東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Detecting Cause of Readmission: A Bi...

Wang, Xiaoyi.

Linked to FindBook

Google Book

Amazon

博客來

Detecting Cause of Readmission: A Big Data Analytics Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Detecting Cause of Readmission: A Big Data Analytics Approach./
Author:	Wang, Xiaoyi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	48 p.
Notes:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
Subject:	Health care management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10810199
ISBN:	9780438239586

Detecting Cause of Readmission: A Big Data Analytics Approach.
Wang, Xiaoyi.

Detecting Cause of Readmission: A Big Data Analytics Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 48 p.

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.S.)--Northeastern University, 2018.

Rehospitalizations within a month discharge lead to tremendous cost to health system in the United States. The Hospital readmission is a constant source of researchers' analysis particular in the US where the hospitals face increased pressure to reduce avoidable readmissions under the federal value-based purchasing program. The readmission rate has dropped since the penalty was acted but it is largely owning to the decline in readmissions to the original hospital. Patients readmitted to a different hospital experienced longer hospital stays. On the other hand, patients with comorbid mental and physical illness are particularly vulnerable to readmission.

ISBN: 9780438239586Subjects--Topical Terms:

2122906
Health care management.

Detecting Cause of Readmission: A Big Data Analytics Approach.
LDR:03073nmm a2200325 4500 001 2164510
005 20181106103646.5
008 190424s2018 ||||||||||||||||| ||eng d
020 $a 9780438239586
035 $a (MiAaPQ)AAI10810199
035 $a (MiAaPQ)coe.neu:10998
035 $a AAI10810199
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wang, Xiaoyi. $3 1026536
245 1 0 $a Detecting Cause of Readmission: A Big Data Analytics Approach.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 48 p.
500 $a Source: Masters Abstracts International, Volume: 58-01.
500 $a Adviser: Noor E. Alam.
502 $a Thesis (M.S.)--Northeastern University, 2018.
520 $a Rehospitalizations within a month discharge lead to tremendous cost to health system in the United States. The Hospital readmission is a constant source of researchers' analysis particular in the US where the hospitals face increased pressure to reduce avoidable readmissions under the federal value-based purchasing program. The readmission rate has dropped since the penalty was acted but it is largely owning to the decline in readmissions to the original hospital. Patients readmitted to a different hospital experienced longer hospital stays. On the other hand, patients with comorbid mental and physical illness are particularly vulnerable to readmission.
520 $a This study had two research aims: Aim 1: To compare different-hospital readmissions among Medicare and non-Medicare patients with three kinds of primary diagnosis: congestive heart failure (CHF), acute myocardial infarction (AMI), and pneumonia; and Aim 2: To examine the effect of comorbid serious mental illness (SMI) on the risk of 30-day rehospitalization among medical and surgical cohorts.
520 $a In Aim 1, we used California Patient Discharge Data from 2010 to 2014. The study sample was composed of three cohorts: congestive heart failure, acute myocardial infarction, and pneumonia inpatients. The main outcome is different-hospital readmissions for each cohort. In Aim 2, we examined hospital discharge records in 2014 from National Readmission Database for medical and surgical inpatients. The main outcome of interest was 30-day readmission among medical and surgical inpatients with comorbid SMI (n = 561,395) and without a comorbid SMI diagnosis (n = 5,767,218).
520 $a Results: In Aim 1, we found the rates of changing hospital in Medicare group were less than that observed in non-Medicare group of CHF, AMI and pneumonia cohort. A consistent decreasing trend of different-hospital readmission with readmission interval was found. Several significant predictors were identified in a multivariate analysis. In Aim 2, we found patients with SMI had higher odds of readmission within 30 days of discharge compared to those without SMI for both medical and surgical hospitalizations.
590 $a School code: 0160.
650 4 $a Health care management. $3 2122906
690 $a 0769
710 2 $a Northeastern University. $b Mechanical and Industrial Engineering. $3 1035359
773 0 $t Masters Abstracts International $g 58-01(E).
790 $a 0160
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10810199