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Frequently Asked Questions

The following questions represent frequently asked questions (FAQs) from the provider community about Comparative Billing Reports (CBRs) and the CBR project in general. FAQs pertaining to a specific CBR release/topic are available with the resources specific to each CBR release/topic.

A Comparative Billing Report (CBR) presents comparative Medicare claims data statistics related to an individual health care provider. These reports help individual providers understand how their specific data compares to the aggregate data of analogous providers in the nation, in their respective states and/or in their respective peer groups. CBRs help the Centers for Medicare & Medicaid Services (CMS) address potential over-utilization in the Medicare Fee-for-Service (FFS) program.

CBRs serve as educational resources that help providers to 1) understand instances when their specific billing statistics differ from the majority of other providers and 2) better understand applicable Medicare billing rules/payment policy.

For a listing of fees used by Medicare to pay providers, please review the “Fee Schedules – General Information” page on the CMS website.

CMS is mandated by law to protect the Medicare Trust Fund. CMS employs a number of strategies to meet this goal, one of which is the provision of CBRs to providers. CBRs are tailored, educational tools made available to individual providers. These reports can help providers proactively assess any areas of increased risk/vulnerability and support efforts to ensure compliance with Medicare payment policy and/or accurate coding.

For all CBR-related questions, please contact the CBR Help Desk.

No, the CBR Team cannot provide a listing of claims that were summarized and/or included in the statistics of your report.

Your Medicare Administrative Contractor (MAC) can assist you with questions about a specific claim or if you identify any claims discrepancies while reviewing your CBR. Your MAC is the contractor to which you submit your claims for Medicare reimbursement. To obtain the contact information for your MAC as well as CMS’ other review contractors, please access the CMS Review Contractor Directory Interactive Map.

CBRs are not available by request at this time. Please contact the CBR Help Desk for more information.

Please contact the CBR Help Desk with your inquiry. You will need to provide your name and National Provider Identifier (NPI).

Yes, a sample is available for each CBR produced. You can access the sample CBR along with other resources and information for each CBR release at CBR.CBRPEPPER.org.

We appreciate your suggestions for additional study topics. Please email your suggestions to the CBR Help Desk.

No, hospitals will not receive CBRs. Hospital-related comparative reports are known as the Program for Evaluating Payment Patterns Electronic Reports (PEPPERs); for more information, please visit the PEPPER website.

No, CMS is not offering CEU credits for attending CBR webinars. Information related to CEU credits can be found on the CMS website.

CMS has contracted with the RELI Group to administer the CBR program. CMS approves all CBR topics and provides access to necessary information and data. RELI Group and its partners, TMF Health Quality Institute and CGS, are responsible for analyzing data, researching, developing, producing and disseminating the CBRs.

Yes, a downloadable version of the handout will be available in advance of the webinar. This handout, along with the webinar recording/transcript, will also be posted to the CBR.CBRPEPPER.org website within two weeks after the completion of each webinar.

All webinars are recorded. A recording of each presentation, along with the handout/presentation slides and a “FAQ” document, will be available at CBR.CBRPEPPER.org within two weeks after the completion of its corresponding webinar.

Due to time constraints, it may not be possible to answer all questions received during the webinar. To address this, a “FAQ” document will be developed that includes all questions asked during the webinar. This document will be made available with the webinar recording within two weeks after the webinar’s conclusion. If you need additional assistance, please contact the CBR Help Desk.

A t-test is a statistical test that is used to determine whether there is statistical difference between the averages of two groups. It is most commonly applied when the test statistic follows a normal distribution. The test is used to support or reject the null hypothesis that the averages from the two sets of data are equal. Significance for the t-test is based on the number of data points and the variability of that data. In CBR, a t-test can be used to compare your average (metric analyzed) with the averages of your peers (e.g., State or National).

Unfortunately, we are not able to provide an exact breakpoint (percentage or average) that a provider would need to warrant a comparison of “higher” as opposed to “significantly higher.” Significance is determined by the results of the chi-square test or t-test, which involves calculations based on the total number of services and the variation of those services in each individual provider’s data.

A chi-square test is a statistical test on the distribution of categorical data. The test is used to support or reject the null hypothesis that the distributions of two sets of data are equal. Significances for the chi-square test are based on the number of data points and the categorical proportions of that data. In CBR, a chi-square test can be used to compare your distribution with your peers’ distribution (e.g., State or National).

Alpha value is the value at which statistical significance is determined. An alpha value of .05 means that there is a five percent chance that the difference between your value and that of your peers is observed purely by chance. P-values are calculated and compared to the alpha value to determine whether the difference is significant. The outcomes to the t-test and chi-square test can be as follows: 1) an individual provider’s utilization measure is significantly higher, 2) it is higher, 3) it does not exceed that of the peer group or 4) there is no data for comparison. The results of statistical significance for each measure will be displayed in a table.