Clinical Data Standards in the era of AI, ML and digital transformation
Shrishaila Patil, VP, Statistical Programming, Navitas Data Sciences, a part of Navitas Life Sciences (a TAKE Solutions Enterprise) highlights how Clinical Trial Data Standards have evolved so far, and how they are continuing to evolve in the era of artificial intelligence (AI), machine learning (ML) and digital transformation to meet future demands
The need of the hour is to reach patients faster. By reducing both the drug development timeline and the overall cost of pharma research and development we can achieve just that. COVID-19 has been a good example of just what can be achieved. Vaccines were developed within a year, pre-COVID, this was unheard of with vaccines typically taking many years to develop.
For biometrics teams, this means that we need to be quick and accurate in data collection, processing and analysis. In order to cut short the timeline and improve efficiency, we need to automate many steps involved in the Clinical Data Life Cycle (planning phase, data collection, tabulation, statistical analysis, and exchange/sharing of data) and, in order to automate, we must have consistent metadata, standards, and technology.
This article highlights how Clinical Trial Data Standards have evolved so far, and how they are continuing to evolve in the era of artificial intelligence (AI), machine learning (ML) and digital transformation to meet future demands.
COVID-19 and the wave of digital transformation
We can all agree that COVID-19 has acted as something of a catalyst, encouraging increased innovation, technology adoption, and a willingness to embrace digital transformation. As a result, we are witnessing a “new normal” – including an increased number of virtual trials being successfully designed, a move away from the more conventional clinical trials, in order to manage the global pandemic situation and ensure the continuation of clinical trials for many other, potentially life-saving, drugs. Virtual trials have helped patient recruitment, retention, real-time access to data, and better quality.
Digital data collection methodologies (mobile technology, wearables, electronic patient-reported outcomes (ePRO), electronic clinical outcome assessment