  Individual clinical Knowledge Artifacts (KA) are designed to be used in
Clinical Decision Support (CDS) systems at the point of care for delivery of
safe, evidence-based care in modern healthcare systems. For formal authoring of
a KA, syntax verification and validation is guaranteed by the grammar. However,
there are no methods for semantic verification. Any semantic fallacy may lead
to rejection of the outcomes by care providers. As a first step toward solving
this problem, we present a framework for translating the logical segments of
KAs into Satisfiability Modulo Theory (SMT) models. We present the
effectiveness and efficiency of our work by automatically translating the logic
fragment of publicly available KAs and verifying them using Z3 SMT solver.
