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Five Ways to Enhance Clinical Operational Efficiencies Utilizing AI

The proven promise and huge potential of using AI and ML to accelerate drug discovery

Artificial intelligence and machine learning tools are transforming how clinical development occurs by delivering significant time and cost efficiencies while providing better and faster insights to inform decision-making. Advances in analytics technology coupled with the availability and integration of vast amounts of healthcare data have already helped automate processes and improve data quality across dozens of clinical development efforts.

As these tools evolve, new opportunities will continue to emerge that drive further benefits to the clinical research landscape. Applications of AI and ML in healthcare are expected to grow to nearly $8 billion by 2022, up from $667.1 million in 2016, and almost half of global life science professionals say they are either using or interested in using AI in their research.[1]

Despite this growth, the industry continues to struggle with what these technologies are and how they work. And there is uncertainty on how to surmount the challenges required to leverage AI and ML.

When sponsors collaborate with partners that have the necessary technical and pharmaceutical expertise, they can achieve significant time and cost savings while reducing risks and improving the quality of their research. Here are five proven areas where AI and ML can directly impact operational efficiencies:

1. Study design Poor study design has a catastrophic impact on the cost, efficiency and success potential of clinical trials. Leveraging vast healthcare data sets, AI, ML and natural language processing tools can be used to assess and select optimal primary and secondary endpoints during study design to ensure the most relevant protocols are defined for regulators, payers and patients. This helps to optimize the study design by informing ideal strategies for host countries and sites, enrollment models, patient recruitment and start-up plans.

Better study design leads to more predictable results, reduced cycle time for protocol development, fewer protocol amendments and higher efficiencies throughout a study. It also results in improved recruitment rates and fewer non-enrolling sites. These improvements facilitate realistic and accurate planning and increase chances of success.

2. Site identification and patient recruitment Identifying trial sites that have access to enough patients who meet inclusion/exclusion criteria is an ongoing challenge. As studies target more specific populations, recruiting goals become even harder to achieve, which drives costs up, increases timelines and raises the risk of failure. According to Tufts Center for the Study of Drug Development, nearly half of all sites miss enrollment targets.

AI and ML can mitigate these risks by identifying and suggesting the sites with the highest recruitment potential and suggesting appropriate recruitment strategies. This involves mapping patient populations and proactively targeting sites with high predicted potential to deliver the most patients — before a single site is opened — and identifying the best avenues to recruit them. This means sponsors can open fewer sites, accelerate recruiting and reduce the risk of under-enrollment.

3. Pharmacovigilance To ensure drug safety, massive amounts of structured and unstructured data must be integrated and reviewed. PV units that seek to harness the power of this data find that AI and ML technologies address many of the challenges they face by providing new levels of insight and predictive analytics. These tools can automate manual processing tasks and translate and digitize case safety reporting and adverse drug reaction documents. They can also monitor digital conversations on social media and other platforms to ensure that adverse events are promptly identified.

Natural language processing, Optical character recognition, and deep neural networks are being used to analyze and format structured and unstructured data for faster and more efficient safety reviews.

The insights derived from using AI for PV tasks lead to faster assessment of subject, site and study risks and overall study performance by domain experts. This allows project leads to increase efficiencies as well as patient safety.

4. Clinical monitoring Tremendous manual effort is spent analyzing site risks and generating “action items” to mitigate those risks. AL and ML concepts can alleviate these pressures by assessing the risk environment and delivering predictive analytics to generate more effective clinical monitoring insights.

Advanced analytics provide composite site rankings for holistic risk assessments, allowing for more specific identification of risks and removal of false positives. Using the composite evaluation of site risks across the study quickly shows high-risk sites, key risk indicators and site risk rank. Evaluations can also be used to proactively identify which sites are more likely to have recruitment and performance issues, or which patients are at higher risk for potential AEs. These insights facilitate faster action and avoid potential problems.

5. Patient care Disease detection algorithms are now being designed to leverage medical information, such as symptoms and procedures that typically precede a diagnosis, to identify patients who are very likely to develop diseases. This allows for proactive care, as well as new recruiting insights for prodromal or early-stage disease studies and those that require treatment-naive patients.

One area where this type of model is having a profound impact is Alzheimer’s disease research. Due to the exponential effects of a delayed AD diagnosis, much clinical research activity in AD is focused on the prodromal stage. Yet, traditional screenings for prodromal AD patients deliver only a 20 percent precision rate.

Writen by: Lucas Glass, Gary Shorter, and Rajneesh Patil, IQVIA

Published on: 10.21.19

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