Systems Modeling of community-based Lung Cancer Screening Programs to Improve Quality
Principal Investigator
Name
Archana Nandakumar
Degrees
M.S, B.Tech
Institution
University of Washington
Position Title
Research Assistant
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-624
Initial CDAS Request Approval
Jan 3, 2020
Title
Systems Modeling of community-based Lung Cancer Screening Programs to Improve Quality
Summary
The main aim of this project is to study the factors that affect the quality of lung cancer screening with special focus on community based lung cancer screening programs. The variation in false positives and other outcomes found across screening centers and across different healthcare elements within a screening center motivates the need to study this problem. Conceptual modeling and simulation modeling are the systems modeling tools employed to study this problem. This conceptual modeling portion of the project, deduces some qualitative insights on the importance of the role of lung cancer screening program coordinators and database management systems used in screening programs. The simulation modeling portion of the project utilizes a Monte Carlo simulation extended from the conceptual model. NLST data is required for modeling assumptions and validation of the simulation model. The scope of the model was restricted to the factors of importance identified by an advisory board from participating institutions. Analysis of the model will help develop quantitative insights on the impact of these identified factors and processes on the overall quality outcomes of the screening program as indicated by the false positive rate, early detection rate, radiation induced harms and quit rates in the smoking cessation program. In addition to the typical factors such as nodule detection sensitivity and nodule length variation, the simulation model observes the effect of recall bias in smoking history and shared decision making visits on the quality outcomes of lung cancer screening.
Aims
1. Build a conceptual model of a community based lung cancer screening system based on information from existing literature and directions from the advisory board.
2. Build a Monte-Carlo simulation model of a community based lung cancer screening system based on assumptions from trends in NLST data.
3. Attempt to extend the model to a discrete event simulation to study resource and scheduling problems.
4. Study the effect of various advisory board identified factors on quality outcomes of screening centers through simulation experiments.
Collaborators
Christina Mastrangelo, Associate Professor, Industrial and Systems Engineering, University of Washington