hospital's bundled DRG payment. Outpatient services provided are paid under the outpatient PPS.

A periodic review of billing practices is a prudent step to avoid incorrectly billing of the TC for radiology services.

About the Author

Randy Wiitala, BS, MT (ASCP) is a senior healthcare consultant with Medical Learning, Inc. (MedLearn), St. Paul, MN. MedLearn is a nationally recognized expert in healthcare compliance and reimbursement. Founded in 1991, MedLearn delivers actionable answers that will equip healthcare organizations with their coding, chargemaster, reimbursement management and RAC solutions.

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ASC Conditions for Coverage: Section 416.40-49 Related survey requirements are published in the State Operations Manual.

wiitalaRAccording to the Centers for Medicare & Medicaid Services, Recovery Auditor Contractors are adjusting claims to align provider payments with Medicare guidelines related to the technical component (TC) of radiology services.

Specifically, Medicare Part B contractors do not directly pay suppliers and physicians for the TC of radiology services furnished in inpatient and outpatient settings of hospitals that are paid under the inpatient prospective payment system (IPPS) or outpatient PPS (OPPS).  That information and other insight can be found in the April issue of the "Medicare Quarterly Provider Compliance Newsletter."

Radiology suppliers that render non-physician outpatient services during inpatient stays must bill the PPS hospitals, not the Medicare carriers, for those services. Radiology professional services furnished to hospital outpatients are paid to the hospital under the OPPS.

Under the IPPS, Medicare contractors reimburse acute-care hospitals (but not critical access hospitals) a predetermined amount for services furnished to patients based on their illnesses and their classifications under diagnosis-related groups (DRGs).

This bundled payment covers non-physician outpatient services that Medicare beneficiaries receive during an inpatient stay, which include radiology services, such as tomography scans, furnished to inpatients by a physician's office, another hospital, or a radiology clinic. Radiology services for beneficiaries in a hospital inpatient stay are part of the hospital bundled payment.

CMS provided the following example of how things work.

An 80-year-old female was admitted to an inpatient hospital stay on January 24, 2010, and discharged on February 8, 2010. A physician billed CPT code 71010 (chest x-ray) for the date of service (DOS) of January 26. This code has a professional component/technical component (PC/TC) indicator of 1 with a global allowed amount of $23.73 and a paid amount of $18.98. The DOS occurred during the inpatient hospital stay, and data analysis confirmed that the patient was not on leave-of-absence from the hospital on that date. The TC portion of this code was only payable to the facility while the patient is in an inpatient setting.

Code 71010 was adjusted to pay only for the PC portion, by applying modifier 26 to the claim. The allowed amount for 71010 with modifier 26 was $9.03. The new provider-paid amount was $7.22. This resulted in a total recouped amount of $11.76.

For more information about the above, go to page 12 of the document located at At the end of the summary, you will find numerous references for more guidance on the parameters around which the TC of radiology services furnished to hospital patients.

About the Author

Randy Wiitala, BS, MT (ASCP) is a senior healthcare consultant with Medical Learning, Inc. (MedLearn), St. Paul, MN. MedLearn is a nationally recognized expert in healthcare compliance and reimbursement. Founded in 1991, MedLearn delivers actionable answers that will equip healthcare organizations with their coding, chargemaster, reimbursement management and RAC solutions.

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Providers challenging audit claim denials through the Medicare appeals process can expect to encounter varying degrees of contractor involvement at each stage of appeal. Providers should be aware of the contractor's role in each stage, the information requests to make at each level and the limits on the scope of the contractor's participation.

Requests for Information

Providers undergoing a Medicare or RAC audit typically are notified of the contractor's findings by way of an audit results letter. Often this letter will provide general conclusions about the audit, and it may include a spreadsheet listing each claim or claim line determination.

