Background: Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. Methods: We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. Results: We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. Conclusions: Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.
|Journal||BMC Medical Informatics and Decision Making|
|Publication status||Published - 21 Jun 2021|
Bibliographical noteFunding Information:
This project was funded by a grant from the Wellcome Trust ref. 202133/Z/16/Z. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. VC is also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' National Health Service NHS) Foundation Trust and King's College London, and the Public Health and Multimorbidity Theme of the National Institute for Health Research’s Applied Research Collaboration (ARC) South London. The opinions in this paper are those of the authors and do not necessarily reflect the opinions of the funders.
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- General Practice
- clinical prediction
- electronic health records
- Alert fatigue
- Primary health care
- General practice
- Clinical decision support