Cited by 60Electronic Health Records SystemsClinical Reasoning and Diagnostic SkillsArtificial Intelligence in Healthcare and Education

Reducing Diagnostic Errors in Primary Care. A Systematic Meta-Review of Computerized Diagnostic Decision Support Systems by the LINNEAUS Collaboration on Patient Safety in Primary Care

Open access
Nurek, Martine, Kostopoulou, Olga, Delaney, Brendan C, Esmail, Aneez·2015·European Journal of General Practice

Abstract

BACKGROUND: Computerized diagnostic decision support systems (CDDSS) have the potential to support the cognitive task of diagnosis, which is one of the areas where general practitioners have greatest difficulty and which accounts for a significant proportion of adverse events recorded in the primary care setting. OBJECTIVE: To determine the extent to which CDDSS may meet the requirements of supporting the cognitive task of diagnosis, and the currently perceived barriers that prevent the integration of CDDSS with electronic health record (EHR) systems. METHODS: We conducted a meta-review of existing systematic reviews published in English, searching MEDLINE, Embase, PsycINFO and Web of Knowledge for articles on the features and effectiveness of CDDSS for medical diagnosis published since 2004. Eligibility criteria included systematic reviews where individual clinicians were primary end users. Outcomes we were interested in were the effectiveness and identification of specific features of CDDSS on diagnostic performance. RESULTS: We identified 1970 studies and excluded 1938 because they did not fit our inclusion criteria. A total of 45 articles were identified and 12 were found suitable for meta-review. Extraction of high-level requirements identified that a more standardized computable approach is needed to knowledge representation, one that can be readily updated as new knowledge is gained. In addition, a deep integration with the EHR is needed in order to trigger at appropriate points in cognitive workflow. CONCLUSION: Developing a CDDSS that is able to utilize dynamic vocabulary tools to quickly capture and code relevant diagnostic findings, and coupling these with individualized diagnostic suggestions based on the best-available evidence has the potential to improve diagnostic accuracy, but requires evaluation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3109/13814788.2015.1043123

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Other observed challenges in various CDSS include static and non-updatable knowledge bases, which restrict the systems' ability to provide the most current and relevant care.

    “Other observed challenges in various CDSS include static and non-updatable knowledge bases, which restrict the systems’ ability to provide the most current and relevant care [232].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [232]

    The Results section (Sample characteristics; page not provided) identifies sustainability and upgradeability as requirements and says diagnostic decision-support ontologies need work to add new problems. It does not establish that various existing CDSS have static, non-updatable knowledge bases or show that this restricts their ability to provide current care. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Nurek, M., Kostopoulou, O., Delaney, B. C., & Esmail, A.
Affiliations
  • King's College London (GB)
  • Manchester Academic Health Science Centre (GB)
Corresponding author
Esmail, Aneez
Venue
European Journal of General Practice
Published
2015-08-14
Volume
21
Issue
sup1
Pages
8-13
ISSN
  • 1381-4788 (ISSN-L)
  • 1751-1402
OpenAlex
W1750045855
Cited by
60
References
32
Citations by year
2026: 3 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 6 · 2019: 10 · 2018: 5 · 2017: 7 · 2016: 5 · 2015: 2
FWCI
12.21
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • European Journal of General Practice (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Explorer (The University of Manchester) (publishedVersion)
  • Research Portal (King's College London) (submittedVersion)
  • Spiral (Imperial College London) (submittedVersion, other-oa)
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
diagnostic errors, primary care, patient safety, electronic health records, knowledge representation, diagnostic accuracy, general practitioners, clinical workflow
MeSH terms
Diagnostic Errors, Humans, Primary Health Care, Decision Support Systems, Clinical, Patient Safety

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review