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 !"#%%**** in other words, information capable of solving the problem does not exist, namely, p: %;*****? @A !) !+! #% !+! 7#7!7/2 (15) When sufficient, relevant, and reliable information has been gathered, the decision-making process, which often starts under uncertain conditions, evolves into a more certain process (14). Since information reflects the degree to which uncertainty is reduced (15), uncertainty can be diminished by obtaining relevant information as a result of certain actions, such as not only observing a new fact or performing an experiment but also finding a historical record (16) that is essential for both the anchoring effect and availability bias. Since strategic decision-making is long-term, information can be quite old (17), meaning that Equations (9) and (11) are valid in terms of timeframe requirements, i. e. , age of information (AoI). However, any model constructed from data is subject to the impact of data staleness. Data staleness occurs when the data become outdated to the point that it no longer represents the current real-world scenario; several reasons contribute to data staleness, i. e. , changes in business conditions, market dynamics, or customer behaviors, while different scientific fields have varied half-lives (half-life is defined as the time for half of a subject's knowledge to be overturned). Thus, data relevance can be directly impacted by the staleness of the source and types of data reviewed. In this sense, using stale data may lead to incorrect decisions (18, 19, 20). Thus, to avoid the anchoring effect as a result of data staleness, decision-makers must recognize the current real-world scenario and its changes. Thus, they must recognize the time point at which the conditions changed. By performing calculations with the help of statistical methods when the user (as a source of truth) should have an updated data element, we can estimate its degree of staleness. On the basis of such estimation, one can distribute resources of an information system in such a way that the repository is partially or entirely as fresh as needed by means of various synchronization techniques (21). In cases of risk and uncertainty, the probability of making an error is high. In this sense, calculating the probability of making an error provides evidence regarding the correctness of the decision. This gives the decision maker the ability to avoid ineffective decisions. Thus, at an early stage, the decision maker has the opportunity to investigate the outcomes of the decision-making process. With respect to the healthcare sector, in the era of DDDM, probabilities reflect the opportunity to reconsider the decision-making process to avoid the anchoring effect and availability bias. This, in turn, may result in medical and financial gains for healthcare organizations, improvements in healthcare quality, and, to a greater extent, patient-centered care. The opportunity to reconsider the age of information (AoI) used, as indicated by the probabilities, is a means of eliminating such mistakes.
Dimitris Zavras (Mon,) studied this question.