data brokers, not all, send data throughout the planet to ensure that, in case of an emergency, it is backed up. Unless a thorough investigation is performed about the platform and someone thinks to ask that question, the hospital or doctor's office may be blissfully unaware that the data is being spread throughout the world.
In the end, big data is about sharing of data and aggregating the right data sets in the right way. That data may or may not be HIPAA data, but may have all the markers of HIPAA data. The data may be collected from applications and shared in ways that we, as consumers, may not be aware of. It also holds the promise of expanding our scientific understanding and taking us into future directions we have only begun to imagine today. Big data is not about the data itself. There are goals and objectives from many different angles that make it important. There are also tools that data scientists use to sort through the volumes of data.
Data Mining Automation
Analyzing the volume of data that comes out of big data is not a minor undertaking. As the volume of data increases and we become more and more subtle in terms of our analysis of data, often it is worthwhile to get some extra help to analyze that data. Unsurprisingly, there are numerous ways to help with that analysis. Most people immediately start to think of artificial intelligence—partially popularized by IBM's Watson that beat out other contestants on Jeopardy in 2011 and partially by science fiction. While certainly artificial intelligence is used, often the term is overused by marketing teams. Some of the subcomponents of artificial intelligence are more than sufficient to meet the data analytics needs of many companies. For example, many tools use machine learning or deep learning for analytics purposes. Each method has its own pros and cons, and each may bring value depending on the context. Nonetheless, while there are many other tools for working with data, the range of tools that data scientists use to correlate data and discern patterns offer vast improvements over the activity that people perform on their own. Figure 3-1 demonstrates the relationship between the different technologies relating to data science and artificial intelligence. For purposes of simplicity, let's use the term artificial intelligence broadly to talk about the full range of available tools, although it is not technically accurate.
Figure 3-1: Relationship of data science to enablement technologies
One thing to keep in mind is that artificial intelligence and many of the related tools are in their infancy and require tremendous amount of maturation to meet their full potential. Even in the healthcare market, the full potential has yet to be reached. McKinsey and company identified three stages of artificial intelligence uses that are helpful to highlight the context. The first phase is that we are striving to work on repetitive and largely administrative tasks to reduce the existing workload. We are beginning to see this for specializations that work with images as well. The second phase is to use artificial intelligence in home care—often related to remote monitoring. In this phase artificial intelligence will be utilized more often as an aspect of the connected devices themselves. Phase three will be focused more on being embedded within the clinical processes.34 Being a cautious technological optimist, I am sure there will be further applications of artificial intelligence in the future, especially when it is tied to robotics.
It is important to look more deeply at these phases described by McKinsey. Having artificial intelligence injected into telemedicine has tremendous potential to push medicine into a more proactive mode than ever before. If we took the technology within health applications and tied them into augmented forms of monitoring devices that tie into artificial intelligence systems, we could begin to detect potential medical issues (or diseases) early prior to onset of symptoms. The proactive measures of some of the simpler forms of artificial intelligence are already saving us millions of dollars and countless lives every year; just imagine how many more people and how much more we could save with more fine- grained medical information.
Another area of interest for artificial intelligence is data mining EHR records, which does include mining the records of IoMT devices to look for predictors of risk. Obviously, this is another proactive measure that companies are focusing on. What is interesting is that this process is valuable from multiple angles. The hospital is doing it to help their patients, and the IoMT device providers are using the information not only to help patients, but also to fuel the next generation of improvements in the devices. The more data you have, the more you know what you need to go after from a data perspective. That information can be used to focus product innovation. Quite often, this ties back to the Silicon Valley business model, which works with hospitals and other health practitioners to get feedback from them about improvements that need to be made. This, in turn, will increase the amount of data, which is a small part of the reason we will continue to see more data in the years to come.
In Summary
We have been on a short exploration of how big data fits into the big picture of both hospitals and IoMT devices. We have explored how big data fits into the overall Medicine 2.0 as data is part of the advancement of not only medical science, but also for protecting patients that utilize IoMT. As our IoMT devices become more sophisticated and capable of even deeper readings than ever before, this will only catapult our understanding of medicine even further. That shift will help us be more preventative and thus save lives and also further reduce our healthcare expenditures.
On the data science side of things, whether you are talking about deep learning, machine learning, or artificial intelligence, these practices help the data scientists to aggregate, correlate, and present key findings more quickly. They are a critical tool to help sort through vast quantities of data. They help to make data science much easier and the role of data scientists more important for the companies that rely on big data whether they are working on population health, advertising, or myriad other disciplines—including information security.
The utilization of this data is a brilliant and clearly fantastic use of resources on many fronts. But, there is clearly room for improvement as privacy rights, from many people's perspective, are being violated. We clearly need a better approach going forward not only from an IoMT perspective, but also from a data perspective.
Notes
1 1 “Using IoMT to serve medically high-risk populations. How Catalytic Health Partners uses IoMT solutions to provide cost-effective, quality care,” https://www.t-mobile.com/business/resources/articles/catalytic-health-partners.
2 2 “Using RFID technology to reduce medication errors,” https://hospitalnews.com/using-rfid-technology-to-reduce-medication-errors/.
3 3 Nir Menachemi and Taleah H. Collum, “Benefits and drawbacks of electronic health records systems,” 2011, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3270933/.
4 4 “4 ways data is improving healthcare,” 2019, https://www.weforum.org/agenda/2019/12/four-ways-data-is-improving-healthcare/.
