Impacts of big data velocity on quality healthcare decision-making in underserved populations

Leveraging big data analytics in healthcare decision making requires vetting the risk of people, processes, and systems. Today, healthcare decision making is poorly organized, inefficient, and largely siloed in operation, resulting in a disproportional disadvantage in quality of care for underserved populations. Big data attributes include volume, velocity, and variety. The purpose of this paper is to highlight how big data velocity will positively impact the quality of healthcare decision making, particularly in underserved populations. The ensuing paper presents information on how integrating procedural data with outside data sources will drive better healthcare decisions, support identification of critical and emerging healthcare needs, and executing on patient specific best practices through decreased data fragmentation. Areas for continued research are proposed including the role of governance in early onboarding and socialization, as well as metrics in big data change adoption.