Tag Archives: Complex adaptive systems

“On Demand”

Note: This post is a continuation of prior posts on complex, adaptive systems. This post focusses on the virtual workplace, the virtual retailer, the virtual employer, and their myriad manifestations in today’s world. These particular complex, adaptive systems will have the ability to rapidly expand or contract based on demand. And this is the point of this post: to explore the notion of “on demand”.

“On Demand”
Its so obvious …. but, then again, its not so obvious: “on demand” is the drumbeat of daily life. But the 21st century is putting the notion of “on demand” on steroids!

What is “on demand”?
I want a glass of wine, right now. I either pour myself one, buy one, or ask someone else to pour me one. “On demand”.

I need a hotel room, right now. Hotels inventory rooms. I rent one. “On demand”.

I need to haircut, right now. Barbers are open for business. I visit one. They are not busy so they take me. I buy the haircut. “On demand”.

Note that “on demand” wine needs an open bottle of wine to be available; the hotel room requires a hotel; the haircut requires a barber open for business;

In the 21st century, it seems clear to me that “on demand” will morph into smaller, more flexible slices. Consumers and companies will be able to purchase these slices when they want them, for as long as they want them.

It’s happening at lightning speed! There are so many examples. You can find them everywhere, in:

On Demand Transportation

The point: in the 20th century, you had to rent a car or bike or ride for a day from a business location, and now you can rent it for exactly as long as you want it from a street location.

Uber revolutionized the taxi business when they broke the paradigm and said “Effectively immediately, and car with driver can pick up a passenger and get paid to take them somewhere.” From a passenger’s POV, the result is revolutionary: I can get anywhere I want, anytime I want, by simply alerting a central intelligence on-line that I need ride from x to y at z time.

Every major city now rents bikes. Grab a bike at one stand and leave it at another stand. Take the bike from x to y at z time.

ZipCars are on-demand cars.

On Demand Work

The point: in the 20th century, you either had a job or you didn’t. Now you can have a job for a half hour of your choosing. “Temporary Help” agencies filled any gaps – when the job-holder was unable to work.

LiveOps revolutionized call center management by organizing workers to be available when the client wants the worker, for as long as the client wants the worker. They keep workers trained and on–the-ready, so they can deploy them virtually as needed.

On Demand Work Space

The point: In the 20th century, you worked someplace and employers employed workers in workplaces. Today workplaces are built for flexibility, so many employers can do their work with employees when they need the workplace and how they need the workplace for as long as they need the workplace.

Metro Studios and others are replacing the Hollywood “studio” with a flexible studio. Studios in the past built spaces for their filming needs. Metro Studios works with any client that will rent their massive spaces – for as long as the client needs the space, and not longer. Note that the “Studio” is a big box, easily repurposed to a warehouse or distribution center or big box store if demand shifts.

“Co-working” is exploding, and has revolutionized office work. A co-working space can be sized up or down as demand requires, for as long as demand requires. Co-working can suit the individual virtual worker, who can come in as they wish and stay as long as they wish. But, importantly, more and more companies are using co-working facilities in order to have flex space that suits them.

Self-storage is exploding, giving companies and consumers the ability to get storage space when they want it, for as long as they want it.

On Demand Housing

The point: Hotels, long term rentals and short term rentals will have their place in tomorrow’s economy but ordinary people with extra space in their houses will make places available when, where, and however long they are needed.

AirB&B and VRBO have revolutionized the way we access temporary housing. Go on line, check out who’s offering what, and when, and then make your selection.

On Demand Entertainment

The point: entertainment was made available at a certain time, at a certain place (a concert venue, a movie theater, a movie channel, or a TV channel). No longer. Increasingly, consumers will get what they want, when they want it, for as long as they want it.

Netflix revolutionized on demand movies by letting consumer get what they want when they want it. They started with on-line movie rentals that requires physical shipping of CD’s, but rapidly moved to on-line downloads and streaming. Amazon is chasing them, but with amazing speed.

On Demand Tools

The point: In the 20th century, the norm was “if you want a tool, buy it and put it in a safe place until you need it. The norm is changing to “when you need a tool, order it up for as when you need it, for as long as you need it.”

