Clinicians need to ask natural-language questions over patient databases across multi-turn conversations, not just isolated questions. Answering correctly requires jointly handling four coupled challenges: ambiguity (mapping informal language like “high blood pressure” to schema literals, and resolving coreference like “those patients” across turns), nested reasoning (questions like “latest HbA1c per patient” that need subqueries or ranking, not a flat filter), join-path reasoning (picking the correct tables and result grain in a normalized clinical schema), and temporal reasoning (anchoring phrases like “currently on insulin” to real date columns and status logic). Getting any of these wrong doesn’t just crash the query, it silently produces the wrong patient cohort, which is worse.

Existing Text-to-SQL research doesn’t fully cover this setting. Most benchmarks score isolated single-turn questions, so dialogue memory and constraint retention across turns are rarely measured. Most systems generate SQL with one monolithic LLM call, making the reasoning behind a join or filter choice implicit and uninspectable. Schema grounding is usually a static text dump rather than the database’s actual current values, and failure recovery is typically a naive retry rather than a structured response to why a query failed. No existing benchmark stratifies clinical queries by which of these reasoning dimensions (or combinations) they stress.

This project explores an alternative to the single, all-at-once response most systems rely on, using a staged, feedback-driven process rather than one monolithic model call. This is still active, evolving work; further details will be shared once the approach is validated and written up.