Healthcare professional
Transforming the field data of a major healthcare player into actionable insights using generative AI
The context
Analyze and utilize the data collected by pharmaceutical representatives in the field
How to synthesize and analyze the very large amount of data reported weekly by sales teams in the pharmaceutical industry? This is the challenge raised by a major player in the European healthcare sector, which recognizes how valuable this data, rich in insights, is for the strategic management of its business in both the short and long term.
To respect professional secrecy and confidentiality agreements, the identity of our clients and certain technical details are intentionally anonymized. We primarily work on strategic, high-value-added innovation projects, for which discretion is essential to preserving our clients' competitive advantage.
The Challenge
Automatically transform field feedback into relevant and usable indicators
The integration of advanced generative AI capabilities represents a major opportunity not only to simplify but also to automate the analysis and use of this type of information. It is on this synthesis aspect that this company chose to focus its efforts. Raw files were thus used to properly populate the LLM and define the semantic field specific to this client's context. From this database, an initial analysis of the extracted and processed content could be conducted to assess its relevance and value.
Our approach
Validate the ROI to concretely launch the generative AI project
Our teams initially designed an automated data transformation process to:
- Validate the value of the approach for automating the processing of this field data (ensure the sustainability of the process, measure the impact of automation, calculate the ROI)
- Identify risks and design remediation mechanisms (QA, human review of the completed synthesis, automatic and/or manual annotation)
- Capitalize on the generated data and possibly go as far as specific fine-tuning (geographic, temporal, professional report, etc. adapted to the reader typology)
The result
Actionable data for business operations and management
Following the initial analysis, our teams were able to implement:
- An autonomous agent that not only studies, but also transforms raw field data into actionable insights, offering a responsive and up-to-date view of the situation at different levels.
- An organized information repository, facilitating rapid understanding and intelligent integration into our processes
This type of project is exciting and promising. Exciting because it shows us the vast potential of generative AI, regardless of the sector or the problem identified. Promising because the results are tangible and measurable very quickly. To scale up, all stakeholders will need to get involved at several levels (technical, technological, business, etc.), but the enthusiasm and expectations are clear!
Guillaume Lanthier
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