RUcore: Rutgers University Community Repository
Conversational artificial intelligence and patient generated health data: transitional patient applications to improve outcomes in the 30 day post-discharge window
Descriptive
TitleInfo
Title
Conversational artificial intelligence and patient generated health data: transitional patient applications to improve outcomes in the 30 day post-discharge window
Name
(type = personal)
NamePart
(type = family)
Mazza
NamePart
(type = given)
Kathleen
NamePart
(type = date)
1961-
DisplayForm
Kathleen Mazza
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Coffman
NamePart
(type = given)
Frederick
DisplayForm
Frederick Coffman
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Srinivisan
NamePart
(type = given)
Shankar
DisplayForm
Shankar Srinivisan
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Mishra
NamePart
(type = given)
Simita
DisplayForm
Simita Mishra
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(type = corporate)
NamePart
School of Health Professions
Role
RoleTerm
(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(encoding = w3cdtf);
(keyDate = yes);
(qualifier = exact)
2020
DateOther
(encoding = w3cdtf);
(qualifier = exact);
(type = degree)
2020-01
Language
LanguageTerm
(authority = ISO 639-3:2007);
(type = text)
English
Abstract
(type = abstract)
Readmission within 30 days of hospital discharge and avoidable emergency room visits have been shown to result in substantial costs and increased risk to patients. Evolving payment models under the ACA are focused on reducing unnecessary costs and decreasing short-term readmissions and will eliminate or reduce payment for 30-day readmissions after treatment for specified conditions and procedures. Evolving communications technology can help providers and patients to exchange critical patient data in the vulnerable post-discharge period and can encourage a shift to shared responsibility and collaboration between patients and providers. However, many providers are not prepared to collect, analyze, or respond to PGHD in their existing workflows, to make it actionable at the point of care, and to establish best practices for the use of PGHD. This study considers the patient’s outcomes of readmission and/or emergency department use in relation to the use of conversational artificial intelligence in the form of chatbots to gather structured PGHD which is integrated directly into the patient’s EHR and into usual provider workflow, making it available in real time for the provider for use in treatment decisions. Additionally, the study describes characteristics of patients elect or decline to participate in the use of chatbots and their preferences for how they receive and send messages. This study may provide valuable insight into developing optimal models of chatbot use for PGHD sharing and for the establishment of best practices for future implementations of this emerging technology.
Subject
(authority = RUETD)
Topic
Biomedical Informatics
Subject
(authority = LCSH)
Topic
Medical informatics
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_10424
PhysicalDescription
Form
(authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 120 pages) : illustrations
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
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/t3-y9c9-tw24
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Mazza
GivenName
Kathleen
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2019-11-27 13:28:37
AssociatedEntity
Name
Kathleen Mazza
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
Back to the top
Technical
RULTechMD
(ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem
(VERSION = 5.1)
windows xp
CreatingApplication
Version
1.4
ApplicationName
Microsoft® Word 2013
DateCreated
(point = end);
(encoding = w3cdtf);
(qualifier = exact)
2019-12-16T21:46:20
DateCreated
(point = end);
(encoding = w3cdtf);
(qualifier = exact)
2019-12-16T21:46:20
Back to the top
Statistical Profile