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Authors: Bernard Hernandez ; Pau Herrero ; Timothy M. Rawson ; Luke S. P. Moore ; Esmita Charani ; Alison H. Holmes and Pantelis Georgiou

Affiliation: Imperial College London, United Kingdom

ISBN: 978-989-758-213-4

Keyword(s): Antimicrobial Resistance, Infection Diseases, Antibiotics, Decision Support System, Case-Based Reasoning, Machine Learning, User Interface, Point of Care.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Cloud Computing ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; e-Health ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Healthcare Management Systems ; Knowledge-Based Systems ; Pattern Recognition and Machine Learning ; Platforms and Applications ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Systems in Medicine ; Symbolic Systems

Abstract: Antimicrobial Resistance (AMR) is a major patient safety issue. Attempts have been made to palliate its growth. Misuse of antibiotics to treat human infections is a main concern and therefore prescription behaviour needs to be studied and modified appropriately. A common approach relies on designing software tools to improve data visualization, promote knowledge transfer and provide decision-making support. This paper explains the design of a Decision Support System (DSS) for clinical environments to provide personalized, accurate and effective diagnostics at point-of-care (POC), improving continuity, interpersonal communication, education and knowledge transfer. Demographics, biochemical and susceptibility laboratory tests and individualized diagnostic/therapeutic advice are presented to clinicians in a handheld device. Case-Based Reasoning (CBR) is used as main reasoning engine to decision support for infection management at POC. A web-based CBR-inspired interface design focused on usability principles has also been developed. The proposed DSS is perceived as useful for patient monitoring and outcome review at POC by expert clinicians. The DSS was rated with a System Usability Scale (SUS) score of 68.5 which indicates good usability. Furthermore, three areas of improvement were identified from the feedback provided by clinicians: thorough guidance requirements for junior clinicians, reduction in time consumption and integration with prescription workflow. (More)

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Paper citation in several formats:
Hernandez B., Herrero P., Rawson T., Moore L., Charani E., Holmes A. and Georgiou P. (2017). Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits and Limitations.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 119-127

@conference{healthinf17,
author={Bernard Hernandez and Pau Herrero and Timothy M. Rawson and Luke S. P. Moore and Esmita Charani and Alison H. Holmes and Pantelis Georgiou},
title={Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits and Limitations},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={119-127},
doi={},
isbn={978-989-758-213-4},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits and Limitations
SN - 978-989-758-213-4
AU - Hernandez B.
AU - Herrero P.
AU - Rawson T.
AU - Moore L.
AU - Charani E.
AU - Holmes A.
AU - Georgiou P.
PY - 2017
SP - 119
EP - 127
DO -

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