Fusing real-time location with clinical and operational data for high-precision characterization of stroke workflows
Stroke care, particularly in ischemic (thrombotic) events, is time-sensitive. Early identification and appropriate management in the initial hours after the event are associated with lower morbidity and mortality as well as a reduction in healthcare costs.
According to the European Heart Network, the annual economic costs of stroke in the European Union is of approximately €38 billion/year (50% direct health costs, 22% productivity loss, 29% informal care of people with stroke).
Recent research also concludes that the number of people living with stroke is estimated to increase by 27% between 2017 and 2047 in the European Union, mainly because of population aging and improved survival rates.
The death rate and level of disability resulting from strokes can be dramatically reduced by early identification and appropriate management in the initial hours after the event. The currently implemented data management systems are not capable of systematically identifying unnecessary time delays, bottlenecks, and other weaknesses in the workflow.
The Stroke management pilot in BigMedilytics —involving Elisabeth-TweeSteden Ziekenhuis (as leader of the pilot), Philips, Universidad Politécnica de Madrid, and the Eindhoven University of Technology— aims to improve outcomes and thereby reduce the overall cost of stroke by using big data to identify and remove bottlenecks in time-critical, hyper-acute stages of the workflow.
The pilot demonstrates how real-time location data can be fused with clinical and operational data in order to accurately characterize the care pathway of stroke patients. RLTS allows non-biased, high accuracy insights into bottlenecks in the care pathway. This has the potential to save lives, reduce costs, through the optimization of workflows.