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persons. Due to social, religious and cultural factors individuals frame decisions
on the basis of their personal experiences and life stories. This can lead to dif-
ferent understandings of situations and thus to different structures of the decisions
- independent of the final outcome. Thus the “concept of culture serves as a re-
minder of local variations in understanding of health, illness, suffering, and death”
[13, p. 308]. In order to take patients seriously these individual perspectives need to
be taken into account for example when doctors seek informed consent from them.
By integrating the notion of culture and religion into ethics the contingent as well
as universally shared aspects of decisions can be investigated in depth.
5.5
Shared Decision Making: Patients' Perspectives
The discussions above can be brought together and made more concrete when
analysing shared decision making processes between doctors and patients. The
structure of informed consent as the typical model of shared decisions is described
as a two pillar model. On the one hand side there is medical information. Doctors
take all information about a patient as well as scientific knowledge into account in
order to find out which therapies are medically indicated. The best option or maybe
different options are presented to the patient. The patient on the other hand pro-
vides his or her consent to one of the options. Only if both conditions are fulfilled a
therapy can start.
This ideally construed decision situation is much more complex in reality. This
starts with the medical side. As already broadly discussed by Sadegh-Zadeh the
medical diagnosis and indication are not easily at hand but needs to be understood
as the result of numerous decisions. At the same time these medical decisions are
not always sharp but based on many uncertainties and vagueness. Here fuzzy logic
can help to restructure information and come to a conclusion - but this conclusion
can only be part of the medical side of the decision. Bates and Young discuss how
fuzzy logic could improve intensive care therapy. They see the advantage that deci-
sions can be made rapidly “on the basis of a large and disparate array of informa-
tion” [2, p. 948]. The alternative and current method in clinical practice is usually
to take the knowledge at hand combined with experience, which can lead to results
that may vary from person to person. For the authors one central aim of the use
of fuzzy systems therefore can be to reduce unwanted variation in clinical practice
and the automation of devices. But the authors also see critical aspects of relying
on fuzzy systems within medical decisions. Since in medicine there are still lots
of unknown interrelationships doctors rely on experience and rules of best practice.
These needs to be integrated into fuzzy algorithms. They state: “Indeed, the perfor-
mance of the fuzzy algorithm depends greatly on whose expertise it encapsules (i.e.
who fuzzified the parameters and set up the rule tables). Thus, we would expect
some disagreement even between different experts” (ibd., p. 951) This might lead
to similar problems as with decisions based on personal experience in areas where
no established algorithm is known: “A fuzzy logic algorithm operating in such an
area is only as good as the expertise of the individual who defined its fuzzy sets