After reviewing the audit letter, providers often are left with unanswered questions about the claim denials and the nature of the audit review conducted by the contractor, information that would be helpful in deciding on an appeal.  In our audit experience, it is important to begin requesting additional information that may be available in the contractor's audit file. This information may include reviewers' credentials, data necessary to recreate the statistical sampling and applied projection, internal notes, memos, correspondence addressing the audit denials, plus guidelines or policies referenced in making the claim determinations. These requests can be made to the contractor by the provider in connection with the appeal and may reference the federal Freedom of Information Act (FOIA) as a basis for the release of information. Providers are advised to calendar the date they submit their requests and to continue to follow up with the contractor on the status of the release of information. Documentation of a provider's ongoing efforts to procure requested information may provide future support of a good-cause argument for the admission of additional evidence at subsequent levels of appeal.

At the Administrative Law Judge (ALJ) stage of appeal, providers may request a copy of the audit file. Throughout the appeals process, the appeal record is developed as the audit file is passed on to the various contractors at subsequent levels of appeal. Providers should direct their requests for audit files to the office of the ALJ assigned to hear the appeal. The Medicare appeal regulations at 42 CFR 405.1042 provide that a party may request and receive a copy of all or part of a record. Obtaining a copy of the audit file can reveal internal contractor correspondence, including the guidelines and policies applied in their review, which also may reveal inappropriate standards or inconsistencies that can be used to challenge the claim denials. Furthermore, the audit file allows the provider to see what documentation the ALJ has in his or her possession going into the hearing so that a determination can be made whether additional information should be submitted.

In a recent audit appeal, review of an audit file revealed that the Medicare contractor that conducted the initial audit review ultimately had discarded the statistical sampling and extrapolation originally applied in favor of a claim review. In a conclusion based on the language of the audit letters and appeal decisions, the provider understood the audit to involve an extrapolated overpayment. It was not until a review of the audit file was performed at the ALJ hearing that the claim-by-claim overpayment calculation was discovered. This type of information is vital to challenging the propriety of an audit.

Contractor Participation

In recent audit appeals, we also have experienced increased contractor participation in ALJ hearings. CMS contractors may elect to participate either as parties or non-party participants in an ALJ hearing (the nature of the contractor's involvement in the hearing often is impacted by how they choose to participate). In our experience, contractors more commonly participate as non-party participants. As participants, contractors may not call witnesses or cross-examine a provider's witnesses, nor may the provider call the contractor as a witness. Pursuant to 42 C.F.R. 405.1010, participation as a non-party participant may include filing position papers and providing testimony to clarify factual or policy issues of the case.

ALJs vary on the scope of involvement they permit from contractors acting as non-party participants. In some instances the ALJ simply will allow the contractor to listen to the hearing or will pose a few questions to the contractor's representatives for clarification. In other instances ALJs have allowed a contractor representative to offer testimony on the substance of the audit review. This can be problematic when the contractor is not placed under oath and when the provider or their attorney is not permitted to engage in cross-examination. That said, contractor participation does not always have a negative impact on a provider's appeal; in some instances contractor testimony and position papers actually have been helpful in revealing shortfalls in an audit review. Regardless, it is helpful for providers to know in advance of the hearing whether contractors plan to participate.


CMS or its contractors also may elect to act as parties to an ALJ hearing on a provider appeal. When acting as a party, the contractor is permitted to call witnesses or cross-examine the witnesses of other parties as well as submit position papers and provide testimony to clarify issues. Notably, limited discovery rights are permitted when a contractor acts as a party to a hearing. While contractors are required to advise the ALJ and the provider of their intent to participate in the hearing as a party or non-party participant within 10 days of receiving a notice of hearing, it is not uncommon to have a contractor attend on the day of the hearing with little or no prior notice. Providers should contact the ALJ's office in advance of the hearing to determine if any of the CMS contractors involved have indicated their intent to participate in the hearing or submit a position paper.

