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    Telehealth

    eICU Research Institute

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    Analytics to drive AI development and evidence-based best practices in the Intensive Care Unit

    Philips eICU Research Institute (eRI), a non-profit institute established by Philips and governed by customers, provides a unique platform to create one of the most comprehensive de-identified databases of ICU care in the world. This integrated dataset is instrumental in product development and is a key enabler for critical research in the intensive care field. The eRI database contains billions of high-quality representative clinical data points that extend over more than 15 years, including the COVID-19 pandemic period.
    Committed to sharing knowledge and the advancement of medicine
    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.
    Clinician with eRI Database
    A comprehensive dataset helping researchers solve real world challenges in critical care
    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. 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.
    Nurse with male patient in ICU CCU
    Unlocking the value of eRI: advancing critical care through collaborative research and innovation
    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.
    Hospital Virtual Care remote nurse
    Critical care and high acuity: predictable and proactive
    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.
    View Philips eICU
    eICU
    Frequently asked questions

    Philips is in collaboration with the Institute for Medical Engineering and Science (IMES) at the Massachusetts Institute of Technology (MIT) to allow health care researchers access to a large scale critical care dataset: eICU Collaborative Research Database eICU-CRD of 200K patients to help advance machine learning and artificial intelligence (AI) in healthcare. This is a subset of the large scale eICU Research Institute dataset. The original eICU-CRD dataset was first released in 2016. More than 3,000 users have used the original database with citations in over 660 published research papers.

    Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study, 2023 (Annals of Intensive Care)
    Impact of COVID-19 pandemic on severity of illness and resources required during intensive care in the greater New York City area, 2020 (medRxiv)
    Five-Year Trends of Critical Care Practice and Outcomes, 2017 (Chest)

    Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias, 2022 (Critical Care Medicine)
    Early prediction of hemodynamic interventions in the intensive care unit using machine learning, 2021 (Critical Care)
    Predicting infection type upon clinical suspicion of hospital-acquired infection, 2020 (Critical Care Medicine)
    Estimating excess ICU time: potential financial impact and variation between ICUs, 2020 (Critical Care Medicine)

    Predicting the risk of mortality and readmission in the ICU, 2012 (Plos one)

    Documentation

    Philips Critical Care Outcomes Prediction models for ICU Length of Stay
    PDF|(3.60 MB)
    Introduction to Philips Critical Care Outcome Prediction Models – Mechanical Ventilation Predictions
    PDF|(2.63 MB)
    Disclaimer
    Results are specific to the institution where they were obtained and may not reflect the results achievable at other institutions. Results in other cases may vary.