Robert H. Dolin

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.

Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative.

Background and objective Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors. Materials and methods We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements. Results We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents. Conclusions We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.

Modeling the Temporal Complexities of Symptoms

Objective : To analyze the temporal aspects of symptoms, including their temporal uncertainty, in order to develop a high-level conceptual data model representation of this domain. Design : A basic tenet of existing temporal models is that events occur not only relative to a particular date or time, but also relative to the time of some other event. The time an event occurs, particularly when the event is a symptom being recalled by a patient or collected by a busy provider, is frequently incomplete or uncertain, and this uncertainty must also be represented in a temporal data model. The object-oriented modeling technique used in this study is becoming popular among U.S. medical informatics standards developers. Results : A conceptual data model for the temporal aspects of symptom data, including temporal uncertainty, has been developed. The object-oriented modeling approach used enables the temporal objects and attributes defined in this model to be inherited by other medical objects, such as problems. Conclusion : The temporal comparators presented here have previously been defined, and may serve as the basis for standardizing the terms used to describe how one event temporally relates to another. In an attempt to achieve domain completeness, this study concentrated more on developing a model that is highly expressive than on developing one that is easily queried. This trade-off in representation versus “queryability” will require further analysis and may require modifications to the underlying model.

Accuracy of Electronically Reported “Meaningful Use” Clinical Quality Measures

Let’s face it. Although we can challenge Kern andcolleagues’ (1) methods (for example, by claiming that electronichealth records [EHRs] are much different today than they were in2008), I don’t think that we can challenge their findings. The Achil-les’ heel of end-to-end quality reporting from EHRs lies with datacapture. The challenges to data capture are many—divergent datarequirements (across quality measures, decision support rules, clinicalpractice guidelines, and others), time pressures, and skepticism (forexample, are all these data elements really necessary?)—which canleave providers overwhelmed and resistant.Quality reporting is required under the federal meaningful useregulations mandated by the 2009 Health Information Technologyfor Economic and Clinical Health (HITECH) Act. A meaningfuluse–certified EHR must be able to export standardized quality re-ports, which can then be fed into a calculation engine to computevarious aggregate scores (such as the number of patients meeting thenumerator and denominator criteria). Interoperability standards forquality reports have been carefully crafted within the Health Level 7standards organization and widely vetted, but we have to acknowl-edge that garbage in equals garbage out and that interoperabilitystandards that support quality reporting cannot compensate for in-consistent or missing data at the source.So, how is the standards community addressing the challenge ofdata capture? We are addressing it head-on by providing the defini-tive source of truth and direction needed by software vendors andclinicians. Through standardization comes a convergence on key dataelements needed for transitions in care, quality reporting, and deci-sion support. Rather than having numerous use cases (for example,transitions of care, clinical decision support, and quality measure-ment), each with its own data requirements converging on the point-of-care provider, standardization leads to a convergence of key dataelements. This convergence sets a clearer path for vendors and userinterface designers, lessens the burden of data capture on clinicians,and focuses data capture on data elements known to be valuable forvarious purposes.In other words, interoperability standards are relevant both onthe afferent and efferent limbs of the EHR. Focusing on standards isa tractable approach for addressing challenges to data capture andtherefore provides a strategy for addressing the very real challengeswith data quality identified by Kern and associates.