University of Sussex
Browse
jamia.ocv180.full.pdf (318.55 kB)

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Download (318.55 kB)
journal contribution
posted on 2023-06-08, 23:47 authored by Elizabeth FordElizabeth Ford, John Carroll, Helen E Smith, Donia Scott, Jackie Cassell
Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall).

Funding

The ergonomics of electric patient records: an interdisciplinary development of methodologies for understanding and exploiting free text to enhance the utility of primary care electronic patient records; G0011; WELLCOME TRUST; 086105/Z/08/Z

History

Publication status

  • Published

File Version

  • Published version

Journal

Journal of The American Medical Informatics Association

ISSN

1067-5027

Publisher

Elsevier

Issue

5

Volume

23

Page range

1007-1015

Department affiliated with

  • BSMS Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-02-16

First Open Access (FOA) Date

2016-02-16

First Compliant Deposit (FCD) Date

2016-02-15

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC