Tim O'Reilly: Solving the Wanamaker problem for health care

Data science and technology give us the tools to revolutionize health care. Now we have to put them to use.

Tim O’Reilly, Julie Steele, Mike Lourdes and Colin Hill,  O'Reilly Radar, August 14, 2012

“The best minds of my generation are thinking about how to make people click ads.” — Jeff Hammerbacher, early Facebook employee

“Work on stuff that matters.” — Tim O’Reilly

In the early days of the 20th century, department store magnate John Wanamaker famously said, “I know that half of my advertising doesn’t work. The problem is that I don’t know which half.” 

The consumer Internet revolution was fueled by a search for the answer to Wanamaker’s question. Google AdWords and the pay-per-click model transformed a business in which advertisers paid for ad impressions into one in which they pay for results. “Cost per thousand impressions” (CPM) was replaced by “cost per click” (CPC), and a new industry was born. It’s important to understand why CPC replaced CPM, though. Superficially, it’s because Google was able to track when a user clicked on a link, and was therefore able to bill based on success. But billing based on success doesn’t fundamentally change anything unless you can also change the success rate, and that’s what Google was able to do. By using data to understand each user’s behavior, Google was able to place advertisements that an individual was likely to click. They knew “which half” of their advertising was more likely to be effective, and didn’t bother with the rest.

Since then, data and predictive analytics have driven ever deeper insight into user behavior such that companies like Google, Facebook, Twitter, Zynga, and LinkedIn are fundamentally data companies. And data isn’t just transforming the consumer Internet. It is transforming finance, design, and manufacturing — and perhaps most importantly, health care.

How is data science transforming health care? There are many ways in which health care is changing, and needs to change. We’re focusing on one particular issue: the problem Wanamaker described when talking about his advertising. How do you make sure you’re spending money effectively? Is it possible to know what will work in advance?

Too often, when doctors order a treatment, whether it’s surgery or an over-the-counter medication, they are applying a “standard of care” treatment or some variation that is based on their own intuition, effectively hoping for the best. The sad truth of medicine is that we don’t really understand the relationship between treatments and outcomes. We have studies to show that various treatments will work more often than placebos; but, like Wanamaker, we know that much of our medicine doesn’t work for half or our patients, we just don’t know which half. At least, not in advance. One of data science’s many promises is that, if we can collect data about medical treatments and use that data effectively, we’ll be able to predict more accurately which treatments will be effective for which patient, and which treatments won’t.

A better understanding of the relationship between treatments, outcomes, and patients will have a huge impact on the practice of medicine in the United States. Health care is expensive.

The U.S. spends over $2.6 trillion on health care every year, an amount that constitutes a serious fiscal burden for government, businesses, and our society as a whole. These costs include over $600 billion of unexplained variations in treatments: treatments that cause no differences in outcomes, or even make the patient’s condition worse. We have reached a point at which our need to understand treatment effectiveness has become vital — to the health care system and to the health and sustainability of the economy overall.

Why do we believe that data science has the potential to revolutionize health care? After all, the medical industry has had data for generations: clinical studies, insurance data, hospital records. But the health care industry is now awash in data in a way that it has never been before: from biological data such as gene expression, next-generation DNA sequence data, proteomics, and metabolomics, to clinical data and health outcomes data contained in ever more prevalent electronic health records (EHRs) and longitudinal drug and medical claims. 

We have entered a new era in which we can work on massive datasets effectively, combining data from clinical trials and direct observation by practicing physicians (the records generated by our $2.6 trillion of medical expense). When we combine data with the resources needed to work on the data, we can start asking the important questions, the Wanamaker questions, about what treatments work and for whom. 

The opportunities are huge: for entrepreneurs and data scientists looking to put their skills to work disrupting a large market, for researchers trying to make sense out of the flood of data they are now generating, and for existing companies (including health insurance companies, biotech, pharmaceutical, and medical device companies, hospitals and other care providers) that are looking to remake their businesses for the coming world of outcome-based payment models.

Making health care more effective

What, specifically, does data allow us to do that we couldn’t do before? For the past 60 or so years of medical history, we’ve treated patients as some sort of an average. A doctor would diagnose a condition and recommend a treatment based on what worked for most people, as reflected in large clinical studies. Over the years, we’ve become more sophisticated about what that average patient means, but that same statistical approach didn’t allow for differences between patients. A treatment was deemed effective or ineffective, safe or unsafe, based on double-blind studies that rarely took into account the differences between patients.

With the data that’s now available, we can go much further. The exceptions to this are relatively recent and have been dominated by cancer treatments, the first being Herceptin for breast cancer in women who over-express the Her2 receptor. With the data that’s now available, we can go much further for a broad range of diseases and interventions that are not just drugs but include surgery, disease management programs, medical devices, patient adherence, and care delivery. 

For a long time, we thought that Tamoxifen was roughly 80% effective for breast cancer patients. But now we know much more: we know that it’s 100% effective in 70 to 80% of the patients, and ineffective in the rest. That’s not word games, because we can now use genetic markers to tell whether it’s likely to be effective or ineffective for any given patient, and we can tell in advance whether to treat with Tamoxifen or to try something else. 

Two factors lie behind this new approach to medicine: a different way of using data, and the availability of new kinds of data. It’s not just stating that the drug is effective on most patients, based on trials (indeed, 80% is an enviable success rate); it’s using artificial intelligence techniques to divide the patients into groups and then determine the difference between those groups. We’re not asking whether the drug is effective; we’re asking a fundamentally different question: “for which patients is this drug effective?” We’re asking about the patients, not just the treatments. A drug that’s only effective on 1% of patients might be very valuable if we can tell who that 1% is, though it would certainly be rejected by any traditional clinical trial. 

More than that, asking questions about patients is only possible because we’re using data that wasn’t available until recently: DNA sequencing was only invented in the mid-1970s, and is only now coming into its own as a medical tool. What we’ve seen with Tamoxifen is as clear a solution to the Wanamaker problem as you could ask for: we now know when that treatment will be effective. If you can do the same thing with millions of cancer patients, you will both improve outcomes and save money. 

Dr. Lukas Wartman, a cancer researcher who was himself diagnosed with terminal leukemia, was successfully treated with sunitinib, a drug that was only approved for kidney cancer. Sequencing the genes of both the patient’s healthy cells and cancerous cells led to the discovery of a protein that was out of control and encouraging the spread of the cancer. The gene responsible for manufacturing this protein could potentially be inhibited by the kidney drug, although it had never been tested for this application. This unorthodox treatment was surprisingly effective: Wartman is now in remission. 

While this treatment was exotic and expensive, what’s important isn’t the expense but the potential for new kinds of diagnosis. The price of gene sequencing has been plummeting; it will be a common doctor’s office procedure in a few years. And through Amazon and Google, you can now “rent” a cloud-based supercomputing cluster that can solve huge analytic problems for a few hundred dollars per hour. What is now exotic inevitably becomes routine. 

But even more important: we’re looking at a completely different approach to treatment. Rather than a treatment that works 80% of the time, or even 100% of the time for 80% of the patients, a treatment might be effective for a small group. It might be entirely specific to the individual; the next cancer patient may have a different protein that’s out of control, an entirely different genetic cause for the disease. Treatments that are specific to one patient don’t exist in medicine as it’s currently practiced; how could you ever do an FDA trial for a medication that’s only going to be used once to treat a certain kind of cancer? 

Foundation Medicine is at the forefront of this new era in cancer treatment. They use next-generation DNA sequencing to discover DNA sequence mutations and deletions that are currently used in standard of care treatments, as well as many other actionable mutations that are tied to drugs for other types of cancer. They are creating a patient-outcomes repository that will be the fuel for discovering the relation between mutations and drugs. 

Foundation has identified DNA mutations in 50% of cancer cases for which drugs exist (information via a private communication), but are not currently used in the standard of care for the patient’s particular cancer.

The ability to do large-scale computing on genetic data gives us the ability to understand the origins of disease. If we can understand why an anti-cancer drug is effective (what specific proteins it affects), and if we can understand what genetic factors are causing the cancer to spread, then we’re able to use the tools at our disposal much more effectively. Rather than using imprecise treatments organized around symptoms, we’ll be able to target the actual causes of disease, and design treatments tuned to the biology of the specific patient. 

Eventually, we’ll be able to treat 100% of the patients 100% of the time, precisely because we realize that each patient presents a unique problem. 

Personalized treatment is just one area in which we can solve the Wanamaker problem with data. Hospital admissions are extremely expensive. Data can make hospital systems more efficient, and to avoid preventable complications such as blood clots and hospital re-admissions. It can also help address the challenge of hot-spotting (a term coined by Atul Gawande): finding people who use an inordinate amount of health care resources. By looking at data from hospital visits, Dr. Jeffrey Brenner of Camden, NJ, was able to determine that “just one per cent of the hundred thousand people who made use of Camden’s medical facilities accounted for thirty per cent of its costs.” Furthermore, many of these people came from two apartment buildings. Designing more effective medical care for these patients was difficult; it doesn’t fit our health insurance system, the patients are often dealing with many serious medical issues (addiction and obesity are frequent complications), and have trouble trusting doctors and social workers. It’s counter-intuitive, but spending more on some patients now results in spending less on them when they become really sick. While it’s a work in progress, it looks like building appropriate systems to target these high-risk patients and treat them before they’re hospitalized will bring significant savings. 

Many poor health outcomes are attributable to patients who don’t take their medications. Eliza, a Boston-based company started by Alexandra Drane, has pioneered approaches to improve compliance through interactive communication with patients. Eliza improves patient drug compliance by tracking which types of reminders work on which types of people; it’s similar to the way companies like Google target advertisements to individual consumers. By using data to analyze each patient’s behavior, Eliza can generate reminders that are more likely to be effective. The results aren’t surprising: if patients take their medicine as prescribed, they are more likely to get better. And if they get better, they are less likely to require further, more expensive treatment. Again, we’re using data to solve Wanamaker’s problem in medicine: we’re spending our resources on what’s effective, on appropriate reminders that are mostly to get patients to take their medications.

More data, more sources

The examples we’ve looked at so far have been limited to traditional sources of medical data: hospitals, research centers, doctor’s offices, insurers. The Internet has enabled the formation of patient networks aimed at sharing data. Health social networks now are some of the largest patient communities. As of November 2011, PatientsLikeMe has over 120,000 patients in 500 different condition groups; ACOR has over 100,000 patients in 127 cancer support groups; 23andMe has over 100,000 members in their genomic database; and diabetes health social network SugarStats has over 10,000 members. These are just the larger communities, thousands of small communities are created around rare diseases, or even uncommon experiences with common diseases. All of these communities are generating data that they voluntarily share with each other and the world. 

Increasingly, what they share is not just anecdotal, but includes an array of clinical data. For this reason, these groups are being recruited for large-scale crowdsourced clinical outcomes research.

Thanks to ubiquitous data networking through the mobile network, we can take several steps further. In the past two or three years, there’s been a flood of personal fitness devices (such as the Fitbit) for monitoring your personal activity. There are mobile apps for taking your pulse, and an iPhone attachment for measuring your glucose. There has been talk of mobile applications that would constantly listen to a patient’s speech and detect changes that might be the precursor for a stroke, or would use the accelerometer to report falls. Tanzeem Choudhury has developed an app called Be Well that is intended primarily for victims of depression, though it can be used by anyone. Be Well monitors the user’s sleep cycles, the amount of time they spend talking, and the amount of time they spend walking. The data is scored, and the app makes appropriate recommendations, based both on the individual patient and data collected across all the app’s users.

Continuous monitoring of critical patients in hospitals has been normal for years; but we now have the tools to monitor patients constantly, in their home, at work, wherever they happen to be. And if this sounds like big brother, at this point most of the patients are willing. We don’t want to transform our lives into hospital experiences; far from it! But we can collect and use the data we constantly emit, our “data smog,” to maintain our health, to become conscious of our behavior, and to detect oncoming conditions before they become serious. The most effective medical care is the medical care you avoid because you don’t need it.

Paying for results

Once we’re on the road toward more effective health care, we can look at other ways in which Wanamaker’s problem shows up in the medical industry. It’s clear that we don’t want to pay for treatments that are ineffective. Wanamaker wanted to know which part of his advertising was effective, not just to make better ads, but also so that he wouldn’t have to buy the advertisements that wouldn’t work. He wanted to pay for results, not for ad placements. Now that we’re starting to understand how to make treatment effective, now that we understand that it’s more than rolling the dice and hoping that a treatment that works for a typical patient will be effective for you, we can take the next step: Can we change the underlying incentives in the medical system? Can we make the system better by paying for results, rather than paying for procedures? 

It’s shocking just how badly the incentives in our current medical system are aligned with outcomes. If you see an orthopedist, you’re likely to get an MRI, most likely at a facility owned by the orthopedist’s practice. On one hand, it’s good medicine to know what you’re doing before you operate. But how often does that MRI result in a different treatment? How often is the MRI required just because it’s part of the protocol, when it’s perfectly obvious what the doctor needs to do? Many men have had PSA tests for prostate cancer; but in most cases, aggressive treatment of prostate cancer is a bigger risk than the disease itself. Yet the test itself is a significant profit center. Think again about Tamoxifen, and about the pharmaceutical company that makes it. In our current system, what does “100% effective in 80% of the patients” mean, except for a 20% loss in sales? That’s because the drug company is paid for the treatment, not for the result; it has no financial interest in whether any individual patient gets better. (Whether a statistically significant number of patients has side-effects is a different issue.) And at the same time, bringing a new drug to market is very expensive, and might not be worthwhile if it will only be used on the remaining 20% of the patients. And that’s assuming that one drug, not two, or 20, or 200 will be required to treat the unlucky 20% effectively.

It doesn’t have to be this way. 

In the U.K., Johnson & Johnson, faced with the possibility of losing reimbursements for their multiple myeloma drug Velcade, agreed to refund the money for patients who did not respond to the drug. Several other pay-for-performance drug deals have followed since, paving the way for the ultimate transition in pharmaceutical company business models in which their product is health outcomes instead of pills. Such a transition would rely more heavily on real-world outcome data (are patients actually getting better?), rather than controlled clinical trials, and would use molecular diagnostics to create personalized “treatment algorithms.” Pharmaceutical companies would also focus more on drug compliance to ensure health outcomes were being achieved. This would ultimately align the interests of drug makers with patients, their providers, and payors.

Similarly, rather than paying for treatments and procedures, can we pay hospitals and doctors for results? That’s what Accountable Care Organizations (ACOs) are about. ACOs are a leap forward in business model design, where the provider shoulders any financial risk. 

ACOs represent a new framing of the much maligned HMO approaches from the ’90s, which did not work. HMOs tried to use statistics to predict and prevent unneeded care. The ACO model, rather than controlling doctors with what the data says they “should” do, uses data to measure how each doctor performs. Doctors are paid for successes, not for the procedures they administer. The main advantage that the ACO model has over the HMO model is how good the data is, and how that data is leveraged. The ACO model aligns incentives with outcomes: a practice that owns an MRI facility isn’t incentivized to order MRIs when they’re not necessary. It is incentivized to use all the data at its disposal to determine the most effective treatment for the patient, and to follow through on that treatment with a minimum of unnecessary testing.

When we know which procedures are likely to be successful, we’ll be in a position where we can pay only for the health care that works. When we can do that, we’ve solved Wanamaker’s problem for health care.

Enabling data

Data science is not optional in health care reform; it is the linchpin of the whole process. All of the examples we’ve seen, ranging from cancer treatment to detecting hot spots where additional intervention will make hospital admission unnecessary, depend on using data effectively: taking advantage of new data sources and new analytics techniques, in addition to the data the medical profession has had all along. 

But it’s too simple just to say “we need data.” We’ve had data all along: handwritten records in manila folders on acres and acres of shelving. Insurance company records. But it’s all been locked up in silos: insurance silos, hospital silos, and many, many doctor’s office silos. Data doesn’t help if it can’t be moved, if data sources can’t be combined. 

There are two big issues here. First, a surprising amount of medical records are still either hand-written, or in digital formats that are scarcely better than hand-written (for example, scanned images of hand-written records). Getting medical records into a format that’s computable is a prerequisite for almost any kind of progress. Second, we need to break down those silos. 

Anyone who has worked with data knows that, in any problem, 90% of the work is getting the data in a form in which it can be used; the analysis itself is often simple. We need electronic health records: patient data in a more-or-less standard form that can be shared efficiently, data that can be moved from one location to another at the speed of the Internet. Not all data formats are created equal, and some are certainly better than others: but at this point, any machine-readable format, even simple text files, is better than nothing. While there are currently hundreds of different formats for electronic health records, the fact that they’re electronic means that they can be converted from one form into another. Standardizing on a single format would make things much easier, but just getting the data into some electronic form, any, is the first step.

Once we have electronic health records, we can link doctor’s offices, labs, hospitals, and insurers into a data network, so that all patient data is immediately stored in a data center: every prescription, every procedure, and whether that treatment was effective or not. This isn’t some futuristic dream; it’s technology we have now. Building this network would be substantially simpler and cheaper than building the networks and data centers now operated by Google, Facebook, Amazon, Apple, and many other large technology companies. It’s not even close to pushing the limits. 

Electronic health records enable us to go far beyond the current mechanism of clinical trials. In the past, once a drug has been approved in trials, that’s effectively the end of the story: running more tests to determine whether it’s effective in practice would be a huge expense. A physician might get a sense for whether any treatment worked, but that evidence is essentially anecdotal: it’s easy to believe that something is effective because that’s what you want to see. And if it’s shared with other doctors, it’s shared while chatting at a medical convention. But with electronic health records, it’s possible (and not even terribly expensive) to collect documentation from thousands of physicians treating millions of patients. We can find out when and where a drug was prescribed, why, and whether there was a good outcome. We can ask questions that are never part of clinical trials: is the medication used in combination with anything else? What other conditions is the patient being treated for? We can use machine learning techniques to discover unexpected combinations of drugs that work well together, or to predict adverse reactions. We’re no longer limited by clinical trials; every patient can be part of an ongoing evaluation of whether his treatment is effective, and under what conditions. Technically, this isn’t hard. The only difficult part is getting the data to move, getting data in a form where it’s easily transferred from the doctor’s office to analytics centers.

To solve problems of hot-spotting (individual patients or groups of patients consuming inordinate medical resources) requires a different combination of information. You can’t locate hot spots if you don’t have physical addresses. Physical addresses can be geocoded (converted from addresses to longitude and latitude, which is more useful for mapping problems) easily enough, once you have them, but you need access to patient records from all the hospitals operating in the area under study. And you need access to insurance records to determine how much health care patients are requiring, and to evaluate whether special interventions for these patients are effective. Not only does this require electronic records, it requires cooperation across different organizations (breaking down silos), and assurance that the data won’t be misused (patient privacy). Again, the enabling factor is our ability to combine data from different sources; once you have the data, the solutions come easily. 

Breaking down silos has a lot to do with aligning incentives. Currently, hospitals are trying to optimize their income from medical treatments, while insurance companies are trying to optimize their income by minimizing payments, and doctors are just trying to keep their heads above water. There’s little incentive to cooperate. But as financial pressures rise, it will become critically important for everyone in the health care system, from the patient to the insurance executive, to assume that they are getting the most for their money. While there’s intense cultural resistance to be overcome (through our experience in data science, we’ve learned that it’s often difficult to break down silos within an organization, let alone between organizations), the pressure of delivering more effective health care for less money will eventually break the silos down. The old zero-sum game of winners and losers must end if we’re going to have a medical system that’s effective over the coming decades.

Data becomes infinitely more powerful when you can mix data from different sources: many doctor’s offices, hospital admission records, address databases, and even the rapidly increasing stream of data coming from personal fitness devices. The challenge isn’t employing our statistics more carefully, precisely, or guardedly. It’s about letting go of an old paradigm that starts by assuming only certain variables are key and ends by correlating only these variables. This paradigm worked well when data was scarce, but if you think about, these assumptions arise precisely because data is scarce. We didn’t study the relationship between leukemia and kidney cancers because that would require asking a huge set of questions that would require collecting a lot of data; and a connection between leukemia and kidney cancer is no more likely than a connection between leukemia and flu. But the existence of data is no longer a problem: we’re collecting the data all the time. Electronic health records let us move the data around so that we can assemble a collection of cases that goes far beyond a particular practice, a particular hospital, a particular study. So now, we can use machine learning techniques to identify and test all possible hypotheses, rather than just the small set that intuition might suggest. And finally, with enough data, we can get beyond correlation to causation: rather than saying “A and B are correlated,” we’ll be able to say “A causes B,” and know what to do about it. 

Building the health care system we want

The U.S. ranks 37th out of developed economies in life expectancy and other measures of health, while by far outspending other countries on per-capita health care costs. We spend 18% of GDP on health care, while other countries on average spend on the order of 10% of GDP. We spend a lot of money on treatments that don’t work, because we have a poor understanding at best of what will and won’t work. 

Part of the problem is cultural. In a country where even pets can have hip replacement surgery, it’s hard to imagine not spending every penny you have to prolong Grandma’s life — or your own. The U.S. is a wealthy nation, and health care is something we choose to spend our money on. But wealthy or not, nobody wants ineffective treatments. Nobody wants to roll the dice and hope that their biology is similar enough to a hypothetical “average” patient. No one wants a “winner take all” payment system in which the patient is always the loser, paying for procedures whether or not they are helpful or necessary. Like Wanamaker with his advertisements, we want to know what works, and we want to pay for what works. We want a smarter system where treatments are designed to be effective on our individual biologies; where treatments are administered effectively; where our hospitals our used effectively; and where we pay for outcomes, not for procedures.

We’re on the verge of that new system now. We don’t have it yet, but we can see it around the corner. Ultra-cheap DNA sequencing in the doctor’s office, massive inexpensive computing power, the availability of EHRs to study whether treatments are effective even after the FDA trials are over, and improved techniques for analyzing data are the tools that will bring this new system about. The tools are here now; it’s up to us to put them into use.

Recommended reading:

We recommend the following books regarding technology, data, and health care reform:

·       Ahier, Brian. “Big data is the next big thing in health IT,” O’Reilly Radar. February 27, 2012.

·       Bigelow, Bruce. “Big Data, Big Biology, and the ‘Tipping Point’ in Quantified Health,” Xconomy. April 26, 2012.

·       Brawley, Otis Webb. How We Do Harm: A Doctor Breaks Ranks About Being Sick in America. St. Marten’s Press, 2012.

·       Christensen, Clayton M. et al. The Innovator’s Prescription: A Disruptive Solution for Health Care. McGraw Hill, 2008.

·       Howard, Alex. “Data for the Public Good,” O’Reilly Radar. February 22, 2012.

·       Manyika, James et al. “Big data: The next frontier for innovation, competition, and productivity,” McKinsey Global Institute. May, 2011.

·       Oram, Andy. “Five tough lessons I had to learn about health care,” O’Reilly Radar. March 26, 2012.

·       Shah, Nigam H and Jessica D Tenenbaum. “The coming age of data-driven medicine: translational bioinformatics’ next frontier,” Journal of the American Medical Informatics Association (JAMIA). March 26, 2012.

·       Trotter, Fred and David Uhlman. Meaningful Use and Beyond. O’Reilly Media, 2011.

·       Wilbanks, John. “Valuing Health Care: Improving Productivity and Quality” [PDF], Ewing Marion Kauffman Foundation. April, 2012.

FT: Big data is watching you

Gillian Tett, The Financial Times, August 10, 2012
 
Information from mobile devices is not just changing the way the western world lives

During the past few years, there has been one constant in my life. Wherever I have travelled, in America or anywhere else, I have carried a mobile phone. Usually, the device is so ubiquitous, I do not think about this habit at all (except when I lose my phone and panic).

But last week, I took part in a seminar organised by America’s Brookings Institution and Blum Center to discuss development and global economics. And now I am looking at that mobile phone with fresh eyes. For what became clear in discussions with aid workers, healthcare officials and US diplomats is that those oft-ignored mobile devices are not just changing the way the western world lives – but changing the lives of poor societies, too. This, in turn, has some intriguing potential to reshape parts of how the global development business is done.

These days, there are about 2.5 billion people in emerging markets countries who own a mobile phone. In places such as the Philippines, Mexico and South Africa, mobile phone coverage is nearly 100 per cent of the population, while in Uganda it is 85 per cent. That has not only left people better connected than before – which has big political and commercial implications – it has also made their movements, habits and ideas far more transparent. And that is significant, given that it has often been extremely hard to monitor poor societies in the past, particularly when they are scattered over large regions.

Consider what happened two-and-a-half years ago when the Haitian earthquake struck. The population scattered when the tremors hit, leaving aid agencies scrambling to work out where to send help. Traditionally, they could only have done this by flying over the affected areas, or travelling on the ground. But some researchers at Columbia University and the Karolinska Institute took a different tack: they started tracking the Sim cards inside mobile phones owned by Haitians, to work out where their owners were located or moving. That helped them to “accurately analyse the destination of more than 600,000 people who were displaced from Port au Prince”, as a UN report says. Then, when a cholera epidemic hit Haiti later, the same researchers tracked the Sim cards again, to put medicine in the correct locations – and prevent the disease from spreading.

Aid groups are not just tracking those physical phones; they are also starting to watch levels of mobile phone usage and patterns of bill payment, too. If this suddenly changes, it can indicate rising levels of economic distress, far more accurately than, say, GDP data. Inside the UN, the secretary general is now launching a project called Global Pulse to screen some of the 2.5 quintillion bytes of so-called “big data” being generated each day around the world, including on social media sites such as Twitter and Facebook. These sites are strikingly popular in parts of the emerging markets world; Indonesia, for example, has one of the most Twitter-addicted populations on the planet. Thus if the UN (or anyone else) spots a sudden increase in certain keywords, this can also provide an early warning of distress. References to food or ethnic strife, for example, may indicate the onset of famine or civil unrest. Similarly, medical researchers have learnt in the past couple of years that social media references to infection area are powerful early warning signal of epidemics – and more timely than official alerts from government doctors.

Such developments are – unsurprisingly – controversial. For just as the spread of social media has sparked a blizzard of concern about privacy in the west, some observers worry about the dark side of this technological revolution in the emerging markets as well. Not everyone who may want to track these data is benign. Facebook might allow activists to express opposition to governments (as in the Arab spring), but social media data could also help repressive governments monitor their populations. Companies can use the data, too; there are initiatives under way to use it to develop credit scores for the poor.

But such concerns are not deterring the UN. On the contrary, Robert Kirkpatrick, a former IT expert who now runs the UN’s Global Pulse unit, argues that we should treat those 2.5 quintillion bytes of big data as an international common good. He dreams of using these data to create the social media equivalent of “metereological stations”, which can test the winds of public debate, spot economic trends and predict looming problems in a beneficial way. Even if this idea sounds far-fetched, economists can already use this information to track how economies are developing in poor regions of the world with much more precision and timeliness than ever before.

That mobile phone in my pocket, in other words, does not just connect me to my friends. It is now part of a shared human experience – and database – that spans the globe, and which is growing in depth and power each day. And that has implications most of us have barely begun to understand. It is both a sobering and exciting thought, whether you are now sitting on a holiday beach, in a humdrum office – or anywhere else in the world.

