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Diagnostic tests can detect diseases, monitor responses, and inform treatments. They are vital to the effective management of disease. There have been significant advances in the engineering of new diagnostic technologies. These technologies may forgo sample extraction, simplify readout, or automate processing. Many researchers design these diagnostics based on test performance in a limited sample subset. This approach ignores the intertwined relationship between patient characteristics and diagnostic test results. Yet, it is important to understand the clinical decision‐making workflow and how the disease manifests in order to optimally design diagnostic tests. This review article explores the three aspects of incorporating patient characteristics to maximize diagnostic performance. 1) Characterize patient populations using patient demographics, disease prevalence, and other unique features. 2) Use the characteristics of the patient population to establish design requirements. 3) Determine the best use case since each case has different performance and target requirements. In this framework the clinical, technological, and unmet needs of a patient population shape the diagnostics design requirements. Following these steps will lead to maximal diagnostic performance and poise new diagnostics for real world use. [ABSTRACT FROM AUTHOR]