Home Depot and Loews both have lucrative side businesses that allow businesses and consumers to rent the exact tool they need for as long as they need it.

Other examples of “On Demand”

On Demand Meals

On Demand Party Essentials

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References:

Co-Working

Virtual Workplace and Virtual Retailer

On-Demand Work Articles and Commentary
The New York Times article below refers to a mega-trend: on-demand work. The author refers to it as a “tectonic shift in how jobs are structured“.

The focus of the article is Liveops, but this is only illustrative of this larger trend. https://www.liveops.com. Their competitor is https://workingsolutions.com .

On their front page, LiveOps says: “It’s a highly skilled workforce of virtual agents who flex to meet customer needs.”

On-demand work is exploding in customer service call centers and sales.

Some points I found interesting:

Roughly 3,000,000 Americans find work this way – as independent contractors working on a virtual basis.

Since 2001, apparently the move to outsource call centers to India has reversed. Today, the focus is on quality, and so the new trend is toward employing American workers, on a contract basis, and on a virtual basis.

They are only paid while on the phone. This is roughly 75% of the time they “commit” (“commits” are made in half hour blocks)

Top performers get the first call. “Performance-Based Routing, so the top-performing agents on your programs get more calls. By aligning our agents’ incentives with your goals, each agent who answers the call will be invested in your business objectives. What’s more, you won’t be paying call centers for idle time.”

Clients hire LiveOp. In this article, TruStage Insurance is the client.

Liveops CEO is Greg Hanover. Their competitor, Working Solutions, was founded in 1996. LiveOps says they have 20,000 agents, which they refer to as “Liveops Nation”.

“We hand-pick our agents for their great phone voices and warm and friendly personalities.”

“Scalable and flexible contract center outsourcing – leveraging an on-demand distributed network.”

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CREDIT: https://www.nytimes.com/2017/11/11/business/economy/call-center-gig-workers.html?smid=nytcore-ipad-share&smprod=nytcore-ipad
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Plugging Into the Gig Economy, From Home With a Headset

A company called Liveops has become the
Uber of call centers by doting on its agents.
But is the work liberating, or dehumanizing?
By NOAM SCHEIBERNOV. 11, 2017

DURHAM, N.C. — The gathering in a private dining room at a Mexican restaurant had the fervent energy of a megachurch service, or maybe an above-average “Oprah” episode — a mix of revival-style confession and extravagant empathy. There were souls to be won.

“By the end of the day, Kelly’s going to be an agent,” the group’s square-jawed leader said. “Kelly went through the process a while ago, then life happens, now she’s back. Her commitment to me that she made earlier, she looked me right in the eyes and told me she’s going to be an agent.”
Paradise, for these pilgrims, lies at one end of a phone line.

The company behind this spectacle, Liveops, had invited several dozen freelance call-center agents to a so-called road show. Some of them may have answered your customer-service calls to Home Depot or AAA. All were among the more than 100,000 agents who work as independent contractors through on-demand platforms like the one Liveops operates, which uses big data, algorithms and gamelike techniques to match its agents to clients. What Uber is to cars, Liveops is to call centers.

The agents are part of a tectonic shift in how jobs are structured. More companies are pushing work onto freelancers, temps, contractors and franchisees in the quest for an ever more nimble profit-making machine. It is one reason a job category seemingly headed offshore forever — customer service representatives — has been thriving in call centers and home offices across the United States, supporting roughly three million workers.

While critics of the arrangement cite rising insecurity, some of Liveops’ star agents — like Emmett Jones in Chicago, who knows of his rivals primarily as numbers on a leader board — say the opportunity has been transformative.

The earnest gratitude of the agents assembled here, not far from the Raleigh-Durham Airport, affirmed that. To them, Liveops is a sustaining force, a way to earn a living while being present at home. A few had driven hours to attend. Many brought friends and family members who were considering joining “Liveops Nation,” too.

There were icebreakers (“Liveops Nation Bingo”). Gift-card raffles (“$150?” the chief executive quipped. “Who approved these things?”). Free enchiladas. Everyone was invited to schmooze.
“John, I heard your story about how you got to us is pretty great,” said the master of ceremonies, an impossibly sunny woman named Tara. “Would you mind telling all these people?”
When the mic came to John, a former insurance claims adjuster with a gray beard and several earrings, there was a sense of imminent revelation.

