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Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. (Contains 3 tables and 7 figures.)

An Electronic Health Record (EHR) is an individual's record of all health events that enables critical information to be documented and shared electronically amongst health care providers and patients. The introduction of an EHR, particularly a patient-accessible EHR, can be expected to lead to an escalation of enquiries, complaints and ultimately, disputes. Prevailing opinion is that Online Dispute Resolution (ODR) systems can help with the mediation of certain types of disputes electronically, particularly systems which deploy Artificial Intelligence (AI) to reduce the need for a human mediator. However, disputes regarding health tend to invoke emotional responses from patients that may conceivably impact ODR efficacy. This raises an interesting question on the influence of emotional intelligence (EI) in the process of mediation. Using a phenomenological research methodology simulating doctor–patient disputes mediated with an AI Smart ODR system in place of a human mediator, we found an association between EI and the propensity for a participant to change their previously asserted claims. Our results indicate participants with lower EI tend to prolong resolution compared to those with higher EI. Future research include trialling larger scale ODR systems for specific cohorts of patients in the area of health related dispute resolution are advanced. [ABSTRACT FROM AUTHOR]

Abstract: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN). We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery. This is important as not all five Ewing tests can always be applied in each situation in practice. Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN. We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests. Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery. We found the best sequences of tests for cost-function equal to the number of tests. The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93. They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests. The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure. We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained. Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure. The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test. Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence. [Copyright &y& Elsevier]