New WEF doc: Big Data, Big Impact: New Possibilities for International Development

Big Data, Big Impact: New Possibilities for International Development,World Economic Forum, 2012

Economic summary 

A flood of data is created every day by the interactions of billions of people using computers, GPS devices, cell phones, and medical devices. Many of these interactions occur through the use of mobile devices being used by people in the developing world, people whose needs and habits have been poorly understood until now. Researchers and policymakers are beginning to realise the potential for channelling these torrents of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises for the benefit of low-income populations. Concerted action is needed by governments, development organisations, and companies to ensure that this data helps the individuals and communities who create it.

Full report at http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf

Atul Gawande: Big Med

Restaurant chains have managed to combine quality control, cost control, and innovation. Can health care?

Atul Gawande,  The New Yorker, August 13, 2012

Medicine has long resisted the productivity revolutions that transformed other industries. But the new chains aim to change this.

It was Saturday night, and I was at the local Cheesecake Factory with my two teen-age daughters and three of their friends. You may know the chain: a hundred and sixty restaurants with a catalogue-like menu that, when I did a count, listed three hundred and eight dinner items (including the forty-nine on the "Skinnylicious" menu), plus a hundred and twenty-four choices of beverage. It's a linen-napkin-and-tablecloth sort of place, but with something for everyone. There's wine and wasabi-crusted ahi tuna, but there's also buffalo wings and Bud Light. The kids ordered mostly comfort food—pot stickers, mini crab cakes, teriyaki chicken, Hawaiian pizza, pasta carbonara. I got a beet salad with goat cheese, white-bean hummus and warm flatbread, and the miso salmon.

The place is huge, but it's invariably packed, and you can see why. The typical entrée is under fifteen dollars. The décor is fancy, in an accessible, Disney-cruise-ship sort of way: faux Egyptian columns, earth-tone murals, vaulted ceilings. The waiters are efficient and friendly. They wear all white (crisp white oxford shirt, pants, apron, sneakers) and try to make you feel as if it were a special night out. As for the food—can I say this without losing forever my chance of getting a reservation at Per Se?—it was delicious.

The chain serves more than eighty million people per year. I pictured semi-frozen bags of beet salad shipped from Mexico, buckets of precooked pasta and production-line hummus, fish from a box. And yet nothing smacked of mass production. My beets were crisp and fresh, the hummus creamy, the salmon like butter in my mouth. No doubt everything we ordered was sweeter, fattier, and bigger than it had to be. But the Cheesecake Factory knows its customers. The whole table was happy (with the possible exception of Ethan, aged sixteen, who picked the onions out of his Hawaiian pizza).

I wondered how they pulled it off. I asked one of the Cheesecake Factory line cooks how much of the food was premade. He told me that everything's pretty much made from scratch—except the cheesecake, which actually is from a cheesecake factory, in Calabasas, California.

I'd come from the hospital that day. In medicine, too, we are trying to deliver a range of services to millions of people at a reasonable cost and with a consistent level of quality. Unlike the Cheesecake Factory, we haven't figured out how. Our costs are soaring, the service is typically mediocre, and the quality is unreliable. Every clinician has his or her own way of doing things, and the rates of failure and complication (not to mention the costs) for a given service routinely vary by a factor of two or three, even within the same hospital.

It's easy to mock places like the Cheesecake Factory—restaurants that have brought chain production to complicated sit-down meals. But the "casual dining sector," as it is known, plays a central role in the ecosystem of eating, providing three-course, fork-and-knife restaurant meals that most people across the country couldn't previously find or afford. The ideas start out in élite, upscale restaurants in major cities. You could think of them as research restaurants, akin to research hospitals. Some of their enthusiasms—miso salmon, Chianti-braised short ribs, flourless chocolate espresso cake—spread to other high-end restaurants. Then the casual-dining chains reëngineer them for affordable delivery to millions. Does health care need something like this?

Big chains thrive because they provide goods and services of greater variety, better quality, and lower cost than would otherwise be available. Size is the key. It gives them buying power, lets them centralize common functions, and allows them to adopt and diffuse innovations faster than they could if they were a bunch of small, independent operations. Such advantages have made Walmart the most successful retailer on earth. Pizza Hut alone runs one in eight pizza restaurants in the country. The Cheesecake Factory's major competitor, Darden, owns Olive Garden, LongHorn Steakhouse, Red Lobster, and the Capital Grille; it has more than two thousand restaurants across the country and employs more than a hundred and eighty thousand people. We can bristle at the idea of chains and mass production, with their homogeneity, predictability, and constant genuflection to the value-for-money god. Then you spend a bad night in a "quaint" "one of a kind" bed-and-breakfast that turns out to have a manic, halitoxic innkeeper who can't keep the hot water running, and it's right back to the Hyatt.

Medicine, though, had held out against the trend. Physicians were always predominantly self-employed, working alone or in small private-practice groups. American hospitals tended to be community-based. But that's changing. Hospitals and clinics have been forming into large conglomerates. And physicians—facing escalating demands to lower costs, adopt expensive information technology, and account for performance—have been flocking to join them. According to the Bureau of Labor Statistics, only a quarter of doctors are self-employed—an extraordinary turnabout from a decade ago, when a majority were independent. They've decided to become employees, and health systems have become chains.

I'm no exception. I am an employee of an academic, nonprofit health system called Partners HealthCare, which owns the Brigham and Women's Hospital and the Massachusetts General Hospital, along with seven other hospitals, and is affiliated with dozens of clinics around eastern Massachusetts. Partners has sixty thousand employees, including six thousand doctors. Our competitors include CareGroup, a system of five regional hospitals, and a new for-profit chain called the Steward Health Care System.

Steward was launched in late 2010, when Cerberus—the multibillion-dollar private-investment firm—bought a group of six failing Catholic hospitals in the Boston area for nine hundred million dollars. Many people were shocked that the Catholic Church would allow a corporate takeover of its charity hospitals. But the hospitals, some of which were more than a century old, had been losing money and patients, and Cerberus is one of those firms which specialize in turning around distressed businesses.

Cerberus has owned controlling stakes in Chrysler and GMAC Financing and currently has stakes in Albertsons grocery stories, one of Austria's largest retail bank chains, and the Freedom Group, which it built into one of the biggest gun-and-ammunition manufacturers in the world. When it looked at the Catholic hospitals, it saw another opportunity to create profit through size and efficiency. In the past year, Steward bought four more Massachusetts hospitals and made an offer to buy six financially troubled hospitals in south Florida. It's trying to create what some have called the Southwest Airlines of health care—a network of high-quality hospitals that would appeal to a more cost-conscious public.

Steward's aggressive growth has made local doctors like me nervous. But many health systems, for-profit and not-for-profit, share its goal: large-scale, production-line medicine. The way medical care is organized is changing—because the way we pay for it is changing.

Historically, doctors have been paid for services, not results. In the eighteenth century B.C., Hammurabi's code instructed that a surgeon be paid ten shekels of silver every time he performed a procedure for a patrician—opening an abscess or treating a cataract with his bronze lancet. It also instructed that if the patient should die or lose an eye, the surgeon's hands be cut off. Apparently, the Mesopotamian surgeons' lobby got this results clause dropped. Since then, we've generally been paid for what we do, whatever happens. The consequence is the system we have, with plenty of individual transactions—procedures, tests, specialist consultations—and uncertain attention to how the patient ultimately fares.

Health-care reforms—public and private—have sought to reshape that system. This year, my employer's new contracts with Medicare, BlueCross BlueShield, and others link financial reward to clinical performance. The more the hospital exceeds its cost-reduction and quality-improvement targets, the more money it can keep. If it misses the targets, it will lose tens of millions of dollars. This is a radical shift. Until now, hospitals and medical groups have mainly had a landlord-tenant relationship with doctors. They offered us space and facilities, but what we tenants did behind closed doors was our business. Now it's their business, too.

The theory the country is about to test is that chains will make us better and more efficient. The question is how. To most of us who work in health care, throwing a bunch of administrators and accountants into the mix seems unlikely to help. Good medicine can't be reduced to a recipe.

Then again neither can good food: every dish involves attention to detail and individual adjustments that require human judgment. Yet, some chains manage to achieve good, consistent results thousands of times a day across the entire country. I decided to get inside one and find out how they did it.

Dave Luz is the regional manager for the eight Cheesecake Factories in the Boston area. He oversees operations that bring in eighty million dollars in yearly revenue, about as much as a medium-sized hospital. Luz (rhymes with "fuzz") is forty-seven, and had started out in his twenties waiting tables at a Cheesecake Factory restaurant in Los Angeles. He was writing screenplays, but couldn't make a living at it. When he and his wife hit thirty and had their second child, they came back east to Boston to be closer to family. He decided to stick with the Cheesecake Factory. Luz rose steadily, and made a nice living. "I wanted to have some business skills," he said—he started a film-production company on the side—"and there was no other place I knew where you could go in, know nothing, and learn top to bottom how to run a business."

To show me how a Cheesecake Factory works, he took me into the kitchen of his busiest restaurant, at Prudential Center, a shopping and convention hub. The kitchen design is the same in every restaurant, he explained. It's laid out like a manufacturing facility, in which raw materials in the back of the plant come together as a finished product that rolls out the front. Along the back wall are the walk-in refrigerators and prep stations, where half a dozen people stood chopping and stirring and mixing. The next zone is where the cooking gets done—two parallel lines of countertop, forty-some feet long and just three shoe-lengths apart, with fifteen people pivoting in place between the stovetops and grills on the hot side and the neatly laid-out bins of fixings (sauces, garnishes, seasonings, and the like) on the cold side. The prep staff stock the pullout drawers beneath the counters with slabs of marinated meat and fish, serving-size baggies of pasta and crabmeat, steaming bowls of brown rice and mashed potatoes. Basically, the prep crew handles the parts, and the cooks do the assembly.

