東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Managing Hospital Care: Data-Driven ...

Hu, Wenqi.

FindBook

Google Book

Amazon

博客來

Managing Hospital Care: Data-Driven Decisions and Comparisons.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Managing Hospital Care: Data-Driven Decisions and Comparisons./
作者:	Hu, Wenqi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	156 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-04, Section: B.
Contained By:	Dissertations Abstracts International80-04B.
標題:	Applied Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10933290
ISBN:	9780438488595

Managing Hospital Care: Data-Driven Decisions and Comparisons.
Hu, Wenqi.

Managing Hospital Care: Data-Driven Decisions and Comparisons. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 156 p.

Source: Dissertations Abstracts International, Volume: 80-04, Section: B.

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

This item must not be sold to any third party vendors.

This dissertation focuses on utilizing data-driven approaches to objectively measure variation in the quality of care across different hospitals, understand how physicians make dynamic admission and routing decisions for patients, and propose potential changes in practice to improve the quality of care and patient flow management. This analysis was performed in the context of Intensive Care Units (ICUs) and the Emergency Department (ED). In the first part, we assess variation in the overall quality of care provided by both urban and rural hospitals under the same integrated healthcare delivery system when augmenting administrative data with detailed patient severity scores from the electronic medical records (EMRs). Using a new template matching methodology for more objective comparison, we found that the use of granular EMR data significantly reduces the variation across hospitals in common patient severity-of-illness levels. Further, we found that hospital rankings on 30-day mortality and estimates of length-of-stay (LOS) are statistically different from rankings based on administrative data. In the second part, we study ICU admission decision-making dynamically throughout a patient's stay in the general ward/the Transitional Care Unit (TCU). We first used an instrumental variable approach and modern multivariate matching methods to rigorously estimate the potential benefits and costs of transferring patients to the ICU based on a real-time risk score for deterioration. We then used the quantified impact to calibrate a comprehensive simulation model to evaluate system performances under various new ICU transfer policies. We show that proactively transferring the most severe patients to the ICU could reduce mortality rates and LOS without increasing ICU congestion and causing other adverse effects. In the third part, we focus on understanding how physicians make ICU admission decisions for patients in the ED. We first used two sets of reduced-form regressions to understand 1) what and how patient risk factors and system controls impact the admission decision from the ED; and 2) what are the potential benefits of admitting patients from the ED to the ICU. We then proposed a dynamic discrete choice structural model to estimate to what extent physicians account for the inter-temporal externalities when deciding to admit a specific patient to the ICU, to the ward or let him/her wait in the ED. Note that the structural model estimation is still an ongoing process and more investigation is required to fine tune the details. Therefore, we will not discuss the structural model estimation results in this chapter, but only present the modeling framework and key estimation strategy.

ISBN: 9780438488595Subjects--Topical Terms:

1669109
Applied Mathematics.

Managing Hospital Care: Data-Driven Decisions and Comparisons.
LDR:03844nmm a2200349 4500 001 2210561
005 20191121124239.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438488595
035 $a (MiAaPQ)AAI10933290
035 $a (MiAaPQ)columbia:14882
035 $a AAI10933290
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hu, Wenqi. $3 3437699
245 1 0 $a Managing Hospital Care: Data-Driven Decisions and Comparisons.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 156 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-04, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Chan, Carri W.;Zubizarreta, Jose R.
502 $a Thesis (Ph.D.)--Columbia University, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation focuses on utilizing data-driven approaches to objectively measure variation in the quality of care across different hospitals, understand how physicians make dynamic admission and routing decisions for patients, and propose potential changes in practice to improve the quality of care and patient flow management. This analysis was performed in the context of Intensive Care Units (ICUs) and the Emergency Department (ED). In the first part, we assess variation in the overall quality of care provided by both urban and rural hospitals under the same integrated healthcare delivery system when augmenting administrative data with detailed patient severity scores from the electronic medical records (EMRs). Using a new template matching methodology for more objective comparison, we found that the use of granular EMR data significantly reduces the variation across hospitals in common patient severity-of-illness levels. Further, we found that hospital rankings on 30-day mortality and estimates of length-of-stay (LOS) are statistically different from rankings based on administrative data. In the second part, we study ICU admission decision-making dynamically throughout a patient's stay in the general ward/the Transitional Care Unit (TCU). We first used an instrumental variable approach and modern multivariate matching methods to rigorously estimate the potential benefits and costs of transferring patients to the ICU based on a real-time risk score for deterioration. We then used the quantified impact to calibrate a comprehensive simulation model to evaluate system performances under various new ICU transfer policies. We show that proactively transferring the most severe patients to the ICU could reduce mortality rates and LOS without increasing ICU congestion and causing other adverse effects. In the third part, we focus on understanding how physicians make ICU admission decisions for patients in the ED. We first used two sets of reduced-form regressions to understand 1) what and how patient risk factors and system controls impact the admission decision from the ED; and 2) what are the potential benefits of admitting patients from the ED to the ICU. We then proposed a dynamic discrete choice structural model to estimate to what extent physicians account for the inter-temporal externalities when deciding to admit a specific patient to the ICU, to the ward or let him/her wait in the ED. Note that the structural model estimation is still an ongoing process and more investigation is required to fine tune the details. Therefore, we will not discuss the structural model estimation results in this chapter, but only present the modeling framework and key estimation strategy.
590 $a School code: 0054.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Economics. $3 517137
650 4 $a Health care management. $3 2122906
650 4 $a Operations research. $3 547123
690 $a 0364
690 $a 0501
690 $a 0769
690 $a 0796
710 2 $a Columbia University. $b Business. $3 1681698
773 0 $t Dissertations Abstracts International $g 80-04B.
790 $a 0054
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10933290