Signal processing, human factors, and modelling to support bedside care in the intensive care unit

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Publication Type dissertation
School or College College of Engineering
Department Biomedical Engineering
Author Görges, Matthias
Title Signal processing, human factors, and modelling to support bedside care in the intensive care unit
Date 2011-08
Description Medical error causes preventable death in nearly 100,000 patients per year in the US alone. Common sources for error include medication related problems, technical equipment failure, interruptions, complicated and error-prone devices, information overload (providing too much patient data for one person to process effectively), and environmental problems like inadequate lighting or distracting ambient noise. Intensive care units are one of the riskiest locations in a hospital, with up to 9 reported events per 100 patient days. This risk is in large contrast to anesthesia in the operating rooms. Here much advancement in the area of patient safety has been made in the past, dropping the average risk for anesthesia related death to less than 1 in 200,000 anesthetics-an improvement by a factor of 20 in the past 30 years. Improvements in technology and other innovations contributing to this success now need to be adapted for and implemented in the intensive care unit setting. Nurses are increasingly regarded as key decision makers within the healthcare team, as they outnumber physicians 4:1. Reducing nurses' workload and improving medical decision making by providing decision support tools can have a significant impact in reducing the chances of medical errors. This dissertation consists of four manuscripts: 1) a review of previous medical display evaluations, providing insight into solutions that have worked in the past; 2) a study on reducing false alarms and increasing the usefulness of the remaining alarms by introducing alarm delays and detecting alarm context;, such as suctioning automatically silencing ventilator alarms; 3) a study of simplifying the frequent but complicated task of titrating vasoactive medications by providing a titration support tool that predicts blood pressure changes 5 minutes into the future; and 4) a study on supporting the triage of unfamiliar patients by introducing a far-view display that incorporates information from previously disparate devices and presents trend and alarm information at one easy to scan and interpret location.
Type Text
Publisher University of Utah
Subject Graphical display; Human factors; Intensive care unit; Nursing; Signal processing
Dissertation Institution University of Utah
Dissertation Name Doctor of Philosophy
Language eng
Rights Management Copyright © Matthias Görges 2011
Format Medium application/pdf
Format Extent 1,416,902 bytes
Identifier us-etd3,59319
Source Original housed in Marriott Library Special Collections, RA4.5 2010 .G67
ARK ark:/87278/s68346rw
Setname ir_etd
Date Created 2012-04-24
Date Modified 2021-05-06
ID 194506
Reference URL
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