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

Statistical based evaluation of effe...

Stone, Kristen M.

FindBook

Google Book

Amazon

博客來

Statistical based evaluation of effectiveness of surgeon predicted procedure time.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical based evaluation of effectiveness of surgeon predicted procedure time./
作者:	Stone, Kristen M.
面頁冊數:	111 p.
附註:	Source: Masters Abstracts International, Volume: 54-06.
Contained By:	Masters Abstracts International54-06(E).
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1593879
ISBN:	9781321902709

Statistical based evaluation of effectiveness of surgeon predicted procedure time.
Stone, Kristen M.

Statistical based evaluation of effectiveness of surgeon predicted procedure time. - 111 p.

Source: Masters Abstracts International, Volume: 54-06.

Thesis (M.S.)--State University of New York at Binghamton, 2015.

Variability in healthcare makes it difficult for resources to be responsive to meet organization and patient needs. Current planning and scheduling processing are highly complex but have difficulty accurately predicting procedure times in the operating room. The objectives of this research are threefold. The first main objective is to conduct a quantitative assessment of current scheduling methods at the University of Arizona Medical Center. Using an electronic medical record software, Epic, as well as experienced surgical staff to schedule procedures in their operating room. Epic uses a predicted algorithm, a 5-running average of past procedures conducted by each specific surgeon. JMP, an analytical software, was used to conduct statistical analysis on the data collected from November 2013 to November 2014 (1 years' worth). Multiple models are made to evaluate the relationship between surgeon predicted scheduling times versus the procedure and room durations. The second objective focuses on understanding the relationships between the surgeon predicted times and actual procedure duration using various statistical modeling techniques. Three models are implemented: decision tree, least squares, and log-linear. Third, to develop a time series forecasting to predict procedure time compared to the existing scheduling approach. . It was determined that decision tree at 30% validation and 70% training, provided the best results with the highest R2 of 0.743 and lowest RMSE of 42.96.

ISBN: 9781321902709Subjects--Topical Terms:

526216
Industrial engineering.

Statistical based evaluation of effectiveness of surgeon predicted procedure time.
LDR:02397nmm a2200289 4500 001 2115186
005 20170217105126.5
008 180830s2015 ||||||||||||||||| ||eng d
020 $a 9781321902709
035 $a (MiAaPQ)AAI1593879
035 $a AAI1593879
040 $a MiAaPQ $c MiAaPQ
100 1 $a Stone, Kristen M. $3 3276825
245 1 0 $a Statistical based evaluation of effectiveness of surgeon predicted procedure time.
300 $a 111 p.
500 $a Source: Masters Abstracts International, Volume: 54-06.
500 $a Adviser: Mohammad Khasawneh.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2015.
520 $a Variability in healthcare makes it difficult for resources to be responsive to meet organization and patient needs. Current planning and scheduling processing are highly complex but have difficulty accurately predicting procedure times in the operating room. The objectives of this research are threefold. The first main objective is to conduct a quantitative assessment of current scheduling methods at the University of Arizona Medical Center. Using an electronic medical record software, Epic, as well as experienced surgical staff to schedule procedures in their operating room. Epic uses a predicted algorithm, a 5-running average of past procedures conducted by each specific surgeon. JMP, an analytical software, was used to conduct statistical analysis on the data collected from November 2013 to November 2014 (1 years' worth). Multiple models are made to evaluate the relationship between surgeon predicted scheduling times versus the procedure and room durations. The second objective focuses on understanding the relationships between the surgeon predicted times and actual procedure duration using various statistical modeling techniques. Three models are implemented: decision tree, least squares, and log-linear. Third, to develop a time series forecasting to predict procedure time compared to the existing scheduling approach. . It was determined that decision tree at 30% validation and 70% training, provided the best results with the highest R2 of 0.743 and lowest RMSE of 42.96.
590 $a School code: 0792.
650 4 $a Industrial engineering. $3 526216
650 4 $a Health care management. $3 2122906
650 4 $a Health sciences. $3 3168359
690 $a 0546
690 $a 0769
690 $a 0566
710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $3 2104041
773 0 $t Masters Abstracts International $g 54-06(E).
790 $a 0792
791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1593879