Medicare Program Integrity Manual (Pub. 100-08), Ch. 12 § 12.3

Overview of the CERT Process

Last amended: 2026Year: 2026Length: 275 wordsOfficial source
12.3 – Overview of the CERT Process (Rev. 13678; Issued: 03-12-26; Effective: 04-13-26; Implementation: 04-13-26) This section applies to Medicare Administrative Contractors (MACs) and Comprehensive Error Rate Testing (CERT) as indicated. The CERT sampling process begins with the CERT statistical contractor sampling from shared systems claims in the Integrated Data Repository (IDR). The CERT statistical contractor will ensure that the sampling universe only contains one record of each unique claim. The CERT statistical contractor transmits files containing the sampled claims to the CERT review contractor twice monthly. The CERT review contractor process begins when claims that have entered the claims processing system are extracted to create a claims universe file. This file is transmitted to the CMS Data Center (CMSDC) daily. Claims sampled from the IDR are matched and reconciled with this CMSDC universe. The sampled claims are held for a predefined period to allow the claim to be processed and paid by the MAC. After this waiting period, the sample information is sent to the MAC as a sampled claim transaction file. The MAC returns specific information about each claim to the CERT review contractor using the sampled claims resolution file, claims history replica file, and the provider address file formats. The CERT program uses the information obtained from the MAC to request documentation from the provider who submitted the sampled claim. The claim and the supporting documentation are reviewed by the CERT review contractor to determine if the claim was paid or denied appropriately based upon Medicare coverage, coding, and billing rules. The CERT program collects additional information from the MACs for each claim considered to be in error via the feedback process.
Medicare Program Integrity Manual (Pub. 100-08), Ch. 12 § 12.3: Overview of the CERT Process | Justis AI