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
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.
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