國立虎尾科技大學 |

Improving healthcare outcomes with business analytics.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Improving healthcare outcomes with business analytics./
Author:	Majidi Zolbanin, Hamed.
Description:	1 online resource (136 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
Contained By:	Dissertation Abstracts International78-05A(E).
Subject:	Business administration. -
Online resource:	click for full text (PQDT)
ISBN:	9781369364859

Improving healthcare outcomes with business analytics.
Majidi Zolbanin, Hamed.

Improving healthcare outcomes with business analytics. - 1 online resource (136 pages)

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.

Thesis (Ph.D.)

Includes bibliographical references

This work includes three studies, in which we develop the fact-based data analytics methodologies to inspect, clean, transform, and model data to discover useful information and make suggestions that enable enhanced decision making in the healthcare sector. We employ this methodology to analyze medium and large physical and mental health data to address questions that could not be easily answered using the traditional methodologies.

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

Mode of access: World Wide Web

ISBN: 9781369364859Subjects--Topical Terms:

1148568
Business administration.
Index Terms--Genre/Form:

554714
Electronic books.

Improving healthcare outcomes with business analytics.
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100 1 $a Majidi Zolbanin, Hamed. $3 1182626
245 1 0 $a Improving healthcare outcomes with business analytics.
264 0 $c 2016
300 $a 1 online resource (136 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
500 $a Adviser: Dursun Delen.
502 $a Thesis (Ph.D.) $c Oklahoma State University $d 2016.
504 $a Includes bibliographical references
520 $a This work includes three studies, in which we develop the fact-based data analytics methodologies to inspect, clean, transform, and model data to discover useful information and make suggestions that enable enhanced decision making in the healthcare sector. We employ this methodology to analyze medium and large physical and mental health data to address questions that could not be easily answered using the traditional methodologies.
520 $a In the first study, we show that the availability of comorbidity data enables more accurate predictive models, which results in better decision making in future. This can be done with minimum changes in existing data storage systems, as most methods of recording comorbidities provide a single score to account for overall health of patients.
520 $a In the second study, we build a predictive analytics model that can inform drug treatment court authorities to make more efficient decisions by favoring offenders that have greater chances of completing the treatment. Our results confirm prior research and add to it by creating a predictive model that could separate completers from dropouts with an accuracy over 90%. We also show how predictive analytics provides an explanation for the behavior of offenders in these programs from a different angle. At the end, we provide a list of factors and the values they take to enable an evidence-based decision making in these treatment programs.
520 $a The third study uses a subset of the Cerner Corporation's massive data warehouse to explore how hospitals have responded to the Hospital Readmission Reduction Program (HRRP) of the Patient Protection and Affordable Care Act. We use the ecological rationality framework of rational choice theory to show that hospitals selected an outcome-oriented approach to lower readmission rates, that is, through fewer inpatient admissions. We also employ a predictive approach to build models that can specify 30-day readmission propensity of patients.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Business administration. $3 1148568
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
690 $a 0310
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Oklahoma State University. $b Management Information Systems. $3 1182627
773 0 $t Dissertation Abstracts International $g 78-05A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10190258 $z click for full text (PQDT)