Precision medical tests play a critical role in tailoring treatment plans to individual patients and optimizing medical resource utilization. This paper establishes a quantitative model to analyze the relationship between the accuracy of a precision medical test and its cost-effectiveness. Two theoretical lower bounds on test accuracy are proposed to be required for a testing strategy to be considered cost-effective. The first bound ensures that the intervention matched to a test result remains locally optimal for the reported patient class, thereby satisfying a local efficiency criterion. The second bound ensures that the testing strategy yields a higher expected net monetary benefit for the entire population compared to no testing, satisfying a global efficiency criterion. A special case analyzing sensitivity and specificity for binary test results illustrates these thresholds intuitively. Numerical simulations support these theoretical findings. The results illustrate how accuracy requirements depend on class prevalence, treatment benefits, and test costs, and may serve as a preliminary screening criterion for further evaluation.
Chen et al. (2026) studied this question.