What does the future of the clinical trial process look like?

We’ve already talked about the details of how the clinical trial process works on this blog, acknowledging each stage in the process and the current issues facing researchers. As artificial intelligence expands and embeds itself more into the research and development stage of drug production, we’ll likely be seeing this process evolve to incorporate new ideas and concepts.

Developing a new treatment or medication is no easy feat – it’s often a decades-long process costing millions of dollars, with the vast majority of new drugs failing during phased trials. Creating a product candidate that can satisfy the needs and expectations of the patient whilst ensuring that the pharmaceutical company in question is able to receive a return on their investment is a difficult balancing act, with huge amounts of time, money, and effort at stake.

Current developments in predictive data analytics are raising a lot of eyebrows in the pharmaceutical world – and for all the right reasons. By harnessing the power of ethical data collection techniques - such as social listening and natural language processing – data analysts are able to produce and deliver actionable insights into patients thoughts and opinions, offering tangible solutions to real-world problems.

Whilst this all sounds well and good, understanding the exact implications the inclusion of these new developments will have on the clinical trial process might not be immediately clear. What happens to all this information once it’s been gathered, and how does it actually make a difference for patients in their day-to-day lives?

Essentially, these insights are able to provide research and development teams with a clearer picture of what patients are looking for in their treatment. There are a lot of different options out there, but the difference between a medication or therapy that’s embraced by many and one that patients don’t respond well to is substantial. Why opt for a drug that causes headaches and nausea as a side effect, when there’s another option out there that allows the patient to avoid these experiences?

Perhaps partly due to the ongoing pandemic, the healthcare sector is opting for a more compassionate approach to treatment options. Research teams are becoming increasingly more involved in the human side to medical treatment – that is, acknowledging that there’s no one-size-fits-all approach to healthcare, and that individual thoughts and opinions matter. Patient response can make or break a new medication or therapy, and by using a combination of artificial intelligence and data analytics, pharmaceutical companies are able to hear what their patients are saying – and listen to them.

By considering the wants, needs and preferences of patients in depth, research teams are increasingly able use these insights to highlight certain approaches that ought to be avoided – saving countless hours and millions of dollars in investment – whilst ensuring that their treatment works as well as it possibly can for the individual. Data analytics can help research and development teams avoid these costly and time-consuming roadblocks by only focusing on the product candidates that are predicted to have a high success rate in the real world. As a result, the end products are cheaper, more effective, and more in line with what patients really want.

Similar posts

Get notified on new Healthcare insights

Be the first to know about new Healthcare insights to build or refine your healthcare function with the tools and knowledge of today’s industry.

Subscribe Now