Computer monitors positioned head-high every few feet flashed the orders for a given station. Luz showed me the touch-screen tabs for the recipe for each order and a photo showing the proper presentation. The recipe has the ingredients on the left part of the screen and the steps on the right. A timer counts down to a target time for completion. The background turns from green to yellow as the order nears the target time and to red when it has exceeded it.

I watched Mauricio Gaviria at the broiler station as the lunch crowd began coming in. Mauricio was twenty-nine years old and had worked there eight years. He'd got his start doing simple prep—chopping vegetables—and worked his way up to fry cook, the pasta station, and now the sauté and broiler stations. He bounced in place waiting for the pace to pick up. An order for a "hibachi" steak popped up. He tapped the screen to open the order: medium-rare, no special requests. A ten-minute timer began. He tonged a fat hanger steak soaking in teriyaki sauce onto the broiler and started a nest of sliced onions cooking beside it. While the meat was grilling, other orders arrived: a Kobe burger, a blue-cheese B.L.T. burger, three "old-fashioned" burgers, five veggie burgers, a "farmhouse" burger, and two Thai chicken wraps. Tap, tap, tap. He got each of them grilling.

I brought up the hibachi-steak recipe on the screen. There were instructions to season the steak, sauté the onions, grill some mushrooms, slice the meat, place it on the bed of onions, pile the mushrooms on top, garnish with parsley and sesame seeds, heap a stack of asparagus tempura next to it, shape a tower of mashed potatoes alongside, drop a pat of wasabi butter on top, and serve.

Two things struck me. First, the instructions were precise about the ingredients and the objectives (the steak slices were to be a quarter of an inch thick, the presentation just so), but not about how to get there. The cook has to decide how much to salt and baste, how to sequence the onions and mushrooms and meat so they're done at the same time, how to swivel from grill to countertop and back, sprinkling a pinch of salt here, flipping a burger there, sending word to the fry cook for the asparagus tempura, all the while keeping an eye on the steak. In producing complicated food, there might be recipes, but there was also a substantial amount of what's called "tacit knowledge"—knowledge that has not been reduced to instructions.

Second, Mauricio never looked at the instructions anyway. By the time I'd finished reading the steak recipe, he was done with the dish and had plated half a dozen others. "Do you use this recipe screen?" I asked.

"No. I have the recipes right here," he said, pointing to his baseball-capped head.

He put the steak dish under warming lights, and tapped the screen to signal the servers for pickup. But before the dish was taken away, the kitchen manager stopped to look, and the system started to become clearer. He pulled a clean fork out and poked at the steak. Then he called to Mauricio and the two other cooks manning the grill station.

"Gentlemen," he said, "this steak is perfect." It was juicy and pink in the center, he said. "The grill marks are excellent." The sesame seeds and garnish were ample without being excessive. "But the tower is too tight." I could see what he meant. The mashed potatoes looked a bit like something a kid at the beach might have molded with a bucket. You don't want the food to look manufactured, he explained. Mauricio fluffed up the potatoes with a fork.

I watched the kitchen manager for a while. At every Cheesecake Factory restaurant, a kitchen manager is stationed at the counter where the food comes off the line, and he rates the food on a scale of one to ten. A nine is near-perfect. An eight requires one or two corrections before going out to a guest. A seven needs three. A six is unacceptable and has to be redone. This inspection process seemed a tricky task. No one likes to be second-guessed. The kitchen manager prodded gently, being careful to praise as often as he corrected. ("Beautiful. Beautiful!" "The pattern of this pesto glaze is just right.") But he didn't hesitate to correct.

"We're getting sloppy with the plating," he told the pasta station. He was unhappy with how the fry cooks were slicing the avocado spring rolls. "Gentlemen, a half-inch border on this next time." He tried to be a coach more than a policeman. "Is this three-quarters of an ounce of Parm-Romano?"

And that seemed to be the spirit in which the line cooks took him and the other managers. The managers had all risen through the ranks. This earned them a certain amount of respect. They in turn seemed respectful of the cooks' skills and experience. Still, the oversight is tight, and this seemed crucial to the success of the enterprise.

The managers monitored the pace, too—scanning the screens for a station stacking up red flags, indicating orders past the target time, and deciding whether to give the cooks at the station a nudge or an extra pair of hands. They watched for waste—wasted food, wasted time, wasted effort. The formula was Business 101: Use the right amount of goods and labor to deliver what customers want and no more. Anything more is waste, and waste is lost profit.

I spoke to David Gordon, the company's chief operating officer. He told me that the Cheesecake Factory has worked out a staff-to-customer ratio that keeps everyone busy but not so busy that there's no slack in the system in the event of a sudden surge of customers. More difficult is the problem of wasted food. Although the company buys in bulk from regional suppliers, groceries are the biggest expense after labor, and the most unpredictable. Everything—the chicken, the beef, the lettuce, the eggs, and all the rest—has a shelf life. If a restaurant were to stock too much, it could end up throwing away hundreds of thousands of dollars' worth of food. If a restaurant stocks too little, it will have to tell customers that their favorite dish is not available, and they may never come back. Groceries, Gordon said, can kill a restaurant.

The company's target last year was at least 97.5-per-cent efficiency: the managers aimed at throwing away no more than 2.5 per cent of the groceries they bought, without running out. This seemed to me an absurd target. Achieving it would require knowing in advance almost exactly how many customers would be coming in and what they were going to want, then insuring that the cooks didn't spill or toss or waste anything. Yet this is precisely what the organization has learned to do. The chain-restaurant industry has produced a field of computer analytics known as "guest forecasting."

"We have forecasting models based on historical data—the trend of the past six weeks and also the trend of the previous year," Gordon told me. "The predictability of the business has become astounding." The company has even learned how to make adjustments for the weather or for scheduled events like playoff games that keep people at home.

A computer program known as Net Chef showed Luz that for this one restaurant food costs accounted for 28.73 per cent of expenses the previous week. It also showed exactly how many chicken breasts were ordered that week ($1,614 worth), the volume sold, the volume on hand, and how much of last week's order had been wasted (three dollars' worth). Chain production requires control, and they'd figured out how to achieve it on a mass scale.

As a doctor, I found such control alien—possibly from a hostile planet. We don't have patient forecasting in my office, push-button waste monitoring, or such stringent, hour-by-hour oversight of the work we do, and we don't want to. I asked Luz if he had ever thought about the contrast when he went to see a doctor. We were standing amid the bustle of the kitchen, and the look on his face shifted before he answered.

"I have," he said. His mother was seventy-eight. She had early Alzheimer's disease, and required a caretaker at home. Getting her adequate medical care was, he said, a constant battle.

Recently, she'd had a fall, apparently after fainting, and was taken to a local emergency room. The doctors ordered a series of tests and scans, and kept her overnight. They never figured out what the problem was. Luz understood that sometimes explanations prove elusive. But the clinicians didn't seem to be following any coördinated plan of action. The emergency doctor told the family one plan, the admitting internist described another, and the consulting specialist a third. Thousands of dollars had been spent on tests, but nobody ever told Luz the results.

A nurse came at ten the next morning and said that his mother was being discharged. But his mother's nurse was on break, and the discharge paperwork with her instructions and prescriptions hadn't been done. So they waited. Then the next person they needed was at lunch. It was as if the clinicians were the customers, and the patients' job was to serve them. "We didn't get to go until 6 P.M., with a tired, disabled lady and a long drive home." Even then she still had to be changed out of her hospital gown and dressed. Luz pressed the call button to ask for help. No answer. He went out to the ward desk.

The aide was on break, the secretary said. "Don't you dress her yourself at home?" He explained that he didn't, and made a fuss.

An aide was sent. She was short with him and rough in changing his mother's clothes. "She was manhandling her," Luz said. "I felt like, 'Stop. I'm not one to complain. I respect what you do enormously. But if there were a video camera in here, you'd be on the evening news.' I sent her out. I had to do everything myself. I'm stuffing my mom's boob in her bra. It was unbelievable."

His mother was given instructions to check with her doctor for the results of cultures taken during her stay, for a possible urinary-tract infection. But when Luz tried to follow up, he couldn't get through to her doctor for days. "Doctors are busy," he said. "I get it. But come on." An office assistant finally told him that the results wouldn't be ready for another week and that she was to see a neurologist. No explanations. No chance to ask questions.

The neurologist, after giving her a two-minute exam, suggested tests that had already been done and wrote a prescription that he admitted was of doubtful benefit. Luz's family seemed to encounter this kind of disorganization, imprecision, and waste wherever his mother went for help.

"It is unbelievable to me that they would not manage this better," Luz said. I asked him what he would do if he were the manager of a neurology unit or a cardiology clinic. "I don't know anything about medicine," he said. But when I pressed he thought for a moment, and said, "This is pretty obvious. I'm sure you already do it. But I'd study what the best people are doing, figure out how to standardize it, and then bring it to everyone to execute."

