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Conversational artificial intelligence and patient generated health data: transitional patient applications to improve outcomes in the 30 day post-discharge window
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Mazza, Kathleen. Conversational artificial intelligence and patient generated health data: transitional patient applications to improve outcomes in the 30 day post-discharge window. Retrieved from https://doi.org/doi:10.7282/t3-y9c9-tw24

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TitleConversational artificial intelligence and patient generated health data: transitional patient applications to improve outcomes in the 30 day post-discharge window
NameMazza, Kathleen (author); Coffman, Frederick (chair); Srinivisan, Shankar (internal member); Mishra, Simita (outside member); Rutgers University; School of Health Professions
Date Created2020
Other Date2020-01 (degree)
SubjectBiomedical Informatics, Medical informatics
Extent1 online resource (xii, 120 pages) : illustrations
DescriptionReadmission 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.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-y9c9-tw24
LanguageEnglish
CollectionSchool of Health Professions ETD Collection
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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