Aladdin Baarah

Improving cardiac patient flow based on complex event processing

Event delivery architectures can collect streams of events from sensors for processing to infer critical medical events in real time. However, to address cardiac patient flow, it is critical to also incorporate business process activity events. Our work seeks to improve cardiac patient flow by integrating events from a wide spectrum of sources within a hospital using complex event processing to support fine grained monitoring of patient flow. In this paper, we use a case study from a large community hospital in Ontario, Canada to illustrate the problem and demonstrate our approach which includes: an analysis of cardiac patient flows in order to understand where the patient could be waiting; a data architecture for collecting and processing basic events from multiple sources in order to create complex events delivered to a real-time dashboard; and a detailed analysis of the complex event processing required to support fine grained monitoring.

Architecture of an Event Processing Application for Monitoring Cardiac Patient Wait Times

Presented is an architecture for event processing applications that manage business processes, and the authors use a case study of monitoring cardiac patient wait times to evaluate their architecture and illustrate our approach. Event processing applications can collect streams of events from sensors for processing to infer critical medical events in real time. However, to manage business processes, it is critical to understand not only where in the hospital those events occur, but also where in the business process those events are occurring. Metrics, such as wait times, can be computed in real-time by using complex event processing to integrate and aggregate events in support of fine grained monitoring of business processes. The authors evaluate their architecture against both current practice and related works in the literature.

Inferring state for real-time monitoring of care processes

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. In this paper, we introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. The research is validated with a case study developed in collaboration with a large community hospital.

A design strategy for health information systems to address care process management

Health information systems (HISs) offer great potential for supporting healthcare delivey, particularly collaborative care delivery that is provided across multiple settings and providers. To date many HISs have focused on digitzing data or processes on a departmental or healthcare provider basis. This approach has resulted in unintended consequences due to different types of interoperability issues. New approaches are needed to design integrated HISs to support collaborative care delivery. There is also a need for studies that articulate interoperability needs for HISs and how to evaluate different aspects (i.e., data, process and technical) of interoperability. This paper uses a three year case study of the design of the Palliative Care Information System (PAL-IS) to study system design and interoperability considerations for a collaborative HIS. We describe the two phases in which PAL-IS was designed and also identify criteria for evaluating interoperability of collaborative HISs.

Engineering a state monitoring service for real-time patient flow management

Addressing wait times is a challenge for the healthcare system. It is difficult for hospitals to measure precisely wait times and root causes. To do so they need to process events from disparate sources in real-time to measure fine grained care states of the patients, providers, beds and other resources related to care processes. In this paper, we present a state monitoring service for patient flow management that has been prototyped with a Toronto area hospital to integrate events from RFID tracking, business process management (BPM) applications and legacy systems. The results show that BPM and RFID provide events in real-time, but on their own could not fully define all states in the care process. Our state monitoring service integrates BPM and RFID and infers new events that define all the states not handled by BPM and RFID.

An Application Meta-model for Community Care

Abstract Care process monitoring is needed to provide performance management reporting to measure how quality of care goals are being met for a specific care process. There are special challenges faced when monitoring community care processes, especially if one wants to manage performance for community care across an entire geographic region. In this paper, we evaluate an application meta-model for defining a care process monitoring application (CPMA) previously developed for monitoring care processes in a hospital, to determine its effectiveness for addressing community care processes. A case study developed in collaboration with a regional health authority is used.

Analytics Driven Application Development for Healthcare Organizations

In response to governmental and regulatory mandates, Healthcare organizations are increasingly interested in assessing the efficiency of their care processes and services. Traditional information systems for healthcare have focused on capturing administrative details related to services and resource usage on a departmental or healthcare provider basis. The resulting interoperability challenges make it difficult for analytics and performance management reporting to provide a detailed view of care processes. This paper presents a methodology and an analytics application framework that focuses on performance and efficiency. Starting from performance goals, the application framework development is driven by the identified key performance indicators. This methodology addresses interoperability challenges by defining the minimal dataset required for measuring outcomes of a care process. It enables an information system design that focuses on analytics and minimizes maintenance and integration issues. The application framework is developed in the context of a multi-year case study of a clinical information system for palliative care.

A mutation-based model to rank testing as a service (TaaS) Providers in cloud computing

With the increase of cloud computing service models, the need to measure and evaluate them are increased as well. In this paper, we proposed a novel measurement approach for the purpose of evaluating the quality of Testing as a Service (TaaS), which is considered as one of the most recent outstanding model within cloud computing environment. (TaaS) as outstanding model include the provision of multi-sub services, such as enabling cloud customer to verify his own code through the use of cloud provider resources. Its goes without questioning that testing over web environment requires high level of resources, time, and effort. Therefore, it should take high attention toward the quality of the used testing technique. Where, the quality of testing technique associated with set of attributes that has the ability to determine testing effectiveness. Thus, in this paper we propose a measurement approach to evaluate the effectiveness of TaaS, over cloud computing environment which relies on the use of mutation score. The main contribution of the proposed model represent in the use of mutation score to evaluate cloud providers ability to perform TaaS, and rank them according to the percentage of TaaS effectiveness.

A Bounded Health Information Technology System Design Approach to Support Community-Based Care Delivery

Health information technology HIT offers great potential for supporting healthcare delivery, particularly collaborative care delivery that is provided across multiple settings and providers. To date much of HIT design has focused on digitizing data or processes on a departmental or healthcare provider basis. However, this bounded approach has not scaled well for supporting community based care across disparate providers or settings because of the lack of boundaries e.g. disprate data and processes that exist in community based care. Cloud computing approaches that leverage mobile form applications for developing integrated HIT solutions have the potential to support collaborative healthcare delivery in the community. However, to date there is a shortage of methods that describe how to develop integrated cloud computing solutions to support community based care delivery. In particular there is a need for methods that identify how to incorporate boundaries into cloud computing systems design. This paper uses a three year case study of the design of the Palliative Care Information System PAL-IS to provide system design insight on cloud computing approaches that leverage mobile forms applications to support community care management.

Engineering a Performance Management System to Support Community Care Delivery

The engineering of health information technology (HIT) often focuses on clinical or hospital focused tasks. As more care is provided in the community there is an increasing need to monitor goals of care related to patient care delivery. These goals are often measured through performance metrics. Before we can track performance metrics we need to articulate the data and processes that define the metrics. However, the data sources are often varied and the processes ill-defined making it hard to engineer systems to collect and analyze metrics. Further, the ability to share data between organizations is impacted by culture, technology and privacy issues. To date there are few methodological approaches for modeling a health system from the perspective of metrics, data sources, and touch points to enable performance management of community based healthcare delivery. This paper addresses those shortcomings and presents a methodology for modeling goals, metrics and data to enable engineering of business intelligence applications for performance management of community based care.