Mosquera, Julia | 2021
Journal of Law and the Biosciences, vol. 8 issue 2
Current patients seem to be subject to certain biases when it comes to the report of their utility. Eyal’s proposal is to calibrate current patients’ scores with the scores that former patients give of the same health conditions. He proposes a measure, the Bias of Adapted Patients (BAP), that adjusts current patients’ scores to reflect more accurate health states. In this paper I analyze Eyal’s measure. First, I review the motivation behind it. Then, I raise some counterintuitive results that follow from the functioning of the formula and explain the possible sources of these results. Finally, I discuss the rationale behind the use of average scores and suggest that a promising departing point for a measure like BAP could consist in a thorough analysis of the distribution of scores from former and current patients for different conditions to try to establish relevant patterns and determine hypothesis that may explain the differences in scores.