Obamacare matters. But the debate about it also misdirects attention away from massive collateral damage to patients. How massive? Dig To Make Hospitals Less Deadly, a Dose of Data, by Tina Rosenberg in The New York Times. She writes,
Until very recently, health care experts believed that preventable hospital error caused some 98,000 deaths a year in the United States — a figure based on 1984 data. But a new report from the Journal of Patient Safety using updated data holds such error responsible for many more deaths — probably around some 440,000 per year. That’s one-sixth of all deaths nationally, making preventable hospital error the third leading cause of death in the United States. And 10 to 20 times that many people suffer nonlethal but serious harm as a result of hospital mistakes.
The bold-facing is mine. In 2003, one of those statistics was my mother. I too came close in 2008, though the mistake in that case wasn’t a hospital’s, but rather a consequence of incompatibility between different silo’d systems for viewing MRIs, and an ill-informed rush into a diagnostic procedure that proved unnecessary and caused pancreatitis (which happens in 5% of those performed — I happened to be that one in twenty). That event, my doctors told me, increased my long-term risk of pancreatic cancer.
Risk is the game we’re playing here: the weighing of costs and benefits, based on available information. Thus health care is primarily the risk-weighing business we call insurance. For generations, the primary customers of health care — the ones who pay for the services — have been insurance companies. Their business is selling bets on outcomes to us, to our employers, or both. They play that game, to a large extent, by knowing more than we do. Asymmetrical knowledge R them.
Now think about the data involved. Insurance companies live in a world of data. That world is getting bigger and bigger. And yet, McKinsey tells us, it’s not big enough. In The big-data revolution in US health care: Accelerating value and innovation (subtitle: Big data could transform the health-care sector, but the industry must undergo fundamental changes before stakeholders can capture its full value), McKinsey writes,
Fiscal concerns, perhaps more than any other factor, are driving the demand for big-data applications. After more than 20 years of steady increases, health-care expenses now represent 17.6 percent of GDP—nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.1 To discourage overutilization, many payors have shifted from fee-for-service compensation, which rewards physicians for treatment volume, to risk-sharing arrangements that prioritize outcomes. Under the new schemes, when treatments deliver the desired results, provider compensation may be less than before. Payors are also entering similar agreements with pharmaceutical companies and basing reimbursement on a drug’s ability to improve patient health. In this new environment, health-care stakeholders have greater incentives to compile and exchange information.
While health-care costs may be paramount in big data’s rise, clinical trends also play a role. Physicians have traditionally used their judgment when making treatment decisions, but in the last few years there has been a move toward evidence-based medicine, which involves systematically reviewing clinical data and making treatment decisions based on the best available information. Aggregating individual data sets into big-data algorithms often provides the most robust evidence, since nuances in subpopulations (such as the presence of patients with gluten allergies) may be so rare that they are not readily apparent in small samples.
Although the health-care industry has lagged behind sectors like retail and banking in the use of big data—partly because of concerns about patient confidentiality—it could soon catch up. First movers in the data sphere are already achieving positive results, which is prompting other stakeholders to take action, lest they be left behind. These developments are encouraging, but they also raise an important question: is the health-care industry prepared to capture big data’s full potential, or are there roadblocks that will hamper its use
The word “patient” appears nowhere in that long passage. The word “stakeholder” appears twice, plus eight more times in the whole piece. Still, McKinsey brooks some respect for the patient, though more as a metric zone than as a holder of a stake in outcomes:
Health-care stakeholders are well versed in capturing value and have developed many levers to assist with this goal. But traditional tools do not always take complete advantage of the insights that big data can provide. Unit-price discounts, for instance, are based primarily on contracting and negotiating leverage. And like most other well-established health-care value levers, they focus solely on reducing costs rather than improving patient outcomes. Although these tools will continue to play an important role, stakeholders will only benefit from big data if they take a more holistic, patient-centered approach to value, one that focuses equally on health-care spending and treatment outcomes.
McKinsey’s customers are not you and me. They are business executives, many of which work in health care. As players in their game, we have zero influence. As voters in the democracy game, however, we have a bit more. That’s one reason we elected Barack Obama.
So, viewed from the level at which it plays out, the debate over health care, at least in the U.S., is between those who believe in addressing problems with business (especially the big kind) and those who believe in addressing problems with policy (especially the big kind, such as Obamacare).
Big business has been winning, mostly. This is why Obamacare turned out to be a set of policy tweaks on a business that was already highly regulated, mostly by captive lawmakers and regulators.
Meanwhile we have this irony to contemplate: while dying of bad data at a rate rivaling war and plague, our physical bodies are being doubled into digital ones. It is now possible to know one’s entire genome, including clear markers of risks such as cancer and dementia. That’s in addition to being able to know one’s quantified self (QS), plus one’s health care history.
Yet all of that data is scattered and silo’d. This is why it is hard to integrate all our available QS data, and nearly impossible to integrate all our health care history. After I left the Harvard University Health Services (HUHS) system in 2010, my doctor at the time (Richard Donohue, MD, whom I recommend highly) obtained and handed over to me the entirety of my records from HUHS. It’s not data, however. It’s a pile of paper, as thick as the Manhattan phone book. Its utility to other doctors verges on nil. Such is the nature of the bizarre information asymmetry (and burial) in the current system.
On top of that, our health care system incentivizes us to conceal our history, especially if any of that history puts us in a higher risk category, sure to pay more in health insurance premiums.
But what happens when we solve these problems, and our digital selves become fully knowable — by both our selves and our health care providers? What happens to the risk calculation business we have today, which rationalizes more than 400,000 snuffed souls per annum as collateral damage? Do we go to single-payer then, for the simple reason that the best risk calculations are based on the nation’s entire population?
I don’t know.
I do know the current system doesn’t want to go there, on either the business or the policy side. But it will. Inevitably.
At the end of whatever day this is, our physical selves will know our data selves better than any system built to hoard and manage our personal data for their interests more than for ours. When that happens the current system will break, and another one will take its place.
How many more of us will die needlessly in the meantime? And does knowing (or guessing at) that number make any difference? It hasn’t so far.
But that shouldn’t stop us. Hats off to leadership in the direction of actually solving these problems, starting with Adrian Gropper, ePatient Dave, Patient Privacy Rights, Brian Behlendorf, Esther Dyson, John Wilbanks, Tom Munnecke and countless other good people and organizations who have been pushing this rock up a hill for a long time, and aren’t about to stop. (Send me more names or add them in the comments below.)