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
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/4
Y1 - 2018/4
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.
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.
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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U2 - 10.1109/TASE.2016.2643627
DO - 10.1109/TASE.2016.2643627
M3 - Article
AN - SCOPUS:85010223395
SN - 1545-5955
VL - 15
SP - 531
EP - 544
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
ER -