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Questioning the Quantitative Imperative: Decision Aids, Prevention, and the Ethics of Disclosure Patients should not always receive hard data about the risks and benefits of a medical intervention.

It is easy to see why many experts recommend that patients be given quantitative information about the risks and benefits of a test or treatment they are considering, especially when it serves a preventive function. The value of prevention, after all, is based on its ability to reduce the risk posed by some disease or condition, and the magnitude of this risk reduction can be stated precisely only by using numbers. Many experts conclude that such data, when available, should be disclosed to patients to help them make an informed decision. Advocates of the “quantitative imperative,” as I call it, often appeal to ethical principles related to informed consent or shared decision-making or, at a deeper level, to the goal of respecting patient autonomy.

The quantitative imperative is perhaps most prominent in discussions of “decision aids,” which are pamphlets, videos, or computer programs designed to inform patients about their options and help them make better choices. While decision aids are not yet a standard part of medical care, they are being created, tested, and recommended for a wide range of medical decisions, and they can present much more information than a health care practitioner can convey during a typical patient visit. The International Patient Decision Aids Standards, the most widely accepted guidelines for the design and evaluation of decision aids, recommend that all patients considering preventive interventions be given information about the baseline risk of the disease in question, the specific magnitude of risk reduction offered by the preventive service, and the probability of negative outcomes.

Even though the ethical reasoning that generates the quantitative imperative appears plausible, I will argue here that there are important problems with it stemming from the way people understand and respond to numerical and graphical information.

It is easy to see why many experts recommend that patients be given quantitative information about the risks and benefits of a test or treatment they are considering, especially when it serves a preventive function. The value of prevention, after all, is based on its ability to reduce the risk posed by some disease or condition, and the magnitude of this risk reduction can be stated precisely only by using numbers. Many experts conclude that such data, when available, should be disclosed to patients to help them make an informed decision. Advocates of the “quantitative imperative,” as I call it, often appeal to ethical principles related to informed consent or shared decision-making or, at a deeper level, to the goal of respecting patient autonomy.

The quantitative imperative is perhaps most prominent in discussions of “decision aids,” which are pamphlets, videos, or computer programs designed to inform patients about their options and help them make better choices. While decision aids are not yet a standard part of medical care, they are being created, tested, and recommended for a wide range of medical decisions, and they can present much more information than a health care practitioner can convey during a typical patient visit. The International Patient Decision Aids Standards, the most widely accepted guidelines for the design and evaluation of decision aids, recommend that all patients considering preventive interventions be given information about the baseline risk of the disease in question, the specific magnitude of risk reduction offered by the preventive service, and the probability of negative outcomes.

Even though the ethical reasoning that generates the quantitative imperative appears plausible, I will argue here that there are important problems with it stemming from the way people understand and respond to numerical and graphical information.

Peter H. Schwartz, "Questioning the Quantitative Imperative: Decision Aids, Prevention, and the Ethics of Disclosure," Hastings Center Report 42, no. 2 (2011): 30-39.