Background
A leading pharmaceutical consortium wanted to enhance market visibility and optimize resource allocation by leveraging a centralized geo-mapping analytics platform. Their aim is to provide a comprehensive view of pharmaceutical spending, prescription trends, and healthcare access patterns at both a demographic and aggregated level.
Potential Objectives
To achieve this, the platform needed to integrate medical aid claims, prescription data, and dispensing records, enabling them to:
- Analyse regional spending trends by mapping prescription costs and medication utilization per geographic area.
- Compare prescribed vs. dispensed medications to identify gaps, inefficiencies, or non-adherence patterns.
- Segment data demographically to assess age, gender, and income-related health spending behaviours.
- Optimize sales and distribution strategies based on real-time prescription and medical aid trends.
Screenshot: The geo-mapping interface showing prescription distribution by region.
By utilizing Sidekick Lab’s AI-powered analytics tool, a geospatial visualization was built allowing pharmaceutical companies to gain real-time insights into prescription fulfilment, and healthcare accessibility. This data-driven approach enables better forecasting, and strategic market planning to improve patient outcomes and business performance.
Proposed Solution Architecture
As before, the Natural Language Query Assistant developed by SideKick Lab is structured across four core layers:
-
User Interface Layer
- Query Input: Users can type questions in English via a web-based interface generating specific answers and graphs related to the data available.
- Formatted Responses: Responses are customized, ensuring reporting relevance as well as aggregated trends across regions.
-
Administration Layer
- User Authentication: Role-based access control linked to enterprise authentication.
- Query Management: Monitors query usage to optimize performance and prevent misuse.
-
Logic Layer
This core AI engine would handle:
- Responsible AI Checks: Ensuring ethical and unbiased responses.
- Query Analysis: Determining relevant data sources (structured databases, APIs, or LLMs).
- SQL & API Query Processing: Translating natural language requests into structured database or API queries.
- Retrieval-Augmented Generation (RAG): Integrating unstructured content like manuals and documents.
- LLM Integration: Utilizing AI models to generate responses when required.
- Response Coordination: Combining data sources for an accurate and context-aware answer.
-
Data Layer
- Structured Data: medical aid claims, prescription data, and dispensing records.
- Unstructured Content: industry trends, spend history, and other documents.
- Third-Party Content: External data sources relevant to medical aid claims, prescription data, and dispensing records.
Screenshot: Advanced geo-mapping analytics overlay for real-time prescription data.
Expected Benefits
- Enhanced claims vs dispensed reporting and analysis via geomapping.
- Improved efficiency with regards mapping claims, prescriptions and dispensing.
- Optimized real-time insights and trends analysis.
- Scalability to support multiple channels without modifying the core assistant.
Conclusion
This GeoMapping feature is an example of the AI querying solutions SideKick Lab can develop to transform how big Pharma interact with complex and fragmented medical data across various sectors. By leveraging Natural Language AI, their stakeholders can drive enhanced health management efficiency, and future-proof their business and inventory strategy.