The arc from enthusiast to entrepreneur

Many of the most interesting technologies of the next decade will involve innovations in hardware, not just software. The Maker movement, like all enthusiast movements, is a harbinger of deeper change.

What Dale Dougherty first recognized in 2005 when he published Make: Magazine and began Maker Faire was that there was a new upwelling of interest in making things, embracing everything from new technologies like 3D printing and other forms of advanced manufacturing, robotics, sensor platforms, to crafting and older hands-on technologies. The early projects in the magazine — aerial photography with kites, a programmable cat feeder made out of an old VCR, hacked robot dogs sniffing out environmental toxins — may have seemed trivial at the time, but they were a sign of things to come.

In 2005, Jeff Han’s work with multitouch interfaces was a maker project at NYU. In February 2006, when he demoed his work at TED, it was a WOW moment. A year and a half later, with the release of the iPhone, the multitouch screen was the foundation of a transformative consumer product.

Multitouch was just the beginning. Smart phones are sensor platforms: GPS, compass, accelerometer, camera, microphone, and dozens more specialized sensors create new possibilities for application design that are only now being exploited more fully. Applications like Square Wallet and Uber are only possible because of these platforms.

The problem is that, as has often been said about AI as well, as soon as something crosses over into the consumer realm, it’s no longer seen as “makerish.” When Nike is selling quantified self devices, when your bathroom scale tweets your weight, it’s hard to see this as part of the Maker movement. Yet thinking about how much further we have to go in applying sensors to transform applications and business processes will help you see important opportunities that you might otherwise miss.

A sensor and control platform like Arduino still seems to belong to the Maker universe, but an application that uses the consumer sensor platform of a smart phone does not. But this is the very heart of the distinction that will help you to see the future more clearly.

To understand the trend line of the Maker movement, ask yourself “What are makers playing with today that has already become mainstream? What other kinds of devices and business processes can be transformed by the additions of sensors? What are the opportunities here for startups?”

When you ask yourself these questions, and then look around, you will realize that the Maker movement is the next big thing.

As a result, we decided it was time to create Maker Media as a standalone vehicle to ride this new wave of innovation. Dale Dougherty, my partner from the early days of O’Reilly, and the creator of both Make magazine and Maker Faire, was the one who recognized this wave coming, and has nurtured it for the last seven years. Now, he has a platform to continue his work and take it to the next level.

Below, a few thoughts from Dale about the origins of Make, and where he wants to take Maker Media.

Making becomes popular

Thoughts from Dale Dougherty

I first mentioned the idea for MAKE Magazine to Tim in a cab in Portland. We were heading to the Open Source Conference and I had a few minutes to pitch him on a magazine that I said would be “Martha Stewart for Geeks.”  We had a good conversation, talking about how hackers were hacking the physical world, applying a mindset learned from developing software to customize, personalize and create physical environments. Tim’s encouragement was the initial step in developing what would become MAKE Magazine. I certainly had no idea that many years later we’d be talking about a global Maker movement. Indeed, what has happened is simultaneously that making and the geeks behind it have broken into the mainstream. Making is now popular.

From the beginning, I was fascinated by makers. I enjoyed meeting makers, getting to know their stories, and seeing firsthand the amazing projects they were doing. I realized that makers would enjoy meeting each other and talking about their projects, sharing the kinds of details that they were able to share with me. That was the inspiration for Maker Faire, and I wondered at the time if other people would find makers as fascinating as I did. Maker Faire was really an experiment to find out. A team headed by Sherry Huss organized the first Maker Faire in the Bay Area, and we chose to hold it at a fairgrounds/expo center. We wanted Maker Faire to be fun and we wanted families to come. We re-invented the fair. In 2012, there were over 60 Maker Faires around the world, most of them organized by community-minded individuals who wanted to support and promote making in their city or region.

