I was at MDRevolution’s La Jolla, Calif., office about a month ago, sitting in on a consultation as a patient huffed away on a treadmill. A staff member hovered nearby, monitoring the patient’s heart rate and pushing her to keep up the pace. As the staff member took note after note on the patient’s performance and tapped away at a calculator and keyboard to analyze the results, I felt like I was in a research lab, not a doctor’s office — and a lab modeled after a gleaming Apple store.

According to its founder, cardiologist Samir Damani, that’s the point. The office — with its sleek, spa-like aesthetic and shelves of connected devices — is a showcase for a data-driven future of medicine that puts technology at the center of the patient experience and moves actual doctors into managerial, less visible roles.

As the era of the more-affordable “$1,000 genome” draws closer, the phrase “personalized medicine” is popping up all over the place. But for the most part, matching medical treatments with a patient’s genetic profile is an option for only the wealthiest Americans.

With his patients, Damani is trying to create customized health care that is more accessible and affordable — and he’s doing it by blending cardiology, nutrition science and genetics with emerging mobile technology. But that’s just the first step: He believes he can build a big business by using the data he’s gathering and the algorithms he’s creating to design software that will allow employers and hospitals around the country to replicate his approach.

“I got tired of people saying this is what it’s going to be like. I said, ‘I’m a 37-year-old cardiologist, I need to know what it’s going to be like today,’” said Damani, a slight, well-dressed man with a seemingly boundless memory for medical literature.

So, in 2011, he rustled up $1.6 million in angel funding from several local MDs, Ph.Ds and other supporters (no traditional “vulture capitalists” allowed). He hired an IT guy, an office manager, a metabolic specialist, nutritionist and medical assistant and, about a year later, opened up his doors — all while keeping an active cardiology practice at a local San Diego clinic.

A new medical specialty?

Since launching in February 2012, MDRevolution has worked with about 250 patients; about a quarter of them have a chronic condition, and the balance are people who are generally in good health and who are willing to take that extra step to stay that way (patients continue to see their regular primary care doctor in addition to Damani). He says that each of the last 60 patients has seen statistically significant changes in every health marker analyzed, including weight, body mass index, metabolism and visceral fat (the notoriously hard-to-lose fat that accumulates around organs).

Damani’s model? A new patient starts with a spin through a lab to determine their resting metabolic rates, visceral fat levels and other fitness indicators. They also get a genetic assessment that tells MDRevolution whether they’re a slow processor of caffeine, whether they’re genetically inclined to overeat, and whether they have any other nutrition and fitness-related predispositions. From there, the practice uses proprietary algorithms to craft personalized health plans that include guidance on things like how high and how often to push their heart rates while exercising, what kinds of food to eat, and the types of foods to avoid.

As patients follow the program, fitness trackers like Fitbits (see disclosure), wireless Withings scales and heart monitors report progress back to MDRevolution, while a website enables specialists to give encouragement and direction online.  For the genetic assessments, the company partners with Pathway Genomics and 23andMe, and it uses Qualcomm’s 2Net platform to integrate all of its technology. For the 10 percent of patients who live outside the area (and even for some who live close by), Damani conducts virtual visits via Skype.

The financial model

Just about every week, a new activity tracker, personal genome service, iPhone-based medical device or online patient program hits the market. But for the most part, those tools and services exist in isolation. MDRevolution is a first stab at trying to show how mobile health tracking tools, genomic assessments and personalized coaching can work together to show real results.

The fact that the company is based in sunny Southern California is no accident. Between wireless health leader Qualcomm Life, genetics company Pathway Genomics, The Scripps Research Institute and plenty of other National Institutes of Health-funded research institution, San Diego is a hotbed for health innovation and research. Scripps is also the academic home of Eric Topol, a longtime cardiologist and researcher as well as one of the most influential voices in digital health. Before launching MDRevolution, Damani published several peer-reviewed articles with Topol, who he said has been a mentor.

As health reform pushes medicine to a model that rewards doctors based on how well they keep patients healthy, not just the procedures they perform, more doctors are turning to concierge-style practices in which patients pay an annual retainer for a higher level of care. One Medical Group, which has offices in five cities, lets patients book appointments online and renew prescriptions as well as email with doctors. CarenaMD, in Seattle, offers patients 24/7 virtual doctor visits via webcam.

MDRevolution says its model is closer to a gym membership. Patients pay between $25 and $75 per month (depending on the level of service and attention needed) for access to the clinical lab and personnel, as well as its web-based service. The practice also takes most insurance plans, so each time patients visit the office, they’re also charged a co-pay.