Providers and their attorneys must recognize the potential impact of contractor involvement during the appeals process and utilize to their advantage contractor contact through information requests and hearing appearances. Providers are advised to monitor contractor participation in light of current Medicare regulations and to raise objections when their involvement goes beyond the permissible scope.

About the Authors

Andrew B. Wachler is the principal of Wachler & Associates, P.C.  He graduated Cum Laude from the University of Michigan in 1974 and was the recipient of the William J. Branstom Award. He graduated Cum Laude from Wayne State University Law School in 1978. Mr. Wachler has been practicing healthcare and business law for over 25 years and has been defending Medicare and other third party payor audits since 1980.  Mr. Wachler counsels healthcare providers and organizations nationwide in a variety of legal matters.  He writes and speaks nationally to professional organizations and other entities on a variety of healthcare legal topics. Mr. Wachler is a member of the American Health Lawyers Association and American Bar Association (Health Law Section).  He sits on the Editorial Board of the ABA Health Law Section publication The Health Lawyer, Fraud and Abuse Section and is the Vice Chair of the ABA Health Law Section Payment and Reimbursement Interest Group.  Mr. Wachler is a member of the State Bar of Michigan, Health Care Law Section.

Jennifer Colagiovanni is an attorney at Wachler & Associates, P.C.  Ms. Colagiovanni graduated with Distinction from the University of Michigan and Cum Laude from Wayne State University Law School.  Upon graduation, Ms. Colagiovanni was nominated to the Order of the Coif. Ms. Colagiovanni devotes a substantial portion of her practice to defending Medicare and other third party payer audits on behalf of providers and suppliers.  She is a member of the State Bar of Michigan Health Care Law Section.

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How to Obtain Increased Reimbursement on Your Out-of-Network Claims

fcohen100In the past I have discussed issues involving audit results based on extrapolation. As a refresher, extrapolation is a statistical technique used to infer a conclusion about a universe of data from a sample randomly extracted from that universe, or at least something like that.

In our world, an extrapolation occurs when it is unreasonable and/or not cost effective to audit every single claim within a practice. For example, let's say that a practice submits 10,000 claims a year and an audit is initiated on claims filed during the past three years. In this case there would be 30,000 claims at risk for audit, but it would be crazy even to think about actually auditing that many claims. In this case, the standard operating procedure would be to sample randomly some number of those claims (say, 100) and use the results from that sample to infer the results to the population.

Continuing with our example, let's say that of those 100 claims, 25 were found to have been paid in error. From the standpoint of extrapolation, we then could infer that approximately 25 percent (plus or minus what is known as the "sample error") of the claims in the universe also likely were paid in error. Most often, at least for government audits, the sample error is translated to a confidence interval, and the lower 90 percent of the confidence interval is used to infer the error to the population. In this case, the 90 percent confidence interval range is 18 percent to 33 percent. I would state this by saying I am 90 percent confident that the true error rate for the universe is somewhere between 18 and 33 percent. Another way to interpret this is like so: for every 100 samples of 100 claim lines that I take, the error rate on 90 of them will be between 18 percent and 33 percent.

In order to avoid arguments about the issue of sample error, most auditors will settle on the lower bound of the 90 percent confidence interval (in this case, 18 percent). One point to note here is that most of the time, the overpayment amounts are not measured in proportions, but rather in dollars.  So let's say that of the 25 claims found to have been paid in error, the total overpayment amount is $2,500, or around an average of $100 per overpaid claim (or, more likely, $25 per claim sampled). Once again we still have the issue of sample error, so using a couple of other values (standard deviation and number of units in the sample) we would calculate the 90 percent confidence interval to be around a mean of $25. For the sake of simplicity, let's say that the 10 percent error comes out to $10. In this case, the range would be $15 to $35, and once again, to avoid argument, the auditor would use the lower bound of $15. To complete the extrapolation, we simply multiply the $15 by the 30,000 claims to come up with a total estimated overpayment of $450,000. As a side note, some auditors will extrapolate using the proportion of overpaid claims and the average overpaid amount per overpaid claim. This method is a bit less accurate, as it has to factor in the sample error for both the proportion of claims in error as well as the sample error for the overpayment on the overpaid claims.