While MAKE started out with geek hobbyists, the audience now includes families who look for fun, educational projects to do together. It also includes makers who are developing new products and services for other makers and other audiences. It includes professional engineers and industrial designers. Makers have become entrepreneurs, sometimes accidentally, by discovering there’s a market for what they do. They build components and kits, and we sell them in Maker Shed, and many other places. They create tools such as 3D printers and CNC machines and microcontrollers. Makers have created a new market ecosystem.

MIT economist Michael Schrage, who wrote an article for MAKE’s Kits issue on kits as an engine of innovation, has a new book called Who Do You Want Your Customers To Become?*  He writes that the best innovation transforms your customers. It engages them in “reimagining, redefining, and redesigning” their future. The mission of Maker Media is to help more people become makers, and participate broadly in making a better future for themselves, their families and their communities.

I’m excited by the opportunity for Maker Media and its team. I’m grateful to Tim, Laura Baldwin, my colleagues at O’Reilly and the extended O’Reilly community for supporting the growth of MAKE. I look forward to developing this new edition of MAKE, and expanding the reach of MAKE as a global brand that brings makers together.

* (Schrage, Michael (2012-07-17). Who Do You Want Your Customers to Become? (Kindle Location 57). Perseus Books Group. Kindle Edition.)

Much of the difficulty stems from stringent legal regulations that make it difficult for companies to compete and do business with government. (Like so many government regulations, these rules were designed with good intentions after scandals involving government officials steering contracts to their friends, but need to be simplified and updated for current circumstances.) The regulations are so complex that often, the people who do business with the federal government are more specialized in understanding that regulation than they are in the technology they’re providing. As a result, there are specialized intermediaries whose sole business is bidding on government jobs, and then subcontracting them to people who can actually do the work.

The problem has been compounded by the fact that many things that were once hard and expensive are now easy and cheap. But government rules make it hard to adopt cutting edge technology.

That’s why I’m excited to see the Small Business Administration launch RFP-EZ as part of the White House’s Presidential Innovation Fellows program. It’s a small step towards getting the door open — towards making it easier for new businesses to sell to government. RFP-EZ simplifies both the process for small companies to bid on government jobs and the process for government officials to post their requests. Hopefully it will increase government’s access to technology, increase competition in the federal space, and lower prices.

This is a huge opportunity for web developers and other commercial technology providers. Government is the largest buyer on the planet, and your potential to work on stuff that matters is unparalleled when you’re working with the platform of government. When government and private industry work together to solve problems, amazing things can happen. RFP-EZ is a step in that direction.

If you’re a startup or consulting firm who has a desire to make a difference, and a desire for revenue, I’d encourage you to check out what RFP-EZ has to offer. There are a few projects awaiting bids now, and from what I hear, more on their way. (This is still an experiment, and successful outcomes will lead to more jobs being posted.) If you’ve got a solution to the problems that are posted, take a step towards working on stuff that matters at scale.

I have another reason for urging innovative companies to participate. This project is an experiment. Take a look at the Federal Register notice about the project. It’s a pilot that has a clear start and end date. They’re using the pilot to gather data, learn from it, and iterate. They’ve given themselves room to succeed and permission to fail.  I’d like to see government do more of both. Your participation will encourage that response.

That’s why I’m so excited to see a joint effort by New York City, San Francisco, and Yelp to incorporate government health inspection data into Yelp reviews. I was involved in some early discussions and made some introductions, and have been delighted to see the project take shape.

My biggest contribution was to point to GTFS as a model. Bibiana McHugh at the city of Portland’s TriMet transit agency reached out to Google, Bing, and others with the question: “If we came up with a standard format for transit schedules, could you use it?” Google Transit was the result — a service that has spread to many other U.S. cities. When you rejoice in the convenience of getting transit timetables on your phone, remember to thank Portland officials as well as Google.

In a similar way, Yelp, New York, and San Francisco came up with a data format for health inspection data. The specification is at http://yelp.com/healthscores. It will reportedly be announced at the US Conference of Mayors with San Francisco Mayor Ed Lee today.

