Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Hospital prioritization). Showing records 1 – 3 of 3 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of Missouri – Columbia

1. Ghavrish, Artem. Prioritization of hospital orders considering cancellation probabilities.

Degree: 2012, University of Missouri – Columbia

In a hospital laboratory premature cancellations of the test orders are driving up the laboratory running cost as they result in wasted resources and time. A test order that is cancelled during its processing results in waste of laboratory resources (labor, materials, etc.). We develop a queuing heuristic that prioritizes orders based on their cancellation probability and tardiness. The cancellation probability is a function of order attributes (type of test, the doctor requested the test, urgency, etc.) and can be estimated from historical data. We compare the cost and timeliness of lab orders from our prioritization scheme against those from a First-Come-First-Served queuing system. Having no control over the nature of the tests ordering (arrival) and their cancellations (departure) we model them as random processes that follow certain probability distributions and include them into a Dynamic Programming model that attempts to minimize the expected total running cost by picking the "right" test to process at every decision epoch. Advisors/Committee Members: Sir, Mustafa (advisor).

Subjects/Keywords: order prioritization; cancellation probability; hospital laboratory; dynamic programming model

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ghavrish, A. (2012). Prioritization of hospital orders considering cancellation probabilities. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/15948

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ghavrish, Artem. “Prioritization of hospital orders considering cancellation probabilities.” 2012. Thesis, University of Missouri – Columbia. Accessed April 24, 2019. http://hdl.handle.net/10355/15948.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ghavrish, Artem. “Prioritization of hospital orders considering cancellation probabilities.” 2012. Web. 24 Apr 2019.

Vancouver:

Ghavrish A. Prioritization of hospital orders considering cancellation probabilities. [Internet] [Thesis]. University of Missouri – Columbia; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10355/15948.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ghavrish A. Prioritization of hospital orders considering cancellation probabilities. [Thesis]. University of Missouri – Columbia; 2012. Available from: http://hdl.handle.net/10355/15948

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

2. Hagen, Matthew. Biological and clinical data integration and its applications in healthcare.

Degree: PhD, Computer Science, 2014, Georgia Tech

Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are often heterogeneous. Most biological data repositories focus on providing only one particular type of data, such as sequences, molecular interactions, protein structure, or gene expression. In many cases, it is required for researchers to visit several different databases to answer one scientific question. It is essential to develop strategies to integrate disparate biological data sources that are efficient and seamless to facilitate the discovery of novel associations and validate existing hypotheses. This thesis presents the design and development of different integration strategies of biological and clinical systems. The BioSPIDA system is a data warehousing solution that integrates many NCBI databases and other biological sources on protein sequences, protein domains, and biological pathways. It utilizes a universal parser facilitating integration without developing separate source code for each data site. This enables users to execute fine-grained queries that can filter genes by their protein interactions, gene expressions, functional annotation, and protein domain representation. Relational databases can powerfully return and generate quickly filtered results to research questions, but they are not the most suitable solution in all cases. Clinical patients and genes are typically annotated by concepts in hierarchical ontologies and performance of relational databases are weakened considerably when traversing and representing graph structures. This thesis illustrates when relational databases are most suitable as well as comparing the performance benchmarks of semantic web technologies and graph databases when comparing ontological concepts. Several approaches of analyzing integrated data will be discussed to demonstrate the advantages over dependencies on remote data centers. Intensive Care Patients are prioritized by their length of stay and their severity class is estimated by their diagnosis to help minimize wait time and preferentially treat patients by their condition. In a separate study, semantic clustering of patients is conducted by integrating a clinical database and a medical ontology to help identify multi-morbidity patterns. In the biological area, gene pathways, protein interaction networks, and functional annotation are integrated to help predict and prioritize candidate disease genes. This thesis will present the results that were able to be generated from each project through utilizing a local repository of genes, functional annotations, protein interactions, clinical patients, and medical ontologies. Advisors/Committee Members: Lee, Eva K. (advisor), Jordan, King (committee member), Song, Le (committee member), Navathe, Shamkant (committee member), Buchman, Timothy (committee member).

