Unraveling the potential of Generative AI (e.g., ChatGPT) in Pharmacovigilance

‘First-time right and complete’ adverse event (AE) intake

Cumbersome AE intake forms often lack essential details, leading to costly follow-ups.

Leveraging Gen AI, we transformed traditional AE forms into dynamic questionnaires, adapting queries to case specifics and reporter profiles. Responses guided subsequent questions, ensuring comprehensive data collection.

Identifying adverse event and other data fields from unstructured/ free text in case reports

Understanding the context to identify an adverse event in free text is essential. Previously, large volumes of data (10,000+ records) were required to train an AI/ ML model to accurately classify an adverse event (with limited success) in free text.

We used Gen AI models to accurately identify AEs in unstructured text, such as case reports. The use case is extendable to AE identification in other scenarios, such as in literature and in patient or HCP transcripts. It can be expanded to identifying other key information, such as medications, indications, timeline surrounding AE occurrence, etc., from unstructured text.

Generating case narratives

Case narratives provide crucial descriptions of adverse event circumstances. Hence, regulators and MAH emphasize authoring high-quality narratives that capture all the information accurately. The structure and content of the narrative vary by context, case type, and sponsor conventions. Therefore, manual authoring is often error-prone.

Using Gen AI, we dynamically generated narratives specific to the case in question and replaced multiple static templates used by case processors or medical writers to author and edit narratives. In addition, Gen AI can also author inferences and conclusions by looking at the case facts based on historical intelligence (and training).

Identifying missing information, generating follow-up letters, and auto-intake of follow-up information

Obtaining complete AE information from reporters is crucial to minimize follow-up interactions. However, achieving this completeness in intricate clinical trials and complex therapy cases can be challenging. The process of identifying missing data and creating personalized follow-up queries is laborious. Furthermore, seamlessly integrating reporter responses into case information for more informed and efficient decision-making remains a continuing challenge.

We used Gen AI to act as a business rules engine and identify critical information missing based on the case type. Further, based on missing information, Gen AI generated a follow-up letter with only the relevant questions and probable response options for the reporter to fill out. Once the reporter responded to questions, our Gen AI models could extract key information such as relevant history and outcome from the responses, which could then be updated to the initial case.

Assisted authoring of aggregate periodic safety reports

A single aggregate report (AR) takes a few hundred hours to author, review, and publish.

We leverage Large Language Models (LLMs) in tandem with other AI/ML models and rule-based systems to facilitate the seamless conversion of tables into textual content and vice versa. It enables the transformation of tabular safety data into meticulously authored reports. Our approach involved identifying pertinent information related to safety topics, analyzing patterns across safety cases, drawing meaningful inferences, and articulating the findings in accordance with approved guidelines and appropriate messaging. While LLMs may not be the sole choice for data analysis, they play a pivotal role in conjunction with other models, such as in formulating precise queries for data analysis. As we continue to explore diverse models for table-to-text conversions, Gen AI emerges as a critical component in creating an automated, high-quality initial draft for these reports.

Evidence generation

Information required for signal detection is scattered across multiple resources, making manual efforts more reactive than proactive

Using Gen AI and other technologies, we gathered insights from various resources for better signal detection and evaluation. Insights generated were then presented to users in a consumable format so that they could further probe on interesting evidence.