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CPIA Certification Program
Module 2: Data Structures and Healthcare Terminologies
Learning the Grammar and Vocabulary of Health Information.
From NDC to RxNorm: Mastering the Language of Data
As a pharmacist, you are already an expert in a highly specialized language. You can instantly differentiate between a generic name, a brand name, and an NDC. You understand that “Lisinopril 10mg Tablet” and “Prinivil 10mg Tablet” are therapeutically equivalent, even though they have different names and codes. This ability to understand, translate, and categorize medication information is a core competency of your profession.
This module is designed to expand that linguistic expertise from the pharmacy shelf to the entire healthcare ecosystem. We will move beyond the familiar world of NDCs and into the universal, standardized terminologies that allow different computer systems to speak to each other without ambiguity. If Module 1 laid the foundation of informatics, this module teaches you the grammar and vocabulary that are used to build upon it.
You will learn how data is structured in health records and why standardized “code sets” like RxNorm and SNOMED CT are as critical to interoperability as a shared language is to a conversation. This is not an abstract, academic topic; it is the absolute bedrock of modern, data-driven healthcare. Mastering these concepts will enable you to build safer systems, perform more meaningful analysis, and speak with authority on how health information should be managed.
Your Guide to the Data Landscape
This module provides a deep dive into the structure of health data and the standardized languages used to ensure it is consistent, accurate, and interoperable.
2.1 Relational vs NoSQL Models in Health Data
An essential primer on how data is stored. We explore the difference between the highly structured “filing cabinet” of relational databases and the flexible “document store” of NoSQL models, and why this matters for healthcare.
2.2 Code Sets and Terminologies (RxNorm, SNOMED CT, LOINC, ICD-10)
A deep dive into the “universal languages” of health data. We’ll master RxNorm for medications, SNOMED CT for clinical findings, LOINC for lab tests, and ICD-10 for diagnoses.
2.3 Mapping and Crosswalks for Drug Databases
Learn the art and science of “translation.” This section covers the critical process of mapping a hospital’s local, proprietary drug formulary to the national standard of RxNorm to enable interoperability.
2.4 Ontology Management and Semantic Normalization
We explore how computers can understand that “heart attack” and “myocardial infarction” are the same concept. This section covers the creation of concept “family trees” (ontologies) to enable advanced analytics and decision support.
2.5 Terminology Services and API Integrations
A look at the future of terminology management. We examine how modern systems use APIs to “call out” to centralized terminology services, ensuring they always have the most up-to-date codes and definitions.
