Big Data Analytics for Remote Patient Monitoring
Remote Health Data
Wireless biometric sensors are rapidly becoming cheap, accurate, and ubiquitous, ranging from simple weight scales to wearable multi-modal devices that can capture high-resolution EKGs. In principle, this data can be used to dramatically alter the way we understand and manage chronic disease. Combining this remote monitoring data with other sources of clinical, environmental, and subjective data has the potential to drive even more powerful predictions. However, transforming this mass of data into actionable information requires complex signal processing and advanced analytics and constantly improving predictions requires intuitive visual and linguistic tools and machine learning: features that are not available in current remote patient monitoring systems.
The Jointly Health Remote Analytics and Monitoring Platform
Jointly Health’s team of world-renowned clinicians, mathematicians, scientists, engineers and healthcare executives are building a next-generation remote patient analytics and monitoring platform with the goal to detect changes in a patient’s health state days, weeks and eventually months in advance. With these predictions it becomes possible to act on these changes before a significant health problem develops so that less risky, and lower cost, interventions can be implemented. This will help “at-risk” healthcare organizations make significant reductions in cost while allowing them to improve quality and alleviate an unquantifiable amount of human suffering. The Jointly Health end-to-end Remote Analytics and Monitoring Platform (RAMP) is designed to continuously identify patients in need of remote monitoring, enable the development of health deterioration detection models, and deploy these models to monitor patients and detect early deterioration before symptoms manifest themselves.
A unique component of the Jointly Health RAMP is a concept we call Adaptive Physiological Modeling, a form of Human-Augmented Machine Learning that combines techniques from natural language processing and machine learning to offer human experts unique tools that let the system learn in detail from their insights. This patent-pending technology will enable Jointly Health to continuously improve the remote detection of patient deterioration.
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