Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned. Issue 6 (June 2019)

• Record Type: Journal Article
• Authors: Yang, Shiming; Stansbury, Lynn G.; Rock, Peter; Scalea, Thomas; Hu, Peter F.
• Is Part Of: Critical care medicine. Volume 47:Issue 6(2019)
• Publication Date: 2019-06
• Subjects: big data -- critical care prediction -- decision support -- intensive care unit prediction -- prediction strategies; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000003739
• Languages: English
• ISSNs: 0090-3493

Abstract

Abstract : Objectives: Modern critical care amasses unprecedented amounts of clinical data—so called "big data"—on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information. Data Sources: We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data, " with special focus on outcomes prediction and major clinical concerns in critical care. Study Selection: Three reviewers independently screened preliminary citation lists. Data Extraction: Summary data were tabulated for review. Data Synthesis: To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices. Conclusions: Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care. Abstract : Supplemental Digital Content is available in the text.