Compel Data to PredictHigh-Perf ormance Clinical Trial Sites:To Reduce Guesswork over Rate of Recruitment and Other Metrics, Use a Platform-Based Approach

摘要

Drug development has been vexed by the following metrics for decades: 5% of eligible patients participate in a clinical trial, 25% of sites do not enroll any patients, and 80% of trials are delayed because of patient recruitment.Despite increased access to data (EMR, claims, registries, feasibility, CTMS, etc.) and advances in analytical techniques (that is, artificial intelligence, machine learning; AI-ML) over the past several years, the aforementioned metrics haven't moved much in the industry. A high degree of reliability in predicting site performance (that is, patient recruitment) and, in turn, trial outcomes, specifically rate of recruitment (RoR), remains elusive. Getting these predictions correct are crucial as net present value calculations and investments in new trials are often contingent on these numbers being reliable and accurate.