Big Data and the Shift Towards Value-Based Health Care

A properly designed health care system should deliver value for patients: high-quality, cost-effective care that includes both preventative medicine and treatment. Unfortunately, medical care today all too often rewards providers, payers, and biopharma for quantity, not quality. The more services doctors perform, the more money doctors and hospitals earn. And pharma earns a paycheck even if outcomes, side effects, or adherence is poor. This model yields more patient visits, more tests, and more procedures, but not necessarily better health outcomes. Case in point: the U.S. spends more per capita on health care than any other developed country yet has notoriously poor health outcomes.

The sad truth is, fee-based medicine isn’t just inefficient: it’s completely broken.

We know that throwing money at the problem isn’t working. We have to buck the status quo and move towards a value-based health care model. Value-based health care is aligned with the patient’s interests: providing high-value care, with the greatest possible health outcomes, at the lowest possible cost.

Last week Congress passed MACRA, repealing the flawed sustainable growth rate for Medicare payments to providers and paving the way to value-based medicine. And the movement is gathering steam; UnitedHealth Group recently announced it will increase value-based payments to doctors and hospitals by 20%, to over $43 billion. Detractors argue that value-based medicine, while good for patients, is not good for the health care system’s bottom line. Well, whether you like it or not, value-based medicine is definitely catching on, and it will disrupt all parties involved — physicians, patients, providers, insurers, technology innovators, and government agencies at the local, state, and federal levels.

So the real question becomes, “Is there a way to make value-based medicine benefit both patients and health care providers?” The answer is yes, thanks to data-driven discovery.

The data explosion in the health care industry gives us increasingly valuable insights that can spur innovation and create better outcomes. Let’s take the Human Genome Project: an international collaborative project to map the complete DNA sequence of human genetic information. By learning more about individual genes, researchers gained an understanding of why certain treatments work better for some people than others and how genes affect the way people respond to drugs (aka, pharmacogenomics). This paves the way toward truly personalized medicine, where patients receive highly targeted treatments that take their unique genome into account.

In search of other examples of Big Data being applied to medicine, we recently traveled to NASA’s Jet Propulsion Lab (JPL). JPL is a fellowship of 6,500 rocket scientists propelling technology forward that just added health care as an area of focus. The effort holds great promise: personalized medicine and cancer research are early waypoints on their exploratory roadmap. JPL’s researchers are experts in leveraging analytics to produce insights and innovations from complex data streams. What’s more, JPL’s approach focuses on “end-to-end observational systems” (i.e., the understanding of data at the time and place you can use it, and how to extract value in real-time from a cacophony of data streams).

Just imagine where this caliber of predictive analytics could take the mission of value-based medicine!

The amount of data on the Internet doubles every three years, so inventing and reinventing new paradigms of information and data science will be imperative. The health care data ecosystem is literally an ever-expanding treasure trove of opportunity. Instead of limiting ourselves to a laboratory-focused “bench to bedside” approach, we must now think “outside to bedside,” using constantly developing sources of rich, complex data to drive health care forward.

Of course, the JPL team doesn’t know how to bring a MVP (minimum viable product) to help a community oncologist make better decisions for her patients and achieve the outcomes of an academic medical center team. Needless to say, we have our own prospecting list of high-impact and high-priority health care data challenges for our design colleagues to rapid-cycle prototype and test.

We see a future that preserves the centrality of the doctor-patient relationship, where doctors continue as thought leaders in health care, with information at their fingertips that can drive new understanding of how to prevent and treat disease. We just have to figure out what to do first, and how to make our foresight actionable.

Leveraging data-driven discovery in pursuit of value-based health care success should yield many attractive investment opportunities. In our opinion —and for all of our sakes —it can’t happen fast enough.