To test or not to test: what is the value of knowing?

This paper develops a conceptual model for understanding the impact of the “value of knowing”, defined as the value of information from medical tests exclusive of treatment or life-planning decisions on a patient’s decision to undergo testing. We draw upon the behavioral economic, loss-aversion, cost-benefit and willingness-to-pay literatures to develop a mathematical model of how a medical diagnostic test affects patients’ sense of wellbeing and how this phenomenon affects their decision to undergo testing. The model allows simultaneous evaluation of the impact of baseline (pre-test) disease risk, test inaccuracy, prior information, worrying over disease onset, time preference and the degree of loss aversion on patients’ net assessment of the value of knowing. We then simulate the net value of knowing under alternative hypothetical scenarios about test accuracy and patient characteristics. Patients agree to testing when the expected benefits from good news (measured by willingness to pay) exceed the psychic costs of bad news (measured by willingness to accept). The value of knowing from testing is shown to depend on test accuracy, pre-test disease risk, the patient’s discount rate, time to disease onset and the patient’s aversion to receiving bad news (loss). Simulation results indicate that the value of knowing increases (and testing becomes more likely) when: tests are more accurate; the baseline expectation of a positive test is low and the adverse consequences of a positive test are either small or occur far in the future or patients do not worry about onset of future disease