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Studies on Using Data-Driven Decision Support Systems to Improve Personalized Medicine Processes.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Studies on Using Data-Driven Decision Support Systems to Improve Personalized Medicine Processes./
Author:	Cameron, Kellas Ross.
Description:	1 online resource (170 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
Contained By:	Dissertation Abstracts International79-11A(E).
Subject:	Management. -
Online resource:	click for full text (PQDT)
ISBN:	9780438146891

Studies on Using Data-Driven Decision Support Systems to Improve Personalized Medicine Processes.
Cameron, Kellas Ross.

Studies on Using Data-Driven Decision Support Systems to Improve Personalized Medicine Processes. - 1 online resource (170 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.

Thesis (Ph.D.)--Boston University, 2018.

Includes bibliographical references

This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient's ability to engage in their own healthcare decisions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438146891Subjects--Topical Terms:

558618
Management.
Index Terms--Genre/Form:

554714
Electronic books.

Studies on Using Data-Driven Decision Support Systems to Improve Personalized Medicine Processes.
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245 1 0 $a Studies on Using Data-Driven Decision Support Systems to Improve Personalized Medicine Processes.
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300 $a 1 online resource (170 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
500 $a Adviser: Nitin R. Joglekar.
502 $a Thesis (Ph.D.)--Boston University, 2018.
504 $a Includes bibliographical references
520 $a This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient's ability to engage in their own healthcare decisions.
520 $a The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider's decision on whether this test would provide beneficial information in selecting a patient's medication. Model analysis shows that healthcare providers' primary focus is to maximize patient health outcomes while considering the impact the patient's economic welfare.
520 $a The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC.
520 $a The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why---whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies.
520 $a Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Management. $3 558618
650 4 $a Health care management. $3 1148454
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0769
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Boston University. $b Management. $3 1190521
773 0 $t Dissertation Abstracts International $g 79-11A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10822396 $z click for full text (PQDT)