Impact of Artificial Intelligence-Assisted Indication Selection on Appropriateness Order Scoring for Imaging Clinical Decision Support.

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  • Additional Information
    • Source:
      Publisher: Elsevier Country of Publication: United States NLM ID: 101190326 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-349X (Electronic) Linking ISSN: 15461440 NLM ISO Abbreviation: J Am Coll Radiol Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Elsevier, c2004-
    • Subject Terms:
      Decision Support Systems, Clinical* ; Medical Order Entry Systems*; Humans ; Female ; Adult ; Middle Aged ; Aged ; Male ; Artificial Intelligence ; Diagnostic Imaging ; Radionuclide Imaging
    • Abstract:
      Purpose: The aim of this study was to assess appropriateness scoring and structured order entry after the implementation of an artificial intelligence (AI) tool for analysis of free-text indications.
      Methods: Advanced outpatient imaging orders in a multicenter health care system were recorded 7 months before (March 1, 2020, to September 21, 2020) and after (October 20, 2020, to May 13, 2021) the implementation of an AI tool targeting free-text indications. Clinical decision support score (not appropriate, may be appropriate, appropriate, or unscored) and indication type (structured, free-text, both, or none) were assessed. The χ 2 and multivariate logistic regression adjusting for covariables with bootstrapping were used.
      Results: In total, 115,079 orders before and 150,950 orders after AI tool deployment were analyzed. The mean patient age was 59.3 ± 15.5 years, and 146,035 (54.9%) were women; 49.9% of orders were for CT, 38.8% for MR, 5.9% for nuclear medicine, and 5.4% for PET. After deployment, scored orders increased to 52% from 30% (P < .001). Orders with structured indications increased to 67.3% from 34.6% (P < .001). On multivariate analysis, orders were more likely to be scored after tool deployment (odds ratio [OR], 2.7, 95% CI, 2.63-2.78; P < .001). Compared with physicians, orders placed by nonphysician providers were less likely to be scored (OR, 0.80; 95% CI, 0.78-0.83; P < .001). MR (OR, 0.84; 95% CI, 0.82-0.87) and PET (OR, 0.12; 95% CI, 0.10-0.13) were less likely to be scored than CT (; P < .001). After AI tool deployment, 72,083 orders (47.8%) remained unscored, 45,186 (62.7%) with free-text-only indications.
      Conclusions: Embedding AI assistance within imaging clinical decision support was associated with increased structured indication orders and independently predicted a higher likelihood of scored orders. However, 48% of orders remained unscored, driven by both provider behavior and infrastructure-related barriers.
      (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)
    • Comments:
      Comment in: J Am Coll Radiol. 2023 Dec;20(12):1267-1268. (PMID: 37379889)
    • Grant Information:
      T32 EB004311 United States EB NIBIB NIH HHS
    • Contributed Indexing:
      Keywords: Imaging clinical decision support; artificial intelligence; implementation science; informatics
    • Publication Date:
      Date Created: 20230630 Date Completed: 20231222 Latest Revision: 20240304
    • Publication Date:
      20240304
    • Accession Number:
      10.1016/j.jacr.2023.04.016
    • Accession Number:
      37390881