CCADD members attended the KAIST CHIP (Bio-Healthcare Innovation and Policy) symposium on September 20, 2017 held at the Novotel Ambassador Seoul Gangnam. At the symposium,  Dr. Howard Lee talked about the team's collective perspective on the utility and future potential of the EHR4CR project by the Innovative Medicines Initiative (IMI) in Europe, particularly in terms of streamlining the process by which clinical trial participants are screened and enrolled based on their eligibility assessment aided by artificial intelligence.

Kim Y, Kim A, Lee S, Choi SH, Lee DY, Song JS, Lee H, Jang IJ, Yu KS. Pharmacokinetics, Safety, and Tolerability of Tedizolid Phosphate After Single-Dose Administration in Healthy Korean Male Subjects. Clin Ther. 2017. Sep;39(9):1849-1857, ePub 2017 Aug 31.

How can we increase the efficiency, accuracy, and speed of patient enrollment into clinical trials? Clinical trial investigators have long been suffering from the low yield, inaccuracy, and delay of patient enrollment. It was mainly because the current practice is labor-intensive and has to be done manually in most cases. For example, based on the eligibility criteria of a clinical trial, a clinical trial investigator or one of his/her staff (e.g., clinical research coordinator) has to go over the entire medical record of a potential patient by eyeballing many scattered fields, which is very much time-consuming while inefficient and inaccurate. A typical clinical trial protocol has an average number of 50 for the eligibility criteria, which can become easily many more up to ~100 in late phase clinical trials. Dr. Lee discussed this issue and how artificial intelligence-guideded deep learning could address it at a recent Bio-Nanomedicine Salon (August 31, 2017) co-sponsored by the Korean Society for Nanomedicine and the Seoul National University Bio-Max Institute.