Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

Developing consensus on hospital prescribing indicators of potential harms amenable to decision support

Aim: To develop a list of prescribing indicators specific for the hospital setting that would facilitate the prospective collection of high severity and/or high frequency prescribing errors, which are also amenable to electronic clinical decision support (CDS). Method: A three-stage consensus technique (electronic Delphi) was carried out with 20 expert pharmacists and physicians across England. Participants were asked to score prescribing errors using a 5-point Likert scale for their likelihood of occurrence and the severity of the most likely outcome. These were combined to produce risk scores, from which median scores were calculated for each indicator across the participants in the study. The degree of consensus between the participants was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or more was achieved. Results: A total of 80 prescribing errors were identified by consensus as being high or extreme risk. The most common drug classes named within the indicators were antibiotics (n=13), antidepressants (n=8), nonsteroidal anti-inflammatory drugs (n=6), and opioid analgesics (n=6).The most frequent error type identified as high or extreme risk were those classified as clinical contraindications (n=29/80). Conclusion: 80 high risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as the basis for a standardised, validated tool for the collection of data in both paperbased and electronic prescribing processes, as well as to assess the impact of electronic decision support implementation or development.

The influence of a decision support tool on real estate valuations

An appraisal task involves the rendering of market value, an unobservable and hypothetical construct. Direct feedback against this objective is typically not possible, so alternative feedback such as confirmation of previous appraised values may be employed. This may alter the appraiser’s perception of the valuation objective leading to divergence from the appraisal normative model. The real estate literature suggests appraisers have been susceptible to the influence of previous appraised values, often resulting in biased valuations. This research focuses on the efficacy of a decision support tool in eliminating or subduing this bias in the appraisal process.

Modelling of control options for an outbreak of Mycoplasma gallisepticum in egg production: a decision support tool

Mycoplasma gallisepticum (MG) is a bacterium that causes respiratory disease in chickens, leading to reduced egg production. A dynamic simulation model was developed that can be used to assess the costs and benefits of control using antimicrobials or vaccination in caged or free range systems. The intended users are veterinarians and egg producers. A user interface is provided for input of flock specific parameters. The economic consequence of an MG outbreak is expressed as a reduction in expected egg output. The model predicts that either vaccination or microbial treatment can approximately halve potential losses from MG in some circumstances. Sensitivity analysis is used to test assumptions about infection rate and timing of an outbreak. Feedback from veterinarians points to the value of the model as a discussion tool with producers.

Incorporating semiotics into fuzzy logic to enhance Clinical Decision Support Systems

In order to enhance the quality of care, healthcare organisations are increasingly resorting to clinical decision support systems (CDSSs), which provide physicians with appropriate health care decisions or recommendations. However, how to explicitly represent the diverse vague medical knowledge and effectively reason in the decision-making process are still problems we are confronted. In this paper, we incorporate semiotics into fuzzy logic to enhance CDSSs with the aim of providing both the abilities of describing medical domain concepts contextually and reasoning with vague knowledge. A semiotically inspired fuzzy CDSSs framework is presented, based on which the vague knowledge representation and reasoning process are demonstrated.