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SummaryObjectives: Describe how the HL7 Clinical Document Architecture (CDA), a foundational standard in US Meaningful Use, contributes to a “big data, incrementally structured” interoperability strategy, whereby data structured incrementally gets large amounts of data flowing faster. We present cases showing how this approach is leveraged for big data analysis.Methods: To support the assertion that semi-structured narrative in CDA format can be a useful adjunct in an overall big data analytic approach, we present two case studies. The first assesses an organization’s ability to generate clinical quality reports using coded data alone vs. coded data supplemented by CDA narrative. The second leverages CDA to construct a network model for referral management, from which additional observations can be gleaned.Results: The first case shows that coded data supplemented by CDA narrative resulted in significant variances in calculated performance scores. In the second case, we found that the constructed network model enables the identification of differences in patient characteristics among different referral work flows.Discussion: The CDA approach goes after data indirectly, by focusing first on the flow of narrative, which is then incrementally structured. A quantitative assessment of whether this approach will lead to a greater flow of data and ultimately a greater flow of structured data vs. other approaches is planned as a future exercise.Conclusion: Along with growing adoption of CDA, we are now seeing the big data community explore the standard, particularly given its potential to supply analytic engines with volumes of data previously not possible.

Realization of the value of reliable codified medical data is growing at a rapid rate. Symptom data in particular have been shown to be useful in decision analysis and in the determination of patient outcomes. Electronic medical record systems are emerging, and attempts are underway to define the structure and content of these systems to support the storage of all medical data. The underlying models upon which these systems are being built continue to be strengthened by a deeper understanding of the complex information they are to store. This report analyzes symptoms as they might be recorded in free text notes and presents a high-level conceptual data model representation of this domain.

This paper describes the approach taken to build Kaiser Permanente's national clinical intranet. A primary objective for the site is to facilitate resource discovery, which is enabled by the use of "metadata", or data (fields and field values) that describe the various resources available. Users can perform full text queries and/or fielded searching against the metadata. Metadata serves as the organizing principle of the site--it is used to index documents, sort search results, and structure the site's table of contents. The site's use of metadata--what it is, how it is created, how it is applied to documents, how it is indexed, how it is presented to the user in the search and the search results interface, and how it is used to construct the table of contents for the web site--will be discussed in detail. The result is that KP's national clinical intranet has coupled the power of Internet-like full text search engines with the power of MedLine-like fielded searching in order to maximize search precision and recall. Organizing content on the site in accordance with the metadata promotes overall consistency. Issues currently under investigation include how to better exploit the power of the controlled terminology within the metadata; whether the value gained is worth the cost of collecting metadata; and how automatic classification algorithms might obviate the need for manual document indexing.

The efficient use of documents from heterogeneous computer systems is hampered by differences in document-naming practices across organizations. Using an open-consensus method, the Document Ontology Task Force, with support from the Veterans Health Administration, addressed this pervasive problem by developing a clinical document ontology. Based on the analysis of over 2000 clinical document names, the ontology was used to formulate a terminology model which is currently being used to guide the creation of fully-specified document names in LOINC (Logical Observations, Identifiers, Names and Codes). Incorporation into LOINC will enable homogeneous management of documents in a widely distributed environment and will also give rise to a rich polyhierarchy of document names.

OBJECTIVES: To validate the ease by which a Clinical Practice Guideline (CPG) can be web-enabled using an XML-based semi-automated process. DESIGN AND IMPLEMENTATION: An XML DTD for Clinical Practice Guidelines and an MS Word authoring template were created in an earlier project. We took an existing guideline, Bedside Smoking Cessation Intervention, placed it into the MS Word template, converted it into XML, and then to HTML for deployment over the Kaiser Permanent intranet. CONCLUSIONS: We were able to use the MS Word authoring template and automatically generate both an XML representation of our guideline, and an HTML representation, which we have deployed on our intranet. The Bedside Smoking Cessation Intervention guideline was automatically merged into the online guidelines collection. Placing it on our intranet allowed for rapid and easy access by physicians and other health care providers throughout the Kaiser Permanente Medical Care Program.

