No abstract

An influence diagram is a graphical representation of a decision problem that is at once a formal description of a decision problem that can be treated by computers and a representation that is easily understood by decision makers who may be unskilled in the art of complex probabilistic modeling. The power of an influence diagram, both as an analysis tool and a communication tool, lies in its ability to concisely summarize the structure of a decision problem. However, when confronted with highly asymmetric problems in which particular acts or events lead to very different possibilities, many analysts prefer decision trees to influence diagrams. In this paper, we extend the definition of an influence diagram by introducing a new representation for its conditional probability distributions. This extended influence diagram representation, combining elements of the decision tree and influence diagram representations, allows one to clearly and efficiently represent asymmetric decision problems and provides an attractive alternative to both the decision tree and conventional influence diagram representations.

L1TC status is the America College of Surgeons' highest level of verification for trauma care. To be certified as a L1TC, hospitals must meet strict criteria in both services and patient care. The donation process is often profoundly affected by the burden of demands made on the resources of these institutions and from divergent responsibilities between specialty services within the facility. Dedicated IHCs (OPO staff) are needed to provide early family intervention and to orchestrate the donation process to maximize organ recovery.

A patient's decision to accept treatment recommended by his dental health care provider will be strongly influenced by the quality of the information he is given. Estimates of prognosis and treatment predictability must be based on the evidence available from the literature and the practitioners' own experience. Thorough, accurate, and relevant clinical and adjunctive diagnostic data will be a major influence in the development of the patient's individualized treatment strategy. Some clinical findings such as severity of disease for age, deepening pockets accompanied by loss of clinical attachment, frequent bleeding on probing, and bone loss can be considered as risk and prognosis factors. "Hard" data implicating specific clinical or diagnostic findings as risk factors or markers are difficult to find because there are few randomized longitudinal trials available. A new approach which attempts to focus on reducing the risk of undesirable outcomes while improving the probability of successful outcomes following treatment has been referred to as the Treatment Predictability Model. A key feature of this approach is the focus on individual patient circumstances and preferences through the use of decision analysis techniques. A large scale, long-term project utilizing a practice-based research network (PBRN) provided some descriptive information about factors that could distinguish between responders and nonresponder patients undergoing treatment for advanced periodontitis. Bacterial colonization, level of post-treatment plaque control, and smoking were major predictive variables in this group of periodontitis patients. The predictive treatment approach may be one way to develop evidence that will improve the predictability of outcomes for individual patients.