In this data quality report, we investigate the capacity of the 2018 Transformed Medicaid Statistical Information System Analytic Files (TAF) to measure medical services among people dually enrolled in Medicare and Medicaid. We examine services for which Medicaid is the primary payer among dual enrollees: long-term services and supports, including personal care, nonemergency transportation, and other home- and community-based services (HCBS); behavioral health care; and nursing home care. Our analysis produced several key findings:

• Five states, New York, South Carolina, Vermont, Utah, and Colorado, have relatively high rates of missing procedure codes for noninstitutional services among Other Services (OT) file claim lines, ranging from 11.5 to 42.1 percent. This raises concerns about the TAF’s ability to accurately measure HCBS, behavioral health services, and nonemergency transportation services in these states.
• Five states, Florida, Nebraska, Missouri, Massachusetts, and Hawaii, have relatively high rates of missing or invalid type-of-service codes among Long Term Care (LT) file claim lines, ranging from 21.0 to 98.6 percent. This raises concerns regarding the TAF’s ability to accurately measure nursing home services in these states.
• Among services studied on OT and LT file claim lines, beginning and end dates of service are of high quality across all states, except Virginia’s nursing home services in the LT file.
• In multiple states, utilization rates for the services of interest are extremely low and in some cases seemingly implausible, even in cases where the analysis of the input data elements needed to measure utilization does not identify quality issues. This underscores the challenges researchers face using the TAF data for specific applications.
• The TAF HCBS taxonomy code data elements are unusable.
• The type-of-service data element is very imprecise compared with procedure codes for measuring HCBS located on the TAF OT file.

Overall, our findings suggest the data fields necessary to identify services commonly used by dual enrollees and paid for by Medicaid are of good quality. However, we identify several states with likely data quality problems based on individual data elements with missing or invalid values or implausibly low levels of implied utilization for specific services. These findings underscore the need to account for both across- and within-state variations in data quality when developing research designs using the TAF. They also suggest TAF data will not support national-level analyses.