Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital

BMC Medical Informatics and Decision Making

Research

O. C. Granmo¹  B. E. Munkvold²  G. T. Berge³  A. L. Ruthjersen⁴  T. O. Tveit⁵  J. Sharma⁶ 

[1] Department of ICT, University of Agder, Grimstad, Norway;Department of Information Systems, University of Agder, Kristiansand, Norway;Department of Information Systems, University of Agder, Kristiansand, Norway;Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway;Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway;Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway;Department of Anaesthesia and Intensive Care, Sørlandet Hospital Trust, Kristiansand, Norway;Research Department, Sørlandet Hospital Trust, Kristiansand, Norway;Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway;Department of ICT, University of Agder, Grimstad, Norway;

关键词: Clinical decision support systems;    Natural language processing;    Technology acceptance;    UTAUT;    Machine learning;    Electronic health record;   

DOI  :  10.1186/s12911-023-02101-x

 received in 2022-06-22, accepted in 2023-01-04,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundNatural 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 real-world 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.MethodsWe 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 rule-based 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.ResultsEvaluation of system adoption was performed by a mixed methods approach applying The Unified 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.ConclusionsOur combined machine learning and rule-based approach benefits system performance, efficiency, 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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 8

Scheme 1