The pharmaceutical and medical research industry is at a crossroads, facing the dual challenges of extended development times and escalating costs. Artificial Intelligence (AI) is sparking a transformative shift in drug discovery and development, with the potential to slash drug discovery expenses by up to 70%, as reported by Insider Intelligence.
The Impact of Generative AI on Clinical Research
Generative AI has rapidly evolved from a novel concept to a pivotal force in redefining the methodologies of clinical trials. Its role is comprehensive, influencing the drafting of protocols, identification of suitable patient groups, selection of trial sites, adherence to regulatory standards, strategies for patient retention, management of clinical documentation, and monitoring of adverse events. The integration of generative AI across these domains seeks to solve what matters through expedited trial timelines, boosted operational efficiency, and enhanced regulatory compliance, aiming to deliver more efficient, cost-effective, and patient-focused healthcare solutions.
With the average cost of patient recruitment reaching around $1.2 billion and clinical trials often lasting 6-7 years, the challenges faced by researchers and clinical professionals are significant. However, generative AI offers promise by providing sophisticated tools and insights. One notable innovation is the Clinical Trials Suite powered by Large Language Models (LLMs), which boasts cognitive functions, predictive analytics, prescriptive insights, and chat assistance capabilities tailored to tackle the critical challenges of patient retention, clinical document management, ensuring data integrity, medical coding, and the reconciliation of adverse events.
Generative AI Use Cases in Clinical Trials
Quantiphi, an AI-first digital engineering leader in Responsible AI solutions, has introduced several generative AI use cases designed to enhance the efficiency and effectiveness of clinical trials:
AI Assistive Protocol Authoring:
Drafting clinical trial protocols, an essential phase in the drug development cycle, often sees delays due to the repetitive nature of the content and the extensive time required to compile data from various sources. Historically, finalizing a protocol can take up to 28 weeks, necessitating the input of a broad team of sometimes more than 50 clinical experts. Quantiphi revolutionizes this intricate process by utilizing Enterprise Search along with cutting-edge Large Language Models (LLMs) to efficiently gather and distill vital information from various sources, including clinicaltrials.gov, PubMed, and exclusive databases. This streamlined approach not only expedites the protocol authoring phase but also holds the potential for substantial cost reductions, offering savings that could reach $50 million.
Clinical Trial Search Assistant:
Quantiphi is tackling the persistent challenge of patient dropout in clinical trials, where retaining a participant can cost anywhere from $100,000 to $150,000. Given trials often require the participation of more than 2,000 individuals, the financial stakes are high. To address this, Quantiphi has developed a Virtual Agent utilizing advanced technologies such as Dialog Flow and Cloud Functions. This innovative solution is designed to enhance the trial experience by offering real-time, responsive communication. By swiftly addressing patient inquiries and concerns, the Virtual Agent ensures participants are fully informed and engaged throughout the study's duration. This approach is instrumental in reducing dropout rates, ensuring more trials can proceed smoothly without the significant cost implications of participant replacement.
Adverse Event Detection:
The surge in reported adverse events (AEs) by 400% alongside 76% of patients expecting pharmaceutical firms to offer more health management information underscores the urgency for proficient AE processing. Quantiphi's advanced Natural Language Processing model excels in spotting and classifying adverse events across diverse textual datasets. Integrated within Quantiphi’s baioniq, an enterprise-ready generative AI platform, this model automates the workflow of adverse event case handling, preparing them promptly for regulatory submission. This breakthrough significantly bolsters pharmacovigilance operations, enhancing efficiency by 40-45% and markedly diminishing the time and effort needed for the identification of AEs.
Patient Matching for Clinical Trials:
On average, identifying and recruiting patients for clinical trials is a time-intensive process that spans approximately 2 years, with costs soaring to at least $100,000 per patient. A significant portion of this time and expense is devoted to the creation of patient cohorts apt for clinical trials. Quantiphi leverages the power of Large Language Models (LLMs) to streamline this process. By integrating clinical protocols with Electronic Health Records (EHR), Quantiphi's solution facilitates the generation of patient cohorts based on specific criteria, including age, gender, demographics, and medical history. This approach significantly reduces the time needed to identify suitable patient groups for clinical trials, cutting down the cohort creation duration by an estimated 40%.
The Next Frontier of Healthcare Innovation
In summary, embracing generative AI within the framework of clinical trials signifies a monumental shift towards more innovative, efficient, and compliant medical research and development practices. This cutting-edge technology opens the door to a realm of possibilities, enhancing the speed, accuracy, and innovation of clinical studies through its advanced algorithms.
Quantiphi remains dedicated to harnessing generative AI to accelerate clinical advancements in a responsible and ethical manner, ensuring a positive impact on healthcare. We invite you to join us in exploring the potential of Generative AI in clinical trials. Contact us to learn how you can be part of this exciting journey towards redefining healthcare innovation.
Read more about Quantiphi in Forbes, Financial Times, Nikkei Asia and visit our Case Studies page today.
