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A predictive model analyses of medicare and medicaid inpatient stays and the role of recovery audit contracting program (RAC)

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TitleInfo
Title
A predictive model analyses of medicare and medicaid inpatient stays and the role of recovery audit contracting program (RAC)
Name (type = personal)
NamePart (type = family)
Kirk
NamePart (type = given)
Kathleen M.
NamePart (type = date)
1955-
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Kathleen M. Kirk
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author
Name (type = personal)
NamePart (type = family)
Srinivasan, PhD
NamePart (type = given)
Shankar
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Shankar Srinivasan, PhD
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Gladson,PT,OT,PhD
NamePart (type = given)
Barbara
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Barbara Gladson,PT,OT,PhD
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Coffman, PhD
NamePart (type = given)
Frederick
DisplayForm
Frederick Coffman, PhD
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Health Professions
Role
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school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Advances in information acquisition techniques and the widespread use of information technologies in healthcare services has resulted in an incredible opportunity for health administrators to utilize data analytics and models to provide better health care,manage risks and improve patient outcomes. In this study two different data analytical models - a Logic Model and a Predictive Model – were formulated using data sets obtained from the Health Cost Utilization Project (HCUP) and the Recovery Audit Contractor (RAC) reports for use in Medicare and Medicaid patient hospitalization outcomes research. Objectives: The overall goal of the study was to (1) to design an appropriate analytical model to explain the operations of the RAC process and identify the hospitalization factors that affect the efficient recovery of claims (2) to formulate a predictive model by using HCUP’s Nationwide Inpatient Sample datasets to help predict those hospitalization factors above affecting the RAC claims recovery process, and (3) to determine other relevant hospital, regional and patient related variables that play a statistically significant role in both the RAC and the Hospitalization Outcomes Models. Methods: To meet the aforementioned objectives data was extracted from both the RACTrac Website and Reports (for developing the RAC Process Model) and the HCUP Nationwide Inpatient Sample (NIS) database. Several analytical models currently in vogue in both health and finance were investigated and it was decided to adopt a Logic Model to describe the RAC claims recovery process and with its help identified the hospitalization factors related to the claims and payment issues. Secondly the Multiple Linear Regression Model was found to be the most suitable predictive model type for the 6 hospitalization factors identified from the RAC Logic Model. Lastly several descriptive and inferential statistics were employed to infer relationships among several patient and hospital variables with the RAC regions and their outcomes. Results: Both Length of Stay (LOS) and Total Charges were found to be intimately related to the RAC claims recovery process and accordingly they both were employed in the development of the Multiple Linear Regression Model with several independent variables such as DRG, RAC region, Payer type (Medicare, Medicaid, Private), Number of Diagnoses and Number of Procedures resulted in a reasonably good fit (54 % to 59 %) of the model in explaining the variance of the outcome of Total Charges and not a very good fit for the LOS which was expected since LOS is not a linear variable and subject to too many constraints and hence not easily predictable. The ANOVA Tests revealed several interesting relationships between the independent variables listed above and the RAC regions with implications of import for the RAC claims recovery process. Conclusion: This study is significant because it demonstrates the validity of the use of analytical models such as Logic Model and the Multiple Linear Regression Model in predicting Hospitalization Outcomes of interest to not only the RAC claims recovery process relevant to this study but also in other health administrative settings involving planning of budget and resource allocation. The complex process of RAC claims recovery mechanism has been duly modeled by the Logic Model technique thus making it available for future configuration modification and studies into their effect on theclaims recovery process.
Subject (authority = RUETD)
Topic
Biomedical Informatics
Subject (authority = ETD-LCSH)
Topic
Medical audit
Subject (authority = ETD-LCSH)
Topic
Medicare Recovery Audit Contractor Program (U.S.)
Subject (authority = ETD-LCSH)
Topic
Medicare
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Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8298
PhysicalDescription
Form (authority = gmd)
electronic resource
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application/pdf
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text/xml
Extent
1 online resource (105 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Kathleen M. Kirk
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School of Health Professions ETD Collection
Identifier (type = local)
rucore10007400001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3FF3WDW
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kirk
GivenName
Kathleen
MiddleName
M.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-08-21 16:39:05
AssociatedEntity
Name
Kathleen Kirk
Role
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Affiliation
Rutgers University. School of Health Professions
AssociatedObject
Type
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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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