A predictive analytics framework for undesired outcomes using vital signs data analysis

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The long-term goal of this research program is to develop decision support tools that provide predictive information to both clinicians and patients in order to personalize treatments and improve wellness. These tools will: 1) gather and integrate data from electronic warehouses in clinical, personal, and community settings; 2) apply smart algorithms to this data to detect potential outcomes; and 3) distribute this outcome information to electronic health records or smart devices (for clinicians) and smartphone applications (for patients). The short-term objective is to develop and test predictive analytics frameworks that analyze and integrate the multitude of digital signals (both data-banked and real-time) that exist in complex machine-assisted environments to: a) improve data access and exchange between medical devices, while ensuring security/privacy/confidentiality; and b) support the implementation and evaluation of algorithms to predict undesired clinical events using vital signs, medical device, and patient data. ******Importance of research: By facilitating data exchange, applying smart algorithms to predict outcomes from complex physiological and environmental data, and using effective means to visualize and communicate the results, we can make an important contribution to modernizing the tools we need for healthcare. The early detection of the deterioration of critically ill patients before undesired clinical events (e.g., cardiac arrest in the ICU or complications after surgery) is such an application, as it allows for re-assigning priority of care and represents an enormous opportunity to improve outcomes and reduce cost.******Anticipated Outcomes: In collaboration with healthcare organizations, clinicians, data scientists, researchers, and potential industry partners, we will develop two complementary components of the predictive analytics framework: a data exchange component, which obtains and integrates real-time and historic data, using ‘Internet of Things' technologies; and an outcome prediction modeling and processing pipeline, which extracts the data and transforms them into actionable information.******Benefit for Research Field & Canada: Innovation in biomedical and computer engineering is integral to advancing Canadian healthcare. This research program will mitigate barriers in machine-to-machine communication and the meaningful use of such data by providing a framework for safe, trusted, and simple medical data exchange. This framework will be widely applicable in research and development settings. It will underpin the development of smart decision support systems, including predictive analytics models, which will support the reduction of false alarms, closed-loop monitoring/assistive systems and early-warning systems to detect and prevent undesirable health outcomes.