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Cited by 89Electronic Health Records SystemsClinical Reasoning and Diagnostic SkillsArtificial Intelligence in Healthcare and Education

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

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Harada, Taku, Miyagami, Taiju, Kunitomo, Kotaro, Shimizu, Taro·2021·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph18168435PMC PMC8391274
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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.

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  • Some CDSS can generate ranked lists of potential diagnoses for disease differentiation, including disease subtype classification or stages, to guide subsequent treatment planning.

    “Some CDSS can further generate ranked lists of potential diagnoses for disease differentiation [219,220,221], including disease subtype classification or stages, to guide subsequent treatment planning [222].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [220]

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Authors
Harada, T., Miyagami, T., Kunitomo, K., & Shimizu, T.
Affiliations
  • SHOWA Medical University Koto Toyosu Hospital (JP)
  • Juntendo University (JP)
  • Kumamoto Medical Center (JP)
  • Dokkyo Medical University Hospital (JP)
Corresponding author
Shimizu, Taro
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2021-08-10
Volume
18
Issue
16
Pages
8435
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph18168435
PMC
PMC8391274
OpenAlex
W3187814586
Cited by
89
References
69
Citations by year
2026: 13 · 2025: 18 · 2024: 28 · 2023: 17 · 2022: 13
FWCI
2.55
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
clinical decision support systems, diagnostic errors, primary care, diagnostic accuracy, chronic disease screening, health information technology
MeSH terms
Mass Screening, Primary Health Care, Decision Support Systems, Clinical

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