Streamlining Clinical Trials with AI

A clinical trial involves a set of complex processes, which include writing the protocol, recruiting patients, conducting the study, and culminating in bringing the drug to market. There are many stakeholders involved, in different capacities, to carry out these processes, ensure adherence to regulations, and keep the trial on track.

Here are some of the most important areas of a clinical trial that require constant attention and have a serious impact on the outcome of the trial:

Clinical Study Spend: This is the most critical aspect of a clinical trial that needs to be always monitored. Almost 80% of clinical trials go over budget, for one reason or another, and trial managers are often under pressure to keep the costs down.

Clinical Study Timelines: Extended timelines are often treated as the norm rather than an anomaly during clinical trials. Many factors contribute directly to these extended timelines, some of which are delays and challenges in patient recruitment, site selection, and investigator analysis. Extended timelines directly impact the cost, therefore knowing which trial processes are lagging is essential for a trial manager.

Clinical Study Quality: This aspect becomes more important as the trial approaches its conclusion. However, ensuring optimum quality requires constant monitoring of all protocols, which is only possible if the trial manager can access the associated data as frequently as needed in near real time. The quality of the study in the pre-approval phase reflects during the post-approval study.

All stakeholders can benefit from access to relevant information quickly and accurately. If they need to sift through the available information, they lose precious time and may even miss the red flags and their root causes. A system that allows access to information and delivers it in a format that is intelligent and flexible is the key to mitigating the risks associated with global portfolio management.

For example, a trial manager is responsible for keeping the pre-approval study on track and ensure superior quality of the data. The regional managers and country managers are responsible for managing the trials in their respective regions and countries. All of them need to be able to look at the data related to clinical trials that is consistent with their roles, to make their decisions, assessments, and recommendations.

An AI-based data management platform is the best choice for performing and managing all the tasks and keeping every aspect of the trial coordinated and on target. With personas defined and assigned their own views, it becomes easy for different stakeholders to stay on top their tasks and KPIs.

With predictive information about at-risk sites, flagging processes that are falling behind like patient recruitment or drop-out patterns, becomes easier and helps to keep the clinical trial within budget and timelines. Such data analysis becomes significantly more effective with AL/ML solutions that can uncover insights from large troves of data and bring it all together into a data lake. This is instrumental in bringing down costs and getting the drug to market faster.

To learn more about global portfolio analysis, download our solution brief.

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