Abstract
This doctoral research explored strategies for the design and statistical development of probability-based nursing decision support tools within the clinical context of in-hospital cardiopulmonary arrest (IHCPA). IHCPA remains a harmful and costly event, and recent attempts to assist with early recognition via probability-based clinical decision support (PBCDS) tools have fallen short of improving patient outcomes. These shortcomings are due, in part, to the complex nature of PB-CDS tools with inadequate attention paid to important design elements during the early stages of the tools' construction.1 Failure to improve patient outcomes may also be a condition of the PB-CDS tools' underlying statistical assumptions. Thus, this paucity of evidence provided an opportunity to examine aspects of PB-CDS tools that influence clinician's decision making which in turn could impact patient outcomes.
Sigma Membership
Iota at-Large, Nu Lambda
Type
Dissertation
Format Type
Text-based Document
Study Design/Type
Descriptive/Correlational
Research Approach
Advanced Analytics
Keywords:
Cardiac Arrest, Patient Care, Improving Patient Outcomes
Advisor
Lorraine C. Mion
Degree
PhD
Degree Grantor
Vanderbilt University
Degree Year
2017
Recommended Citation
Jeffery, Alvin Dean, "Statistical modeling approaches and user-centered design for nursing decision support tools predicting in-hospital cardiopulmonary arrest" (2020). Dissertations. 1079.
https://www.sigmarepository.org/dissertations/1079
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
All permission requests should be directed accordingly and not to the Sigma Repository.
All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
None: Degree-based Submission
Acquisition
Proxy-submission
Date of Issue
2020-06-12
Full Text of Presentation
wf_yes
Description
This dissertation has also been disseminated through the ProQuest Dissertations and Theses database. Dissertation/thesis number: 13835084; ProQuest document ID: 2179190128. The author still retains copyright.