• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Mental Health Readmissions Among Veterans: An Exploratory Endeavor Using Data Mining

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_etd_14270_sip1_m.pdf
    Size:
    3.623Mb
    Format:
    PDF
    Description:
    Dissertation
    Download
    Thumbnail
    Name:
    BEST+CHAID (2).png
    Size:
    340.8Kb
    Format:
    PNG image
    Description:
    Supplemental File
    Download
    Author
    Price, Lauren Emilie
    Issue Date
    2015
    Keywords
    Data Mining
    Mental Health
    Nursing
    Readmission
    Veteran
    Big Data
    Advisor
    Shea, Kimberly D.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    The purpose of this research is to inform the understanding of mental health readmissions by identifying associations between individual and environmental attributes and readmissions, with consideration of the impact of time-to-readmission within the Veterans Health Administration (VHA). Mental illness affects one in five adults in the United States (US). Mental health disorders are among the highest all-cause readmission diagnoses. The VHA is one of the largest national service providers of specialty mental health care. VHA's clinical practices and patient outcomes can be traced to US policy, and may be used to forecast national outcomes should these same policies be implemented nationwide. In this research, we applied three different data mining techniques to clinical data from over 200,000 patients across the VHA. Patients in this cohort consisted of adults receiving VHA inpatient mental health care between 2008 and 2013. The data mining techniques employed included k-means cluster analysis, association-rule mining, and decision tree analysis. K-means was used during cluster analysis to identify four statistically distinct clusters based on the combination of admission count, comorbidities, prescription (RX) count, age, casualty status, travel distance, and outpatient encounters. The association-rule mining analysis yielded multiple frequently occurring attribute values and sets consisting of service connection type, diagnoses/problems, and pharmaceuticals. Using the CHAID algorithm, the best decision tree model achieved 80% predictive accuracy when no readmissions were compared to 30-day readmissions. The strongest predictors of readmissions based on this algorithm were outpatient encounters, prescription count, VA Integrated Service Network (VISN), number of comorbidities, region, service connection, and period of service. Based on evidence from all three techniques, individuals with higher rates of system-wide utilization, more comorbidities, and longer medication lists are the most likely to have a 30-day readmission. These individuals represented 25% of this cohort, are sicker in general and may benefit from enrollment in a comprehensive nursing case management program.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Nursing
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.