Clinical outcomes in residential care : setting benchmarks for quality

Aim Australian residential aged care does not have a system of quality assessment related to clinical outcomes, or comprehensive quality benchmarking. The Residential Care Quality Assessment was developed to fill this gap; and this paper discusses the process by which preliminary benchmarks representing high and low quality were developed for it. Methods Data were collected from all residents (n = 498) of nine facilities. Numerator–denominator analysis of clinical outcomes occurred at a facility-level, with rank-ordered results circulated to an expert panel. The panel identified threshold scores to indicate excellent and questionable care quality, and refined these through Delphi process. Results Clinical outcomes varied both within and between facilities; agreed thresholds for excellent and poor outcomes were finalised after three Delphi rounds. Conclusion Use of the Residential Care Quality Assessment provides a concrete means of monitoring care quality and allows benchmarking across facilities; its regular use could contribute to improved care outcomes within residential aged care in Australia.

Effects of supervision on tax compliance : evidence from a field experiment in Austria

We conduct a field experiment on tax compliance, focusing on newly founded firms. As a novelty the effect of tax authorities’ supervision on timely tax payments is examined. Interestingly, results show no positive overall effect of close supervision on tax compliance.

Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31 -- September 3, 2015, Proceedings

BPM 2015 was the 13th International Conference on Business Process Management. It provided a global forum for researchers to meet and exchange views over research topics and outcomes in business process management. BPM 2015 was hosted by the University of Innsbruck and took place August 31 to September 3.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case,and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.