This is not at all the normal way of doing things in medicine. ("You're scaring me," he said, when I told him.) But it's exactly what the new health-care chains are now hoping to do on a mass scale. They want to create Cheesecake Factories for health care. The question is whether the medical counterparts to Mauricio at the broiler station—the clinicians in the operating rooms, in the medical offices, in the intensive-care units—will go along with the plan. Fixing a nice piece of steak is hardly of the same complexity as diagnosing the cause of an elderly patient's loss of consciousness. Doctors and patients have not had a positive experience with outsiders second-guessing decisions. How will they feel about managers trying to tell them what the "best practices" are?

In March, my mother underwent a total knee replacement, like at least six hundred thousand Americans each year. She'd had a partial knee replacement a decade ago, when arthritis had worn away part of the cartilage, and for a while this served her beautifully. The surgeon warned, however, that the results would be temporary, and about five years ago the pain returned.

She's originally from Ahmadabad, India, and has spent three decades as a pediatrician, attending to the children of my small Ohio home town. She's chatty. She can't go through a grocery checkout line or get pulled over for speeding without learning people's names and a little bit about them. But she didn't talk about her mounting pain. I noticed, however, that she had developed a pronounced limp and had become unable to walk even moderate distances. When I asked her about it, she admitted that just getting out of bed in the morning was an ordeal. Her doctor showed me her X-rays. Her partial prosthesis had worn through the bone on the lower surface of her knee. It was time for a total knee replacement.

This past winter, she finally stopped putting it off, and asked me to find her a surgeon. I wanted her to be treated well, in both the technical and the human sense. I wanted a place where everyone and everything—from the clinic secretary to the physical therapists—worked together seamlessly.

My mother planned to come to Boston, where I live, for the surgery so she could stay with me during her recovery. (My father died last year.) Boston has three hospitals in the top rank of orthopedic surgery. But even a doctor doesn't have much to go on when it comes to making a choice. A place may have a great reputation, but it's hard to know about actual quality of care. Unlike some countries, the United States doesn't have a monitoring system that tracks joint-replacement statistics. Even within an institution, I found, surgeons take strikingly different approaches. They use different makes of artificial joints, different kinds of anesthesia, different regimens for post-surgical pain control and physical therapy.

In the absence of information, I went with my own hospital, the Brigham and Women's Hospital. Our big-name orthopedic surgeons treat Olympians and professional athletes. Nine of them do knee replacements. Of most interest to me, however, was a surgeon who was not one of the famous names. He has no national recognition. But he has led what is now a decade-long experiment in standardizing joint-replacement surgery.

John Wright is a New Zealander in his late fifties. He's a tower crane of a man, six feet four inches tall, and so bald he barely seems to have eyebrows. He's informal in attire—I don't think I've ever seen him in a tie, and he is as apt to do rounds in his zip-up anorak as in his white coat—but he exudes competence.

"Customization should be five per cent, not ninety-five per cent, of what we do," he told me. A few years ago, he gathered a group of people from every specialty involved—surgery, anesthesia, nursing, physical therapy—to formulate a single default way of doing knee replacements. They examined every detail, arguing their way through their past experiences and whatever evidence they could find. Essentially, they did what Luz considered the obvious thing to do: they studied what the best people were doing, figured out how to standardize it, and then tried to get everyone to follow suit.

They came up with a plan for anesthesia based on research studies—including giving certain pain medications before the patient entered the operating room and using spinal anesthesia plus an injection of local anesthetic to block the main nerve to the knee. They settled on a postoperative regimen, too. The day after a knee replacement, most orthopedic surgeons have their patients use a continuous passive-motion machine, which flexes and extends the knee as they lie in bed. Large-scale studies, though, have suggested that the machines don't do much good. Sure enough, when the members of Wright's group examined their own patients, they found that the ones without the machine got out of bed sooner after surgery, used less pain medication, and had more range of motion at discharge. So Wright instructed the hospital to get rid of the machines, and to use the money this saved (ninety thousand dollars a year) to pay for more physical therapy, something that is proven to help patient mobility. Therapy, starting the day after surgery, would increase from once to twice a day, including weekends.

Even more startling, Wright had persuaded the surgeons to accept changes in the operation itself; there was now, for instance, a limit as to which prostheses they could use. Each of our nine knee-replacement surgeons had his preferred type and brand. Knee surgeons are as particular about their implants as professional tennis players are about their racquets. But the hardware is easily the biggest cost of the operation—the average retail price is around eight thousand dollars, and some cost twice that, with no solid evidence of real differences in results.

Knee implants were largely perfected a quarter century ago. By the nineteen-nineties, studies showed that, for some ninety-five per cent of patients, the implants worked magnificently a decade after surgery. Evidence from the Australian registry has shown that not a single new knee or hip prosthesis had a lower failure rate than that of the established prostheses. Indeed, thirty per cent of the new models were likelier to fail. Like others on staff, Wright has advised companies on implant design. He believes that innovation will lead to better implants. In the meantime, however, he has sought to limit the staff to the three lowest-cost knee implants.

These have been hard changes for many people to accept. Wright has tried to figure out how to persuade clinicians to follow the standardized plan. To prevent revolt, he learned, he had to let them deviate at times from the default option. Surgeons could still order a passive-motion machine or a preferred prosthesis. "But I didn't make it easy," Wright said. The surgeons had to enter the treatment orders in the computer themselves. To change or add an implant, a surgeon had to show that the performance was superior or the price at least as low.

I asked one of his orthopedic colleagues, a surgeon named John Ready, what he thought about Wright's efforts. Ready was philosophical. He recognized that the changes were improvements, and liked most of them. But he wasn't happy when Wright told him that his knee-implant manufacturer wasn't matching the others' prices and would have to be dropped.

"It's not ideal to lose my prosthesis," Ready said. "I could make the switch. The differences between manufacturers are minor. But there'd be a learning curve." Each implant has its quirks—how you seat it, what tools you use. "It's probably a ten-case learning curve for me." Wright suggested that he explain the situation to the manufacturer's sales rep. "I'm my rep's livelihood," Ready said. "He probably makes five hundred dollars a case from me." Ready spoke to his rep. The price was dropped.

Wright has become the hospital's kitchen manager—not always a pleasant role. He told me that about half of the surgeons appreciate what he's doing. The other half tolerate it at best. One or two have been outright hostile. But he has persevered, because he's gratified by the results. The surgeons now use a single manufacturer for seventy-five per cent of their implants, giving the hospital bargaining power that has helped slash its knee-implant costs by half. And the start-to-finish standardization has led to vastly better outcomes. The distance patients can walk two days after surgery has increased from fifty-three to eighty-five feet. Nine out of ten could stand, walk, and climb at least a few stairs independently by the time of discharge. The amount of narcotic pain medications they required fell by a third. They could also leave the hospital nearly a full day earlier on average (which saved some two thousand dollars per patient).

My mother was one of the beneficiaries. She had insisted to Dr. Wright that she would need a week in the hospital after the operation and three weeks in a rehabilitation center. That was what she'd required for her previous knee operation, and this one was more extensive.

"We'll see," he told her.

The morning after her operation, he came in and told her that he wanted her getting out of bed, standing up, and doing a specific set of exercises he showed her. "He's pushy, if you want to say it that way," she told me. The physical therapists and nurses were, too. They were a team, and that was no small matter. I counted sixty-three different people involved in her care. Nineteen were doctors, including the surgeon and chief resident who assisted him, the anesthesiologists, the radiologists who reviewed her imaging scans, and the junior residents who examined her twice a day and adjusted her fluids and medications. Twenty-three were nurses, including her operating-room nurses, her recovery-room nurse, and the many ward nurses on their eight-to-twelve-hour shifts. There were also at least five physical therapists; sixteen patient-care assistants, helping check her vital signs, bathe her, and get her to the bathroom; plus X-ray and EKG technologists, transport workers, nurse practitioners, and physician assistants. I didn't even count the bioengineers who serviced the equipment used, the pharmacists who dispensed her medications, or the kitchen staff preparing her food while taking into account her dietary limitations. They all had to coördinate their contributions, and they did.

Three days after her operation, she was getting in and out of bed on her own. She was on virtually no narcotic medication. She was starting to climb stairs. Her knee pain was actually less than before her operation. She left the hospital for the rehabilitation center that afternoon.

The biggest complaint that people have about health care is that no one ever takes responsibility for the total experience of care, for the costs, and for the results. My mother experienced what happens in medicine when someone takes charge. Of course, John Wright isn't alone in trying to design and implement this kind of systematic care, in joint surgery and beyond. The Virginia Mason Medical Center, in Seattle, has done it for knee surgery and cancer care; the Geisinger Health Center, in Pennsylvania, has done it for cardiac surgery and primary care; the University of Michigan Health System standardized how its doctors give blood transfusions to patients, reducing the need for transfusions by thirty-one per cent and expenses by two hundred thousand dollars a month. Yet, unless such programs are ramped up on a nationwide scale, they aren't going to do much to improve health care for most people or reduce the explosive growth of health-care costs.

