Page 1 |
Save page Remove page | Previous | 1 of 200 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
BRIM: A PERFORMANCE-BASED BAYESIAN MODEL TO IDENTIFY USE-ERROR RISK LEVELS IN MEDICAL DEVICES
by
Kathryn R. Rieger
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(INDUSTRIAL AND SYSTEMS ENGINEERING)
May 2011
Copyright 2011 Kathryn R. Rieger
Object Description
| Title | BRIM: A performance-based Bayesian model to identify use-error risk levels in medical devices |
| Author | Rieger, Kathryn R. |
| Author email | krrieger@usc.edu; kathrynrose@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-05-02 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-05-04 |
| Advisor (committee chair) | Rahimi, Mansour |
| Advisor (committee member) |
John, Richard S. Meshkati, Najmedin |
| Abstract | Increasing pressure from both regulatory agencies and the consumer market has led to the need for medical use error reduction. BRIM (Bayesian Risk Identification Model) integrates human performance testing with risk management to quantifiably predict human factors issues and illuminate design mitigation strategies during development of medical devices. Upfront analytical modeling permits a significant reduction in required expertise and application of empirical methodologies. BRIM asserts that a common set of performance influencing conditions (PICs) determine how a human will interact with a medical device, and that a unique set of resulting human response failures (HRFs) manifest differently depending on the specific product interface. Probability of HRF occurrence can be derived via a Bayesian Belief Network representation of PICs. By understanding the root causes of why an interface, environment, or contextual combination lead to human error, we can predict how a product will perform with respect to human interaction, and by testing BRIM’s targeted set of design characteristics across human performance metrics, we can specify this use-error likelihood per product interface. Swift and early identification of use-error can provide for increased design flexibility, ultimately leading toward development of a safer product, at a lower cost, that increases productivity and decreases patient mortality. |
| Keyword | human factors; use error; industrial engineering; human performance; modeling |
| Coverage date | 2000/2010 |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3888 |
| Contributing entity | University of Southern California |
| Rights | Rieger, Kathryn R. |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Rieger-4550 |
| Archival file | uscthesesreloadpub_Volume29/etd-Rieger-4550.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | BRIM: A PERFORMANCE-BASED BAYESIAN MODEL TO IDENTIFY USE-ERROR RISK LEVELS IN MEDICAL DEVICES by Kathryn R. Rieger A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (INDUSTRIAL AND SYSTEMS ENGINEERING) May 2011 Copyright 2011 Kathryn R. Rieger |
