Evolving Congress Competitive Intelligence With Artificial Intelligence

The following is the abstract for an upcoming poster presentation at the International Society of Medical Publication Professionals' 17th annual meeting to be held April 12-14, 2021.

Authors:  Jennifer Ghitha, Cheryl Truchana, Zena Askerovaa, Dheepa Charia, Raj Patela and Vivekanandan Mathivananb

aPfizer Inc., New York, NY, USA,
bFerma AI, ZoomRx

Objective: Congress competitive intelligence (CI) traditionally involves labor-intensive manual collection and synthesis of large volumes of data and social media postings. Our objective was to determine whether artificial intelligence (AI)-based tools could improve congress CI and data management.

Research design and methods: Several companies are developing AI tools to extract information from the literature and congress sites, but these tools are not widely adopted. In 2020, Pfizer initiated a pilot to discern whether the Ferma.AI platform could improve implementation of CI at the American Society of Hematology (ASH) annual meeting. Examples of objective tasks executed via AI included identification and retrieval of session data, as well as capturing and ranking impact of social media posts.

Results: Prioritization of abstracts using AI allowed for more interactive and rapid ranking of sessions as compared with manually executed CI. During the congress, individuals were able to access content via a central platform while still benefitting from distilled information provided via daily reports. Social media influencers were identified from >33,000 tweets, and insights were integrated within the platform. Using AI resulted in cost savings of 40-50% relative to traditional coverage. Data obtained with the platform showed that the number of abstracts and attendees at ASH were similar relative to previous years, despite the evolution to the virtual setting.

Conclusions: Leveraging AI for congress CI enables broader understanding of data across information sources. AI efficiently retrieved presentations of interest and performed objective tasks quickly. Interpretation of the data in the context of the landscape required subject matter expertise. Using these insights, we developed a roadmap for use of AI for future CI in a rapidly transforming landscape.

Word count, including title (limit 275): 272

Keywords (limit: 3): Competitive intelligence, Congresses, artificial intelligence

Acknowledgment: The authors would like to thank Sashi Nadanaciva for her support in the pilot and submission.

Disclosure: All authors participated in the research process or development of the concept discussed, were actively involved in writing the abstract, and approved the submitted version.

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