To many in the healthcare industry, big data is viewed as a powerful tool in improving healthcare as we know it. And indeed it is, both in terms of operations and patient care. However, that is only part of the story. Big data is also a disruptive force. As data becomes increasingly democratized and consumerized, changes will be wrought on the industry and upheaval will become the new norm.
Here are two areas where big data plays a role in disrupting common healthcare models.
1. Healthcare pricing.
Reducing healthcare costs is a global concern today and attempts to address this issue exist on every level from stockholders and insurance companies to providers and patients. Big data tools enable these and other groups to demystify healthcare pricing practices, do comparative shopping, rate prices vs patient results, and discover new ways to deliver cheaper healthcare.
Examples of a government taking a step in this direction include the U.S. Health and Human Services (HHS) and the Centers for Medicare & Medicaid Services (CMS) federal agencies.
In May 2014, the HHS released data showing significant pricing variation across the country and within communities in what hospitals charge for common inpatient services. The data is regularly updated and can be found online. For the first time, U.S. healthcare consumers can compare provider prices before selecting a provider or having a test or procedure.
“Currently, consumers have limited information about how physicians and other health care professionals practice medicine,” said [now former] Secretary Sebelius. “This data will help fill that gap by offering insight into the Medicare portion of a physician’s practice. The data released today afford researchers, policymakers and the public a new window into health care spending and physician practice patterns.”
Meanwhile, CMS launched Hospital Compare “a consumer-oriented website that provides information on how well hospitals provide recommended care to their patients.”
The Hospital Compare website allows consumers to select multiple hospitals and directly compare performance measure information related to heart attack, heart failure, pneumonia, surgery, and other conditions. Results are organized by:
• Patient Survey Results
• Timely and Effective Care
• Readmissions, Complications, and Deaths
• Use of Medical Imaging
• Linking Quality to Payment
• Medicare Volume
Expect governments in other nations to do similar price (or cost) and cost vs patient outcome analyses to discover which providers and healthcare organizations are both most effective and most efficient, whether or not those governments or agencies release such data for public consumption. Further analyses of one procedure or treatment vs another will be similarly commonplace. Last but not least, where healthcare exists as a for-profit industry, expect consumer protection groups to do similar analyses and drive consumers (and their money) to the best performing and affordable providers.
Such activity will bring new and growing price/cost and performance pressures to healthcare providers the world over.
2. The rise of nontraditional competitors.
Historically healthcare has been the province of the classically trained few. Doctors and medical researchers, at least in modern history, were nearly identically trained, licensed and sanctioned by peer groups. Pharmaceuticals also followed a set and supervised track from discovery to testing to market. Ditto for medical and lab equipment. But that model is eroding under new competitive forces.
For example, biohackers do their work much more cheaply and faster than their traditional lab counterparts. To see an example of DNA extraction done in a tent by a biohacker using common household chemicals and a home refitted dremel tool, watch the video below. Towards the end of the video Cathal Garvey explains how biohackers can produce antibiotics in the field this way. But make no mistake, biohackers can wield DNA for a variety of medical uses. Biohacking is so powerful that Bill Gates told Wired magazine that if he were starting out today, he would be hacking biology instead of computers.
If you want to learn more about biohackers and how they work, see my post “Biohacking 101: Tools of the Biopunk Trade” in Genome Alberta.
Other non-traditional researchers are also blazing new trails in developing faster and cheaper tests, treatments and cures. Take for example, 15-year-old Jack Andraka who was dubbed the “Teen Prodigy of Pancreatic Cancer” by the Smithsonian for developing a highly effective, faster and cheaper test for the lethal cancer.
“Andraka created his potentially revolutionary pancreatic cancer detection tool at nearby Johns Hopkins University, though he does sometimes tinker in a small basement lab at the family’s house in leafy Crownsville, Maryland, where a homemade particle accelerator crowds the foosball table,” writes Abigail Tucker in an article about the boy genius in Smithsonian magazine.
Then there’s 30-year-old Stanford dropout, Elizabeth Holmes who just became the youngest self-made woman billionaire on the Forbes 400 list. She developed a cheaper way to do traditional blood tests with only a drop or two of blood in a matter of four hours instead of four weeks. And a multitude of tests can be run on the same drop of blood at a pharmacy rather than sent off to a lab.
At a recent Cleveland Clinic innovation summit in the U.S., Andraka said to the audience:
"You don't have to be an expert in the field you're trying to change. Knowledge has never been more accessible."
And he is right. But that knowledge doesn’t flow one way to nontraditional researchers. Their work often flows back again to traditional researchers.
There are many big data efforts, such as Project Data Sphere, currently underway designed to capture the work of both traditional and nontraditional researchers and make their work available to all researchers. In this way, advancements come faster.
“The point [to Project Data Sphere] is to create an ecosystem where traditional and nontraditional researchers can independently and collaboratively solve problems,” Charles Hugh-Jones, vice president of Medical Affairs North America at Sanofi, a multinational pharmaceutical company, and a member of the CEO Roundtables on Cancer’s Life Services Consortium (LSC) told me when I interviewed him for my book, Data Divination: Big Data Strategies.
Advancements coming from sources such as these present opportunities in both patient care and cost cutting but also create upheaval, for good or ill, in current healthcare models. For example, Holmes’ new way of blood testing can conceivably erode revenues in both hospital and independent labs and biohackers can potentially negatively impact pharmaceutical and medical equipment revenues.
While we should always be thankful for medical advancements and quick to deploy them, it is also important to consider their business impact and adapt accordingly. Big data is among the best ways to predict impact and develop new models likely to succeed thereafter. It is also the fastest and most effective means to develop and implement your own innovations to remain competitive in an increasingly changing marketscape.
Pam Baker is a regular nuviun contributor, the editor of FierceBigData and author of Data Divination: Big Data Strategies. For more expert insights from Pam, follow her on Twitter @bakercom1 and at FierceBigData.
The nuviun blog is intended to contribute to discussion and stimulate debate on important issues in global digital health. The views are solely those of the author.