Health care today usually consists of various services covering various parts of the total health care of a region or country. These services are required to coordinate and collaborate, often using procedures and IT collaboration tools that may not be designed for interoperating across the evolving wider landscape of health care services. We posit that it is necessary to train personnel in collaboration skills using whatever infrastructure is in place. To this end, we present design principles for simulation-based collaboration training scenarios that emphasizes the inclusion of suboptimal infrastructure elements. We applied the principles in a co-creational workshop with healthcare stakeholders from a hospital and surrounding municipalities in Norway where we discussed cases where collaboration training is perceived as critical. We elicited five training vignettes concerning the general case of   detecting, and following up on, clinical deterioration in a patient at home or in a nursing home. We found that the design principles spurred highly relevant discussions among participants and that novel ideas for collaboration training were brought forth on the basis of these principles. We conclude that there is a potential in using these principles for eliciting training vignettes that address the actual situation more accurately. 

We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language AL. Generic causal and temporal logic is added when the causal theory is compiled into the underlying Answer Set Programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.

Context: The mean magnitude of relative error (MMRE) is an error measure frequently used to evaluate and compare the estimation performance of prediction models and software professionals.

Objective: This paper examines conditions for proper use of MMRE in effort estimation contexts.

Method: We apply research on scoring functions to identify the type of estimates that minimizes the expected value of the MMRE.

Results: We show that the MMRE is a proper error measure for estimates of the most likely (mode) effort, but not for estimates of the median or mean effort, provided that the effort usage is approximately log-normally distributed, which we argue is a reasonable assumption in many software development contexts. The relevance of the findings is demonstrated on real-world software development data.

Conclusion: MMRE is not a proper measure of the accuracy of estimates of the median or mean effort, but may be used for the accuracy evaluation of estimates of most likely effort.