While there are a lot of places between the audit notice and the audit results in which to make mistakes, in theory the process is pretty simple. That is, until we introduce the concept of stratification.


Stratification by definition is the process of layering, and in our case it defines a situation in which the universe of claims is broken out into different subsets sometimes called "sample frames."

Stratification is supposed to be based on some logical distribution of unique features, but I find that this is not always the case. Most often, strata are defined by criteria like code category (i.e. E/M, surgery, pathology, etc.), paid claim amount, date range or other factors.  When this happens, the sample sizes are diluted a bit for each stratum, but the extrapolation is based on the universe of claims for that stratum only.

In our example, let's say that the universe was divided into three strata separated by paid claim amount. Claims paid at under $100 are designated as stratum 1, claims greater than $100 but less than $500 are stratum 2 and claims paid at $500 or more are stratum 3.

The sample size is 30 claims per stratum. So consider that stratum 1 has a universe of 20,000 claims, stratum 2 a universe of 8,000 claims and stratum 3 a universe of 2,000 claims.

In order to estimate the total overpayment amount, we use the same technique as above, only we apply it to each stratum individually and then add together the extrapolated overpayment estimate for each to get the grand total. Again, in theory at least, pretty simple.

The Nitty Gritty

After an 800-plus word introduction, here's the important part of the story. In a few of the audits I have reviewed of late, I came across a situation in which a claim being audited was incomplete. Remember, at the claim level there can be more than one claim line. For example, if I see a patient, perform a procedure and maybe run a lab test or two, there can be three or four claims lines that make up the claim. So when we talk about the provider's paid amount per claim (a very common metric), we are referencing the paid amount per claim line within that claim. This discrepancy, when it occurs, often just looks like an innocent mistake, and in some cases the client just wanted to let it go since it would make sense (at least at first glance) that the fewer claim lines within a claim, the lower the overpayment amount. Why? Because the claim paid amount would be lower.

By Way of Example

Here's an example from our mock practice: claim X is reported with four claim lines and a claim paid amount of $485. When the practice looked at the actual claim record (or the chart), it found that there were actually five claim lines, and if the fifth had been included the paid claim amount would have been $535. The first thought is "great, they forgot to include a claim line so that eliminates the possibility that it was paid improperly, and adds to the overpayment amount."  It's definitely a logical assumption. But let's take a closer look at this.

A Closer Look

In its current position, claim X falls into stratum 2, which has a universe of 8,000 claims. Not only does it fall into this stratum, it is at the higher end of the stratum's range. So the overpayment amount determined for this claim goes into the bucket, which, when averaged, will use the universe of 8,000 claims to calculate the extrapolated amount. Just for the sake of argument, let's say that all of the claim lines were determined to have been paid in error, and as such, the overpayment amount is reported as $485. That's only $15 shy of the highest amount that could have been found, since the top of the range for this stratum is $500. Now let's look at this from the other side. Let's say that the fifth claim line was included and the entire claim was paid in error, increasing the overpayment amount from $485 to $535 (an increase of $50). Again, logic says that the former case is better than the latter. But is it really? In the former case we have an overpayment amount at the upper range of the stratum factored into a universe of 8,000, while in the latter case we have an overpayment at the bottom of the stratum factored into a universe of only 2,000.

Whether these omissions occur on purpose or not, when a sample is stratified based on a paid claim amount, it is important that practices review the claims in the sample to make sure that all of the claim lines are included. If they are not, it would be a good idea to try to reconstruct at least some of them to determine whether the exclusion results in a change to a lower stratum, and whether that might result in a higher overpayment estimate. There are many ways to skin a cat(fish), and I am afraid we have just discovered another.

