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Analyses of medical device failures related to computing technology

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TitleInfo
Title
Analyses of medical device failures related to computing technology
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Khanal
NamePart (type = given)
Deepak
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Deepak Khanal
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author
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Srinivasan
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Shankar
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Shankar Srinivasan
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Advisory Committee
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chair
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Gohel
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Suril
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Suril Gohel
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
AbdelHady
NamePart (type = given)
Mohamed F
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Mohamed F AbdelHady
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Advisory Committee
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outside member
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Heierman
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Edwin O
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Edwin O Heierman
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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NamePart
School of Health Professions
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school
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2018
DateOther (type = degree); (qualifier = exact)
2018-05
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2018
Place
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xx
Language
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eng
Abstract (type = abstract)
Background: The adoption of computing technology in modern medical devices is ubiquitous. However, limited research currently exists on the role of computing technology on medical device failures and patient safety. The U.S. Food and Drug Administration (FDA) collects and publishes reports of medical device events as a part of its Medical Device Reporting program, but the problem codes assigned to events in the published database do not appear to be reliable to identify computing technology-related events. Methods: A supervised machine learning technique was designed and implemented to classify over 11 million natural-language narratives of medical device events reported to the FDA between 2007 and 2016 to identify events related to computing technology. The result of the classification was then used to analyze the events from several dimensions. Results: A total of 5,110,200 reports of medical device events were submitted to the FDA between 2007 and 2016. Of these, 1,155,516 (22.61%) were related to computing technology. Number of computing technology-related medical device events reported to the FDA jumped nearly 7-fold from 37,679 in 2007 to 262,407 in 2016. Nearly all (99.36%) of these reports were submitted to the FDA by the manufacturers of devices, even though patients were the original reporters of the issues leading up to the submission in nearly a third (32.46%) of the events. A total of 3,449 medical device events related to computing technology were associated with patient deaths in the 10-year period. Also, events published by the FDA on its Manufacturer and User Facility Device Experience (MAUDE) database were found to be missing problem codes in 62.74% of a sampled set (N=102) of events related to computing technology and inaccurate in 26.47% of the sampled events. Conclusions: Computing technology-related events constitute a significant portion of medical device events reported to the FDA every year. Overall, these events are on an increasing trend on an absolute basis. Manufacturers are the submitters of nearly all of the computing technology-related medical device events reported to the FDA. Medical device events related to computing technology can cause serious adverse patient events, including death. Problem codes assigned to computing technology-related medical device events in the MAUDE database published by the FDA are inaccurate at a significant rate and should not be used in research.
Subject (authority = RUETD)
Topic
Biomedical Informatics
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8760
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiv, 249 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Medical instruments and apparatus--Defects
Note (type = statement of responsibility)
by Deepak Khanal
RelatedItem (type = host)
TitleInfo
Title
School of Health Professions ETD Collection
Identifier (type = local)
rucore10007400001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3TX3JPK
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Khanal
GivenName
Deepak
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-04-05 16:55:40
AssociatedEntity
Name
Deepak Khanal
Role
Copyright holder
Affiliation
Rutgers University. School of Health Professions
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2018-04-05T15:54:01
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2018-04-05T15:54:01
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