In the ICU, patients can become emergent quickly, and their care needs are constant and everchanging. This demanding care setting requires constant patient evaluation and care from the clinical staff. The Philps eICU program uses evidence-based decision-support tools, comprehensive analytics and specific predictive algorithms to provide data the virtual care team needs to help improve patient outcomes.
Philips eICU programs generate an incredible amount of data on ICU patient stays every year. Philips eICU Research Institute (eRI), a non-profit institute established by Philips and governed by customers, is a platform built from a repository of de-identified data, collected since 2008, that is used to advance knowledge of critical and acute care. For Philips customers that participate, eRI provides a unique platform to create one of the most comprehensive databases of ICU care in the world. In addition, eRI directly benefits customer telehealth programs as research is frequently translated into new and advanced tools and programs.
Philips established the eRI platform as a key enabler for critical research in the intensive care field. The database is a repository of de-identified data collected in collaboration with our customers. This integrated dataset contains billions of high quality representative clinical data points that extend over more than 15 years including the COVID-19 pandemic period. The secure database is instrumental in product development and includes detailed clinical information such as vital signs, pharmacy and medication orders, lab results, diagnoses, and novel severity of illness scores. The dataset gives comprehensive insights on patient admissions, treatments, co-morbidities, readmissions, and clinical outcomes.
The impact of eRI extends from product development to critical research and beyond, enabling academia and member organizations to collaborate, innovate and advance critical care together. This database has played a pivotal role in developing solutions for multiple clinical challenges, including epidemiology/large data analytics, AI and predictive analytics and learning data patterns and clinical strategies.
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