When a hospital aims to maximize the number of potential cancer cases identified, which metric should they prioritize?

When a hospital seeks to maximize the number of potential cancer cases identified, prioritizing recall is essential. Recall, also known as sensitivity, measures the ratio of true positive predictions to the actual positive cases. In the context of detecting cancer, maximizing recall ensures that as many actual cancer cases as possible are identified, reducing the risk of false negatives—instances where patients with cancer are not identified by the model.

This is particularly critical in a healthcare setting, where failing to detect a cancer case can have serious consequences for patient outcomes. By focusing on recall, the hospital can ensure that it captures all potential cancer cases, which is the primary goal when attempting to increase the detection rate of a serious condition like cancer.

In contrast, metrics like precision, accuracy, and specificity do not align with the goal of identifying as many cases as possible. Precision focuses on the percentage of relevant instances among the identified cases, accuracy considers both false positives and false negatives across all predictions, and specificity measures the ability to correctly reject negative cases. While these metrics have their utility in different contexts, they are not the optimal choice when the primary aim is to identify every possible case of cancer.