Medication error is a severe patient safety event in the United States. Medication error reports collected by Patient Safety Organizations provide an opportunity to analyze and learn from previous errors. However, the current workflow of analyzing the error reports is labor-intensive and time-consuming. To reduce the workloads for clinicians and save time, we developed a pipeline for medication error report pre-analysis by applying automated text classification techniques. The pipeline was proven functional in two tasks, i.e., identifying the error originated stages, error types and error causes from the medication error reports, and calculating the similarity scores between the error reports for re-organization. The proposed pipeline holds promise in helping clinicians understand the nature of medication error in an error report, and better manage the error reports, which could further facilitate the prevention of medication errors in healthcare settings.