In medicine, good ideas still take an appallingly long time to trickle down. Recently, the American Academy of Neurology and the American Headache Society released new guidelines for migraine-headache-treatment. They recommended treating severe migraine sufferers—who have more than six attacks a month—with preventive medications and listed several drugs that markedly reduce the occurrence of attacks. The authors noted, however, that previous guidelines going back more than a decade had recommended such remedies, and doctors were still not providing them to more than two-thirds of patients. One study examined how long it took several major discoveries, such as the finding that the use of beta-blockers after a heart attack improves survival, to reach even half of Americans. The answer was, on average, more than fifteen years.

Scaling good ideas has been one of our deepest problems in medicine. Regulation has had its place, but it has proved no more likely to produce great medicine than food inspectors are to produce great food. During the era of managed care, insurance-company reviewers did hardly any better. We've been stuck. But do we have to be?

Every six months, the Cheesecake Factory puts out a new menu. This means that everyone who works in its restaurants expects to learn something new twice a year. The March, 2012, Cheesecake Factory menu included thirteen new items. The teaching process is now finely honed: from start to finish, rollout takes just seven weeks.

The ideas for a new dish, or for tweaking an old one, can come from anywhere. One of the Boston prep cooks told me about an idea he once had that ended up in a recipe. David Overton, the founder and C.E.O. of the Cheesecake Factory, spends much of his time sampling a range of cuisines and comes up with many dishes himself. All the ideas, however, go through half a dozen chefs in the company's test kitchen, in Calabasas. They figure out how to make each recipe reproducible, appealing, and affordable. Then they teach the new recipe to the company's regional managers and kitchen managers.

Dave Luz, the Boston regional manager, went to California for training this past January with his chief kitchen manager, Tom Schmidt, a chef with fifteen years' experience. They attended lectures, watched videos, participated in workshops. It sounded like a surgical conference. Where I might be taught a new surgical technique, they were taught the steps involved in preparing a "Santorini farro salad." But there was a crucial difference. The Cheesecake instructors also trained the attendees how to teach what they were learning. In medicine, we hardly ever think about how to implement what we've learned. We learn what we want to, when we want to.

On the first training day, the kitchen managers worked their way through thirteen stations, preparing each new dish, and their performances were evaluated. The following day, they had to teach their regional managers how to prepare each dish—Schmidt taught Luz—and this time the instructors assessed how well the kitchen managers had taught.

The managers returned home to replicate the training session for the general manager and the chief kitchen manager of every restaurant in their region. The training at the Boston Prudential Center restaurant took place on two mornings, before the lunch rush. The first day, the managers taught the kitchen staff the new menu items. There was a lot of poring over the recipes and videos and fussing over the details. The second day, the cooks made the new dishes for the servers. This gave the cooks some practice preparing the food at speed, while allowing the servers to learn the new menu items. The dishes would go live in two weeks. I asked a couple of the line cooks how long it took them to learn to make the new food.

"I know it already," one said.

"I make it two times, and that's all I need," the other said.

Come on, I said. How long before they had it down pat?

"One day," they insisted. "It's easy."

I asked Schmidt how much time he thought the cooks required to master the recipes. They thought a day, I told him. He grinned. "More like a month," he said.

Even a month would be enviable in medicine, where innovations commonly spread at a glacial pace. The new health-care chains, though, are betting that they can change that, in much the same way that other chains have.

Armin Ernst is responsible for intensive-care-unit operations in Steward's ten hospitals. The I.C.U.s he oversees serve some eight thousand patients a year. In another era, an I.C.U. manager would have been a facilities expert. He would have spent his time making sure that the equipment, electronics, pharmacy resources, and nurse staffing were up to snuff. He would have regarded the I.C.U. as the doctors' workshop, and he would have wanted to give them the best possible conditions to do their work as they saw fit.

Ernst, though, is a doctor—a new kind of doctor, whose goal is to help disseminate good ideas. He doesn't see the I.C.U. as a doctors' workshop. He sees it as the temporary home of the sickest, most fragile people in the country. Nowhere in health care do we expend more resources. Although fewer than one in four thousand Americans are in intensive care at any given time, they account for four per cent of national health-care costs. Ernst believes that his job is to make sure that everyone is collaborating to provide the most effective and least wasteful care possible.

He looked like a regular doctor to me. Ernst is fifty years old, a native German who received his medical degree at the University of Heidelberg before training in pulmonary and critical-care medicine in the United States. He wears a white hospital coat and talks about drips and ventilator settings, like any other critical-care specialist. But he doesn't deal with patients: he deals with the people who deal with patients.

Ernst says he's not telling clinicians what to do. Instead, he's trying to get clinicians to agree on precise standards of care, and then make sure that they follow through on them. (The word "consensus" comes up a lot.) What I didn't understand was how he could enforce such standards in ten hospitals across three thousand square miles.

Late one Friday evening, I joined an intensive-care-unit team on night duty. But this team was nowhere near a hospital. We were in a drab one-story building behind a meat-trucking facility outside of Boston, in a back section that Ernst called his I.C.U. command center. It was outfitted with millions of dollars' worth of technology. Banks of computer screens carried a live feed of cardiac-monitor readings, radiology-imaging scans, and laboratory results from I.C.U. patients throughout Steward's hospitals. Software monitored the stream and produced yellow and red alerts when it detected patterns that raised concerns. Doctors and nurses manned consoles where they could toggle on high-definition video cameras that allowed them to zoom into any I.C.U. room and talk directly to the staff on the scene or to the patients themselves.

The command center was just a few months old. The team had gone live in only four of the ten hospitals. But in the next several months Ernst's "tele-I.C.U." team will have the ability to monitor the care for every patient in every I.C.U. bed in the Steward health-care system.

A doctor, two nurses, and an administrative assistant were on duty in the command center each night I visited. Christina Monti was one of the nurses. A pixie-like thirty-year-old with nine years' experience as a cardiac intensive-care nurse, she was covering Holy Family Hospital, on the New Hampshire border, and St. Elizabeth's Medical Center, in Boston's Brighton neighborhood. When I sat down with her, she was making her rounds, virtually.

First, she checked on the patients she had marked as most critical. She reviewed their most recent laboratory results, clinical notes, and medication changes in the electronic record. Then she made a "visit," flicking on the two-way camera and audio system. If the patients were able to interact, she would say hello to them in their beds. She asked the staff members whether she could do anything for them. The tele-I.C.U. team provided the staff with extra eyes and ears when needed. If a crashing patient diverts the staff's attention, the members of the remote team can keep an eye on the other patients. They can handle computer paperwork if a nurse falls behind; they can look up needed clinical information. The hospital staff have an OnStar-like button in every room that they can push to summon the tele-I.C.U. team.

Monti also ran through a series of checks for each patient. She had a reference list of the standards that Ernst had negotiated with the people running the I.C.U.s, and she looked to see if they were being followed. The standards covered basics, from hand hygiene to measures for stomach-ulcer prevention. In every room with a patient on a respirator, for instance, Monti made sure the nurse had propped the head of the bed up at least thirty degrees, which makes pneumonia less likely. She made sure the breathing tube in the patient's mouth was secure, to reduce the risk of the tube's falling out or becoming disconnected. She zoomed in on the medication pumps to check that the drips were dosed properly. She was not looking for bad nurses or bad doctors. She was looking for the kinds of misses that even excellent nurses and doctors can make under pressure.

The concept of the remote I.C.U. started with an effort to let specialists in critical-care medicine, who are in short supply, cover not just one but several community hospitals. Two hundred and fifty hospitals from Alaska to Virginia have installed a version of the tele-I.C.U. It produced significant improvements in outcomes and costs—and, some discovered, a means of driving better practices even in hospitals that had specialists on hand.

After five minutes of observation, however, I realized that the remote I.C.U. team wasn't exactly in command; it was in negotiation. I observed Monti perform a video check on a middle-aged man who had just come out of heart surgery. A soft chime let the people in the room know she was dropping in. The man was unconscious, supported by a respirator and intravenous drips. At his bedside was a nurse hanging a bag of fluid. She seemed to stiffen at the chime's sound.

"Hi," Monti said to her. "I'm Chris. Just making my evening rounds. How are you?" The bedside nurse gave the screen only a sidelong glance.

Ernst wasn't oblivious of the issue. He had taken pains to introduce the command center's team, spending weeks visiting the units and bringing doctors and nurses out to tour the tele-I.C.U. before a camera was ever turned on. But there was no escaping the fact that these were strangers peering over the staff's shoulders. The bedside nurse's chilliness wasn't hard to understand.

In a single hour, however, Monti had caught a number of problems. She noticed, for example, that a patient's breathing tube had come loose. Another patient wasn't getting recommended medication to prevent potentially fatal blood clots. Red alerts flashed on the screen—a patient with an abnormal potassium level that could cause heart-rhythm problems, another with a sudden leap in heart rate.

Monti made sure that the team wasn't already on the case and that the alerts weren't false alarms. Checking the computer, she figured out that a doctor had already ordered a potassium infusion for the woman with the low level. Flipping on a camera, she saw that the patient with the high heart rate was just experiencing the stress of being helped out of bed for the first time after surgery. But the unsecured breathing tube and the forgotten blood-clot medication proved to be oversights. Monti raised the concerns with the bedside staff.