Brad Lally, a 46-year-old San Diego executive with an outdoor sports company, said he decided to see Damani in 2011 after a brief episode of cardiac arrhythmia. He wanted to try treating his heart condition without drugs, and also wanted to get rid of his belly fat. In addition to putting more proteins and vegetables in his diet, MDRevolution told him to do metabolic interval training workouts twice a week to push his heart rate into the anaerobic zone. Since genetic testing revealed that he was a slow caffeine metabolizer, Damani’s team told him to stay away from evening cups of coffee.

For the next 12 months, he reported his workouts and diet to MDRevolution — even on frequent overseas business trips his blood pressure cuff sent back data — and received nearly bi-weekly feedback from staff. After three months on the program, he says he lost 10 percent of his visceral fat and increased his resting metabolic rate and VO2 level — two measures that Damani believes are more predictive of heart health than cholesterol — by more than 10 percent. It’s been a year since he finished the program, and he said he hasn’t had any heart irregularities. Even when he slips, he said, he knows how to quickly correct his diet and exercise plan.

“It’s empowering to know what’s going on,” Lally said. “And the level of interaction you get — it’s really good. It’s nothing like what you get with your regular primary care doctor because they’re so busy seeing patients.”

The real ‘billion-dollar’ business

As MDRevolution tries to help patients reach new levels of fitness and heart health, it is building a robust data set to support what Damani said could be the company’s real ‘billion-dollar’ business idea: a patient-engagement platform.

RevUp, which MDRevolution uses internally for its 250 patients, is a web-based dashboard that aggregates all of a patient’s information, from fitness trackers and other devices to genetic and other health data. It provides each patient with a personalized health plan, including custom fitness and nutrition guidelines depending on their needs and goals. Physicians and other health experts can use it to track patient progress and send updates and guidance. Corporate and health system administrators are able to see what employees are doing in aggregate but not an individuals’ specific information.

According to a recent study from human resources company Aon Hewitt, the average employer spends about 40 percent more on health care now than it did six years ago. Another report found that poor health costs the U.S. economy $576 billion annually, with nearly 40 percent of that due to employee absenteeism or low productivity.

To address those issues, companies like Keas, Healthrageous and ShapeUp pitch employers on corporate wellness programs that integrate with digital devices to keep employees active, healthy and out of the doctor’s office. Damani argues that RevUp will have an edge in this market because its program is backed by clinical results.

He says the data that he’s compiling with just 250 patients is already leading to fresh insights. For example, he said, people can increase their heart and lung capacity independent of age, and women increase oxygen consumption (an indicator of fitness) slower than men. When the company reaches 1,000 patients, he believes, their research could be used to influence public debate about how to maintain health in the population at large.

“Our competitive advantage is that we have a lab driving the software. We’re always going to be creating software based on needs for the practice and outcomes,” Damani says. He says that while other corporate wellness programs tend to only focus on basic health markers like cholesterol and body mass index, the indicators underpinning his program (resting metabolic rates, visceral fat levels and oxygen consumption) will prove to be the key to preventative medicine.

Can MDRevolution compete against tech companies?

The ultimate plan is for the dashboard to serve as a vehicle for gathering even more data, he said; creating a repository that could be a licensable asset in itself. It could also inform the development of future products or make MDRevolution a valuable partner for companies developing medical devices or conducting clinical trials, he said.

With a doctorate, a master’s degree and a medical degree, Damani is clearly not one to shy away from a new challenge. But in turning MDRevolution into a technology company, he’s moving into an uncertain and increasingly competitive terrain. While Damani has led the company so far — he said it has already signed up four corporate customers, a mix of companies and hospital systems, and should be profitable by next year — running a doctor’s office arguably requires a different kind of skill set than building a technology company. The company now employs 15 people, but only the COO, CTO and a new database manager have technical backgrounds. Six contractors work full-time on its software development, and Damani said he plans to bring several programmers in-house in the next year.

Damani may find that the market for his data isn’t what he anticipated. Abhas Gupta, a partner at venture firm Mohr Davidow Partners who focuses on digital health, said that he’s seen promising health startups that have amassed strong and unique datasets but that haven’t been able to generate revenue like they expected. “Who do you sell that data to?” he asked. “There may not be individuals ready to do something with that data.”

