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A predictive model for inpatient major joint replacement or reattachment of lower extremity

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TitleInfo
Title
A predictive model for inpatient major joint replacement or reattachment of lower extremity
Name (type = personal)
NamePart (type = family)
Pascal
NamePart (type = given)
Forrest
NamePart (type = date)
1962-
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Forrest Pascal
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RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Srinivasan
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Shankar
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Shankar Srinivasan
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Advisory Committee
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chair
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Gohel
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Suril
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Suril Gohel
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Strandberg
NamePart (type = given)
Ericka
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Ericka Strandberg
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Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = personal)
NamePart (type = family)
Cull
NamePart (type = given)
Tamara
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Tamara Cull
Affiliation
Advisory Committee
Role
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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School of Health Professions
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school
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Text
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theses
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2019
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2019-05
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2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract
Many have suggested that the United States healthcare system is broken. Costs are higher than ever, access is limited, and at times quality is questionable. One area of opportunity to lower cost, increase quality, and provide greater access is major joint replacement or reattachment of lower extremity—the most common inpatient surgical procedure for Medicare beneficiaries. Existing research points to emergency department visits, readmissions, and mortality as strong determinants of risk in an inpatient stay for major joint replacement or reattachment of lower extremity. For the current study, approximately 2.3 million inpatient claims were ingested from Medicare, resulting in 74 187 major joint replacement claims being extracted, cleansed, processed, and transformed. For each claim, emergency department visits, readmissions, mortality, and length of stay were calculated, along with the creation of an ICD crosswalk from 9 to 10 and Elixhauser Comorbidity Indexes for mortality & readmissions. A novel algorithm was developed to determine the risk of each claim. SAS Enterprise Miner, MATLAB, and MLJAR were used to mine the claims using supervised machine learning algorithms, and Tableau was used to visualize correlations and create 2D plots. This research provided the following insights: Matlab’s Ensemble Boosted Tree algorithm predicted the novel risk 8.out of 10 times across both the training and test dataset, proving its portability and reliability. Consistently, the physicians, provider (hospital), claim payment, type of admission and beneficiary county yielded the strongest predictor strength in predicting the outcome novel risk derived from emergency department visits, mortality, and readmissions. These predictors present areas of opportunity to lower cost, increase access, and improve quality by being used as indicators for early warning & surveillance systems for case workers, clinicians, and hospital administrators. Furthermore, machine learning models utilized in value-based care can assist healthcare leaders, payers, and providers with decision making on which care models may be most effective in facilitating associations to data on outcomes about patients with the highest risk profiles—specifically to identify which patients to follow more closely, which physicians and hospitals have the most successful results, and which geographic areas have differing results. Additionally, white females made up over 60% of observations, and both white females and males had the costliest claim payments. Lastly, obesity and hypertension (complicated & uncomplicated) were the most frequent comorbidities across gender, race and age group.
Subject (authority = local)
Topic
LEJR
Subject (authority = RUETD)
Topic
Biomedical Informatics
Subject (authority = ETD-LCSH)
Topic
Artificial joints
Subject (authority = ETD-LCSH)
Topic
Algorithms
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9900
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (xiii, 141 pages)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Health Professions ETD Collection
Identifier (type = local)
rucore10007400001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-sjgv-fs40
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
Pascal
GivenName
Forrest
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-04-22 20:58:35
AssociatedEntity
Name
Forrest Pascal
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
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Technical

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2019-04-22T18:49:41
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2019-04-22T18:54:08
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