It’s 8:15 on Friday evening. I’m almost through editing the job description for a user interface engineer after sending off an introductory slide deck to a potential client. Today I met with a business development prospect, held calls with a potential advisor, a potential client, and finally made those changes to the website. There’s not time to write this but when will there be?
I’m part of a growing trend of academics, programmers, and clinicians taking the startup path to try to make healthcare a better place. In fact, record breaking amounts of venture funding are pouring into healthcare with 2014 seeing $4.13 billion in digital health venture funding and 2015 showing no signs of slowing. Established tech companies not typically associated with healthcare including Apple, Samsung, Google, and IBM are getting in on the act with substantial investments. It seems that nearly every hospital and insurer is launching its own incubator or innovation fund.
The real question is why, after decades of lagging behind nearly all other industries in the adoption and use of information technology, does healthcare seem to suddenly be such a hotbed of activity?
The answer: data matters like never before in healthcare.
For as long as there have been 3rd party insurers, the vast majority of care in the US has operated under a perverse incentive system. And while every clinician I know is dedicated to providing the best care possible, today’s healthcare systems are far more likely to invest in technologies that fetch handsome reimbursement returns. That’s why a robot to perform surgery that fetches tens of thousands of dollars per procedure (regardless of the lack of evidence of improved outcomes) has trumped investment in systems designed to keep people out of the hospital.
If you’re interested in building systems that improve care, and said systems consume resources beyond those available from your typical governmental or philanthropic grant, you either work in another country or you take a job in one the few US systems with appropriately aligned incentives. And for the past 8 years that’s exactly what I’ve done, chasing interesting work in government, in the non-profit sector, and overseas.
However, a US healthcare market driven by value demands efficiency + effectiveness. Efficiency + effectiveness requires measurement and improvement. In this new world, data is currency. It points out inefficiencies, speeds and streamlines effective communication, and highlights opportunity. In fact, the ability of healthcare organizations to use their data most effectively – from the C-suite to the visiting nurse – will soon determine whether or not that system survives.
In order to measure, data silos will be opened by their owners in search of new insights and best practices to tackle the complex challenges of human health. Solutions that are most capable of learning relevant patterns and automating the best practice tend to incorporate information technology.
The answer has everything to do with a gradual and long overdue shift in healthcare payment incentives from rewarding volume to rewarding value. The credit goes to changes in public policy via the American Recovery and Reinvestment Act (ARRA), the Affordable Care Act, new risk-sharing contracts offered by insurers to providers, and growing demand by large employers that buy health insurance. This shift is a prerequisite for our health system to make progress in addressing its ranking of 1st in cost and 37th in quality worldwide. And organizations will fail or thrive based, in large part, on their ability to learn from and react to data.
Enter the startups and increased attention by retail IT companies.
Of course, today’s new entrants are not the first to seek truth in healthcare data. Health services researchers, epidemiologists, and quality improvement specialists have filled the stacks of medical libraries with not only important discoveries but often times proven formulae for improvement.
For the last 10 years these were my “customers.” They posed the questions of the greatest clinical importance and I built software tools designed to let them get at the truth as quickly and easily as possible. They published papers citing newly unlocked clinical discoveries. I published papers demonstrating the ability of machine learning and natural language processing technologies to improve healthcare’s use of data. No, we weren’t curing cancer, replacing doctors, or anything nearly as ambitious as some of today’s ‘big data’ marketing suggests. But we were figuring out who actually had certain diseases, what was being done, and whether or not it was working – fundamental information that can be difficult and sometimes impossible to unlock using traditional business intelligence approaches.
I believed and argued at conferences that the adoption of such technologies by clinics, hospitals, payors, and providers could make a real difference. Only, they didn’t. Not in a meaningful way. Because there is a greater incentive to not know the answers when it only pays to treat, not prevent. But for the first time in my career, that’s starting to change. Today those most interested and knowledgeable about improving healthcare can finally take up the mantle of change with a new and most important champion – the bottom line.
Of course, startups are far from the only way to have impact in this new regime. In fact, if you’re currently suffering from a moderate to severe case of startup fatigue, I can’t say that I blame you. It’s hard to grab a coffee at Voltage (or your local equivalent) without stumbling across the pitch of an app hoping to add order to the chaos that is healthcare. And one need only ask “and how will you get the data?” to encounter a sort of naive optimism that has long been beaten out of those on the inside.
However, if improving healthcare is truly your interest, there is another angle to consider. Behind every healthcare startup, no matter how impractical the idea, is a group of usually bright, ambitious, and well-meaning individuals looking to help. There may be value in listening to their ideas and asking the tough questions. This army of the unindoctrinated, not yet bloodied by battles for IT’s attention or empty promises of “after the EPIC install” just might become your army. You may learn that, with your navigational support, their concept has a chance and realize a new opportunity for yourself to contribute to their team. Frankly, it doesn’t matter which banner you carry, as long as your enemy is the estimated 400,000 deaths a year attributed to completely preventable medical mistakes.
It’s 6:23 p.m. Tues, and I’m editing this one last time (I hope). The next few minutes of editing will come at the expense of writing up the outline of a proposal that will bring together the CMOs and CFOs of a health plan and a hospital around a joint program. Claims, EMR, and free text notes will be combined to predict people in their shared population that are likely to benefit from additional support. The project does not aim to sell more drugs or procedures but to demonstrate the value their organizations can offer to clinicians operating in an accountable care model (i.e., value based).
If value-based financial reimbursement becomes a permanent fixture in our health system, such collaborations will become the norm and not the exception. Why? Because “value” in healthcare can only be achieved when healthcare’s various stakeholders work together to keep people healthy throughout their continuum of care. Data will be the engine driving these partnerships forward and new companies may be formed to meet emerging needs.
In time, healthcare will realize the same levels of innovation, efficiency, and capability that the US high tech industry is globally famous for. And for a nice change of pace, billions of dollars will be exchanged not in the pursuit of better ad placement or market timing, but in turning data into better human health.
Leonard D’Avolio Ph.D., is the CEO and co-founder of Cyft, Inc., assistant professor at Harvard Medical School, an advisor to Ariadne Labs and the Helmsley Charitable Trust Foundation. He can be followed on twitter @ldavolio and his writings and bio appear at http://scholar.harvard.edu/len.