Yesterday, at our annual firm-wide meeting, keynote speaker and board member Sanju K. Bansal shared some thoughts on the nature of innovation. Highlighting the role of technology, he cited a 2011 WSJ essay by Marc Andreessen titled, “Why Software Is Eating The World.”
In it, Andreessen describes the “dramatic and broad technological and economic shift in which software companies are poised to take over large swathes of the economy”. “Health care and education, in my view,” writes Andreessen, “are next up for fundamental software-based transformation.” (Which, incidentally, are the two industries that my current employer focuses on.)
Ignoring Andreessen’s apparent miss with his market-is-undervaluing-Internet-companies theory (Two days ago, Facebook’s shares were below 50% of its IPO price again), I found the most prescient part of his argument (at least for health care) embodied in the following paragraph:
“Today’s leading real-world retailer, Wal-Mart, uses software to power its logistics and distribution capabilities, which it has used to crush its competition. Likewise for FedEx, which is best thought of as a software network that happens to have trucks, planes and distribution hubs attached. And the success or failure of airlines today and in the future hinges on their ability to price tickets and optimize routes and yields correctly—with software.”
The next wave of technological disruption in health care isn’t necessarily electronic medical records (EMRs), even if you could get doctors to agree to use them (which we don’t seem to be doing a good job of doing). It’s data analytics. What are the drivers of unnecessary variation (and thus, cost)? Where are the bottlenecks that are slowing ED throughput? Which procedures produce the best outcomes for each particular patient, not in the generalizable randomized controlled trial sense, but for the specific patients in your specific locale entering your specific institution?
I’ll provide a more concrete example using an area I’m currently focusing on: device implants. This subset of supplies are also often called “physician preference items”—because other than the peculiar preferences of each particular surgeon, there doesn’t appear to be much else evidence in support of using one brand over another. In fact, the decisions are more often governed by which sales representative the surgeon met during residency training, or who their golfing buddies are. Given the power of physicians to take their business elsewhere if pressured to change brands, hospitals face immense challenges trying to get physicians to standardize to a less costly brand or more clinically effective model.
That second goal is particularly challenging, given that we currently don’t really know which brands work better than others. The traditional academic literature has failed us here. Perhaps more accurately, the problem is that we’ve been asking the wrong question. The question isn’t which brands work better than others universally. It’s which brands work better for which types of patients under what circumstances.
The Patient Centered Outcomes Research Institute (PCORI), established by the ACA, takes some important steps toward promoting so-called “comparative effectiveness research” that recognizes the messiness of real life medicine. However, for the hospital executive, what will be more useful will be real-time data on which brands are providing the best outcomes for his/her hospital’s patients. And that’s where the data analytics comes in.
Physicians (and surgeons especially) may be at times stubborn, but I believe they are first and foremost committed to their patients’ outcomes, and they are strong believers in data. If you can set up a well-running Materials Management Information System (MMIS), link it to your EMR system, and tack on the spending data so you can evaluate cost-effectiveness, you’ll have a powerful tool to win over your surgeons while identifying the best care practices for your patients.
Most hospitals are far from the kind of systems interoperability described here—in fact, it’s been described as the “Holy Grail” of physician preference items. But it’s coming—in the end, software will eat health care too.