Changes in coverage and claim submission requirements can have significant impact on claims research data. Understanding how upstream policies impact downstream data can protect researchers from over-purchasing data for their project. The information I gained from working on the Identifying Clinical Trials in CMS FFS Claims Data article provides a clear example of this.

At the time of writing, there are two important dates related to clinical trial coverage in Medicare. The first is July 9^th, 2007, when Medicare started covering routine costs of clinical trials. The second is January 1^st, 2014, when CMS mandated reporting on clinical trial items and services. This means there are varying use cases for CMS RIF claims data depending on the year of data.

Applied to a project where CMS RIF data is the sole data source, these dates restrict clinical trial identification and service reporting. Prior to 2007, this project would have great difficulty finding any clinical trial utilization claims. Between 2007 and 2014, they would have a greater likelihood of identifying clinical trial services but potentially a difficult time linking to specific clinical trials. From 2014 onwards, this project should generally find both services and clinical trial numbers. However, in the article linked above, recent data can still contain missing or unknown clinical trial numbers.

Does this mean that CMS RIF data is useless for clinical trial studies prior to 2007? Of course not. CMS RIF data is still an excellent source of healthcare utilization for Medicare beneficiaries. CMS RIF data can supplement existing clinical trial data if there is a way to link beneficiaries between the two datasets.

Clinical trials have a very straightforward timeline of coverage and claims submission requirements, so it works as an excellent example for understanding how upstream decisions impact data downstream. Applying this logic to your own research topic can protect you from purchasing unusable data.