Sometimes they resist. "You have got to be careful from patient to patient," Gerard Hayes, the tele-I.C.U. doctor on duty, explained. "Pushing hard on one has ramifications for how it goes with a lot of patients. You don't want to sour whole teams on the tele-I.C.U." Across the country, several hospitals have decommissioned their systems. Clinicians have been known to place a gown over the camera, or even rip the camera out of the wall. Remote monitoring will never be the same as being at the bedside. One nurse called the command center to ask the team not to turn on the video system in her patient's room: he was delirious and confused, and the sudden appearance of someone talking to him from the television would freak him out.

Still, you could see signs of change. I watched Hayes make his virtual rounds through the I.C.U. at St. Anne's Hospital, in Fall River, near the Rhode Island border. He didn't yet know all the members of the hospital staff—this was only his second night in the command center, and when he sees patients in person it's at a hospital sixty miles north. So, in his dealings with the on-site clinicians, he was feeling his way.

Checking on one patient, he found a few problems. Mr. Karlage, as I'll call him, was in his mid-fifties, an alcoholic smoker with cirrhosis of the liver, severe emphysema, terrible nutrition, and now a pneumonia that had put him into respiratory failure. The I.C.U. team injected him with antibiotics and sedatives, put a breathing tube down his throat, and forced pure oxygen into his lungs. Over a few hours, he stabilized, and the I.C.U. doctor was able to turn his attention to other patients.

But stabilizing a sick patient is like putting out a house fire. There can be smoldering embers just waiting to reignite. Hayes spotted a few. The ventilator remained set to push breaths at near-maximum pressure, and, given the patient's severe emphysema, this risked causing a blowout. The oxygen concentration was still cranked up to a hundred per cent, which, over time, can damage the lungs. The team had also started several broad-spectrum antibiotics all at once, and this regimen had to be dialled back if they were to avoid breeding resistant bacteria.

Hayes had to notify the unit doctor. An earlier interaction, however, had not been promising. During a video check on a patient, Hayes had introduced himself and mentioned an issue he'd noticed. The unit doctor stared at him with folded arms, mouth shut tight. Hayes was a former Navy flight surgeon with twenty years' experience as an I.C.U. doctor and looked to have at least a decade on the St. Anne's doctor. But the doctor was no greenhorn, either, and gave him the brushoff: "The morning team can deal with that." Now Hayes needed to call him about Mr. Karlage. He decided to do it by phone.

"Sounds like you're having a busy night," Hayes began when he reached the doctor. "Mr. Karlage is really turning around, huh?" Hayes praised the doctor's work. Then he brought up his three issues, explaining what he thought could be done and why. He spoke like a consultant brought in to help. This went over better. The doctor seemed to accept Hayes's suggestions.

Unlike a mere consultant, however, Hayes took a few extra steps to make sure his suggestions were carried out. He spoke to the nurse and the respiratory therapist by video and explained the changes needed. To carry out the plan, they needed written orders from the unit doctor. Hayes told them to call him back if they didn't get the orders soon.

Half an hour later, Hayes called Mr. Karlage's nurse again. She hadn't received the orders. For all the millions of dollars of technology spent on the I.C.U. command center, this is where the plug meets the socket. The fundamental question in medicine is: Who is in charge? With the opening of the command center, Steward was trying to change the answer—it gave the remote doctors the authority to issue orders as well. The idea was that they could help when a unit doctor got too busy and fell behind, and that's what Hayes chose to believe had happened. He entered the orders into the computer. In a conflict, however, the on-site physician has the final say. So Hayes texted the St. Anne's doctor, informing him of the changes and asking if he'd let him know if he disagreed.

Hayes received no reply. No "thanks" or "got it" or "O.K." After midnight, though, the unit doctor pressed the video call button and his face flashed onto Hayes's screen. Hayes braced for a confrontation. Instead, the doctor said, "So I've got this other patient and I wanted to get your opinion."

Hayes suppressed a smile. "Sure," he said.

When he signed off, he seemed ready to high-five someone. "He called us," he marvelled. The command center was gaining credibility.

Armin Ernst has big plans for the command center—a rollout of full-scale treatment protocols for patients with severe sepsis, acute respiratory-distress syndrome, and other conditions; strategies to reduce unnecessary costs; perhaps even computer forecasting of patient volume someday. Steward is already extending the command-center concept to in-patient psychiatry. Emergency rooms and surgery may be next. Other health systems are pursuing similar models. The command-center concept provides the possibility of, well, command.

Today, some ninety "super-regional" health-care systems have formed across the country—large, growing chains of clinics, hospitals, and home-care agencies. Most are not-for-profit. Financial analysts expect the successful ones to drive independent medical centers out of existence in much of the country—either by buying them up or by drawing away their patients with better quality and cost control. Some small clinics and stand-alone hospitals will undoubtedly remain successful, perhaps catering to the luxury end of health care the way gourmet restaurants do for food. But analysts expect that most of us will gravitate to the big systems, just as we have moved away from small pharmacies to CVS and Walmart.

Already, there have been startling changes. Cleveland Clinic, for example, opened nine regional hospitals in northeast Ohio, as well as health centers in southern Florida, Toronto, and Las Vegas, and is now going international, with a three-hundred-and-sixty-four-bed hospital in Abu Dhabi scheduled to open next year. It reached an agreement with Lowe's, the home-improvement chain, guaranteeing a fixed price for cardiac surgery for the company's employees and dependents. The prospect of getting better care for a lower price persuaded Lowe's to cover all out-of-pocket costs for its insured workers to go to Cleveland, including co-payments, airfare, transportation, and lodging. Three other companies, including Kohl's department stores, have made similar deals, and a dozen more, including Boeing, are in negotiations. Big Medicine is on the way.

Reinventing medical care could produce hundreds of innovations. Some may be as simple as giving patients greater e-mail and online support from their clinicians, which would enable timelier advice and reduce the need for emergency-room visits. Others might involve smartphone apps for coaching the chronically ill in the management of their disease, new methods for getting advice from specialists, sophisticated systems for tracking outcomes and costs, and instant delivery to medical teams of up-to-date care protocols. Innovations could take a system that requires sixty-three clinicians for a knee replacement and knock the number down by half or more. But most significant will be the changes that finally put people like John Wright and Armin Ernst in charge of making care coherent, coördinated, and affordable. Essentially, we're moving from a Jeffersonian ideal of small guilds and independent craftsmen to a Hamiltonian recognition of the advantages that size and centralized control can bring.

Yet it seems strange to pin our hopes on chains. We have no guarantee that Big Medicine will serve the social good. Whatever the industry, an increase in size and control creates the conditions for monopoly, which could do the opposite of what we want: suppress innovation and drive up costs over time. In the past, certainly, health-care systems that pursued size and market power were better at raising prices than at lowering them.

A new generation of medical leaders and institutions professes to have a different aim. But a lesson of the past century is that government can influence the behavior of big corporations, by requiring transparency about their performance and costs, and by enacting rules and limitations to protect the ordinary citizen. The federal government has broken up monopolies like Standard Oil and A.T. & T.; in some parts of the country, similar concerns could develop in health care.

Mixed feelings about the transformation are unavoidable. There's not just the worry about what Big Medicine will do; there's also the worry about how society and government will respond. For the changes to live up to our hopes—lower costs and better care for everyone—liberals will have to accept the growth of Big Medicine, and conservatives will have to accept the growth of strong public oversight.

The vast savings of Big Medicine could be widely shared—or reserved for a few. The clinicians who are trying to reinvent medicine aren't doing it to make hedge-fund managers and bondholders richer; they want to see that everyone benefits from the savings their work generates—and that won't be automatic.

Our new models come from industries that have learned to increase the capabilities and efficiency of the human beings who work for them. Yet the same industries have also tended to devalue those employees. The frontline worker, whether he is making cars, solar panels, or wasabi-crusted ahi tuna, now generates unprecedented value but receives little of the wealth he is creating. Can we avoid this as we revolutionize health care?

Those of us who work in the health-care chains will have to contend with new protocols and technology rollouts every six months, supervisors and project managers, and detailed metrics on our performance. Patients won't just look for the best specialist anymore; they'll look for the best system. Nurses and doctors will have to get used to delivering care in which our own convenience counts for less and the patients' experience counts for more. We'll also have to figure out how to reward people for taking the time and expense to teach the next generations of clinicians. All this will be an enormous upheaval, but it's long overdue, and many people recognize that. When I asked Christina Monti, the Steward tele-I.C.U. nurse, why she wanted to work in a remote facility tangling with staffers who mostly regarded her with indifference or hostility, she told me, "Because I wanted to be part of the change."

And we are seeing glimpses of this change. In my mother's rehabilitation center, miles away from where her surgery was done, the physical therapists adhered to the exercise protocols that Dr. Wright's knee factory had developed. He didn't have a video command center, so he came out every other day to check on all the patients and make sure that the staff was following the program. My mother was sure she'd need a month in rehab, but she left in just a week, incurring a fraction of the costs she would have otherwise. She walked out the door using a cane. On her first day at home with me, she climbed two flights of stairs and walked around the block for exercise.

The critical question is how soon that sort of quality and cost control will be available to patients everywhere across the country. We've let health-care systems provide us with the equivalent of greasy-spoon fare at four-star prices, and the results have been ruinous. The Cheesecake Factory model represents our best prospect for change. Some will see danger in this. Many will see hope. And that's probably the way it should be. ♦

Read more http://www.newyorker.com/reporting/2012/08/13/120813fa_fact_gawande#ixzz23Io1MBPF