Code for America built a site for other municipalities to pledge support. I’d also love to see support in other local restaurant review services from companies like Foursquare, Google, Microsoft, and Yahoo!  This is, as Chris Anderson of TED likes to say, “an idea worth spreading.”

But this line has never seemed more prescient than today, in the new wave of software that blends mobile devices in the hands of more than one person, big data back ends, and a profound re-imagination of services, business processes, and interfaces. Yesterday’s announcement that 7,000 Starbucks locations now accept Square Wallet drives home just how much technology is changing the game for business. It isn’t just the web, big data, or even mobile, it’s the combination of them all into new systems of interaction between companies and their customers.

If you’ve never experienced the magic of walking into a coffee shop, having the cashier glance down at their iPad-based Square Register to verify your face and payment credentials already provided by your phone’s automatic check-in, and buying your coffee simply by confirming your name, you haven’t yet tasted the future.

Square Wallet and Square Register aren’t just mobile applications, they are a profound rethinking of the entire business process of buying something at a retail location. They combine not just one but two mobile applications, a cloud-based data backend with payment information, identity, and perhaps even your purchase preferences at a merchant you frequent, location-based check-in, and more, all woven into a seamless experience. Software above the level of a single device. Retail will never be the same again.

The Apple Store has got a lot of the same magic. Gone is the cash register. Clerks instead wander the store, offering advice, and, when you’re ready to buy, they hand you your product, and offer to email you your receipt. Your name and credit card are already on file. You and the sales clerk are already part of the system. Software above the level of a single device.

Or consider Uber. You look on your phone. The nearest car is three minutes away. You choose the car and driver you want – perhaps based on proximity, but perhaps on the basis of user ratings of the driver. When the driver is outside, you receive a text message. When you arrive at your destination, you simply thank the driver and step out. Payment information is already on file. Software above the level of a single device. Magic.

This is only the beginning of a great rewiring of every aspect of business processes and interactions. The web was never just about content, but always about building the infrastructure for a kind of internet operating system. The first apps on that operating system were thinly upgraded versions of what went before, but the true native apps are starting to arrive. Software above the level of a single device. Magic.

Unfortunately, much of the data that will enable an entrepreneurial explosion is still locked up — in paper records, in proprietary data formats, and by well-intentioned but conflicting privacy regulations.

We’re making progress towards open data in healthcare, but there are still so many obstacles!  Ann Waldo recently introduced me to one of these.

A 2009 law modernized patient access rights by allowing individuals to get copies of their medical records in electronic format. Unfortunately, however, these patients’ access rights surprisingly do not include lab test results – one of the types of medical records that people are most likely to find urgent and useful. Due to the interaction of HIPAA (the Federal medical privacy law), CLIA (a Federal laboratory regulatory law), and state laws, patients can only get direct access to their their test results from labs in a handful of states.

A recent New York Times story highlighted just how much pain and suffering can be caused by this inability to get access to your own lab results.

In 2011, the Department of Health and Human Services put forward a proposed Rule that would give patients the right to get their test results directly from laboratories. This Rule is still waiting to be finalized. In hopes of breaking the logjam, O’Reilly Media and a variety of other players have written a consensus letter that voices our whole-hearted support for that proposed Rule and encourages the Federal government to finalize it promptly.

We’d love to invite you to join us in signing this letter.

Patients’ rights should include direct access to their lab results, just like all their other medical records!

“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 began the transformation of a business in which advertisers paid for ad impressions into one in which they pay for results. “Cost per thousand impressions” (CPM) was outperformed by “cost per click” (CPC), and a new industry was born. It’s important to understand why CPC outperformed 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,  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 always 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 enough 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 health care 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 only 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 exhaust,” 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 number 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 articles and 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.

I’ve been thinking, though, that it may be useful to frame the notion of cyberwarfare far more broadly.  What if we thought of JP Morgan’s recent trading losses not simply as a “bad bet” but as the outcome of a cyberwar between JP Morgan and hedge funds?  More importantly, what if we thought of the Euro’s current troubles in part as the result of a cyberwar between the financial industry and the EU?

