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dc.contributor.authorBerge, Geir Thore
dc.contributor.authorGranmo, Ole- Christoffer
dc.contributor.authorTveit, Tor Oddbjørn
dc.contributor.authorMunkvold, Bjørn Erik
dc.contributor.authorRuthjersen, Anna Linda
dc.contributor.authorSharma, Jivitesh
dc.identifier.citationBerge, G.T., Granmo, O.C., Tveit, T.O. (2023). Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital.BMC Medical Informatics and Decision Making 23, 5en_US
dc.description.abstractBackground Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because realworld EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use. Methods We present our results from the evaluation of an NLP-driven CDSS developed and implemented in a Norwegian Hospital. The system incorporates unsupervised and supervised machine learning combined with rulebased algorithms for clinical concept-based searching to identify and classify allergies of concern for anesthesia and intensive care. The system also implements a semi-supervised machine learning approach to automatically annotate medical concepts in the narrative. Results Evaluation of system adoption was performed by a mixed methods approach applying The Unifed Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens. Most of the respondents demonstrated a high degree of system acceptance and expressed a positive attitude towards the system in general and intention to use the system in the future. Increased detection of patient allergies, and thus improved quality of practice and patient safety during surgery or ICU stays, was perceived as the most important advantage of the system. Conclusions Our combined machine learning and rule-based approach benefts system performance, efciency, and interpretability. The results demonstrate that the proposed CDSS increases detection of patient allergies, and that the system received high-level acceptance by the clinicians using it. Useful recommendations for further system improvements and implementation initiatives are reducing the quantity of alarms, expansion of the system to include more clinical concepts, closer EHR system integration, and more workstations available at point of care.en_US
dc.publisherBioMed Central (BMC)en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleMachine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospitalen_US
dc.typeJournal articleen_US
dc.rights.holder© 2023 The Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.journalBMC Medical Informatics and Decision Makingen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal