News & Publications

Inside Century Health's approach to extracting reliable, structured insights from the messy, multimodal world of clinical data, and how we use reinforcement learning to continuously improve our models.

CHARM (Century Health Abstraction and Retrieval Model) automates the extraction of structured variables from unstructured clinical records using Large Language Model (LLM) reasoning and clinical Natural Language Processing (NLP). The result: highly accurate, scalable data abstraction that enables RWE studies to move faster and at lower cost than traditional methods.

Century Health, an AI company focused on real-world clinical data, announced a partnership with Dallas Renal Group to better understand rare kidney diseases, where diagnoses and signs of disease progression often end up buried in doctors' notes and pathology reports.

IgA nephropathy (IgAN) and related glomerular diseases (GD) are increasingly being evaluated under a more stringent evidence framework than in the past. Regulatory agencies, clinicians, and payers now focus on two endpoints when assessing disease progression and treatment value: early proteinuria reduction as an indicator of near-term risk, and longitudinal eGFR slope as a measure of sustained kidney preservation.

Large language models used for clinical abstraction are sensitive to prompt wording, yet most work treats prompts as fixed and studies uncertainty in isolation. We argue these should be treated jointly. Across two clinical tasks (MedAlign applicability/correctness and MS subtype abstraction) and multiple open and proprietary models, we measure prompt sensitivity via flip rates and relate it to calibration and selective prediction. We find that higher accuracy does not guarantee prompt stability, and that models can appear well-calibrated yet remain fragile to paraphrases. We propose a dual-objective prompt optimization loop that jointly targets accuracy and stability, showing that explicitly including a stability term reduces flip rates across tasks and models, sometimes at modest accuracy cost. Our results suggest prompt sensitivity should be an explicit objective when validating clinical LLM systems.

Century Health, a pioneer in applying AI to real-world clinical data to accelerate research, and Dallas Renal Group, one of the largest nephrology practices in Texas, today announced a partnership aimed at improving identification and understanding of rare glomerular diseases including IgA nephropathy (IgAN) and C3 glomerulopathy.