The amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

In Phase I of this project, reported in SAND97-1922, Sandia National Laboratories applied a systems approach to identifying innovative biomedical technologies with the potential to reduce U.S. health care delivery costs while maintaining care quality. The effort provided roadmaps for the development and integration of technology to meet perceived care delivery requirements and an economic analysis model for development of care pathway costs for two conditions: coronary artery disease (CAD) and benign prostatic hypertrophy (BPH).Phases II and III of this project, which are presented in this report, were directed at detailing the parameters of telemedicine that influence care delivery costs and quality. These results were used to identify and field test the communication, interoperability, and security capabilities needed for cost-effective, secure, and reliable health care via telemedicine.