An Online Tool for Correcting Performance Measures of Electronic Phenotyping Algorithms for Verification Bias - Ajay Bhasin; Sue Bielinski; Abel N. Kho; Nicholas Larson; Laura J. Rasmussen-Torvik (ACI Open)
Objectives Computable or electronic phenotypes of patient conditions are becoming more commonplace in quality improvement and clinical research. During phenotyping algorithm validation, standard classification performance measures (i.e., sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) are often employed. When validation is performed on a randomly sampled patient population, direct estimates of these measures are valid. However, studies will commonly sample patients conditional on the algorithm result prior to validation, leading to a form of bias known as verification bias.