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  • CMS has developed a new Medicaid DQ Atlas, an interactive, web-based tool that helps policymakers, analysts, researchers, and other stakeholders explore the quality and usability of the Medicaid TMSIS Analytic File (TAF). The charts, maps, and tables in the new DQ Atlas show state-level DQ assessments and associated measure values for topics that are pertinent to Medicaid and CHIP.

  • This document shows each state's DQ concerns from all TAF DQ briefs, with DQ concerns grouped by content group.

  • This document shows Alabama’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Alaska’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Arizona’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows California’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Colorado’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Connecticut’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Delaware’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows District of Columbia’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Florida’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Georgia’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Hawaii’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Idaho’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Illinois’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Indiana’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Iowa’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Kansas’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Kentucky’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Louisiana’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Maine’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Maryland’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Massachusetts’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Michigan’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Minnesota’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Mississippi’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Missouri’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Montana’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Nebraska’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Nevada’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows New Hampshire’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows New Jersey’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows New Mexico’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows New York’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows North Carolina’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows North Dakota’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Ohio’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Oklahoma’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Oregon’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Pennsylvania’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Rhode Island’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows South Carolina’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows South Dakota’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Tennessee’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Texas’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Utah’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Vermont’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Virginia’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Washington’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows West Virginia’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Wisconsin’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document shows Wyoming’s TAF data quality assessments from each TAF DQ brief. TAF DQ briefs assess the reliability, accuracy, and usability of TAF data for conducting analyses of specific Medicaid and CHIP topics.

  • This document describes the development and content of the TAF annual Demographic & Eligibility (DE) file. The TAF DE includes information on the demographic, eligibility, and enrollment characteristics of beneficiaries who were enrolled in Medicaid or in CHIP for at least one day during any given calendar year.

  • This document describes the development and content of the four claims files that contain service use and payment records: (1) the IP file includes institutional inpatient services and payments, (2) the LT file includes institutional long-term care services and payments, (3) the OT file includes all other medical services and payments, and (4) the RX file includes prescription drug fills and pharmacy payments.

  • This document provides instructions for analyzing claims data submitted by Illinois. This is a short-term solution that will help make the data usable until the state can implement changes to address this issue.

  • The claims files that make up part of the TAF system rely on the final action status appended to each T-MSIS claim to identify which record in a claim family is the final version and therefore most appropriate for research. States with high proportions of claim families where a final action claim cannot be identified may have TAF data that will under-count services and expenditures. This data quality brief examines the number and percent of claims and claims families without final action among T-MSIS inpatient, long-term care, pharmacy, and other claims files.

  • All states providing services through managed care plans must report enrollment for beneficiaries covered under such plans; however, the accuracy of T-MSIS enrollment reporting varies considerably both across states and across plan types. This data quality brief examines alignment between the TAF and the benchmark, the Medicaid Managed Care Enrollment and Program Characteristics (MMCEPC) report, using data from June 2016.

  • This brief examines the accuracy of the CHIP enrollment reported in TAF by comparing it to an external benchmark, the Performance Indicators data set.

  • This brief examines the accuracy of the adult expansion enrollment reported in TAF by comparing it to an external benchmark, enrollment data from the Medicaid Budget and Expenditure System.

  • This brief examines the accuracy of the total Medicaid and CHIP population with comprehensive benefits reported in TAF by comparing it to an external benchmark, the Performance Indicators data set.

  • This brief examines the accuracy of the total Medicaid population with comprehensive benefits reported in TAF by comparing it to an external benchmark, the Performance Indicators data set.

  • This brief evaluates the accuracy of the dually eligible population (beneficiaries eligible for both Medicaid and Medicare) by benchmarking it to an external data source, the Medicare-Medicaid Enrollment Snapshot.

  • This brief identifies states in which the effective eligibility start and end dates may have data quality issues, by examining states with unlikely enrollment patterns.

  • This brief examines the completeness and distribution of five key demographics variables in the TAF data: age, gender, income, race/ethnicity, and ZIP code.

  • This brief examines the completeness and distribution of the CHIP code or the dual status code, which identify beneficiaries enrolled in Title XXI CHIP or who are dually eligible for Medicare.

  • Eligibility group codes allow TAF users to identify the basis on which an individual was deemed eligible for Medicaid or CHIP. This brief identifies states in which (1) the eligibility group code had high rates of missing values or (2) codes for certain large, mandatory Medicaid eligibility groups were not used.

  • This brief presents information on how to interpret the values of the restricted benefits code variable, instruction on determining the scope of benefits based on this variable, and guidance for TAF users about what to do when there is not enough information in the restricted benefits code variable to make this determination.