When two nations with differing goals attack each other, we call it warfare.  But when financial firms attack each other, or the financial industry attacks the economy of nations, we tell ourselves that it’s “the efficient market” at work.  In fact the Eurozone crisis is  a tooth-and-claw battle between central bankers and firms seeking  profit for themselves despite damage to the livelihoods of millions.

When I see headlines like “Merkel says Euro Rescue Funds Needed Against Speculators” or “Speculators Attacking the Euro” or “Banksters Take Us to the Brink” it’s pretty clear to me that we need to stop thinking of the self-interested choices made by financial firms as “just how it is,” and to think of them instead as hostile activities.  And these activities are largely carried out by software trading bots, making them, essentially, a cyberwar between profiteers and national economies (i.e. the rest of us).

I know that the reality is more complex than those statements might suggest, but reality always is. Financial firms have legitimate interests in profit seeking, and sometimes the discipline of the market is just what national economies need. But when does it go too far? The US and Israel had legitimate security interests in undermining the Iranian nuclear program too.  That doesn’t mean that we didn’t call Stuxnet an act of cyberwarfare.

One of the things we try to do on the Radar blog is to frame things in a way that help people to see the future more broadly.  I do believe that one of the major long-term trends that we need to include in our thinking is that foreign policy (including the possibility of cyberwarfare) is no longer just between nations, but between nations and individuals (whether the collective activists of popular revolutions or the terrorist as the oft-discussed “violent non-state actor“), between nations and big companies, and between companies and industries.

And just as we expect nations not to act out of untrammeled self-interest lest the world go to hell in a handbasket, I think it’s reasonable to ask financial firms to show self-restraint as well. Either that, or expect that at some point, nations may decide to fight back with more than their central banks.

I’ve been thinking for some time how the web is “legacy” software, and that so many media companies just getting on the web are already behind the curve. This tweet says it all.

Or almost all. Because of course, the web isn’t just one thing. At Vidcon a couple of weeks ago, I was struck by the remark of one panelist, a young YouTube video star, advising his peers to get a Facebook page as well as their YouTube channel: “The audience there is older, and more male, but it’s still worthwhile….”

So even though YouTube is older than Facebook, the audience is younger…

It’s a wonderful, evolving media landscape these days. So much change, so much opportunity!

In my recent review of “John Carter,” I damned the movie for failing to be true to the book, taking liberties with the story and with the character of John Carter himself. Yet when watching “Captain America” on the plane the other day, I found myself completely satisfied despite that fact that it too was unfaithful to the original in many ways.

I asked myself why, and concluded that the answer is central to understanding O’Reilly’s brand marketing, and by extension the authenticity that is at the heart of all great brands.

For me as a young reader, the appeal of “Captain America” (as with “Spider-Man” and other Marvel comics) was the notion that a nerd, a kid who wasn’t good at sports and was scorned by popular society, could be transformed into a hero. His smarts, his will, his character were what mattered — all that was required was a chance spark that would transform him into who he really was inside.

The movie version of “Captain America” is completely true to this fantasy. The character of Steve Rogers is so right that I was willing to forgive the many changes to the story (e.g. that Bucky was not his young sidekick but his pre-transformation protector and military buddy), improbabilities such as that the notion of riding a zipline from a mountain down onto the roof of a fast-moving train begs the question of just how they strung that zipline. (I’ve done it, and it’s non-trivial, and gets harder the longer the line.) These are the kinds of errors that I found offensive in “John Carter” but didn’t mind at all in “Captain America.” I found myself moved by scenes in the movie that demonstrated Steve Rogers’ courage, his indomitable will, his loyalty to friends — hell, his nobility. Exactly what Andrew Stanton took away from “John Carter”!

This was equally true in the second installment of “Sherlock Holmes,” which I likewise saw on a plane last week. It takes even more liberties with Conan Doyle’s original stories than “John Carter” took with Burroughs. Yet once again I consumed it with relish! Why? Because the character of Holmes was so true — his incredible ability to observe tiny details, to think ahead, his remarkable strength (which features in only a few of the stories, but is there nonetheless), his flawed character. And even though the character of Watson was nothing like the Watson of Doyle’s stories, I forgave the director, because he made Watson better, not worse than the original.

