An Electronic Health Record Based on Structured Narrative

Design: We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. Validation: The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. Discussion: The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. Conclusion: Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research. J Am Med Inform Assoc. 2008;15:54–64. DOI 10.1197/jamia.M2131. uest on Jauary 6, 2016 Introduction Electronic health records have been a major objective of research in biomedical informatics for decades. This research seeks to improve the legibility, accessibility and quality of health records to support patient care. Tremendous effort has been expended in representing health information in highly structured ways that support subsequent computer processing, such as decision support and quality assurance. This vision of electronic health records transcends the basic function of communication Affiliations of the authors: Department of Biomedical Informatics (SBJ, SB, DD, SH, EM, FM, TB, TVV, JW, PS), School of Nursing (SB, SH), Department of Medicine (PS), Columbia University, New York, NY. This work was supported by NLM 5R01 LM007268 and 1K22 LM008805. The authors acknowledge important conceptual contributions by Justin Starren, MD, PhD, and the involvement of Jason Shapiro, MD and Genevieve Melton, MD. Correspondence: Stephen B. Johnson, PhD, 622 West 168 St, VC557, New York, NY 10032; e-mail: [email protected] . Received for review: 04/20/06; accepted for publication: 09/20/07. among providers, and enables computers to support and augment care processes in myriad ways. Despite these efforts, electronic health records have relatively low penetration into health care delivery. Acquisition of data directly from clinicians remains one of the largest potential obstacles. As a result, many electronic health records often lack key pieces of documentation, such as progress notes or admission notes. Low utilization is caused in part by the difficulty of capturing data in structured form. While effective in narrow, predictable domains, structured data entry can be quite slow when events are broad in scope and exhibit high variation. Clinicians are pressed for time, and cannot assume the burden of data entry without significant returns for their efforts. Diffusion of electronic health record technology will remain low until these barriers are addressed. Clinicians have a long tradition of using paper forms and dictation services, and are beginning to adopt direct entry of text and speech recognition. This abiding preference for narrative data (clinical text written in a natural language such as English) deserves further consideration. Narrative has the advantage of familiarity, ease of use and freedom to express anything the clinician wishes. But more than this, Journal of the American Medical Informatics Association Volume 15 Number 1 Jan / Feb 2008 55 by gest on Jauary 6, 2016 ht://jam ia.oxfournals.org/ D ow nladed from clinicians need a way to interpret raw data, synthesize the facts and weave them into a coherent narrative. Natural language provides many mechanisms that augment or enrich simple facts, for example to qualify their severity or degree, convey temporal relationships, indicate patterns of causality, provide rationale, propose hypotheses, and suggest alternatives. This paper proposes a new model for electronic health records called structured narrative, in which unstructured text and coded data are fused into a single representation combining the advantages of both. Each major clinical event (e.g., encounter or procedure) is represented as a document to which Extensible Markup Language (XML) is added to indicate gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). This form of representation is known as semi-structured because the gross structure imposes restrictions on the clinician (standard fields for data entry), while allowing freedom of expression within those units (free text paragraphs). However, unlike traditional structured documentation, the free text paragraphs are marked up (using natural language processing) to yield a fine structure that identifies facts within the free text (diseases, medications, procedures, etc.), as well as modifiers (severity, certainty, etc.), and ways of connecting these facts (temporally, causally, etc.). All of these structured elements are associated with standardized codes that enable them to be reused for various computational purposes. At the same time, the extra verbiage in the narrative helps to weave the facts together, conveying temporal, causal, and reasoning relationships among the facts that are essential to contextualize, interpret, and synthesize the information. The structured narrative model is intended to be convenient for data entry, highly synthesized, and amenable to computer processing.