The Stanford Translational Research Integrated Database Environment (STRIDE)1
is an informatics research and development project at Stanford University Medical Center (SUMC) to create a standards-based informatics platform supporting clinical and translational research (CTR). STRIDE receives clinical data, for research use, via HL7 messages from SUMC information systems supporting patient care at Lucile Packard Children’s Hospital at Stanford (LPCH) and Stanford Hospital & Clinics (SHC). This data is integrated into the STRIDE Clinical Data Warehouse (CDW), an Oracle-based system that uses a data model based on the HL7 Version 3 Reference Information Model (RIM)2
. STRIDE supports integrated access to clinical data, for research purposes, from the pediatric and adult patient populations at SUMC. A Java application, called the STRIDE Anonymous Patient Cohort Identification Tool, gives Stanford researchers the ability to identify research patient cohorts in the CDW, using a variety of clinical criteria, without exposing protected health information (PHI).
Medication information is an important data type for CTR. Accurate, standards-based, representation of medications assures a common understanding of the data, which facilitates retrieval, analysis, and sharing of pharmacy data for CTR. However, many clinical and pharmacy systems use drug information databases from commercial vendors, which may use different proprietary identifiers, naming conventions and drug models. This is the case at SUMC, where LPCH and SHC operate two separate EHR systems that use different commercial drug databases. Thus, even though SUMC hospitals are cooperating to share content with STRIDE, their data are incompatible. To support integrated representation and searching of pharmacy data across both SUMC hospitals, STRIDE needed a standards-based drug representation model within its CDW.
The National Library of Medicine (NLM) and the Food and Drug Administration (FDA) set out to standardize drug information identifiers to support interoperability by creating RxNorm3
, a free, robust and current drug representation system, which is updated weekly. RxNorm allows navigation between ingredients, generic drug names, brand names, and National Drug Codes (NDC) identifiers through the use of defined relationships. RxNorm is one of the source vocabularies of NLM’s Unified Medical Language System (UMLS). It provides a unified drug representation model and maintains a mapping between different proprietary drug identifiers. The major drug information vendors submit some level of their terminologies to the UMLS for mapping within RxNorm.
This paper describes the use of RxNorm as a standards-based drug representation model within STRIDE. RxNorm and its built-in relationships were leveraged to provide mapping between pharmacy data from two SUMC EHR systems employing different proprietary drug vendor information systems. In particular, we are interested in the following outcomes: (1) RxNorm coverage for the drug concepts derived from two sources of SUMC pharmacy orders; (2) utilization of the linkages within RxNorm, particularly those linking brand names to generic ingredients (3) characterization of the pharmacy message that could not be automatically mapped to RxNorm and (4) mapping from RxNorm concepts to the SNOMED-CT substance hierarchy.
The approach of using algorithms to map biomedical concepts to standardized terminologies, followed by manual review of the mapping results by medical domain experts, is well-documented4
. Alternative approaches to integration of drug terminologies include the use of ontologies5
. RxNorm has been used to extract drug names from narrative text clinical documents6
and for computable exchange of drug allergy information between the Department of Veterans Affairs (VA) and the Department of Defense (DoD)7
. RxNorm was selected as a Consolidated Health Informatics (CHI) designated standard for Trade names and Drug Names. RxNorm and the VA National Drug File Reference Terminology’s (NDF-RT)8
are the recommended standards for representing drug names and drug classification.
Given RxNorm’s emerging role as a national standard, its use within STRIDE was felt to offer a scalable strategy for representing drug orders obtained from different EHR systems using different drug vendor information models. This approach may be of interest to others who have to merge pharmacy data from multiple clinical systems into a common standards based representational framework.