The new role for doctors in a tech-driven world

One thing patients at MDRevolution don’t see much of is a doctor. The practice is still small enough that patients know Damani oversees the entire operation but he has delegated much of the day-to-day interacting to patients to metabolic experts and nurse practitioners. Even though the office sees patients five days a week, his face time with them is just five to eight hours weekly. He says MDRevolutions in other locations could operate with barely any doctor oversight at all. (To bill as a doctor’s office, they will need to be led by doctor, but day-to-day operations could be entirely run by a nurse practitioner, he said.)

It makes you wonder if Vinod Khosla’s controversial prediction that technology will replace 80 percent of medical experts is coming true. Damani said his vision isn’t one that minimizes doctors (although the impending doctor shortage means the country will have to make do with fewer doctors, relatively speaking) or one where doctors play CEO and tech entrepreneur. But it is a world where physicians will have to adapt.

“Physicians are going to have to be in a management role as opposed to being the primary person seeing [patients],” he said. “Most physicians out there are unhappy. Because cost-containment pressures are so great, they’ve become assembly-line type physicians who see as many patients as possible in as little time as possible. I want to offer those doctors a new process.”

Disclosure: Fitbit is backed by True Ventures, a venture capital firm that is an investor in the parent company of this blog, Giga Omni Media. Om Malik, founder of Giga Omni Media, is also a venture partner at True.

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Overall, health technology startups received $120 million from investors in March, a 12 percent increase from the same period last year. Deal volume nearly tripled, from 13 deals last March to 36 this year.

Here’s an at-a-glance look at activity last month:

• Although practice management was the dominant sector by funding amount last month (largely because of One Medical Group’s Series F round), sensor technology was a bigger winner in terms of deal volume.  In addition to the nearly $12 million raised by health-tracking wristwatch startup Basis, companies including Rock Health-backed Podimetrics, which makes an intelligent floor mat for helping diabetic patients detect foot ulcers, and Sensiotec, which develops technology that monitors heart and respiration rates without any direct patient contact, added new funding.
• Personalized medicine got a boost last month. Five startups – from those that speed up the analysis of DNA sequence data to those that give chronic disease patients in-depth reports and analysis on their personal condition – raised funding, including Spiral Genetics, Bina Technologies and MetaMed.
• Last month, Nike announced the 10 companies participating in the first class of its TechStars-powered accelerator for fitness-related startups. This points to a growing trend of strategic investors attempting to drive innovation around specific themes. Earlier this year, GE announced a partnership with Startup Health to invest in consumer health startups. “I think we’re going to start to see strategic partners do more of these types of programs in the future,” said Unity Stoakes, president of Startup Health.

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Have access, will innovate

Wired published an interesting profile Tuesday morning that exemplifies what’s possible when smart people have access to good data. The piece showcases the work of a man named Fred Trotter who has accessed reams of buried Medicare data via a Freedom of Information Act request and is uncovering some potentially valuable information. Already, the article explains, he has built a “Doctor Social Graph” by analyzing some “60 million relationships between doctors, and how often they refer patients to one another.” His next mission is to build a doctor rating system based on data he’s uncovered about credentials, nursing home inspections and other relevant info.

Elsewhere, companies such as Palo Alto, Calif., startup Apixio are trying to make hospitals more efficient by using semantic analysis to connect the dots between patient charts, electronic medical records, billing data and whatever other sources of information that hospitals generate. (We covered Apixio in early 2011, although the company has significantly expanded its services since then.) In health care, everyone seems to have their own way of doing things, as Apixio natural-language-processing scientist Vishnu Vyas told me recently, so “the variety of the data becomes as important as the volume of the data.”

Linda Drumright, GM of the Clinical Trial Optimization Solutions group at IMS Health, agreed. She explained that her company is able to do its job because it has access to mountains of data from pharmacies, insurance claims, medical records, partners and other sources. All told, it houses 17 petabytes of data spread across 5,000 databases. Her division’s clients, which generally include pharmaceutical and biotech companies running patient trials, need all this data in order to ensure their trials will actually be successful.

One recent customer wasn’t able to recruit test subjects fast enough, she noted, and IMS helped it comb through its criteria about who to or not to include in the trial only to find “that the patient population they were looking for didn’t exist.” As IMS went back and began eliminating criteria and iterating design, it realized that trial never should have begun in the first place.

There are a million ways to think about how to use this data, Drumright said, and as more customers begin to fully understand what they can do with it, her goal is to “make this information accessible in a way where it’s easy at the point where it’s needed, and consumable where it’s needed.”

