Taming complex healthcare data models with dictionary tooling

Information models used in the healthcare domain tend to be complex, in part because they were designed to be as flexible and generic as possible. This complexity presents a steep learning curve for implementers, which can lead to partial or poorly-implemented solutions. In this paper, we present a tool that facilitates the creation of sets of modular and composable clinical data abstractions. Using these, implementers can produce and consume standards-compliant clinical data correctly and efficiently without being experts in the underlying information models or the medical terminologies they reference.