Critical Care, Critical Data

Abstract

As big data, machine learning , and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*

“…However, this requires large amounts of patient data, ideally from multiple hospitals. Fortunately, electronic health records (EHRs) are widely adopted amongst ICUs ( 4 ), and ICU admissions generate vast amounts of data from patient monitors and life support devices ( 3 ).…”

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

“…Content may change prior to final publication. [57], [58], [59], [25], [60] [61], [62], [63], [64] [65], [66], [67], [68], [69] [70], [71], [72] demands in less-developed countries without fully open healthcare systems [74]. Nowadays, a wide range of mobile devices of various sizes, from cell phones and smartwatches to small phones and tablets, along with integrated wearables, implants, and location-based trackers and sensors are available.…”

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

“…In the United States, the Health Information Technology for Economic and Clinical Health (HITECH) Act has allowed for the rapid implementation of EHR nationwide, and this has had a global impact [35]. In the field of intensive care, a National Institutes of Health-funded initiative entitled Medical Information Mart for Intensive Care (MIMIC), developed by a partnership between Beth Israel Deaconess Medical Center (BIDMC), Massachusetts Institute of Technology (MIT) and Philips Healthcare, has led to rapid advancement in the application of data science to clinical medicine [36]. MIMIC is a publicly available, de-identified database of close to 100,000 patients admitted to the ICUs at BIDMC in Boston, MA, USA from 2002 to 2018 and is maintained by the Laboratory for Computational Physiology at MIT [37].…”