About the Author

Frank Cohen is the senior analyst for The Frank Cohen Group, LLC. He is a healthcare consultant who specializes in data mining, applied statistics, practice analytics, decision support and process improvement.

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Contract Involvement in the Medicare Appeals Process

tforce100Many physicians and other healthcare practitioners ("practices") need to go "in-network" with managed care organizations because these network agreements drive patients into their office. These practitioners typically enter into managed care participation provider agreements with the health insurers for the privilege of becoming an "in-network" provider. The benefits of being in-network include the following:

  • Patients are referred to the practice by virtue of their inclusion on PPO and HMO networks;
  • Medical claim checks are issued more quickly;
  • Medical claims checks are issued to the practice directly, not to patients; and
  • Claim denials are reduced.

The major disadvantage of becoming an "in-network" provider is that the healthcare practitioner is forced to accept very low reimbursement rates for their medical services, and there is virtually no give and take on the contract terms. Providers essentially sign the contract as is, without any revisions. Consequently, the contract provisions are typically one-sided, favoring the health insurer.

As a result, more and more health care practitioners are deciding to go "out-of-network." This means the healthcare practitioner chooses to forego participation in health insurer PPO and HMO networks, and no managed care contracts are executed. The obvious benefit of being an out-of-network provider is that reimbursement rates typically are higher than that of in-network providers.

The disadvantages of being out-of-network are that the frequency of denied claims increases and the practice received no referrals from  HMO and PPO networks. In addition, many health insurers refuse to accept assignment of benefits (AOB) between patients and the practice.

An AOB is a form through which a patient agrees to assign a claim benefit or payment to the practice, meaning that claim checks are sent to the practice and not to the insured member. When an insurer rejects an AOB, it results in claim checks being sent directly to the practice's patients. Many insurers policy forms contain a provision that all AOB are rejected.  This essentially forces a provider to chase their patients for claim checks that rightfully belong to the practice. Many patients cash the claim checks, especially in a slow economy, and then outright refuse to return the funds to the practice. The practice is then faced with instituting debt collection actions, which could result in a public relations disaster. As a result, many practices simply decide to write off of the debt, which has a detrimental impact on revenue.

Many physician advocates have questioned whether the failure to recognize the validity of a patient AOB is really nothing more than an attempt by a health insurer to punish a practice for daring to go out-of-network ("you don't want to go in-network and accept our unreasonably low fee schedule? No problem, we'll just send your claim checks to the patients, and good luck collecting from your patients.")   Health insurers counter that it is a simple contract issue, asserting that they cannot accept a patient AOB because they simply do not have privity of contract with the provider. The only privity of contract that exists is with their own members, hence the reason the AOB is rejected and checks are sent to members and not to the practice directly.

New York: Case in Point

In any event, by way of background, then-New York State Attorney General Andrew Cuomo, early in 2008, conducted an investigation into what he viewed as under-reimbursement of out-of-network claims by most insurers in New York. Mr. Cuomo investigated what he referred to as "industry-wide," typical, customary and reasonable underpayments that affected consumers in New York State and nationwide. At the conclusion of his investigation, Mr. Cuomo described out-of-network reimbursement by certain health insurers as a scheme to defraud consumers by manipulating reimbursement rates for out-of-pocket medical expenses.

Specifically, in a press release Mr. Cuomo stated that the "scheme by health insurers (is) to defraud consumers by manipulating reimbursement rates" by using a "defective and manipulated database (Ingenix)." He further stated that most major health insurance companies used this database with full knowledge that it is artificially and intentionally well below reasonable and customary reimbursement rates.

Moreover, Mr. Cuomo's investigation "found that by distorting the "reasonable and customary" rates, insurers were "able to keep their reimbursements artificially low and force patients to absorb a higher share of the costs." He stated that "getting insurance companies to keep their promises and cover medical costs can be hard enough as it is, but when insurers create convoluted and dishonest systems for determining the rate of reimbursement, real people get stuck with excessive bills and are less likely to seek the care they need."

