Calvin Beebe

HL7 Clinical Document Architecture, Release 2

Clinical Document Architecture, Release One (CDA R1), became an American National Standards Institute (ANSI)-approved HL7 Standard in November 2000, representing the first specification derived from the Health Level 7 (HL7) Reference Information Model (RIM). CDA, Release Two (CDA R2), became an ANSI-approved HL7 Standard in May 2005 and is the subject of this article, where the focus is primarily on how the standard has evolved since CDA R1, particularly in the area of semantic representation of clinical events. CDA is a document markup standard that specifies the structure and semantics of a clinical document (such as a discharge summary or progress note) for the purpose of exchange. A CDA document is a defined and complete information object that can include text, images, sounds, and other multimedia content. It can be transferred within a message and can exist independently, outside the transferring message. CDA documents are encoded in Extensible Markup Language (XML), and they derive their machine processable meaning from the RIM, coupled with terminology. The CDA R2 model is richly expressive, enabling the formal representation of clinical statements (such as observations, medication administrations, and adverse events) such that they can be interpreted and acted upon by a computer. On the other hand, CDA R2 offers a low bar for adoption, providing a mechanism for simply wrapping a non-XML document with the CDA header or for creating a document with a structured header and sections containing only narrative content. The intent is to facilitate widespread adoption, while providing a mechanism for incremental semantic interoperability.

The HL7 Clinical Document Architecture

Many people know of Health Level 7 (HL7) as an organization that creates health care messaging standards. Health Level 7 is also developing standards for the representation of clinical documents (such as discharge summaries and progress notes). These document standards make up the HL7 Clinical Document Architecture (CDA). The HL7 CDA Framework, release 1.0, became an ANSI-approved HL7 standard in November 2000. This article presents the approach and objectives of the CDA, along with a technical overview of the standard. The CDA is a document markup standard that specifies the structure and semantics of clinical documents. A CDA document is a defined and complete information object that can include text, images, sounds, and other multimedia content. The document can be sent inside an HL7 message and can exist independently, outside a transferring message. The first release of the standard has attempted to fill an important gap by addressing common and largely narrative clinical notes. It deliberately leaves out certain advanced and complex semantics, both to foster broad implementation and to give time for these complex semantics to be fleshed out within HL7. Being a part of the emerging HL7 version 3 family of standards, the CDA derives its semantic content from the shared HL7 Reference Information Model and is implemented in Extensible Markup Language. The HL7 mission is to develop standards that enable semantic interoperability across all platforms. The HL7 version 3 family of standards, including the CDA, are moving us closer to the realization of this vision.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium

RESEARCH OBJECTIVE To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

Image display for clinicians on medical record workstations.

Image display on electronic medical record (EMR) workstations is an important step in widespread implementation of picture archiving and communications systems (PACS). We describe a pilot project for implementing image display capability that is integrated with the EMR software, and will allow display of images on the physician’s workstation. We believe this pilot will provide valuable information about usage patterns in image display needs, which will be valuable in planning further expansion of PACS in our institution.

Clinician image review patterns in an outpatient setting

We have previously described a system for delivering radiology information to the desktop computers used for the electronic medical record (EMR). The system was built with the ability to record physician usage to a database. This usage information was then studied to help understand the value and requirements of an application that could display radiology information on the EMR workstations. This system was used by both primary care physicians and specialists primarily in the out-patient setting. We found that while there was substantial variation in usage both within and between the two physician groups, there was a high degree of support for maintaining image display capabilities on the workstations.