Tagging is a process of applying a set of labels to the included studies in a review. AutoLit offers a built-in hierarchical tagging system connected to the data element set up for extraction. Hierarchical or non-hierarchical tagging systems can be implemented in other systematic review setups.

Tagging is a form of data gathering and study organization. Even in quantitative meta-analyses, there are often qualitative properties that are useful to include in the study report, such as study type (ex. RCT vs. cohort vs. case series or prospective vs. retrospective) or differences in reported population (ex. adults vs. pediatric patients). Tagging also assists in tracking how many studies in a review report each data element or include each intervention, which can be helpful context for the statistical analysis. Different forms of review may use tagging as the main form of data extraction. For example, in a common data elements review, which assesses what data elements are reported in a given field and how consistently they are reported, studies can be tagged with each data element reported, allowing the information to be synthesized.

In order for the tags to be useful, tagging hierarchies need to be kept simple. If every study has a completely unique tag, then using the organization systems and drawing a synthesis from the tagging becomes more difficult. This issue can often be solved by removing the bottom level of tags. For example, in the tagging hierarchy below, if only a few studies report each individual drug, tagging with the drug class (outlined in blue) may be more useful, and the individual drug tags (outlined in red) can be deleted. In a built-in hierarchical tagging system like the one in AutoLit, merging tags and sorting by higher level tags are both easy to do.