At the conclusion of his investigation in January 2009, Mr. Cuomo published a document referred to as "Code Blue" (found at, which concluded that insurance payers in New York State and nationwide were under-reimbursing consumers and providers in out-of-network situations due to a flawed database, Ingenix, which was used to calculate such payments. He found an inherent conflict of interest involving the Ingenix database in that it was owned by an insurer, United Healthcare, and created by the insurance industry, which was motivated to under-reimburse claims.

Mr. Cuomo concluded that a reliance on the Ingenix database contributed to a lack of transparency that made it impossible for patients and their families to obtain key information on costs they would have to bear personally in seeking out-of-network services. In subsequent months, Mr. Cuomo spearheaded multi-million dollar settlements with most of the major insurance companies, with those funds to be applied to a replacement database to calculate out-of-network payments more consistent with actual prevailing rates and reasonable and customary standards.

At the same time that Mr. Cuomo's investigation was ongoing, U.S. Sen. John Rockefeller, the chairman of the Senate Commerce, Science and Transportation Committee, solicited information from 18 insurance companies (representing about 33 percent of the health insurance market in the United States) about whether their companies used the Ingenix database.   With the exception of one company, all of the respondent insurance firms stated that either they or at least one of their affiliates or subsidiaries used Ingenix data to calculate reimbursements for out-of-network healthcare or dental services. The report found flaws in the out-of-network reimbursement system for those health insurers that used the Ingenix database that inhibited them from calculating "reasonable and customary" reimbursement rates for providers and consumers across the nation. The report essentially made the same conclusions as Mr. Cuomo made in his Code Blue Report. Rockefeller's committee report was published on June 24, 2009 and is available at

In January 2009, the American Medical Association announced a settlement of its massive class-action federal lawsuit against United Healthcare (UHC) for $350 million. The lawsuit alleged that UHC under-reimbursed thousands of consumers nationwide.

As part of Mr. Cuomo's settlements with the various insurers, a nonprofit organization called FAIR Health was established to work with leading academic researchers to create an enhanced database utilizing a fair and open methodology for collecting and analyzing healthcare provider charges nationwide. The data was to be made available to the public to assist consumers with researching charges for medical and dental services in advance of making a decision to go out-of-network. The new consumer website is now available at The tools and data developed by FAIR should produce realistic and more competitive out-of-network reimbursement rates, however, most insurers are not yet using these databases to calculate out-of-network medical claims reimbursement.

Under Mr. Cuomo's settlement, health insurers could continue to use Ingenix and other similarly flawed databases until FAIR was completely up and running, which it is not. Other health insurers are changing their policy forms to remove "reasonable and customary" language and replace it with fee reimbursement schedules for calculation of out-of-network claims based on a percentage of Medicare rates - which, in most experts' opinions, are unreasonably low.

Lessons Learned

What does this all mean? It means that healthcare providers and practices can obtain increased reimbursement for their paid out-of-network claims by appealing all out-of-network paid claims. At time of receipt of an insurer's EOB, a written appeal should be prepared to the appeal address listed in the EOB citing the precedents created by the following:

The former New York Attorney General's investigation and settlements (cite the Code Blue Report);

Sen. Rockefeller's investigation and report findings;

The UHC AMA settlement; and

The various class actions that have emerged since the UHC AMA settlement.

These precedents can and should be used to establish that your out-of-network claims are being under-reimbursed and that an increased payment is warranted. It is truly a unique concept to appeal paid claims, as healthcare providers generally are used to appealing only denials. However, practitioners should appeal all out-of-network paid medical claims, without exception. You will be surprised by the results.

Many payers such as Empire Blue Cross, United Healthcare, United Healthcare Empire Plan, Aetna, Cigna and many more will pay additional reimbursement on plans that reimburse based on "reasonable and customary" fees. My clients frequently receive 80 to 100 percent of charges after appeal.

