Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.