Abstract

Decision aids can help patients make medical decisions, which is especially advantageous in situations with equipoise. However, when there is no correct answer, it is difficult to assess whether a decision aid is helpful. The goal of this research is to propose and validate an objective method for measuring decision aid effectiveness by quantifying the clarity participants achieved when making decisions. The measure of decisional clarity was tested in a convenience sample of 131 college-aged students making hypothetical decisions about 2 treatment options for depression and anxiety. The treatments varied with respect to potential benefits and harms. Information was presented numerically or with an accompanying data visualization (an icon array) that is known to aid decision making. Decisional clarity was better with the icon arrays. Furthermore, the results showed that decisional clarity can be used to identify situations for which patients will be more likely to struggle making their decision. These included situations for which financial considerations were relevant to the decision and situations for which the probabilities of potential benefits were higher. The measure of decisional clarity and the situations identified as lacking clarity should be validated with a larger, more representative sample. These findings demonstrate that decisional clarity can be used to both empirically evaluate the effectiveness of a decision aid as well as test factors that can cloud clarity and disrupt medical decision making. Researchers and medical providers interested in developing decision aids for situations with equipoise can use decisional clarity as an objective measure to assess the effectiveness of their decision aid. Financial considerations and higher probabilities may also cloud judgments. An objective measure of decisional clarity is supported.Decisional clarity can be used to evaluate decision aids in the context of equipoise for which there is no objectively correct choice.Decisional clarity can also be used to identify scenarios for which patients are likely to struggle to make a medical decision.

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