Subjects/Keywords: Biological database integration; Clinical data warehouse; Candidate gene prioritization; Disease; Diffusion kernel; Data mining; Ontology; Semantic similarity; Clustering; Intensive care unit; Hospital prioritization; Patient; Machine learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hagen, M. (2014). Biological and clinical data integration and its applications in healthcare. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54267

Chicago Manual of Style (16th Edition):

Hagen, Matthew. “Biological and clinical data integration and its applications in healthcare.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/54267.

MLA Handbook (7th Edition):

Hagen, Matthew. “Biological and clinical data integration and its applications in healthcare.” 2014. Web. 24 Apr 2019.

Vancouver:

Hagen M. Biological and clinical data integration and its applications in healthcare. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/54267.

Council of Science Editors:

Hagen M. Biological and clinical data integration and its applications in healthcare. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/54267

3. Naiman, Melissa I. Systematically Gathering Clinician Opinions on Health Care Technology.

Degree: 2013, University of Illinois – Chicago

Q-methodology appears to be effective for systematically gathering clinician opinions regarding health care technology. One-on-one interviews and focus groups provided sufficient data to create a concourse of radical innovations. Analysis of the radical innovation concourse revealed themes that guided market research into technologies that were currently, or would eventually be, marketed to emergency departments. Generic descriptions of these technologies populated the 43 item Q-set. Clinicians were able to complete Q-sorts independently using a web-based instrument in under 20 minutes. Participants represented a variety of backgrounds and experience levels. Quantitative analysis supported a four component PCA solution which explained 53% of the study variance and contained 33 significantly aligned sorts. The results were interpreted from two frames of reference: 1) in terms of the unique innovation profiles reflected by members of each factor and 2) in terms of consensus and controversial items. The four innovation profiles were: Speed Oriented, Holism Oriented, Acuity Oriented, and Information Availability Oriented. Nine positive consensus items were identified in the areas of health communication, in vitro diagnostics, and imaging. Six negative consensus items were identified in the areas of health communication, non-invasive monitoring, and new treatment options. One significant and extremely controversial item, a government-controlled database that contains a comprehensive patient history, was discussed at length. The results provide streamlined guidance on specific technologies for administrators to explore as well as valuable insights into the underlying attitudes clinicians hold that may influence their technology adoption behavior. Advisors/Committee Members: Valenta, Annette L (advisor).

Subjects/Keywords: health technology; technology adoption; technology prioritization; health care innovation; hospital; Emergency Medicine

…that will allow hospital administrators to efficiently incorporate clinician preferences into… …these results provide a new mechanism by which hospital administrators can include clinician… …all hospital capital spending (1). In 2003, the medical device sector (… …revenues (2). In 2004, hospital investment in IT alone reached $26 billion and… …important to improving the provision of care in a hospital department. Most hospitals currently… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Naiman, M. I. (2013). Systematically Gathering Clinician Opinions on Health Care Technology. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/9976

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Naiman, Melissa I. “Systematically Gathering Clinician Opinions on Health Care Technology.” 2013. Thesis, University of Illinois – Chicago. Accessed April 24, 2019. http://hdl.handle.net/10027/9976.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Naiman, Melissa I. “Systematically Gathering Clinician Opinions on Health Care Technology.” 2013. Web. 24 Apr 2019.

Vancouver:

Naiman MI. Systematically Gathering Clinician Opinions on Health Care Technology. [Internet] [Thesis]. University of Illinois – Chicago; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10027/9976.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Naiman MI. Systematically Gathering Clinician Opinions on Health Care Technology. [Thesis]. University of Illinois – Chicago; 2013. Available from: http://hdl.handle.net/10027/9976

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.