MSD uses Amazon Bedrock and Claude 3.5 Sonnet to translate natural language into SQL for complex healthcare databases
MSD analysts and data scientists spend considerable time manually querying complex healthcare databases, slowing productivity and delaying data-driven decision-making. Healthcare datasets pose additional challenges including coded columns, non-intuitive column names, long medical code lists, and ambiguous queries.
Out-of-the-box text-to-SQL libraries required a custom approach because they could not handle coded columns, non-intuitive names, excessively long medical code lists, and query ambiguity inherent in healthcare datasets.
The text-to-SQL solution at MSD markedly accelerated data access and boosted analyst productivity by simplifying the SQL query process, allowing analysts to dedicate more time to data interpretation and strategic decision-making.
Frequently asked questions
What did this team achieve with this AI workflow?
The text-to-SQL solution at MSD markedly accelerated data access and boosted analyst productivity by simplifying the SQL query process, allowing analysts to dedicate more time to data interpretation and strategic deci…
What tools did this team use?
Amazon Bedrock, Claude 3.5 Sonnet, Converse API, SQLite, Streamlit.
What results were reported?
Query time: potentially reducing query time from hours to minutes; Data access speed: markedly accelerated data access; Analyst productivity: boosted analyst productivity (source-reported, not independently verified).
What failed first in this deployment?
Out-of-the-box text-to-SQL libraries required a custom approach because they could not handle coded columns, non-intuitive names, excessively long medical code lists, and query ambiguity inherent in healthcare datasets.
How is this medical records processing AI workflow structured?
Analyst submits NL question → System prompt assembled → Claude generates SQL or tool call → Lookup tool invoked if needed → SQL query returned to user.