Big Data Illustration: Connecting our Bodies to the Cloud

Editor's Notes

• #3: Analyst firm IDC projects net-new data growth by 2020 being 40 Zettabytes per year (that’s 40 x 1021 bytes). As you can see, where we are today in 2013, we are still near the base of the curve. And we expect this will continue to explode as IDC extends their forecast into later years. This 1 to 2 year doubling type of growth is something we are familiar with from Moore’s Law, and in increases in network bandwidth, etc., but unlike those curves this one has no physical limits. Processors, systems and switches will never alone be able to keep pace again -- they are in the rear view mirror of data growth rates. Even as colleagues in Almaden and Watson and Zurich continue to chase advances to the atomic level. But there’s even more to the story.
• #4: The healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care [1]. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducing the costs, these massive quantities of data (known as ‘big data’) hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support, disease surveillance, and population health management [2-5]. Reports say data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes) scale and, not long after, the yottabyte (1024 gigabytes) [6]. Kaiser Permanente, the California-based health network, which has more than 9 million members, is believed to have between 26.5 and 44 petabytes of potentially rich data from EHRs, including images and annotations [6]. By definition, big data in healthcare refers to electronic health data sets so large and complex that they are difficult (or impossible) to manage with traditional software and/or hardware; nor can they be easily managed with traditional or common data management tools and methods [7]. Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed [7]. The totality of data related to patient healthcare and well-being make up “big data” in the healthcare industry. It includes clinical data from CPOE and clinical decision support systems (physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic patient records (EPRs); machine generated/sensor data, such as from monitoring vital signs; social media posts, including Twitter feeds (so-called tweets) [8], blogs [9], status updates on Facebook and other platforms, and web pages; and less patient-specific information, including emergency care data, news feeds, and articles in medical journals.
• #5: Portal services to Doctors and medical practitioners Watson in Healthcare EHR Services
• #7: Apple Push Notification Service (APNS) or Google Cloud Messaging (GCM) MQTT (formerly Message Queue Telemetry Transport) MQTT is a Client Server publish/subscribe messaging transport protocol. It is light weight, open, simple, and designed so as to be easy to implement. These characteristics make it ideal for use in many situations, including constrained environments such as for communication in Machine to Machine (M2M) and Internet of Things (IoT) contexts where a small code footprint is required and/or network bandwidth is at a premium. The protocol runs over TCP/IP, or over other network protocols that provide ordered, lossless, bi-directional connections.
• #11: 10