How Can AI Help in Making Clinical Operations More Proficient?

How Can AI Help in Making Clinical Operations More Proficient?

By Healthcare Tech Outlook | Wednesday, December 11, 2019

Clinical AIArtificial intelligence and machine learning help deepen clinical development insights.

FREMONT, CA: The two technologies used in the transformation of clinical development are artificial intelligence (AI) and machine learning (ML). The use of Machine Learning (ML) algorithms for clinical operations offers better and quicker insights into decision-making at a reduced time and cost. Analytics aims to automate processes using large quantities of healthcare data. New opportunities will keep developing with the latest tools that will lead to further benefits for the field of clinical research. Here are some techniques in which AI and ML can have a direct impact on operational efficiencies.

Pharmacovigilance

Ensuring the safety of drugs is not an easy task since the integration and analysis of massive quantities of structured and unstructured data are required. The study and design of organized and unstructured data are performed using deep neural networks, natural language processing, and optical character recognition. Such tools can automate manual processing activities and interpret and digitize safety reporting and documentation on adverse drug reactions.

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Insights from AI and other tools above mentioned in the Pharmacovigilance tasks contribute to a faster evaluation of the subject, the site, and the dangers associated with the research, as well as to the overall analysis performed by domain experts. This leads to increased efficiency and patient safety for projects.

Clinical Monitoring

The theories AL and ML will replace massive manual attempts to assess and mitigate risk at the site. This is achieved through risk assessment while delivering predictive analytics to gain valuable clinical surveillance insights. Composite site ratings for holistically assessing risks are provided by advanced analytics, allowing more particular risk detection and elimination of false positive. Assessments can also be used in order to identify sites with potential problems with recruitment and performance or which patients are more vulnerable to AEs. Such insights allow quicker action and prevent possible problems.

AI applications have potentially significant value for the clinical trial process and pharmaceutical drug discovery industry when applied for rising volume and data complexity.

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