A System-Theoretic Method for Modeling, Analysis, and Improvement of Lung Cancer Diagnosis-to-Surgery Process

Hyo Kyung Lee; Feng Ju; Raymond U. Osarogiagbon; Nicholas Faris; Xinhua Yu; Fedoria Rugless; Shan Jiang; Jingshan Li

doi:10.1109/TASE.2016.2643627

A System-Theoretic Method for Modeling, Analysis, and Improvement of Lung Cancer Diagnosis-to-Surgery Process

Hyo Kyung Lee
, Feng Ju
, Raymond U. Osarogiagbon
, Nicholas Faris
, Xinhua Yu
, Fedoria Rugless
, Shan Jiang
, Jingshan Li

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

11 Citations (Scopus)

Abstract

Early diagnosis and treatment of lung cancer are of significant importance. In this paper, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections. The complex care delivery process is decomposed into a collection of serial processes, each consisting of combinations of various tests and procedures. Closed formulas are derived to estimate the mean and coefficient of variation of waiting time during the diagnosis-to-surgery process. Simple indicators based on the data collected on the clinic/hospital floor are derived to identify the bottlenecks, i.e., the waiting times that impede the whole delivery process in the strongest manner. In addition, by approximating waiting times using Gamma distributions, an algorithm is introduced to evaluate the waiting-time performance, i.e., the probability to finish the diagnosis-to-surgery process within a desired or given time interval. Finally, a case study at Baptist Memorial Health System is introduced to illustrate the applicability of the method and provide recommendations for improvement. © 2017 IEEE.

Original language	English
Pages (from-to)	531-544
Journal	IEEE Transactions on Automation Science and Engineering
Volume	15
Issue number	2
DOIs	https://doi.org/10.1109/TASE.2016.2643627
Publication status	Published - 1 Apr 2018
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the Patient-Centered Outcomes Research Institute under Award IH-1304-6147 and in part by the Baptist Memorial Healthcare Foundation.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Keywords

Bottleneck
coefficient of variation (CV)
diagnose-to-surgery process
lung cancer
mean waiting time
waiting-time performance (WTP)

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10.1109/TASE.2016.2643627

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title = "A System-Theoretic Method for Modeling, Analysis, and Improvement of Lung Cancer Diagnosis-to-Surgery Process",

abstract = "Early diagnosis and treatment of lung cancer are of significant importance. In this paper, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections. The complex care delivery process is decomposed into a collection of serial processes, each consisting of combinations of various tests and procedures. Closed formulas are derived to estimate the mean and coefficient of variation of waiting time during the diagnosis-to-surgery process. Simple indicators based on the data collected on the clinic/hospital floor are derived to identify the bottlenecks, i.e., the waiting times that impede the whole delivery process in the strongest manner. In addition, by approximating waiting times using Gamma distributions, an algorithm is introduced to evaluate the waiting-time performance, i.e., the probability to finish the diagnosis-to-surgery process within a desired or given time interval. Finally, a case study at Baptist Memorial Health System is introduced to illustrate the applicability of the method and provide recommendations for improvement. {\textcopyright} 2017 IEEE.",

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A System-Theoretic Method for Modeling, Analysis, and Improvement of Lung Cancer Diagnosis-to-Surgery Process. / Lee, Hyo Kyung; Ju, Feng; Osarogiagbon, Raymond U. et al.
In: IEEE Transactions on Automation Science and Engineering, Vol. 15, No. 2, 01.04.2018, p. 531-544.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - A System-Theoretic Method for Modeling, Analysis, and Improvement of Lung Cancer Diagnosis-to-Surgery Process

AU - Lee, Hyo Kyung

AU - Ju, Feng

AU - Osarogiagbon, Raymond U.

AU - Faris, Nicholas

AU - Yu, Xinhua

AU - Rugless, Fedoria

AU - Jiang, Shan

AU - Li, Jingshan

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PY - 2018/4/1

Y1 - 2018/4/1

N2 - Early diagnosis and treatment of lung cancer are of significant importance. In this paper, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections. The complex care delivery process is decomposed into a collection of serial processes, each consisting of combinations of various tests and procedures. Closed formulas are derived to estimate the mean and coefficient of variation of waiting time during the diagnosis-to-surgery process. Simple indicators based on the data collected on the clinic/hospital floor are derived to identify the bottlenecks, i.e., the waiting times that impede the whole delivery process in the strongest manner. In addition, by approximating waiting times using Gamma distributions, an algorithm is introduced to evaluate the waiting-time performance, i.e., the probability to finish the diagnosis-to-surgery process within a desired or given time interval. Finally, a case study at Baptist Memorial Health System is introduced to illustrate the applicability of the method and provide recommendations for improvement. © 2017 IEEE.

AB - Early diagnosis and treatment of lung cancer are of significant importance. In this paper, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections. The complex care delivery process is decomposed into a collection of serial processes, each consisting of combinations of various tests and procedures. Closed formulas are derived to estimate the mean and coefficient of variation of waiting time during the diagnosis-to-surgery process. Simple indicators based on the data collected on the clinic/hospital floor are derived to identify the bottlenecks, i.e., the waiting times that impede the whole delivery process in the strongest manner. In addition, by approximating waiting times using Gamma distributions, an algorithm is introduced to evaluate the waiting-time performance, i.e., the probability to finish the diagnosis-to-surgery process within a desired or given time interval. Finally, a case study at Baptist Memorial Health System is introduced to illustrate the applicability of the method and provide recommendations for improvement. © 2017 IEEE.

KW - Bottleneck

KW - coefficient of variation (CV)

KW - diagnose-to-surgery process

KW - lung cancer

KW - mean waiting time

KW - waiting-time performance (WTP)

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EP - 544

JO - IEEE Transactions on Automation Science and Engineering

JF - IEEE Transactions on Automation Science and Engineering

IS - 2

ER -

A System-Theoretic Method for Modeling, Analysis, and Improvement of Lung Cancer Diagnosis-to-Surgery Process

Abstract

Bibliographical note

Funding

UN SDGs

Research Keywords

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