  Electronic health records capture patient information using structured
controlled vocabularies and unstructured narrative text. While structured data
typically encodes lab values, encounters and medication lists, unstructured
data captures the physician's interpretation of the patient's condition,
prognosis, and response to therapeutic intervention. In this paper, we
demonstrate that information extraction from unstructured clinical narratives
is essential to most clinical applications. We perform an empirical study to
validate the argument and show that structured data alone is insufficient in
resolving eligibility criteria for recruiting patients onto clinical trials for
chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is
essential to solving 59% of the CLL trial criteria and 77% of the prostate
cancer trial criteria. More specifically, for resolving eligibility criteria
with temporal constraints, we show the need for temporal reasoning and
information integration with medical events within and across unstructured
clinical narratives and structured data.

Computers and Society;Artificial Intelligence;Computation and Language