When dealing with out-of-network claims, the following are a few helpful tips that can assist you in receiving fair, reasonable and quicker payments:

Provide patients with instructional information explaining the following:

You are an out-of-network provider;

What it means to be an out-of-network provider;

Advising that checks will be sent to the patient because insurers do not accept AOB;

What to do with checks received (where to send them, how to endorse them, etc.);

Providing contact information for billing staff (or a billing company representative) who can answer questions about out-of-network claims and payments; and

Advising patients not to be alarmed by large patient responsibilities on EOBs (this is to be expected, so assure them that all paid claims will be appealed multiple times and that you expect appeals to be successful and patient responsibilities to be reduced.)

Get e-mail addresses and cell phone numbers of patients (patients often will respond better to e-mails and text messages than to traditional phone calls).

Get patient AOB and authorizations to discuss patient medical information and to file appeals and lawsuits against health insurers on their behalf if necessary.

Develop an e-mail policy whereby patients agree to accept communications by e-mail or text messaging  (you will have to communicate with patients much more frequently when dealing with out-of-network claims).

Give patients self-addressed envelopes to use for returning claim checks to the practice.

Provide patients copies of all filed appeals and encourage them to contact the health insurer for status of appeals (patients should be advised that successful appeals will reduce patient responsibility, thus motivating them to push payers to resolve appeals quickly).

Keep track of payments by payers and the percentages of payments to charges, as this can be used in appeal letters to establish a precedent for additional payment (i.e. Blue Cross's "YLK" Plan paid 80 percent of charges for patients Smith, Jones and Mann, thus they should pay 80 percent of charges for the appealed claims).

Utilize state department of insurance and state attorney generals' complaint procedures (i.e. the New York State Attorney General's Health Bureau has a department dedicated to complaints involving out-of-network claims).

Utilize small claims courts and courts of lesser jurisdiction (i.e. if your appeal for increased reimbursement is denied, file a small-claims lawsuit, which can be accomplished cheaply and without counsel).

You can and will receive additional reimbursement on your out-of-network claims. Remember, appeal all paid claims, be organized, track payments by payers and remain diligent. These claims are like found money, and can increase a practice's revenue and bottom line.

Good luck.

About the Author

Thomas J. Force, Esq., is the founder, president and chairman of the board of The Patriot Group,, a full service revenue recovery company that provides billing, collections,and follow-up services as well as assistance with managed care appeals, managed care contracting, credentialing and compliance. Mr. Force is nationally recognized as an expert in revenue collection techniques, managed care contracting and appeal strategies.

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RAC 201 - After the Demand Letter Receipt

fcohen100In our recent survey of medical practices that have undergone Recovery Audit Contractor (RAC) audits, respondents reported that in more than 50 percent of cases random sampling was claimed to have been used by a RAC auditor.

Normally, there is only one reason to do this: to establish that there is a high and sustained rate of error, which authorizes the auditor to use extrapolation to determine overpayments. Yet only 21 percent of these same respondents indicated that they were aware that extrapolation had taken place. So either there is a communication problem that is occurring during the audit process or a there is a great deal of confusion over what constitutes extrapolation.

I have read many RAC audit letters and reports, so I can testify that contractors are not always clear regarding what was done and how. This is a very important component of the audit to understand, because if random sampling drives extrapolation, practices need to have an awareness of which (if either) are being applied to their specific audit.

In the past I have discussed how a practice can perform an initial randomness assessment by comparing the average (or median) paid amount per claim for the sample to the universe. Without getting into the details of how to conduct a statistically significant two-sample test, the purpose is to eye the figures and get help if the variance seems too large.

Lately, however, I have found some situations in which, when comparing these figures, the sample appeared random at first - but when I dug deeper and looked at the distribution of the codes being audited, I discovered a great deal of disparity.

