The article examines the potential sources of errors in the International Classification of Diseases (ICD) coding process, which is crucial for various applications in healthcare, including reimbursement, administration, and research. The authors trace the history of ICD coding, detailing the inpatient coding process from patient admission to diagnostic code assignment. They identify key error sources along the "patient trajectory" and the "paper trail," such as communication issues, clinician knowledge, and coder training. The article also reviews methods for assessing code accuracy, including sensitivity, specificity, positive predictive value, negative predictive value, and the $\kappa$ coefficient. By understanding these error sources and evaluation methods, code users can better evaluate the applicability and limitations of ICD codes in their specific contexts, leading to more accurate and appropriate use of these codes in healthcare decisions. The authors emphasize the importance of continuous education for coders and the need for a dynamic resource to help users evaluate code accuracy.The article examines the potential sources of errors in the International Classification of Diseases (ICD) coding process, which is crucial for various applications in healthcare, including reimbursement, administration, and research. The authors trace the history of ICD coding, detailing the inpatient coding process from patient admission to diagnostic code assignment. They identify key error sources along the "patient trajectory" and the "paper trail," such as communication issues, clinician knowledge, and coder training. The article also reviews methods for assessing code accuracy, including sensitivity, specificity, positive predictive value, negative predictive value, and the $\kappa$ coefficient. By understanding these error sources and evaluation methods, code users can better evaluate the applicability and limitations of ICD codes in their specific contexts, leading to more accurate and appropriate use of these codes in healthcare decisions. The authors emphasize the importance of continuous education for coders and the need for a dynamic resource to help users evaluate code accuracy.