The anchoring effect and availability bias in healthcare decision-making

However, making mistakes in all aspects of life is a reality. At the executive level in healthcare, this reality is also true (11), while mistakes can be viewed as making a decision to act or not act without thoroughly assessing known evidence (12). Under this consideration, we may argue that the role of data in healthcare management mistakes is critical. The anchoring effect is one of the decision-making mistakes attributed to data. It is a common type of error in decision-making and refers to the condition in which decision-makers fixate on initial information as a starting point and then, once set, fail to adequately adjust for subsequent information. First, information carries unwarranted weights relative to the information that is received later. The anchoring effect is linked to time. Availability bias is also linked to time. It occurs when decision-makers tend to remember events that are the most recent and vivid in their memory. Such bias distorts managers' ability to recall events in an objective manner and results in distorted judgments and probability estimates (13). For example, by using monthly data on economic uncertainty and the number of new confirmed COVID-19 cases and deaths, one can investigate whether governments' decisions regarding policy responses to the pandemic were subject to anchoring and availability biases. However, since both mistakes have only been conceptually defined, the objective of this study is to develop and present their probabilistic formalism.An incorrect decision in a decision-making problem for which a correct solution exists can be characterized as a problem for which needed data are not used. Thus, the probabilityp !"#$%%&#' )&*&#'+$" $, -*'&%".'+/& )$*0'+$" = ∑ ∑ (3 ! #$$%$% 43 ! #&! '($%|#$$%$% ) ∈ [0, 1] (1)corresponds to a condition in which, during the decision-making process, the pieces of multidisciplinary (j = 1, 2, … , k) information q ' 6 s that were used, namely, q ' 07&8 = q ' "&&8&8 -q ' "$' 07&8|"&&8&8 (2), where needed refers to needed information and where not used refers to information that was not used, are not sufficient to correctly solve the problem. Thus, this probability reflects the inability to solve the decision-making problem. In Equation (1), we may assume that q ' 6 s are defined in terms of time t, i.e., the duration of the decision-making process. The disciplines of information are the same in the numerator and denominator of Equation (1), indicating that an informed decision maker would know what information was needed, i.e., what pieces can solve the problem and then select among them. Under the aforementioned framework, the capacity to solve, namely, the ability of information pieces to solve a decision-making problem, is defined as the probability of such a problem being solved correctly, as shown in Equation (3):p :%$;*&< =$%%&#'*> )$*/&8 = ∑ ∑ 3 ! * ./0/12$ !& (&23$ !4$ 05&12$6(3)with q ' 6 #.?.;*& '$ 7$*/& '@& ?%$;*&< s ∈ q ' 6 07&8 s and thus p :%$;*&< =$%%&#'*> )$*/&8 takes values in the unit interval. In Equation ( 3), q ' 6 07&8 represents the pieces of information that are used by the decision maker, and q ' 6 #.?.;*& '$ 7$*/& s represents the pieces of information that can correctly solve the problem.In addition, if j = 0, the data cannot solve the problem; in other words, information capable of solving the problem does not exist, namely, p :%$;*&< =$%%&#*'> )$*/&8 is not defined (the numerator and denominator are 0). This case corresponds to complete uncertainty. On the other hand, if the decision maker does not consider the total number (q ' 6 07&8 > q ' 6 #.?.;*& '$ 7$*/& ) of information pieces, p :%$;*&< =$%%&#'*> )$*/&8 )$*/&8 =1 corresponds to certainty.Since according to decision-making theory, the outcomes of the decision-making process are functions of uncertainty, risk, or certainty, p :%$;*&< =$%%&#'*> )$*/&8 is a catalytic factor in the decision-making process. On the other hand, p :%$;*&< =$%%&#'*> )$*/&8 is linked to decision effectiveness. In this work, effectiveness is defined as the extent to which the targets have been achieved. Thus, we measure effectiveness through probability p A,,&#'+/& B@%$0C@ D.'. = E0