ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data

Evaluating medications for potential adverse events is a time-consuming process, typically involving manual lookup of information by physicians. This process can be expedited by CDS systems that support dynamic retrieval and filtering of adverse drug events (ADE's), but such systems require a source of semantically-coded ADE data. We created a two-component system that addresses this need. First we created a natural language processing application which extracts adverse events from Structured Product Labels and generates a standardized ADE knowledge base. We then built a decision support service that consumes a Continuity of Care Document and returns a list of patient-specific ADE's. Our database currently contains 534,125 ADE's from 5602 product labels. An NLP evaluation of 9529 ADE's showed recall of 93% and precision of 95%. On a trial set of 30 CCD's, the system provided adverse event data for 88% of drugs and returned these results in an average of 620ms.