Can Big Data Make Us Healthier?
I spent some time this week at the Mobile Health Summit, an annual Washington event featuring the latest in mobile health-related technologies. The exhibition hall was filled with sensor-based devices that can track blood pressure, body weight, blood glucose, pill-taking behavior, and just about any other facet of human life. There was even a $199 sled from AliveCor that turns an iPhone into an electrocardiogram.
Nearly all of these devices feature either Wi-Fi or cellular wireless capability, making them part of an ever growing machine-to-machine network, also known as the Internet of Things. There is little doubt that they are becoming an important part of individualized treatment that can help keep us healthier, albeit at a sometimes creepy loss of privacy. (One company was showing connected motion sensors that could alert a care giver if it didn’t sense you moving about your room when you were supposed to be up and about. I can see the usefulness, but still find the idea disturbing.)
To be truly useful, the data from these sensors should feed into the patient’s medical record in a way that gives a health care provider a big-picture idea of what is going on. Infrastructure providers, including Verizon, AT&T, and Qualcomm, are building systems that can consolidate data from a variety of sensor sources.
But the question in my mind is whether we can go beyond individual medicine and use the staggering mass of data that will be produced by our quantified futures to improve health in general. The practice of medicine remains, in many ways, stunningly unscientific. Treatments are often selected without solid statistical knowledge of outcomes because data is hard to come by. Many decisions are based more on instinct and custom. What studies do exist too often reach sweeping conclusions on the basis of painfully small numbers of patients involved.
I have no doubt that researchers could gain tremendous insight into medicine, particularly what does and does not work to keep us healthy, if they could use big data approaches to study treatments and outcomes from an aggregation of the information that is starting to flow. However, many challenges–technical, business, and regulatory–have to be met before this can happen.
Today, what data does exist is likely to be stored in completely disconnected silos. Changes in technology, insurance company practice, and government regulations are forcing the adoption of electronic medical records (EMR) at a rapid rate, but EMR systems often cannot talk to each other. If you land in the hospital, you will be very lucky if its records system can communicate directly with your doctor’s. The government’s Center for Medicare and Medicaid Services.the payment agent for two massive programs, has a vast collection of data on treatments and outcomes hidden away in an assortment of mutually incompatible legacy databases. (CMS has launched a modernization program mandated by Obamacare, but it could take years to bear fruit.)
There are many obstacles in the way of turning a big collection of individual medical records into useful big data. An obvious one is privacy. Medical records are about as sensitive as personal data gets and we have to make sure that the identity of individuals is not exposed when the information is aggregated. There are already extensive protections in place, most significantly in the U.S., the Health Insurance Portability and Accountability Act (HIPAA.) Some experts, notably Jane Yakowitz Bambauer, fear that excessive concern with making sure that data remain anonymous threatens to cripple valuable research.
There are also major issues in making sense of the data. If you are researching outcomes, it does help to be able to find all the patients with the condition you are studying. But the metadata accompanying today’s medical records, often designed more for the needs of insurance companies and other payers than for doctors, can make identifying the relevant data hard. “People are carefully coding the financial side, but that provides very little help on the clinical side,” says Dr. David Delaney, chief medical officer for SAP Healthcare. A new system called ICD-10 is in use in much of the developed world, but won’t be fully implemented in the U.S. for another two years.
Venture capitalist Vinod Khosla, writing for Fortune, argues that data analytics will eventually replace 80% of what doctors now do. Fortunately for his prediction, Khosla does not put a time on “eventually.” I have no doubt the time will come, but given the myriad difficulties, I suspect it will take a lot longer than any of us would like.