Automated Identification of Lung Cancer Patients Eligible for Clinical Trial Recruitment

Clinical trials (CTs) are an essential function necessary for the advancement of basic and clinical research. Slow or diminished recruitment of patients into clinical trials delays the validation and translation of research findings. Under-recruitment in underserved populations, such as Kentucky’s Appalachian residents or poor urban black populations, may result in perceptions of inequalities in access to the latest treatments and medical technologies. Failure to adequately identify the entire cohort of eligible patients in a recruitment population may also lead to biased study results that are not generalizable to the greater population. Determining the eligibility of every potential patient is essential to assuring adequate and unbiased enrollment in CTs. Clinical research organizations often rely on manual processes to assess the eligibility of cancer patients. Such processes are inherently inefficient and prone to miss potential candidates. Studies have shown that manual methods may miss up to 60% of eligible patients. Recruitments of lung cancer patients are particularly challenging with less than 1% of all patients participating. Rich electronic data sources managed by the Kentucky Cancer Registry and UK Clinical and Translational Science Program could be used to implement an electronic screening system to increase the completeness and efficiency of identifying patients for lung cancer CTs. Investigators on this proposal have previously utilized these data sources to recruit lung cancer patients into a population-based gene environment interaction study and to identify lung cancer biospecimens for a study of the MRP2 genotype expression and treatment outcomes. We propose a study to improve the efficiency of lung cancer clinical trial screening at the Markey Cancer Center (MCC) and increase accruals through the development of an automated screening application. The primary study population will include lung cancer patients seen at the MCC. Automated screening of lung cancer patients will be evaluated against the manual screening methods currently utilized by clinical research associates. Successful methods in lung cancer will be immediately applicable to patient recruitment from Kentucky’s entire lung cancer population and for other cancers. This project will be used to target additional extramural funding through the NIH and NCI.