Computer modeling of lung cancer diagnosis-to-treatment process

Feng Ju, Hyo Kyung Lee, Raymond U. Osarogiagbon, Xinhua Yu, Nick Faris, Jingshan Li

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

Original languageEnglish (US)
Pages (from-to)404-414
Number of pages11
JournalTranslational Lung Cancer Research
Volume4
Issue number4
DOIs
StatePublished - 2015
Externally publishedYes

Fingerprint

Lung Neoplasms
Markov Chains
Neoplasm Staging
Quality Improvement
Delivery of Health Care

Keywords

  • Analytical model
  • Closed formula
  • Discrete event simulation (DES)
  • Lung cancer quality improvement
  • Markov chain
  • Process modeling

ASJC Scopus subject areas

  • Oncology

Cite this

Computer modeling of lung cancer diagnosis-to-treatment process. / Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U.; Yu, Xinhua; Faris, Nick; Li, Jingshan.

In: Translational Lung Cancer Research, Vol. 4, No. 4, 2015, p. 404-414.

Research output: Contribution to journalArticle

Ju, Feng ; Lee, Hyo Kyung ; Osarogiagbon, Raymond U. ; Yu, Xinhua ; Faris, Nick ; Li, Jingshan. / Computer modeling of lung cancer diagnosis-to-treatment process. In: Translational Lung Cancer Research. 2015 ; Vol. 4, No. 4. pp. 404-414.
@article{e3726a5880fa4de79ffad110fc9898a0,
title = "Computer modeling of lung cancer diagnosis-to-treatment process",
abstract = "We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.",
keywords = "Analytical model, Closed formula, Discrete event simulation (DES), Lung cancer quality improvement, Markov chain, Process modeling",
author = "Feng Ju and Lee, {Hyo Kyung} and Osarogiagbon, {Raymond U.} and Xinhua Yu and Nick Faris and Jingshan Li",
year = "2015",
doi = "10.3978/j.issn.2218-6751.2015.07.16",
language = "English (US)",
volume = "4",
pages = "404--414",
journal = "Translational Lung Cancer Research",
issn = "2226-4477",
publisher = "Society for Translational Medicine (STM)",
number = "4",

}

TY - JOUR

T1 - Computer modeling of lung cancer diagnosis-to-treatment process

AU - Ju, Feng

AU - Lee, Hyo Kyung

AU - Osarogiagbon, Raymond U.

AU - Yu, Xinhua

AU - Faris, Nick

AU - Li, Jingshan

PY - 2015

Y1 - 2015

N2 - We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

AB - We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

KW - Analytical model

KW - Closed formula

KW - Discrete event simulation (DES)

KW - Lung cancer quality improvement

KW - Markov chain

KW - Process modeling

UR - http://www.scopus.com/inward/record.url?scp=84960126488&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84960126488&partnerID=8YFLogxK

U2 - 10.3978/j.issn.2218-6751.2015.07.16

DO - 10.3978/j.issn.2218-6751.2015.07.16

M3 - Article

AN - SCOPUS:84960126488

VL - 4

SP - 404

EP - 414

JO - Translational Lung Cancer Research

JF - Translational Lung Cancer Research

SN - 2226-4477

IS - 4

ER -