Traditional Workflow Management Systems (WFMSs) are not flexible enough to support loosely-structured processes. Furthermore, flexibility in contemporary WFMSs usually comes at a certain cost, such as lack of support for users, lack of methods for model analysis, lack of methods for analysis of past executions, etc. DECLARE is a prototype of a WFMS that uses a constraint-based process modeling language for the development of declarative models describing loosely-structured processes. In this paper we show how DECLARE can support loosely-structured processes without sacrificing important WFMSs features like user support, model verification, analysis of past executions, changing models at run-time, etc.

Abstract. In today's fast changing business environment flexible Process Aware Information Systems (PAISs) are required to allow companies to rapidly adjust their business processes to changes in the environment. However, increasing flexibility in large PAISs usually leads to less guidance for its users and consequently requires more experienced users. In order to allow for flexible systems with a high degree of support, intelligent user assistance is required. In this paper we propose a recommendation service, which, when used in combination with flexible PAISs, can support end users during process execution by giving recommendations on possible next steps. Recommendations are generated based on similar past process executions by considering the specific optimization goals. In this paper we also evaluate the proposed recommendation service, which is implemented in ProM, by means of experiments.

Abstract-Traditional Workflow Management Systems (WFMSs) are not flexible enough to support loosely-structured processes. Furthermore, flexibility in contemporary WFMSs usually comes at a certain cost, such as lack of support for users, lack of methods for model analysis, lack of methods for analysis of past executions, etc. DECLARE is a prototype of a WFMS that uses a constraint-based process modeling language for the development of declarative models describing loosely-structured processes. In this paper we show how DECLARE can support loosely-structured processes without sacrificing important WFMSs features like user support, model verification, analysis of past executions, changing models at run-time, etc.

ObjectivesPopulation-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario.SettingsThe five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL).ParticipantsResponsible teams for regional data management in the five ACT regions.Primary and secondary outcome measuresWe characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction.ResultsThere was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment.ConclusionsThe results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.

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