Big Data: Moving Toward the Information Revolution

Every day, we create 2.5 quintillion bytes of data. This is happening so fast that 90% of the world’s data has been created in the last 2 years. Each minute, there are over 400 hours of video uploaded to YouTube, 350,000 Tweets, more than 4 million “likes” on Facebook, and 4 million texts in the United States alone. Let that sink in for a minute. We’re living in a day and age where we are producing content and sharing information (and misinformation) unlike ever before. While the notion of “big data” typically conjures images of NSA snooping, this information revolution is impacting several other areas of life, ranging from banking to health care. While a big data system holds much promise, there are important factors we must take into consideration as we move in that direction. 

The big data revolution in health care is of particular significance because of its impact on each and every one of us, and with health care costs increasing every year, the demand for a big data driven system is skyrocketing. 

The hope for a big data system is that it will enable health care providers to work more efficiently and with a deeper understanding of health trends—and cut costs in the process. While the health care industry has been relatively slow at adopting big data—perhaps due to providers preferring to use their own clinical judgment rather than data—we have arrived at a tipping point where demand, supply, technical capability, and government support have come together in a way that has served as a catalyst for change.

It is clear that the information revolution holds much promise: imagine a scenario in which we could aggregate a patient’s lifestyle habits, medical history, and medications. We would be able to get a full picture of the efficacy of certain drugs, which could help doctors make better decisions when prescribing medications. It would also help to solve an interesting problem that has come about in our health care system: the shift of control away from the doctor-patient relationship. The medical community has become demoralized by government and insurance companies creating endless payment imbroglios to enrich themselves. This has caused some doctors to compensate by shifting their scope-of-practice into areas where they have little training in an effort to meet their bottom line. Because overutilization and inappropriate use leads to higher costs and poorer patient outcomes, big data could help fix the doctor-patient relationship.  

For example, the Society for Vascular Surgery has a Patient Safety Organization, which manages the national Vascular Quality Initiative (VQI).  The VQI is a great example of how big data can be leveraged to improve outcomes. Taken a step further, if the VQI doctors were allowed access to claims data, they could partner with major health insurance companies to better adjudicate claims and identify inappropriate use and overutilization. Based on this, more effective insurance coverage policies could be developed for patients. The ultimate goal being that the right provider (selected based on skill sets and performance records) work with payers to improve value (right value) using the right innovation—to ultimately improve quality of care at reduced cost.

The government is certainly keen to unlock the benefits of big data and in order to do so, it has made efforts to improve transparency and facilitate information sharing. The Open Government Directive, for instance, opened up data from the FDA, the Centers for Medicare and Medicaid, and the Centers for Disease Control. The government is also working to encourage big data at the state level, with the Department of Health and Human Services (HHS) awarding hundreds of millions of dollars to states to encourage health information exchanges. 

Unfortunately, there have already been some major problems with doctors who cannot or choose not to abide by government mandates aimed at bringing the big data revolution to the health care system. One example is electronic health records (EHRs). Doctors cannot meet certain Obamacare provisions without using EHRs, which were intended to make operations easier for both consumers and doctors, encourage the exchange patient information, and avoid duplication. In reality, though, they have failed to improve patient care and only succeeded in taking up time and resources from the medical community.  

The problem: The technology just is not there yet. Right now EHRs simply take too much time—they are not intuitive and, worst of all, they have hurt the doctor-patient relationship because doctors have to be more focused on a screen than on the patient. In order for EHR data to be more useful, it should be structured, (ie, captured and used in a digital format). This “relational database” can then be organized as a set of formally-described data sets from which data can be accessed or reassembled in many different ways without having to reorganize the database.  Currently, 80% of EHR data is unstructured—meaning much of the data is scanned in as PDFs and not easily accessed and reassembled. 

Another major issue the medical community faces as we move toward the information revolution is security. A Washington, DC, hospital was hacked this past May, a Texas hospital was hacked in November, and the insurance conglomerate Anthem was hacked in 2015. As we continue moving in this direction, we will undoubtedly see more and more hacks of sensitive patient information. The medical community recognizes the need to work with IT professionals who are HIPPA compliant, but EHRs in their current form pose increased liability risks for the medical community.

While we are faced with many challenges in moving toward the information revolution, the future benefits will probably outweigh the growing pains. This is a major industry change, and some bumps in the road are to be expected. As we do continue moving forward, however, it is important to keep these potential threats in mind and work to address them before they become severe problems. We must invest in people with expertise in data analysis, data management, and systems management. Policymakers should consider mechanisms to attract more students to these big data related fields, and the private sector should play a bigger role in offering on-the-job training in data science. 

But most importantly, as we move toward a big data system we must not lose sight of the end goal: to provide the best possible patient care and optimize patient outcomes.