  The rapid digitization of real-world data offers an unprecedented opportunity
for optimizing healthcare delivery and accelerating biomedical discovery. In
practice, however, such data is most abundantly available in unstructured
forms, such as clinical notes in electronic medical records (EMRs), and it is
generally plagued by confounders. In this paper, we present TRIALSCOPE, a
unifying framework for distilling real-world evidence from population-level
observational data. TRIALSCOPE leverages biomedical language models to
structure clinical text at scale, employs advanced probabilistic modeling for
denoising and imputation, and incorporates state-of-the-art causal inference
techniques to combat common confounders. Using clinical trial specification as
generic representation, TRIALSCOPE provides a turn-key solution to generate and
reason with clinical hypotheses using observational data. In extensive
experiments and analyses on a large-scale real-world dataset with over one
million cancer patients from a large US healthcare network, we show that
TRIALSCOPE can produce high-quality structuring of real-world data and
generates comparable results to marquee cancer trials. In addition to
facilitating in-silicon clinical trial design and optimization, TRIALSCOPE may
be used to empower synthetic controls, pragmatic trials, post-market
surveillance, as well as support fine-grained patient-like-me reasoning in
precision diagnosis and treatment.
