Accelerate Precise Medical Coding In Clinical Trials

In clinical trials, meticulous documentation of symptoms, procedures, and medication descriptions is imperative for reporting adverse events, medical history, and concomitant medications. These descriptions, known as verbatims, necessitate precise coding into industry-standard dictionaries like the MedDRA (Medical Dictionary for Regulatory Activities) for adverse events and medical history and the WHODrug (World Health Organization Drug) for concomitant medications.

Traditionally, medical coders undertake the arduous task of coding thousands of terms throughout a study, which can be time-consuming and labor-intensive. However, recent advancements in machine learning offer a promising solution to expedite this process and enhance accuracy.

Medidata's Rave Coder medical coding solution, utilized in over 10,000 studies, harnesses a vast repository of over 60 million historic coding decisions. Leveraging this wealth of data, Medidata has developed, trained, and rigorously tested an algorithm for their next-generation medical coding solution, Rave Coder+. This algorithm offers highly accurate predictions for coding clinical verbatims, automating the coding process and drastically reducing the time required.

This white paper delves into the methodology behind the algorithm's development, training, and testing within Rave Coder+, shedding light on how it revolutionizes medical coding in clinical trials, offering unparalleled efficiency and precision.