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The Effectiveness of Electronic Differential Diagnoses (DDX) Generators: A Systematic Review and Meta-Analysis

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  • Nicholas Riches
  • Maria Panagioti
  • Rahul Alam
  • Sudeh Cheraghi-Sohi
  • Stephen Campbell
  • Aneez Esmail
  • Peter Bower

Abstract

Background: Diagnostic errors are costly and they can contribute to adverse patient outcomes, including avoidable deaths. Differential diagnosis (DDX) generators are electronic tools that may facilitate the diagnostic process. Methods and Findings: We conducted a systematic review and meta-analysis to investigate the efficacy and utility of DDX generators. We undertook a comprehensive search of the literature including 16 databases from inception to May 2015 and specialist patient safety databases. We also searched the reference lists of included studies. Article screening, selection and data extraction were independently conducted by 2 reviewers. 36 articles met the eligibility criteria and the pooled accurate diagnosis retrieval rate of DDX tools was high with high heterogeneity (pooled rate = 0.70, 95% CI = 0.63 to 0.77; I2 = 97%, p

Suggested Citation

  • Nicholas Riches & Maria Panagioti & Rahul Alam & Sudeh Cheraghi-Sohi & Stephen Campbell & Aneez Esmail & Peter Bower, 2016. "The Effectiveness of Electronic Differential Diagnoses (DDX) Generators: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-26, March.
    • Handle: RePEc:plo:pone00:0148991
    DOI: 10.1371/journal.pone.0148991
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    References listed on IDEAS

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    1. Yukinori Harada & Shinichi Katsukura & Ren Kawamura & Taro Shimizu, 2021. "Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study," IJERPH, MDPI, vol. 18(11), pages 1-8, May.
    2. Takanobu Hirosawa & Yukinori Harada & Masashi Yokose & Tetsu Sakamoto & Ren Kawamura & Taro Shimizu, 2023. "Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study," IJERPH, MDPI, vol. 20(4), pages 1-10, February.

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