Purpose -The paper aims to provide business process designers a formal yet user friendly technique to evaluate the implications of a process design on process performance even before its implementation. Design/methodology/approach -Based on practical experience, the paper has built on past research to hypothesize structural metrics for business processes that help assess the influence of process design on organizational goals. Findings -This paper suggests a list of structural metrics that can be used to approximate common performance goals (i.e. soft goals) at the stage of process design. Distinct views for process depiction are discussed to explain how each metric can be calculated and what kind of performance goals it can approximate.Research limitations/implications -The paper has assumed an intuitive relationship between process structure and process performance which has to be validated empirically. There is scope for developing formal methods to translate changes in structural metrics to monetary value for business and also to refine the structural metrics further if required. Practical implications -The suggested list of structural metrics and the corresponding process views can be used to compare process design alternatives to select a process design better aligned to organization goals. Originality/value -A list of structural metrics based on practical experience can be easily applied by business process designers to create a formal yet user friendly approach for process design evaluation.
With greater pressures of providing high-quality care at lower cost due to a changing financial and policy environment, the ability to understand variations in care delivery and associated outcomes and act upon this understanding is of critical importance. Building on prior work in visualizing health-care event sequences and in collaboration with our clinical partner, we describe our process in developing a multiple, coordinated visualization system that helps identify and analyze care processes and their conformance to existing care guidelines. We demonstrate our system using data of 5,784 pediatric emergency department visits over a 13-month period for which asthma was the primary diagnosis.
This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies. Importantly, this new approach to the labeling process, which we name Dynamic Automatic Conflict Resolution (DACR), does not require a ground truth dataset and is instead based on inter-project annotation inconsistencies. This makes DACR not only more accurate but also available to a broad range of labeling tasks. In what follows we present results from a text classification task performed at scale for a commercial personal assistant, and evaluate the inherent ambiguity uncovered by this annotation strategy as compared to other common labeling strategies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.