Investigating Differences between Graphical and Textual Declarative Process Models

Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding declarative process models often impede their adoption. Particularly, a study revealed that aspects that are present in both imperative and declarative process modeling languages at a graphical level-while having different semantics-cause considerable troubles. In this work we investigate whether a notation that does not contain graphical lookalikes, i.e., a textual notation, can help to avoid this problem. Even though a textual representation does not suffer from lookalikes, in our empirical study it performed worse in terms of error rate, duration and mental effort, as the textual representation forces the reader to mentally merge the textual information. Likewise, subjects themselves expressed that the graphical representation is easier to understand

Investigating the trade-off between the effectiveness and efficiency of process modeling

Despite recent efforts to improve the quality of process models, we still observe a significant dissimilarity in quality between models. This paper focuses on the syntactic condition of process models, and how it is achieved. To this end, a dataset of 121 modeling sessions was investigated. By going through each of these sessions step by step, a separate ‘revision’ phase was identified for 81 of them. Next, by cutting the modeling process off at the start of the revision phase, a partial process model was exported for these modeling sessions. Finally, each partial model was compared with its corresponding final model, in terms of time, effort, and the number of syntactic errors made or solved, in search for a possible trade-off between the effectiveness and efficiency of process modeling. Based on the findings, we give a provisional explanation for the difference in syntactic quality of process models

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

For more than a decade, the interest in aligning information systems in a process-oriented way has been increasing. To enable operational support for business processes, the latter are usually specified in an imperative way. The resulting process models, however, tend to be too rigid to meet the flexibility demands of the actors involved. Declarative process modeling languages, in turn, provide a promising alternative in scenarios in which a high level of flexibility is demanded. In the scientific literature, declarative languages have been used for modeling rather simple processes or synthetic examples. However, to the best of our knowledge, they have not been used to model complex, real-world scenarios that comprise constraints going beyond control-flow. In this paper, we propose the use of a declarative language for modeling a sophisticated healthcare process scenario from the real world. The scenario is subject to complex temporal constraints and entails the need for coordinating the constraint-based interactions among the processes related to a patient treatment process. As demonstrated in this work, the selected real process scenario can be suitably modeled through a declarative approach.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Who is behind the Model? Classifying Modelers based on Pragmatic Model Features

\u3cp\u3eProcess modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.\u3c/p\u3

Medicine / Does the choice of intraoperative fluid modify abdominal aneurysm repair outcomes? : A cohort analysis

Intraoperatively administered hydroxyethyl starch could be a risk indicator for postoperative acute kidney injury (AKI) in vascular surgical patients. In a single-center retrospective cohort analysis, we assessed the impact of hydroxyethyl starch and other risk indicators on AKI and mortality in 1095 patients undergoing elective open abdominal aneurysm repair (AAA-OR) or endovascular aortic repair (EVAR). We established logistic regression models to determine the effect of various risk indicators, including hydroxyethyl starch, on AKI, as well as Cox proportional hazard models to assess the effect on mortality. The use of intravenous hydroxyethyl starch was not associated with an increased risk of AKI or mortality. Patients undergoing EVAR were less likely to develop AKI (4% vs 18%). Multivariate risk indicators associated for AKI included suprarenal or pararenal aortic cross-clamp [odds ratio (OR), 4.44; 95% confidence interval (95% CI), 2.5387.784; P < .001] and procedure length (OR, 1.005; 95% CI, 1.0031.007; P < .001), and favored EVAR (OR, 0.351; 95% CI, 0.1180.654; P < .01). Main multivariate risk indicators associated with mortality included patients needing an urgent procedure [hazard ratio (HR), 2.294; 95% CI, 1.5413.413; P < .001], those with suprarenal or pararenal aortic cross-clamp (HR, 1.756; 95% CI, 1.2472.472; P < .01), and patients undergoing EVAR (HR, 1.654; 95% CI, 1.2922.118; P < .001). We found neither a benefit nor a negative effect of hydroxyethyl starch on the risk of AKI or mortality. Instead, other variables and comorbidities were found to be relevant for the development of postoperative AKI and survival. Nevertheless, clinicians should be aware of the high risk of postoperative AKI, particularly among those undergoing AAA-OR procedures.(VLID)489229