  • The purpose of this brief is to provide TAF users with an assessment of the quality of the restricted benefits code in each state, including how often it is missing or shows an unusual distribution that may indicate the data element is unreliable for analytic purposes.

  • This brief presents a recommended approach for defining and counting inpatient hospital stays in TAF.

  • This issue brief assesses the reliability of the TAF for analyzing inpatient hospital stays among adults enrolled in Medicaid by comparing the number of annual inpatient hospital stays reported in the TAF to an external benchmark from the Healthcare Cost and Utilization Project (HCUP).

  • This brief examines the completeness and quality of the type of bill field in the TAF and whether the distribution of values within each medical claim file reflects the types of claims that states are expected to submit.

  • The type of hospital field in the IP file offers information on the characteristics of hospitals serving Medicaid and CHIP beneficiaries. This brief examines how often states report missing or unexpected information in this field.

  • This brief examines the percentage of beneficiaries in each state with any medical or pharmacy claims in the TAF data, as a way to identify states with potentially incomplete service use data.

  • This brief examines the extent to which four key National Provider Identifiers (NPIs)—the Billing Provider NPI, the Servicing Provider NPI, the Prescribing Provider NPI, and the Dispensing Provider NPI—are available on fee-for-service claims and managed care encounters in the TAF.

  • The billing provider type code allows users to examine the characteristics of providers who receive payments for Medicaid- and CHIP-funded services. This brief assesses the extent to which this data element is missing, or uses unexpected or non-specific values that are not usable for analysis.

  • The generic indicator on TAF claims can be used to differentiate between fills for branded prescription drugs, fills for generic prescription drugs, and fills for non-drug products. This brief identifies states in which the generic indicator should not be used because of high rates of missing values or unexpected patterns in valid values that suggest data quality problems.

  • This brief examines the extent to which admission and discharge dates are available in the 2016 TAF IP and LT claims files.

  • All service-use records in the T-MSIS Analytic Files are structured to include one header (which includes summary information about the claim) and one or more associated line records (which include details about individual goods and services on the claim) per claim. This brief examines the number of header- and line-level service records included in the IP, LT, OT, and RX files, adjusted for the size of a state’s Medicaid and CHIP population, to identify states that may have incomplete service use data.

  • This brief examines the extent to which the 2016 claims records in the IP, LT, and OT files were completely coded with at least one valid diagnosis code. It also provides the average  number of valid diagnosis codes on claims in the IP and LT files to identify states with potentially incomplete diagnosis code data.

  • This brief examines (1) how often the states’ TAF data have a missing or invalid type of service code and (2) whether states appear to be consistent in assigning type of service values to similar claims. This second analysis has implications for the usability of the type of service data element for conducting cross-state comparisons.

  • This brief examines the number of header- and line-level encounter records from comprehensive managed care plans in the IP, LT, OT, and RX files, adjusted for the number of Medicaid and CHIP beneficiaries enrolled in comprehensive managed care plans. A low volume of encounters may signal incomplete managed care service use data, and a high volume of encounters may signal other data quality issues.

  • This brief provides an overview of the service use and payment data captured in the service tracking, supplemental payment, or other claim type categories. It also provides information on possible data quality issues with these records in the 2016 TAF. 

  • This brief describes the extent to which TAF users can determine service setting in the OT file for each state.

  • Procedure codes provide detailed information on the exact goods or services delivered as part of a health care encounter. This brief examines how often the procedure codes were missing on claims in the OT and IP files, and how often the non-missing values were valid national or state-specific codes.

  • This brief examines the extent to which states are reporting FFS claims and managed care encounters with missing or invalid payment data.

  • This brief examines the extent to which the Medicaid fee-for-service expenditures captured in the 2016 TAF align with what states report on the CMS-64, both overall and by major expenditure category.

  • State Medicaid programs use a range of monthly beneficiary payments to cover care provided to beneficiaries, including payments to Medicaid managed care plans, to providers for primary care case management services, to Medicare for premium payments on behalf of dually eligible beneficiaries, and to other private plans for premium assistance. This brief examines how well the total expenditures for monthly beneficiary payments captured in the 2016 TAF align with what states report on the CMS-64.

  • States report Medicaid payments for a covered service both on claim headers and on claim lines. This brief identifies how often the payment amounts captured in these two places are inconsistent, and recommends how TAF users can identify which payment amount to use.

  • In this brief, we benchmark the count of 1915(c) waiver participants found in the 2016 TAF, using participant counts from the most recent annual report summarizing states’ CMS 372 submissions. We also explore how many of the TAF data elements that can be used to identify 1915(c) waiver participants are populated in each state.

  • This brief describes how state-reported T-MSIS data becomes the T-MSIS Analytic Files (TAF) and how the TAF is used to create the publicly-available TAF Research Identifiable File (RIF).