“I was working in another glass box over near here for six years,” he began. “I reached the point where it was either jump off the roof or walk out the front door.” The other agents laughed knowingly.

He continued: “My commute now is I walk down the hall, close the bedroom door behind me.” More laughter.
Then John’s voice softened: “This is good, this is good. I get paid for when I’m working, instead of souring when you get paid for 40 hours and work some more. So, I’m here.”
“Awesome,” Tara said, applause drowning her out. “I feel like John’s story mimics a lot of what we hear from people.”
According to Greg Hanover, a longtime Liveops official who became chief executive this summer, the company’s goal is to make agents feel as if they’re part of a movement, not just earning a wage.

“Where we want to be with this is what Mary Kay has done, multilevel marketing companies,” Mr. Hanover said, referring to the cosmetics distributor and its independent sales force. “The direction we need to head in for the community within Liveops Nation is that the agents are so happy, so satisfied with the purpose and meaning there, that they’re telling their story.”

It’s an ambition that feels almost radical compared with Uber, whose best-known exercise in worker outreach is a video of its former chief executive berating a driver. It was heartening to discover that on-demand work could be both financially viable and emotionally fulfilling.

That is, until I began to speak with Mr. Jones and some of his Liveops competitors. The more you talk with them, the more you detect a kind of Darwinian struggle behind the facade of community and self-actualization. You start to wonder: Is there really such a thing as a righteous gig-economy job, even if the company is as apparently well intentioned as Liveops? Or is there something about the nature of gig work that’s inescapably dehumanizing?

Just the Right Tone
Mr. Jones, who lives in Chicago, was the top rated Liveops agent for an insurer called TruStage for much of this year.
An AT&T technician for decades, he decided that he needed to be at home not long after his wife was diagnosed with vertigo in 2008. “I can’t work and be worried about how she’s doing,” he said.

A few years later, when his daughter told him of a friend who worked with Liveops, he was eager to sign up — but refused to send in his required voice test until it was close to perfect. “I must have did the voice test four or five times,” he said. “I wanted to make sure I gave the right tone that they were looking for.”

As a Liveops agent, Mr. Jones sells life policies to callers, often those who have just seen a television commercial for TruStage insurance. He estimates that he works roughly 40 hours each week, beginning around 8 most mornings, and that he makes about $20 an hour. He is such a valued worker that TruStage invited him to its headquarters earlier this year for a two-day visit by an elite group of agents, in which executives pumped them for insights about how to increase sales.

Roughly two decades ago, Liveops and its competitors typically connected callers to psychic hotlines, and in some cases less reputable services. Such businesses had frequent spikes in call volume, making it helpful to have an on-demand work force that could be abruptly ramped up.

“The only thing people were interested in was the abandonment rate” — that is, the number of people who would hang up in frustration from being kept on hold — said Kim Houlne, the chief executive of a Liveops rival called Working Solutions, which she founded in 1996.

The call center industry took a hit during the 2001 recession, when cost consciousness unleashed a wave of outsourcing to India. But within 10 years, many companies decided that the practice, known as offshoring, had been oversold. The savings on wages were often wiped out by lost business from enraged customers, who preferred to communicate with native English speakers.
“People don’t feel comfortable,” Ms. Houlne said, alluding to the overseas agents.

By the early part of this decade, quality was in fashion. The enormous amounts of data that companies like Liveops and Working Solutions collect allowed them to connect callers to the best possible agent with remarkable precision, while allowing big clients to avoid the overhead of a physical call center and full-time workers.

Today, in addition to sales calls, Liveops agents handle calls from people trying to file insurance claims, those in need of roadside assistance, even those with medical or financial issues relating to prescription drugs. The agents must obtain a certification before they can handle such calls, which sometimes takes weeks of online coursework.

Liveops goes to great lengths to attend to their needs, addressing technical-support issues, even answering agents’ emails to the chief executive within 24 hours.

