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Clinical Decision Support Systems for Diagnosis in Primary Care: A Scoping Review

Author

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  • Taku Harada

    (Department of General Medicine, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
    Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital, Tochigi 321-0297, Japan)

  • Taiju Miyagami

    (Department of General Medicine, Juntendo University Faculty of Medicine, Tokyo 113-8421, Japan)

  • Kotaro Kunitomo

    (Department of General Medicine, Kumamoto Medical Center, Kumamoto 860-0008, Japan)

  • Taro Shimizu

    (Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital, Tochigi 321-0297, Japan)

Abstract

Diagnosis is one of the crucial tasks performed by primary care physicians; however, primary care is at high risk of diagnostic errors due to the characteristics and uncertainties associated with the field. Prevention of diagnostic errors in primary care requires urgent action, and one of the possible methods is the use of health information technology. Its modes such as clinical decision support systems (CDSS) have been demonstrated to improve the quality of care in a variety of medical settings, including hospitals and primary care centers, though its usefulness in the diagnostic domain is still unknown. We conducted a scoping review to confirm the usefulness of the CDSS in the diagnostic domain in primary care and to identify areas that need to be explored. Search terms were chosen to cover the three dimensions of interest: decision support systems, diagnosis, and primary care. A total of 26 studies were included in the review. As a result, we found that the CDSS and reminder tools have significant effects on screening for common chronic diseases; however, the CDSS has not yet been fully validated for the diagnosis of acute and uncommon chronic diseases. Moreover, there were few studies involving non-physicians.

Suggested Citation

  • Taku Harada & Taiju Miyagami & Kotaro Kunitomo & Taro Shimizu, 2021. "Clinical Decision Support Systems for Diagnosis in Primary Care: A Scoping Review," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8435-:d:611658
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    References listed on IDEAS

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    1. Kathrin M Cresswell & Sukhmeet S Panesar & Sarah A Salvilla & Andrew Carson-Stevens & Itziar Larizgoitia & Liam J Donaldson & David Bates & Aziz Sheikh & on behalf of the World Health Organization's (, 2013. "Global Research Priorities to Better Understand the Burden of Iatrogenic Harm in Primary Care: An International Delphi Exercise," PLOS Medicine, Public Library of Science, vol. 10(11), pages 1-6, November.
    2. Yukinori Harada & Shinichi Katsukura & Ren Kawamura & Taro Shimizu, 2021. "Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study," IJERPH, MDPI, vol. 18(4), pages 1-10, February.
    3. Taku Harada & Taro Shimizu & Yuki Kaji & Yasuhiro Suyama & Tomohiro Matsumoto & Chintaro Kosaka & Hidefumi Shimizu & Takatoshi Nei & Satoshi Watanuki, 2020. "A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools," IJERPH, MDPI, vol. 17(17), pages 1-6, August.
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    Cited by:

    1. Taku Harada & Yukinori Harada & Kohei Morinaga & Takanobu Hirosawa & Taro Shimizu, 2022. "Bandemia as an Early Predictive Marker of Bacteremia: A Retrospective Cohort Study," IJERPH, MDPI, vol. 19(4), pages 1-8, February.
    2. Yoshito Nishimura, 2022. "Primary Care, Burnout, and Patient Safety: Way to Eliminate Avoidable Harm," IJERPH, MDPI, vol. 19(16), pages 1-3, August.

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