Archive (2016–2006)

Tool-supported Interactive Correction and Semantic Annotation of Narrative Clinical Reports

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
DOI: https://doi.org/10.3414/ME16-01-0083
Issue: 2017 (Vol. 56): Issue 3 2017
Pages: 217-229
Ahead of Print: 2017-04-28

Tool-supported Interactive Correction and Semantic Annotation of Narrative Clinical Reports

Original Article

K. Zvára (1, 2), M. Tomečková (2), J. Peleška (2), V. Svátek (3), J. Zvárová (1, 2)

(1) Charles University, First Faculty of Medicine, Institute of Hygiene and Epidemiology, Prague, Czech Republic; (2) EuroMISE Mentor Association, Prague, Czech Republic; (3) University of Economics, Faculty of Informatics and Statistics, Department of Information and Knowledge Engineering, Prague, Czech Republic

Keywords

classification systems, Electronic health record, structured information, Narrative clinical report, tokens, nomenclatures

Summary

Objectives: Our main objective is to design a method of, and supporting software for, interactive correction and semantic annotation of narrative clinical reports, which would allow for their easier and less erroneous processing outside their original context: first, by physicians unfamiliar with the original language (and possibly also the source specialty), and second, by tools requiring structured information, such as decision-support systems. Our additional goal is to gain insights into the process of narrative report creation, including the errors and ambiguities arising therein, and also into the process of report annotation by clinical terms. Finally, we also aim to provide a dataset of ground-truth transformations (specific for Czech as the source language), set up by expert physicians, which can be reused in the future for subsequent analytical studies and for training automated transformation procedures.

Methods: A three-phase preprocessing method has been developed to support secondary use of narrative clinical reports in electronic health record. Narrative clinical reports are narrative texts of healthcare documentation often stored in electronic health records. In the first phase a narrative clinical report is tokenized. In the second phase the tokenized clinical report is normalized. The normalized clinical report is easily readable for health professionals with the knowledge of the language used in the narrative clinical report. In the third phase the normalized clinical report is enriched with extracted structured information. The final result of the third phase is a semi-structured normalized clinical report where the extracted clinical terms are matched to codebook terms. Software tools for interactive correction, expansion and semantic annotation of narrative clinical reports has been developed and the three-phase preprocessing method validated in the cardiology area.

Results: The three-phase preprocessing method was validated on 49 anonymous Czech narrative clinical reports in the field of cardiology. Descriptive statistics from the database of accomplished transformations has been calculated. Two cardiologists participated in the annotation phase. The first cardiologist annotated 1500 clinical terms found in 49 narrative clinical reports to codebook terms using the classification systems ICD 10, SNOMED CT, LOINC and LEKY. The second cardiologist validated annotations of the first cardiologist. The correct clinical terms and the codebook terms have been stored in a database.

Conclusions: We extracted structured information from Czech narrative clinical reports by the proposed three-phase preprocessing method and linked it to electronic health records. The software tool, although generic, is tailored for Czech as the specific language of electronic health record pool under study. This will provide a potential etalon for porting this approach to dozens of other less-spoken languages. Structured information can support medical decision making, quality assurance tasks and further medical research.

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