Extracting information from textual documents in the electronic health record: a review of recent research

Savova, G. K.; Kipper-Schuler, K. C.; Hurdle, J. F.; Meystre, S. M.

doi:10.1055/s-0038-1638592

Cited by 895Biomedical Text Mining and OntologiesMachine Learning in HealthcareElectronic Health Records Systems

Extracting information from textual documents in the electronic health record: a review of recent research

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Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., Meystre, S. M.·2008·Yearbook of Medical Informatics

DOI 10.1055/s-0038-1638592

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Abstract

Summary: Objectives We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.

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EHR databases contain large heterogeneous data sets combining structured and formatted data elements with rich unstructured data.
“The resulting underlying databases created by EHR systems contain large heterogeneous data sets that combine structured and formatted data elements such as diagnoses (International Classification of Diseases-10), procedures (Current Procedural Terminology® code), and medications (RxNorm), but also rich unstructured data such as clinical narratives, which represent over 80% of the data in EHRs (5).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [5]
Clinical narratives represent over 80% of the data in EHRs.
“clinical narratives, which represent over 80% of the data in EHRs (5).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [5]

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Authors: Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., & Meystre, S. M.
Affiliations: Mayo Clinic (US)
University of Utah (US)
Corresponding author: Meystre, S. M.
ORCID: Savova, G. K.
Hurdle, J. F.
Venue: Yearbook of Medical Informatics
Publisher: Georg Thieme Verlag KG
Published: 2008-08
Volume: 17
Issue: 01
Pages: 128-144
ISSN: 0943-4747 (ISSN-L)
2364-0502
DOI: 10.1055/s-0038-1638592
OpenAlex: W2114388055
Cited by: 895
References: 173
Citations by year: 2026: 28 · 2025: 41 · 2024: 22 · 2023: 32 · 2022: 32 · 2021: 48 · 2020: 61 · 2019: 91 · 2018: 55 · 2017: 66 · 2016: 63 · 2015: 63 · 2014: 78 · 2013: 62 · 2012: 57
FWCI: 13.15
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Yearbook of Medical Informatics (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords: information extraction, clinical text, electronic health records, clinical text mining, clinical decision support, clinical surveillance, de-identification, text preprocessing, contextual features
MeSH terms: Humans, Natural Language Processing, Population Surveillance, Information Storage and Retrieval, Medical Records Systems, Computerized, Vocabulary, Controlled, Biomedical Research

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