Meta-analyses examine the results of different clinical studies to determine whether a treatment is effective or not. Meta-analyses provide the gold standard for medical evidence. Despite their importance, meta-analyses are time-consuming and this poses a challenge where timeliness is important. Research articles are also increasing rapidly and most meta-analyses become outdated after publication since they have not incorporated new evidence. Therefore, there is increasing interest to automate meta-analysis so as to speed up the process and allow for automatic update when new results are available. In this preliminary study we present AUTOMETA, our proposed system for automating meta-analysis which employs existing natural language processing methods for identifying Participants, Intervention, Control, and Outcome (PICO) elements. We show that our system can perform advanced meta-analyses by parsing numeric outcomes to identify the number of patients having certain outcomes. We also present a new dataset which improves previous datasets by incorporating additional tags to identify detailed information.