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.

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.