The focus of the article is on the nuts and bolts of those standards relevant to the exchange of data between a clinical laboratory and an electronic health record. These include: Health Level 7 (HL7), Logical Observation Identifier Names and Codes (LOINC), Systematized Nomenclature of Human and Veterinary Medicine (SNOMED), and, most recently, the Extensible Markup Language (XML).

Collaborative development involving both individuals and groups is often less efficient than independent development because of communication overhead and integration costs. Despite the decreased development efficiency, collaborations promise more general-purpose products because of the opportunity for integration, with negotiation and reconciliation of diverse perspectives. Collaborations are also perhaps less costly when considered in contexts where there is significant duplication of effort. Computer-facilitated collaboration can reduce the communication and integration burden such that the increased effort required to manage a successful collaboration focuses primarily on the development of shared conceptual model among the developers by requiring that the work product be independently reproducible. This reproducibility requirement incorporates formal quality assurance processes into the development process. In this paper, we describe our initial experiences developing SNOMED-RT using such a computer-facilitated collaborative process. We quantify the extra costs incurred to achieve consistency in our efforts and reproducibility of our results.

INTRODUCTION: In 1993, The European Committee for Standardization (CEN) studied several syntaxes for interchange formats in healthcare, but excluded SGML due to resource constraints. We sought to extend the CEN report and formally evaluate the use of SGML as a message interchange format. METHODS: We followed the methodology set forth by CEN, using their example scenarios and healthcare data model. General message descriptions based on this model set the functional requirements for the interchange format. These general requirements are then mapped into SGML to see how well they can be supported. RESULTS: Results follow the CEN format, enabling a direct comparison of SGML with ASN.1, ASTM E1238, EDIFACT, EUCLIDES, and ODA (those syntaxes studied by CEN). CONCLUSION: SGML compares favorably with other syntaxes investigated by CEN. None of the interchange formats support all functional requirements. Optimal and standard mechanisms of combining different formats through a modular approach to achieve greater overall functionality requires further study.

The concept of analyzing large observational databases, including fully electronic health records, to determine optimal patterns of care and ideal treatment strategies has blossomed now that large organizations and government agencies are recognizing the potential value of computer-stored patient information. However, computer-based patient records may not offer the detail and uniformity in presentation of clinical data that are needed for accurate analysis of their contents. In addition, pooling data from multiple electronic sources to perform outcome studies is impeded by the absence of widely used standards for data interchange and data representation. This report examines the requirements for outcome analysis and the features that a computerized clinical database should possess in order to minimize data ambiguity and ensure that the results of outcome-studies are meaningful.

Realization of the value of reliable codified medical data is growing at a rapid rate. Symptom data in particular have been shown to be useful in decision analysis and in the determination of patient outcomes. Electronic medical record systems are emerging, and attempts are underway to define the structure and content of these systems to support the storage of all medical data. The underlying models upon which these systems are being built continue to be strengthened by a deeper understanding of the complex information they are to store. This report analyzes symptoms as they might be recorded in free text notes and presents a high-level conceptual data model representation of this domain.

The importance of electronic medical records to improve the quality and cost-effectiveness of medical care continues to be realized. This growing importance has spawned efforts at defining the structure and content of medical data, which is heterogeneous, highly inter-related, and complex. Computer-assisted data modeling tools have greatly facilitated the process of representing medical data, however the complex inter-relationships of medical information can result in data models that are large and cumbersome to manipulate and view. This report presents a high-level object-oriented model for representing the relationships between objects or entities that might exist in an electronic medical record. By defining the relationship between objects at a high level and providing for inheritance, this model enables relating any medical entity to any other medical entity, even though the relationships were not directly specified or known during data model design.

The utility of drug-interaction software is limited by the fact that products on the market are unable to interface with preexisting stored medication lists, so that it is necessary to record medication lists twice. With the variety of automated medical records now in existence, it may be impractical for vendors of drug-interaction software to provide enough interfaces to overcome this problem. However, if vendors allowed greater access to their drug-interaction data, existing automated records could easily be altered to incorporate this clinically relevant information. This article describes a commercially available drug-interaction software package and the logic used to build an interface to a preexisting automated medical record.