Lay, professional, and artificial intelligence perspectives on risky medical decisions and COVID-19: How does the number of lives matter in clinical trials framed as gains versus losses?. Issue 5 (May 2022)

• Record Type: Journal Article
• Authors: Mukherjee, Sumitava; Reji, Divya
• Is Part Of: Quarterly journal of experimental psychology. Volume 75:Issue 5(2022)
• Page Start: 784
• Page End: 795
• Publication Date: 2022-05
• Subjects: Valuation of lives -- risk-aversion -- framing effect -- Asian Disease -- COVID-19 -- medical decision making; Psychology, Experimental -- Periodicals; Psychophysiology -- Periodicals; Psychology, Comparative -- Periodicals; 150.72405
• DOI: 10.1177/17470218211052037
• Languages: English
• ISSNs: 1747-0218

Abstract

Outcomes of clinical trials need to be communicated effectively to make decisions that save lives. We investigated whether framing can bias these decisions and if risk preferences shift depending on the number of patients. Hypothetical information about two medicines used in clinical trials having a sure or a risky outcome was presented in either a gain frame (people would be saved) or a loss frame (people would die). The number of patients who signed up for the clinical trials was manipulated in both frames in all the experiments. Using an unnamed disease, lay participants (experiment 1) and would-be medical professionals (experiment 2) were asked to choose which medicine they would have administered. For COVID-19, lay participants were asked which medicine should medical professionals (experiment 3), artificially intelligent software (experiment 4), and they themselves (experiment 5) favour to be administered. Broadly consistent with prospect theory, people were more risk-seeking in the loss frames than the gain frames. However, risk-aversion in gain frames was sensitive to the number of lives with risk-neutrality at low magnitudes and risk-aversion at high magnitudes. In the loss frame, participants were mostly risk-seeking. This pattern was consistent across laypersons and medical professionals, further extended to preferences for choices that medical professionals and artificial intelligence programmes should make in the context of COVID-19. These results underscore how medical decisions can be impacted by the number of lives at stake while revealing inconsistent risk preferences for clinical trials during a real pandemic.