Chapter 23 - Big Data Translation Into Clinical Decision Algorithms

Abstract

Big data has many areas of application in health care and is becoming an important tool for knowledge generation, necessary to deal with unmet needs of patients, clinicians, health policy makers, and researchers. The constraints on using big data, by regulators, protecting confidentiality cannot be underestimated, especially when data is shared between organizations. This should not stifle innovation. The ability of health systems to utilize data effectively, predicting risk, and improving real clinical outcomes acts as a counterbalance to these constraints. Personal care can be enhanced and lives can be saved from careful use of health data. Big data offers many applications in health care, often as an early warning system in predicting harm, indicating aberrant clinician activity, and reducing waste. In this chapter, we discuss the possibility to use big data to advance prediction and comparative research methods to address the complexity of patients, clinicians, and health organizations. We discuss how big data can be used and interpreted, and what the challenges are. Incorporating big data analysis into the clinical decision making for personalized medicine will not only depend on new, big data sources but also needs a clear consensus on practical benefit, legal, ethical, and clinical integration issues. Adequately using this massive amount of information to design computerized clinical decision algorithms for both patients and care takers will generate novel ideas to nourish the use of big data, ultimately advancing the concept of personalized medicine.