This notion of understanding the essence of what matters about a book, a story, a character, also applies to business.

I think about the common thread that runs through all the books we created at O’Reilly — however different they might be. Consider the range of treatment shown by books as diverse as “Linux in a Nutshell,” “Programming Perl,” “Unix Power Tools,” “The Perl Cookbook,” “Head First Java,” “Mac OS X: The Missing Manual,” or “Make: Electronics.” From the point of view of external details, each of these books was a radical departure from what had gone before, and therefore a potential opportunity to confuse customers and dilute the brand.

Yet these books have a common essence: a practical bent, respect for the intelligence of the reader, a clear path to what you need to know, the authentic voice of experience, a willingness to take risks with new tools and new ideas that have been taken up by people on the cutting edge. When they stray from these core features, our books fail.

O’Reilly conferences display the same brand essence. In their deepest core, an O’Reilly book and an O’Reilly technical conference have more in common than a technical book from O’Reilly and those from some of our competitors. Like many of our pioneering books, our most successful new conferences were launched because we thought they were needed, not because we necessarily knew how successful they’d be. We weren’t chasing dollars; we were trying to help the early adopter communities who are our core customers to change the world.

(Of course, it also helped that we created “brand affordances” whenever we introduced a new type of book. I remember in the old days hearing that competitors would cheer every time we put out a new book without an animal on the cover. They thought we were throwing away our brand advantage. Little did they know that we were preserving it. Over time, we created a house of powerful brands with a common core but with clearly visible differences and distinct audiences.)

This brand essence is also true in our advocacy. We stand up for issues that matter in our industry. We tackle big problems that we don’t yet know how to solve, and try to grow markets in ways that benefit others besides ourselves.

Hmmm. Maybe that’s why I hated “John Carter” but loved “Captain America” and “Sherlock Holmes.” Andrew Stanton’s John Carter was a self-absorbed adventurer, a reluctant hero and an anti-romantic, not the noble figure I remembered from my childhood.

There’s a way in which the O’Reilly brand essence is ultimately a story about the hacker as hero, the kid who is playing with technology because he loves it, but one day falls into a situation where he or she is called on to go forth and change the world. Our editors and conference chairs, our authors and our conference presenters, are drawn from the ranks of our customers, and like all true nerds, we have a secret hunger to be heroes.

See comments and join the conversation about this topic at Google+.

Related:

• The stories behind a few O’Reilly “classics”

The idea is to make data from the vaults of HHS (and other sources) available in electronic, machine-readable, downloadable, easily accessible form, and promote its availability to entrepreneurs and innovators (via meetups, challenges, and codeathons) who can turn it into all kinds of applications and services that can help improve health and create jobs at the same time.

This is a great example of what I’ve called “Government as a platform,” in which government provides key capabilities that are then expanded upon by the private sector for delivery to the public in a variety of ways. We’re all familiar with this model for weather data. The National Oceanic and Atmospheric Administration (NOAA) launches weather satellites, tracking buoys, and all kinds of other tools for measuring weather, and releases the data to be delivered by commercial companies via television, web applications, and smartphones. The goal of the Health Data Initiative is to be the NOAA of health data.

Machine-readable data resources available from HHS include health care provider quality information, health and human service provider directories, community health performance statistics, the latest and greatest medical knowledge from the National Library of Medicine, Medicare claims data in a variety of forms, and much more.

Many companies have built new or upgraded products and services using this data that are already helping millions of Americans.

Each year, the initiative hosts a conference (the “Health Datapalooza“) in June to feature the latest and best open health data applications. The deadline for applications for this year’s event is March 30. There’s more information here.

Related:

• Big data is the next big thing in health IT
• Building the health information infrastructure for the modern epatient
• Challenges aired at Health Data Initiative Forum
• See more health IT and health data coverage