Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

Design: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. Measurements: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. Results: An average of 95 medical events (range, 46–151) and 234 temporal assertions (range, 118–388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. Abstractions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%–1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005–1.114). Conclusion: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records. j J Am Med Inform Assoc. 2005;12:55–63. DOI 10.1197/jamia.M1623. The electronic medical record is a temporally rich document filled with assertions about the timing of medical events, such as visits, laboratory values, symptoms, signs, diagnoses, and procedures. Allen, in his review of temporal formalisms, describes how such temporal information can be specified with the use of points anchored in absolute time, qualitative constraints among intervals, and metric relations that specify explicit durations between points. Using such formalisms, past researchers in medical informatics have attempted to model temporal assertions found in the electronicmedical record. The expressiveness of these methods has not been tested with data from actual medical reports, however. Determining and validating a suitable temporal formalism for the electronic medical record remain a challenge in medical informatics. Much of the work in temporal querying and temporal reasoning in medical informatics has used temporal formalisms based on points anchored in absolute time. The available data in such work consist of discrete coded events with known times, such as visit information, laboratory test results, and medication administration logs. Typically, events are represented as points or pairs of points, in which each point is a time stamp stored in a database. The primary focus of this research has been on temporal abstraction: recognizing temporal patterns among the time stamps and determining what can be inferred medically based on the temporal semantics of the events. For example, given a series of hemoglobin Affiliations of the authors: Department of Biomedical Informatics, Columbia University, New York, NY (GH, LZ, SBJ); Department of Computer and Information Science, Brooklyn College, Brooklyn, NY (SP); Department of Psychiatry, Columbia University, New York, NY (AKD). Dr. Das is currently with Stanford Medical Informatics, Stanford University, Stanford, CA. Abstract presented at the meeting of Intelligent Data Analysis in Medicine and Pharmacology, Stanford University, Palo Alto, CA, September 2004.presented at the meeting of Intelligent Data Analysis in Medicine and Pharmacology, Stanford University, Palo Alto, CA, September 2004. Supported by National Library of Medicine grants R01 LM06910, ‘‘Discovering and Applying Knowledge in Clinical Databases’’; R01 LM07659, ‘‘Capturing and Linking Genomic and Clinical Information’’; and R01 LM07268, ‘‘Using Narrative Data to Enrich the Online Medical Record.’’ Correspondence and reprints: George Hripcsak, MD, MS, Department of Medical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10032; e-mail: . Received for publication: 05/24/04; accepted for publication: 09/08/04. 55 Journal of the American Medical Informatics Association Volume 12 Number 1 Jan / Feb 2005