19 Sep, 13 | by Iain Brassington
Guest post by Morten Magelssen, Reidar Pedersen, and Reidun Førde
A difficult case involving a patient in an intensive care unit is brought to a clinical ethics consultant. The ethics consultant argues that intensive care is futile and should be withdrawn. The clinicians are grateful for the advice, and, with the assent of the patient’s relatives, decide to withdraw intensive care accordingly.
Clinical ethics consultation – by committees or individual consultants – involves reflection upon ethically and medically challenging cases. When reflection is carried out in a systematic manner, then ideally the ethically salient points are brought out and discussed in a comprehensive and unbiased way.
But what if the consultation itself introduces new biases and implicit value judgments? We won’t take a stand on how often this in fact happens, but rather draw attention to how easily it may occur and the dangers involved. In our JME article we identify six sources of bias – or conflicts of interest – in clinical ethics consultation. For instance, in the case above, the ethics consultant could be biased towards the interests of health-care professions, or towards the hospital’s interests in keeping costs down and maintaining an unblemished public image. In general, we argue, the potential for harmful biases is greater when the consultation is performed by an individual consultant rather than by a committee.
The introduction of new, harmful biases through ethics consultation, a process intended to reduce biases, would be problematic (and somewhat ironic). Especially considering that, in the eyes of clinicians, the conclusion and advice of ethics consultation may appear to bear a stamp of ”ethically approved”.
We are fundamentally optimistic about the positive role clinical ethics consultation can play in aiding clinicians in the management of ethically complex cases. However, ensuring the quality of clinical ethics case deliberations is vital. Although biases can never be completely eradicated, the identification of potentially significant biases is an important part of quality improvement.