The dataset includes detailed information on Medicare FFS claims that underwent CERT medical review for the FY 2020 report period (claims submitted July 1, 2018 through June 30, 2019). These claims were used to calculate the FY 2020 Medicare FFS improper payment rate.
The Comprehensive Error Rate Testing (CERT) program’s public data contains the claims reviewed from the Medicare Fee-for-Service (FFS) universe for each reporting period. To minimize the risk of beneficiary identification of each claim, Healthcare Common Procedure Coding System (HCPCS) codes that are considered rare (defined as having 10 or fewer claims in the Medicare FFS universe for the reporting period) are removed from the public data for each reporting period. Additionally, a service that is provided by a singular provider or supplier can be removed from the public data dependent upon stakeholder feedback.
This list includes all Medicare enrolled Ambulatory Surgical Centers that converted to Hospitals or Independent Free Standing Emergency Departments enrolled as Hospitals during the COVID-19 public health emergency
The Medicare Shared Savings Program (Shared Savings Program) facilitates coordination among providers to improve the quality of care for Medicare fee-for-service beneficiaries while reducing the growth in health care costs. Eligible providers, hospitals, and suppliers may apply to participate in the Shared Savings Program by creating or participating in an Accountable Care Organization (ACO).
NOTE (1): Data descriptions are available in the Data Dictionary. See the Attachments section on this page to download the Data Dictionary.
NOTE (2): Excel may drop the leading zero from the ACO’s zip code in the aco_zip column when exporting the CSV file. To retain this information, we recommend using the “Import” feature of Excel to manually import the relevant data or exporting the file using one of the other supported file formats.
NOTE (3): The Spanish letter “ñ” may appear as “Ã±” or a question mark when downloaded as a CSV file in some software versions. To retain the correct character, we recommend converting the file from CSV to a different format or opening in another software.
DISCLAIMER: This information is current as of January 1, 2021. Changes to ACO information occur periodically. Each ACO has the most up-to-date information about their organization. Consider contacting the ACO for the latest information.
The MCBS COVID-19 Fall 2020 Supplement is a nationally representative, cross-sectional telephone survey of persons who were continuously enrolled in Medicare from the beginning of 2020 and alive and living in the community in Fall 2020, fielded from October 5, 2020 through November 15, 2020. These data are complemented by additional MCBS Community interview data already collected in Fall 2019 on beneficiaries’ health status and demographics as part of the in-person, nationally representative, longitudinal MCBS survey. The MCBS is sponsored by the Centers for Medicare & Medicaid Services (CMS) and directed by the Office of Enterprise Data and Analytics (OEDA).
The Medicare Current Beneficiary Survey sample is designed to produce national estimates of the Medicare population. The use of the analytic weights in this PUF ensures that the estimates accurately reflect the Medicare population. Failure to use analytic weights provided in the PUF to analyze the data will result in estimates that may misrepresent characteristics of Medicare beneficiaries. For a full explanation of the use of survey weights on the Medicare Current Beneficiary Survey and analytic examples please see our tutorial on weighting and variance estimation located here: https://www.cms.gov/research-statistics-data-and-systemsresearchmcbsdata-briefs/mcbs-advanced-tutorial-weighting-and-variance-estimation. The PUF includes a full-sample weight (CPWFWGT) and a set of 100 replicate weights (CPWF001—CPWF100). The full-sample weight is the primary weight used to ensure that the Medicare population is appropriately represented when producing point estimates. The replicate weights are used for variance estimation under the Balanced Repeated Replication (BRR) method.