Case in Point

In a recent case, a particular procedure code was ranked No. 50 (out of 220) in terms of frequency in the universe, yet this same code ranked No. 1 in the sample. It also had the highest average overpayment estimate. Of the 72 codes in the 30-claim sample, this code appeared 26 times, whereas if it was distributed based on the universe we should have seen it appear only four times.  So by having a representation order of magnitude greater than it should have been, the overpayment amount contributed more to the total than it should have.  In this case, the code contributed 65 percent more to the total overpayment estimate than it would have if it had been represented properly in the sample.

Another Example

In a similar case, we saw a procedure code ranked No. 1 in the universe that ranked last in the sample - and it also reported the lowest overpayment estimate. The same situation as that of the above case was present, only this time the overpayment estimate should have been considerably less because more of the distribution would have been absorbed by a lower overpayment per unit. In this case, had it been represented properly it would have reduced the overpayment estimate by 40 percent. So be aware, there are sneaky ways to make an otherwise random-looking sample turn up with an unexpected bias.

Stratification of Sample Frame

The other issue that practices deal with regularly during audits has to do with the stratification of the sample frame. "Strata" is short for stratification, and this is a method used to divide the claims universe into sections based on some specific criteria. Usually this ratio is provider-paid amount per claim.

Strata: An Example

In a recent case, strata were designated as under $100, $100 to $250, $250 to $500 and over $500. This means that those claims for which the total paid amount was less than $100 in essence were treated as a separate analysis. The same went for those claims falling in the other three strata. In this case, when I analyzed the full claims database I found that pretty much everything over $500 was a statistical outlier, meaning that these claims should not have been included in any type of extrapolation.

In another recent case, we found that the average overpayment amount per claim for an "outlier" stratum actually exceeded the average paid amount per claim, meaning that the practice would have been required to pay back more than they were paid.

The Stratified Sample

When a stratified sample is recommended or used, it is important to take a look at the universe first. One way to do this is to use a histogram-type graph to see what the distribution looks like. If you see a long right tail on the graph, many times this means there are a bunch of particularly high paid-amount-per-claim values on the right; these may be outliers and should be excluded from extrapolation. The only appropriate way to handle outlier audits is on a single-claim basis, with overpayment determinations limited only to individual claims.

Appealing Overpayments

The final issue has to do with appeals. Depending on what study you read, anywhere between 35 and 65 percent of all overpayment findings are overturned on appeal - meaning that if a RAC finds that 20 of your claims have been overpaid, you will get about 10 of them back on appeal. The downside is that only around one-third of practices are appealing, creating a huge shortfall in collectible data.

An Appeal that Worked

One of my clients recently underwent an audit of 30 claims, with the RAC finding 24 of them to have been paid improperly. The practice appealed each of the denied claims, and the result was that 20 were reversed back in favor of the practice. Unfortunately, though, the cost of appealing a claim sometimes can outweigh the value of the overpayment demand. Whether or not a practice chooses to do this has to be an individual business decision. But remember, every time a RAC gets away with improperly tagging a claim as being paid in error, it only motivates them to do this more in the future.

Best Practices

In summary, here are my suggestions:

1.   Make sure that your auditor provides you with enough information to replicate their study and findings. This includes what filters were used to scan from the universe of all claims to the sample frame.  Exclusions may include items like zero-paid claims, secondary payers, etc.

2.   Review the sampling methodology from different angles, including paid per claim, distribution of codes and claim types. Make sure the sample is random from all perspectives.

3.   Determine whether the auditor is using extrapolation. Ask specifically, and if they are not and instead claim to be pulling a random sample, find out why.

4.   Appeal every denial with which you disagree on the auditor's determination of overpayment. Pay particularly close attention when overpayment determinations are due to medical necessity issues.

About the Author

Frank Cohen is the senior analyst for The Frank Cohen Group, LLC. He is a healthcare consultant who specializes in data mining, applied statistics, practice analytics, decision support and process improvement.

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Left in the Dark: The Blackout in Region A