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Exploring prevalence of wound infections and related patient characteristics in homecare using natural language processing

International wound journal, 2022-01, Vol.19 (1), p.211-221 [Peer Reviewed Journal]

2021 The Authors. published by Medicalhelplines.com Inc (3M) and John Wiley & Sons Ltd. ;2021 The Authors. International Wound Journal published by Medicalhelplines.com Inc (3M) and John Wiley & Sons Ltd. ;ISSN: 1742-4801 ;EISSN: 1742-481X ;DOI: 10.1111/iwj.13623 ;PMID: 34105873

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  • Title:
    Exploring prevalence of wound infections and related patient characteristics in homecare using natural language processing
  • Author: Woo, Kyungmi ; Song, Jiyoun ; Adams, Victoria ; Block, Lorraine J. ; Currie, Leanne M. ; Shang, Jingjing ; Topaz, Maxim
  • Subjects: Algorithms ; home health care ; Humans ; Natural Language Processing ; natural language processing (NLP) ; nursing notes ; Original ; Outcome and Assessment Information Set (OASIS) ; Prevalence ; Retrospective Studies ; wound infection ; Wound Infection - epidemiology
  • Is Part Of: International wound journal, 2022-01, Vol.19 (1), p.211-221
  • Description: We aimed to create and validate a natural language processing algorithm to extract wound infection‐related information from nursing notes. We also estimated wound infection prevalence in homecare settings and described related patient characteristics. In this retrospective cohort study, a natural language processing algorithm was developed and validated against a gold standard testing set. Cases with wound infection were identified using the algorithm and linked to Outcome and Assessment Information Set data to identify related patient characteristics. The final version of the natural language processing vocabulary contained 3914 terms and expressions related to the presence of wound infection. The natural language processing algorithm achieved overall good performance (F‐measure = 0.88). The presence of wound infection was documented for 1.03% (n = 602) of patients without wounds, for 5.95% (n = 3232) of patients with wounds, and 19.19% (n = 152) of patients with wound‐related hospitalisation or emergency department visits. Diabetes, peripheral vascular disease, and skin ulcer were significantly associated with wound infection among homecare patients. Our findings suggest that nurses frequently document wound infection‐related information. The use of natural language processing demonstrated that valuable information can be extracted from nursing notes which can be used to improve our understanding of the care needs of people receiving homecare. By linking findings from clinical nursing notes with additional structured data, we can analyse related patients' characteristics and use them to develop a tailored intervention that may potentially lead to reduced wound infection‐related hospitalizations.
  • Publisher: Oxford, UK: Blackwell Publishing Ltd
  • Language: English
  • Identifier: ISSN: 1742-4801
    EISSN: 1742-481X
    DOI: 10.1111/iwj.13623
    PMID: 34105873
  • Source: Journals@Ovid Open Access Journal Collection Rolling
    MEDLINE
    PubMed Central
    DOAJ Directory of Open Access Journals
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