The key to curing cancer might be more data

But whatever Trotter, Apixio, IMS and others accomplish will have been made possible because they have access to some valuable datasets, albeit not always with great ease. Many individuals who’d like to improve the health care system — if not our health, generally — aren’t so lucky. Take, for example, the world’s genetic researchers. It’s very possible the data they need to discover the medical Holy Grail of a cure for cancer is locked in gene sequence data that only very few people will ever see.

David Haussler

According to University of California, Santa Cruz researcher David Haussler, the limited access that many geneticists and computer scientists like himself have to valuable genetic data is “a crime.”

“We are on the brink of a real new understanding of cancer by being able to sequence cancer genomes,” he told me during a recent interview, but big data will be the key to unlocking it.

There are 1.6 million cases of cancer in the United States every year, Haussler explained, and most of the information from those tumors is being ignored. This is partially because of privacy restrictions about who can access personal medical data and for what purposes, and partially because there isn’t yet a concerted effort to collect the necessary genetic samples. As genome sequencing gets faster and cheaper, he says researchers need access to healthy and cancerous samples from the same person — and as many of these samples as possible — in order to analyze the “astounding” number of molecular changes that occur in every type and variation of cancer.

“We can’t completely understand what we’ll find, but we know we the only way we’ll pull out signal from the noise is to [analyze all these genes],” Haussler said.

Haussler understands the need for privacy regulations, but thinks there’s an opportunity to at least ease some current restrictions on how researchers access data. Even when there are relatively large (if not ideal) datasets available such as with the Cancer Genome Atlas project, researchers must apply to the National Institutes of Health for access, and the data must always remain behind an organizational firewall. Every cancer patient in the country could agree to having their data available to researchers, he said, but as long as that data isn’t accessible over the internet it’s only of limited utility.

He — along with others in the field — thinks cloud computing could be the solution because it gives genetic researchers a central location where they can access and perform computations on the data. Haussler and his team that house the Cancer Genome Atlas and a couple other projects currently have more than 400 terabytes of data and expect to have around 5 petabytes of data eventually. Downloading that is infeasible save for access to high-speed research networks, so “we need a place where people can experiment with these big data problems,” Haussler said.

In the meantime, Haussler and his peers will keep on collecting and accessing genome data however they can. And they’ll keep building software packages and algorithms that analyze that data better and faster than ever before. However, he lamented, “If we had the big data out there in an unrestricted setting, then all the best minds in the world would already be crunching on it.”

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It’s a noble goal, but also a necessary one. Big data does have the potential to change our lives, but to get there it’s going to take more than startups created to feed us better advertisements.

Consumer data is easy to get, and profitable

It’s not fair to call the current state of big data problematic, but it is largely focused on profit-centric technologies and techniques. That’s because as companies — especially those in the web world — realized the value they could derive from advanced data analytics, they began investing huge amounts of money in developing cutting-edge techniques for doing so. For the first time in a long time, industry is now leading the academic and scientific research communities when it comes to technological advances.

As Brenda Dietrich, IBM Fellow and vice president for business analytics for IBM Software (and former VP of IBM’s mathematical sciences division), explained to me, universities are still doing good research, but students are leaving to work at companies like Google and Facebook as soon as their graduate or Ph.D. studies are complete, often times beforehand. Research begun in universities is continued in commercial settings, generally with commercial interests guiding its direction.

And this commercial focus isn’t ideal for everyone. For example, Sultan Meghji, vice president of product strategy at Appistry, told me that many of his company’s government- and intelligence-sector customers aren’t getting what they expected out of Hadoop, and they’re looking for alternative platforms. Hadoop might well be the platform of choice for large web and commercial applications — indeed, it’s where most of those companies’ big data investments are going — but it has its limitations.

Enter federal dollars for big data

However, as John Holdren, assistant to the president and director of White House Office of Science and Technology Policy, noted during a White House press conference on Thursday afternoon, the Obama administration realized several months ago that it was seriously under-investing in big data as a strategic differentiator for the United States. He was followed by leaders from six government agencies explaining how they intend to invest their considerable resources to remedy this under-investment. That means everything from the Department of Defense, DARPA and the Department of Energy developing new techniques for storage and management, to the U.S. Geological Survey and the National Science Foundation using big data to change the way we research everything from climate science to educational techniques.

How’s it going to do all this, apart from agencies simply ramping up their own efforts? Doling out money to researchers. As Zach Lemnios, Assistant Secretary of Defense for Research & Engineering for the Department of Defense, put it, “We need your ideas.”

