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Data analysis and simulation approach to capacity planning

Author(s)
Chafac, Melvis Ngemasong Ngimndoh
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Massachusetts Institute of Technology. Engineering Systems Division.
Advisor
John Carroll.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
In 2012, President Obama signed an Executive Order to improve access to mental health service for active duty members and for veterans. Two years later, in 2014, the President outlined 19 new executive actions to improve the lives of service members with a focus on improving access to mental health care. These actions placed a priority on improving the capacity to provide mental health care. This thesis examines ways of improving the capacity of the mental health system with a focus on system redesign. I review capacity planning, provide a literature review of simulation methods and present a simulation, and data analysis of Site Alpha, a U.S. Army Installation. I also use causal loop diagrams to explore other feasible scenarios that affect care capacity. The key take-away from this work is that system inefficiencies should be dealt with before more resources can be effectively added and used in the system. Another pertinent finding is that the distribution of the providers in the system should be improved. The system also contains high utilizer patients who must be considered when planning for care. The mental health system is extremely complex and risks becoming even more complex. However, by adopting a holistic, systems approach to capacity planning the complexity can be managed.
Description
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015.
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 72-97).
 
Date issued
2015
URI
http://hdl.handle.net/1721.1/110893
Department
Massachusetts Institute of Technology. Engineering Systems Division; Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Publisher
Massachusetts Institute of Technology
Keywords
Institute for Data, Systems, and Society., Engineering Systems Division.

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