Datum builds a pharma and biomedical knowledge graph infrastructure to make complex evidence accessible to healthcare applications. The platform powers trustworthy and explainable AI decision support tools for precision medicine and pharmacogenomics. This queryable infrastructure enables innovators to develop safe, scalable, and evidence-based next-generation healthcare solutions.
Funding
Funding not disclosed
Founders
Product
Problem
Clinicians often struggle to translate complex pharmacogenomics (PGx) data into actionable treatment decisions at the point of care. Existing clinical decision support tools may lack comprehensive evidence integration, real-time updates, and seamless integration with electronic health record (EHR) systems. This can lead to suboptimal medication selection and dosing, potentially impacting patient outcomes.
Solution
Datum offers an AI-powered clinical decision support platform that delivers precise, evidence-based pharmacogenomics recommendations to clinicians. The system leverages large language models trained on validated clinical guidelines, peer-reviewed publications, and other relevant data sources. By integrating diverse evidence within a graph database, Datum provides clinicians with up-to-date PGx insights, including information on phenoconversion, genotype implications, and clinical considerations. The platform's AI-based chat interface allows clinicians to query the knowledge base using natural language and receive instant, clinically validated answers.
Target Audience
The primary target audience includes physicians, pharmacists, and other healthcare professionals who need access to reliable and up-to-date pharmacogenomics information to optimize medication management for their patients.
Features
- AI-powered chat interface for natural language querying of PGx knowledge base
- Integration of global PGx recommendations from CPIC, FDA, and DPWG guidelines
- Clinically validated recommendations by licensed practitioners
- Regularly updated evidence base to reflect the latest clinical research
- Support for phenoconversion analysis and genotype interpretation
- Precision-guided recommendations based on patient-specific details