Academic Medical Research Consortium · Academic Research
The consortium had accumulated 12 years of longitudinal patient data from 340,000 participants across 6 research institutions. The data existed in heterogeneous formats across multiple systems, and the cost and complexity of applying meaningful analysis to the full dataset had meant that most research drew on pre-specified subsets of the total record. An estimated 80% of the accumulated data had never been included in a primary analysis. Rare disease research was particularly constrained — the small patient populations required to identify statistically significant patterns demanded full cohort analysis that was computationally and financially out of reach.
LLM reasoning applied to the complete 12-year longitudinal dataset. The model was configured to identify temporal patterns, cross-condition associations, and rare event clusters across the full record — surfacing candidate hypotheses for researcher review.
"We had been doing research with one hand tied behind our back for 12 years. The data was there. The answers were there. We just couldn't afford to look at all of it."
Director of Research Informatics
The three novel disease associations identified in the first 90 days included a previously unreported correlation between a common medication class and reduced incidence of a rare autoimmune condition — a signal present in the data for 9 years but requiring the full longitudinal dataset to achieve statistical significance. The finding has since been submitted for peer review and is in Phase II validation.