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Amber Stubbs, Özlem Uzuner, New approaches to cohort selection, Journal of the American Medical Informatics Association, Volume 26, Issue 11, November 2019, Pages 1161–1162, https://doi.org/10.1093/jamia/ocz174
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Extract
Cohort selection for clinical trials is a critical component of modern medicine, yet it remains one of the most difficult, time-consuming, and expensive aspects of testing new treatments and interventions. Each clinical trial defines inclusion and exclusion criteria that describe the required patient population for the trial to accurately determine efficacy of the treatment. These criteria can be broad, limited only to specific ages or genders, or can be very specific, requiring certain medications be taken in a time period, or certain intentions on the parts of the patients (ie, an intention to become pregnant).
While a simple database search can often identify patients of the right age, or even those with particular diagnoses or test results, the more complex criteria often require study staff to manually examine records to identify qualified patients. Or, studies may rely on the patients to seek out the trial, or to be directed to trial possibilities by their doctors—both of which may lead to representation bias and misleading conclusions for the trial.1,2
