Data Analysis in Healthcare Has Finally Arrived!
Although the term “Big Data” has been around for close to a decade now, it’s no surprise that healthcare and medicine in general is finally starting to pick up on it. Having shadowed a few physicians in various healthcare systems and interacting with some of the IT department staff in those clinics and hospitals, these large healthcare organizations are often running on software and technology stacks and infrastructure many generations behind the current “status quo” for organizations. In fact, in a non-sponsored or funded security audit of a few healthcare systems, I was able to determine that they were running on technologies that had long reached their end-of-life support terms. In technology, everything moves so fast and new things are always coming out which is why companies will continue to offer support on their now “obsolete” technologies, however, there comes a point far down the road where there have been so many new updates and new technologies released, that they eventually reach “end-of-life” in terms of support. The company no longer supports it, offers help, release new updates among other things. At this point, this should be the very last moment you hold out. Unfortunately, many industries and sectors continue trucking along with technologies that even your grandparents have given up on using, namely the military and healthcare.
Regardless, health care over recent years have started to harness the mass amounts of information that it generates allowing for large-scale integration and analysis to make better sense of the ever-changing healthcare environment. Not only is it used for patients and improving the professional workflow, but it can be used in administrative and financial purposes, integrating these key aspects together. All in all, big data is crucial moving forward in healthcare because it can not only improve the quality of care being given but it can also cut down on exponentially increasing costs as well all the helping to generate new insights to modify existing strategies or create new ones. An example I have seen is using patient generated data to help identify the right treatment (through machine learning) for the right individual based off of certain criterion and parameters. Of course, this will still a beta test but it was definitely very eye opening to the extent of technologies today.
Of course, it is relatively easy enough to gather data from within the hospital. However, the challenge arises from the need to establish a federal repository of information from across the nation through various healthcare systems to enable better research, access, management, and utilization of all this glorious data. Another challenge arises on the tools and knowledge to actually make use of this data. Although having this vast repository is terrific, if it is not being efficiently used and even managed it can go from this valuable data lake to being this congested data swamp where nothing can ever be found. In earlier posts of mine, I talked about the Internet of Things (IOT) and how wearables could push the wave of predictive analytics and preventative car, big data is utilized in these situations by integrating these various kinds of data about the patient and his or her environment that can help make better predictions and target interventions. Not only can this improve quality and efficiency in healthcare but also reduce readmissions and costs, further pushing the benefits for the upcoming pay-for-performance model from MACRA. Other benefits include avoiding adverse events, treatment optimization and early identification of worsening health states.
As earlier mentioned the next big thing is actually applying all of this knowledge and insight that we are gaining from big data analytics into clinical practices and improving the best practices in the organization. Everybody needs to be deeply involved so there is a clear understanding on how this generated knowledge can be translated into practice.