A Coordinated Approach to Implementing Low-Dose CT Lung Cancer Screening in a Rural Community Hospital

Purpose: The authors describe a rural community hospital’s approach to lung cancer screening using low-dose CT (LDCT) to address the high incidence of lung cancer mortality. Methods: An implementation project was conducted, documenting planning, education, and restructuring processes to implement a lung cancer screening program using LDCT in a rural community hospital (population 64,917, Rural-Urban Continuum Code 5) located in a region with the highest lung cancer mortality in Oregon. The hospital and community partners organized the implementation project around five recommendations for an efficient and effective lung cancer screening program that accurately identifies high-risk patients, facilitates timely access to screening, provides appropriate follow-up care, and offers smoking cessation support. Results: Over a 3-year period (2018-2020), 567 LDCT scans were performed among a high-risk population. The result was a 4.8-fold increase in the number of LDCT scans from 2018 to 2019 and 54% growth from 2019 to 2020. The annual adherence rate increased from 51% in 2019 to 59.6% in 2020. Cancer was detected in 2.11% of persons scanned. Among the patients in whom lung cancer was detected, the majority of cancers (66.6%) were categorized as stage I or II. Conclusions: This rural community hospital’s approach involved uniting primary care, specialty care, and community stakeholders around a single goal of improving lung cancer outcomes through early detection. The implementation strategy was intentionally organized around five recommendations for an effective and efficient lung cancer screening program and involved planning, education, and restructuring processes. Significant stakeholder involvement on three separate committees ensured that the program’s design was relevant to local community contexts and patient-centered. As a result, the screening program’s reach and adherence increased each year of the 3-year pilot program

Data mining using parallel multi-objective evolutionary algorithms on graphics processing units

An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profits to a company under resource constraints. In this chapter, we first formulate this learning problem as a constrained optimization problem and then convert it to an unconstrained multi-objective optimization problem (MOP), which can be handled by some multi-objective evolutionary algorithms (MOEAs). However, MOEAs may execute for a long time for theMOP, because several evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. Thus we propose a parallel MOEA on consumer-level graphics processing units (GPU) to tackle the MOP. We perform experiments on a real-life direct marketing problem to compare the proposed method with the parallel hybrid genetic algorithm, the DMAX approach, and a sequential MOEA. It is observed that the proposed method is much more effective and efficient than the other approaches