IBM’s Deitrich thinks increased availability of government grants can play a major role in keeping researchers in academic and scientific settings rather than bolting for big companies and big paychecks. Grants can help steer research away from targeted advertising and toward areas that will “be good … for mankind at large,” she said.

The 1,000 Genomes Project data is now freely available to researchers on Amazon's cloud.

Additionally, she said, academic researchers have been somewhat limited in what they can do because they haven’t always had easy access to meaningful data sets. With the government now pushing to open its own data sets, and as well as for collaborative research among different scientific disciplines, she thinks there’s a real opportunity for researchers to do conduct better experiments.

During the press conference, Department of Energy Office of Science Director William Brinkman expressed his agency’s need for better personnel to program its fleet of supercomputers. “Our challenge is not high-performance computing,” he said, “it’s high-performance people.” As my colleague Stacey Higginbotham has noted in the past, the ranks of Silicon Valley companies are deep with people who might be able to bring their parallel-programming prowess to supercomputing centers if the right incentives were in place.

Self-learning systems, a storage revolution and a cure for cancer?

As anyone who follows the history of technology knows, government agencies have been responsible for a large percentage of innovation over the past half century, taking credit for no less than the Internet itself. “You can track every interesting technology in the last 25 years to government spending over the past 50 years,” Appistry’s Meghji said.

Now, the government wants to turn its brainpower and money to big data. As part of its new, roughly $100-million XDATA program, DARPA Deputy Director Kaigham “Ken” Gabriel said his agency “seek[s] the equivalent of radar and overhead imagery for big data” so it can locate a single byte among an ocean of data. The DOE’s Brinkman talked about the importance of being able to store and visualize the staggering amounts of data generated daily by supercomputers, or by the second from CERN’s Large Hadron Collider.

IBM’s Dietrich also has an idea for how DARPA and the DOE might spend their big data allocations. “When one is doing certain types of analytics,” she explained, “you’re not looking at single threads of data, you tend to be pulling in multiple threads.” This makes previous storage technologies designed to make the most-accessed data the easiest to access somewhat obsolete. Instead, she said, researchers should be looking into how to store data in a manner that takes into account the other data sets typically accessed and analyzed along with any given set. “To my knowledge,” she said, “no one is looking seriously at that.”

Not surprisingly given his company’s large focus on genetic analysis, Appistry’s Meghji is particularly excited about the government promising more money and resources in that field. For one, he said, the Chinese government’s Beijing Genomics Institute probably accounts for anywhere between 25 and 50 percent of the genetics innovation right now,  and “to see the U.S. compete directly with the Chinese government is very gratifying.”

But he’s also excited about the possibility of seeing big data turned to areas in genetics other than cancer research — which is presently a very popular pastime — and generally toward advances in real-time data processing. He said the DoD and intelligence agencies are typically two to four years ahead of the rest of the world in terms of big data, and increased spending across government and science will help everyone else catch up. “It’s all about not just reacting to things you see,” he said, “but being proactive.”

Indeed, the DoD has some seriously ambitious plans in place. Assistant Secretary Lemnios explained during the press conference how previous defense research has led to technologies such as IBM’s Watson system and Apple’s Siri that are becoming part of our everyday lives. Its latest quest: utilize big data techniques to create autonomous systems that can adapt to and act on new data inputs in real time, but that know enough to know when they need to invite human input on decision-making. Scary, but cool.

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1TB times 1 million equals …

How much data is anybody’s guess, but the exponential increases in productivity suggest it will be in the exabyte range within a few years. A fully sequenced human genome results in about 100GB of raw data, although DNAnexus Founder and CEO Andreas Sundquist told me that volume increases to about 1TB by the time the genome has been analyzed. He also says we’re on pace to have 1 million genomes sequenced within the next two years. If that holds true, there will be approximately 1 million terabytes (or 1,000 petabytes, or 1 exabyte) of genome data floating around by 2014.

A few years ago, Complete Genomics publicly announced its plan to sequence a million genomes by 2014, but it has been woefully behind schedule to this point. It was hoping to do 50,000 genomes in 2011, but finished the year at only 3,000.

Life's Benchtop Ion Proton Sequencer

However, sequencing instruments are evolving in a manner similar to mainstream computers, which is to say they’re always getting faster and more affordable. Whereas sequencers used to cost more than half a million dollars and take up a room, Life’s genome-in-a-day instrument, the one that claims a $1,000-per-genome price point, sits on a desk and will cost only $149,000 when it’s available later this year. Upgrading to Illumina’s new instrument from the previous model costs only $50,000.

