Menu ≡ ╳
According to visionary leader Steve Jobs, if one defines the problem correctly, that person almost has the solution. And here we are discussing AI as the bellwether solution to every business/operational challenge in this world. Throw in a few AI-related words and the conversation suddenly sounds futuristic and efficient. Sure, AI is already impacting us in various walks of life from online ads to streaming services to smart cars but what about business? In particular, drug development. Is AI ready to make its magic work for clinical trials? Let’s deep dive and try to find the answer.
How well have we defined the problem?
Challenges in patient recruitment, medication adherence, population health management, risk monitoring, data collection and aggregation for clinical trials are well documented. With the introduction of CDISC standards and ICH regulations, the process of recording and reporting data has undergone much-needed standardization. Yet as technology evolves, it adds new operational challenges. Here I am talking about new data sources, in particular, the rise in wearable technologies.
More and more patients are now using wearable devices to track health data thus creating a stream of continuous flowing data. This is a problem for an industry grappling with archaic systems and suboptimal processes for data management. The existing infrastructure is not capable of supporting big data and the processes require far too many human interventions to achieve the required level of efficiency. This is where cloud-based platforms and AI-driven workflow automation can make things better.
The Focus Areas of Clinical Trial Operations
To ensure efficient operations one must focus on the critical parameters of a clinical trial, which are
It all boils down to one crucial aspect, quick access to relevant information. Sounds easy but this is the deciding factor between the success and failure of a study. Currently, trial managers have to sift through tonnes of recorded information which requires a lot of time and patience. As a consequence, many of them miss crucial red flags leading to delays and in many cases, failure of the study itself.