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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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)8-13
Number of pages6
JournalEuropean Journal of General Practice
Publication statusPublished - 14 Aug 2015

King's Authors


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.

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