PubMed · 12873001
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Jay Lehr, Herbert Inhaber. A creative solution to the NIMBY problem.. https://doi.org/10.1111/j.1745-6584.2003.tb02371.x
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Enhancing patient choice is a central theme of medical ethics and law. Informed consent is the legal process used to promote patient autonomy; shared decision making is a widely promoted ethical approach. These processes may most usefully be seen as distinct in clinically and ethically important respects. The approach outlined in this article uses a model that arrays all medical decisions along 2 axes: risk and certainty. At the extremes of these continua, 4 decision types are produced, each of which constrains the principal actors in predictable ways. Shared decision making is most appropriate in situations of uncertainty, in which 2 or more clinically reasonable alternatives exist. When there is only 1 realistic choice, patient and physician may gather and exchange information; however, the patient cannot be empowered to make choices that do not exist. In contrast, informed consent does not require the presence of clinical choice; it is appropriate for all decisions of significant risk, even if there is only one option. When a clinical decision contains both risk and uncertainty, shared decision making and informed consent are both appropriate. For decisions of lower risk, consent should still be present, but it can be simple rather than informed. Clinicians may use this analysis as a guide to their own interactions with patients. In the continuing effort to provide patients with appropriate decisional authority over their own medical choices, shared decision making, informed consent, and simple consent each has a distinct role to play.
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This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.