A Chatbot-Like Platform to Enhance the Discovery of OMOP CDM Databases

Area of Science:

• Biomedical Informatics
• Health Data Science
• Database Interoperability

Background:

• Multicentre studies rely on database interoperability, which has been addressed by current strategies.
• Widespread adoption of these strategies has led to new challenges in data discovery.
• Existing solutions like database catalogues and dashboards become obsolete as research communities grow.

Purpose of the Study:

• To propose a novel platform to aid medical researchers in identifying relevant databases.
• To overcome the limitations of existing data discovery methods in large-scale research.

Main Methods:

• Developed a platform featuring a chatbot-like mechanism for database identification.
• Utilized metadata extracted from Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) databases.
• Focused on addressing challenges in data discovery for growing research communities.

Main Results:

• The proposed platform effectively assists medical researchers in pinpointing databases of interest.
• The chatbot mechanism provides an intuitive interface for data discovery.
• The system is designed to scale with growing research communities and data.

Conclusions:

• The developed platform offers a viable solution to data discovery challenges in multicentre studies.
• Leveraging OMOP CDM metadata through a chatbot enhances research efficiency.
• This approach supports the advancement of medical research by improving data accessibility.