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Ruba Alamad_Master_Thesis2017.pdf (2.01 MB)

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SURGERY DURATION ESTIMATION USING MULTI-REGRESSION MODEL

Alamad, Ruba Amin
http://rave.ohiolink.edu/etdc/view?acc_num=akron1498073495501962

Abstract Details

2017, Master of Science in Engineering, University of Akron, Mechanical Engineering.
ABSTRACT The health care system in the US faces many issues and difficulties, high cost with relatively low quality compared with other developed countries. Surgical departments are the main source of cost and revenue in hospitals. Overestimating and underestimating surgery duration is a very common issue facing the OR (Operating Room) managers and Surgeons; when overestimating the length of a surgery, that surgery will end earlier than expected, leading to reduced utilization of already booked resources. Underestimation leads to delays in the upcoming surgeries and increases the possibility to cancellation. Exploiting the (OR) effectively and efficiently by improve the surgery duration estimation accuracy could increase the revenue, avoid extra costs and increase both surgeon and patient satisfaction. Traditionally, surgery duration is estimated using the average of duration or/and the estimate of the surgeon. Other techniques like Regression have been performed to improve the surgery duration estimation and make better prediction. In this research the surgery duration estimation has been investigated for the top five surgery types which count about 80% of all surgeries, and regression models were built for each of them in order to identify the factors that significantly affect the surgery duration. Regression assumptions were checked and the models validated, iii then the results were compared with the current estimated duration scheduled by OR team using Paired t-test and Boxplot, also the number of defects before and after improvement have been compared. Factors were investigated in this study includes; Surgeon, Primary CPT, Second, Third and Forth Procedures, Time of the day, Patient’s Age, Gender and Physical Status. The results show that Data Mining and Regression could improve the prediction of the surgery duration. The factors tested vary in their impact based on the type of surgery. However, Surgeon and Procedures performed have significant impact on all surgery types. In conclusion, guidelines have been created to help surgeons and OR management departments to make accurate estimates.
Wang Shengyong, Associate Professor (Advisor)
Chen Ling, Associate Professor (Advisor)
Sergio Felicelli, Professor and chair (Committee Member)
87 p.
Industrial Engineering; Statistics
Surgery duration, Regression model, Estimate surgery duration, System and Industrial engineering

Recommended Citations

Citations

  • Alamad, R. A. (2017). SURGERY DURATION ESTIMATION USING MULTI-REGRESSION MODEL [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1498073495501962

    APA Style (7th edition)

  • Alamad, Ruba. SURGERY DURATION ESTIMATION USING MULTI-REGRESSION MODEL. 2017. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1498073495501962.

    MLA Style (8th edition)

  • Alamad, Ruba. "SURGERY DURATION ESTIMATION USING MULTI-REGRESSION MODEL." Master's thesis, University of Akron, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1498073495501962

    Chicago Manual of Style (17th edition)

akron1498073495501962
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© 2017, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.