The fast rate of improvement comes from genomics’ own version of Moore’s Law, Sundquist said: data throughput and cost both improve by tenfold every 18 months. When Life rival Illumina set a world record in February 2008, it took “less than four weeks at a cost of about $100,000.” At this rate, we’ll have $100 genome sequencing by 2014.

Sundquist added that medical systems have tens of thousands of patients queued up for sequencing, many of which they might start doing now that it can be done so fast and at such a low cost.

Hidden costs: ‘The quest for the $1,000 genome interpretation’

Where things get hairy for IT vendors is figuring out how to make it affordable to store, process and analyze all that data — something Sundquist calls the quest for the $1,000 genome interpretation. It’s still not an inexpensive proposition to buy and maintain a system capable of storing and processing potentially petabytes of data. And if doctors or researchers want to collaborate with colleagues, their facilities bandwidth likely won’t cut it for sending even the raw data for a single genome. That’s why many research institutions are connecting to high-speed research networks designed solely to move massive scientific data sets.

As Forbes’ Matthew Herper opined early last year, even though research costs for genomes will soon cost only $1,000, it costs a lot more to employ people and pay for software capable of analyzing it. Because research genomes aren’t accurate enough for medical use, they often must be sequenced multiple times. Herper’s ultimate analysis:

I’d think if we’re talking about actual medical use, $10,000 is a more accurate number. Certainly, it is not going to drop below the $2,000 level for a magnetic resonance imaging scan. And once the technology is in use, I think it is possible that the costs will go back up.

So, even if genome sequencing itself becomes less expensive, hospitals and patients will both be paying well more than $1,000 for the procedure. Presently, $10,000 is about the going rate from Complete Genomics to sequence, analyze and deliver research results to an individual, although the costs certainly are subject to change if hospitals start performing sequencing workloads themselves.

Cloud computing to the rescue?

Sundquist thinks cloud computing is the answer. His company, DNAnexus, provides a cloud-based platform for storing and analyzing genomics data, something we’ve covered before. “A 100-megabit connection could more than keep up with about a dozen of these machines,” he said, and once the data is in DNAnexus’s cloud platform, institutions no longer have to worry about keeping up with exploding data volumes, sending terabytes of data across the Internet or paying software licenses. Access is centralized and everything takes place on DNAnexus’s virtual infrastructure.

Additionally, cloud computing is ideal for spiky use cases, as is generally the case with genome sequencing.  A general rule of “cloudonomics” is that the cloud costs more on a per-unit basis, but generally will cost less over time unless it’s being used for a steady workload flow better suited to an on-premise system.

Whether it’s DNAnexus or some other cloud service, Sundquist’s reasoning is sound. As prices for gene sequencing continue to fall, doctors should be increasingly likely to do it, but they’ll be limited by the infrastructure in place to support them. Unless the costs of doing this on-premise come down significantly, the cloud might be the only place where storing and analyzing potentially petabytes per hospital isn’t such a daunting undertaking.

Feature image courtesy of Flickr user Robert Gaal.

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The switch has just received 510(k) clearance from the FDA, so Siemens is cleared to begin using it as soon as the system is in place. FDA 510(k) clearance is a series of requirements any new medial devices must meet, by law, before they can be implemented in practice. The clearance opens the door for other health science and service companies to make similar use of the Apple operating system.

Siemens lists increased patient data storage capacity, expandable patient data management capabilities, faster sequencing times and more customizable reporting functions as the reasons for the selection of OS X.

The Genotyping Kit and DNA Sequencing System the new iMacs will be supporting monitors HIV-1 resistance in patients, and resistance to different drugs. The information gathered is used to generate algorithms that allow oncologists to maintain an adaptive treatment routine, designed to minimize the potential negative effects of mutations which leave HIV resistant to specific medicines.

Macs are gaining ground in medical applications, especially when graphics are important. Radiology departments, specifically, are increasingly using a rewritten, FDA-approved version of an open-source program called OsiriX to produce high-quality x-ray imaging. The reason for the growing presence of OS X in radiology is its superior 3D visualization capabilities, and its cost relative to other high-end imaging software solutions.

Apple is accumulating more FDA approvals, greater industry presence, and getting buy-in from medical students in a wide range of fields. It might not be too long before you find an iMac waiting for you at your next regular check up, or before OS X saves your life…and not just in the usual “Vista was killing me!” way.

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