Generating insights in uncharted territories: real-time learning from data in critically ill patients-an implementer report.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 34535448.
- Also identified by DOI 10.1136/bmjhci-2021-100447 and PMC identifier 8450955.
- Licence recorded as CC BY-NC.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
<b>Introduction</b> In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data.<b>Methods</b> In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure.<b>Conclusion</b> By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future.
Medical subject headings
- COVID-19
- Critical Illness