Mr. Jones, like many of his fellow agents, thinks of himself as helping others in need. He said that many families will gather around a table after a loved one has died to discuss the burial. If the deceased relative had no insurance, he said, “A lot of times that table is going to clear.” If, on the other hand, he had even $2,000 in life insurance — the minimum that TruStage sells — “the family members are more inclined to say, ‘He did what he could, let me see what I could do to help out.’ You end up with $5,000 to $6,000. You can do a decent burial rather than none at all.”

Still, there is undeniably a brass-tacks quality to the work. Shortly after we hung up, I turned my attention to an assignment due that afternoon, only to receive more calls from Mr. Jones’s number. When I finally answered, he apologized for interrupting me, then came to the point. “I have a question for you,” he said. “Do you have life insurance?”

‘Where the Price Point Is’
Like Uber, Liveops expends considerable effort calculating demand for its agents. For example, if an auto insurance company is running a commercial on ESPN, Liveops will ask the company’s media buyer — that is, the intermediary that placed the ad — to predict how many calls such an ad is likely to generate. Liveops will adjust that prediction, using its own data showing how many calls similar ads have produced from similar audiences during a comparable time of year.

And like Uber, the Liveops focuses on “utilization” — in the Liveops case, the percentage of working agents actually on a call. Depending on the client, Liveops strives for rates of 65 percent to 75 percent. Lower than that and the agents, who make money only when they’re on a call, will complain that they’re not busy enough. Significantly higher and the system is vulnerable to a sudden increase in demand that could tie up the phone lines and keep callers waiting.

Liveops asks agents to schedule themselves in half-hour blocks, known as “commits,” for the upcoming week. If the company expects demand to be higher than the number of commits, it sends agents a message urging them to sign up. (Uber does something similar, except without formal scheduling.) Sometimes it will even offer financial incentives, like a bump in the rate earned for each minute they’re on a call, or a raffle-type scheme in which people accumulate tickets for the giveaway of an iPad or a cruise.

Again like Uber, Liveops relentlessly tests the effectiveness of these tools. Referring to financial incentives, Jon Brown, the Liveops senior director of client services, said, “We’ve zeroed in on exactly what we need for an agent to go from 10 to 15 commits, from 15 commits to 20 commits. We know where the price point is, what drives behavior.”

And then there are the performance metrics. Liveops agents are rated according to what are called key performance indicators, which, depending on the customer, can include the number of sales they make, their success at upselling customers, and whether a caller would recommend the service based on their interaction.

Liveops makes clear that its agents’ ability to earn more money is closely tied to performance. “You’ve heard the term meritocracy?” said a Liveops official named Aimee Matolka at the North Carolina event. “When a call comes in, it routes in to that best agent. Yes, our router is that smart. You guys want to be that agent, I know you do. Otherwise you wouldn’t be here.”

It allows the agents to track their rankings obsessively through internal leader boards. (Liveops officials say that while the pressures of the job can preoccupy agents, it is up to them how much time to invest.)

“I lost the No. 1 spot, now I’m No. 2,” Mr. Jones said in early August, acknowledging that he checks his ranking frequently. “I thought about researching to find out who it is — you always want to know who’s the competition — but I said leave it.”

He added: “I’m a competitive person. We just toggle back and forth. If they see me jump back in, they work harder. They want that spot back.”

‘This Is My Phone Call’
My flight to Bangor, Me., was due after 9 p.m., and apparently sensing my unease with the North Country, the firefighter seated next to me asked if I had to far to drive when we landed. “About three hours north,” I confessed. “Watch out for moose,” he said. I assured him I’d driven around deer before. He stopped me short: If you hit a deer, you’ll kill them, he said. If you hit a moose, they’ll kill you.

I found Troy Carter, the agent who had recently surpassed Emmett Jones, at his home in Fort Fairfield the next day, wearing jeans, a button-down short-sleeve shirt, and a New England Patriots hat. There were no shoes on his feet, only white socks.

Like Mr. Jones, Mr. Carter said Liveops had been a blessing, allowing him to earn a living in a part of Maine so remote that my cellphone carrier welcomed me to Canada shortly after I pulled into his driveway.

When I told Mr. Carter that I had been in touch with his top competitor, he quickly pulled up the latest monthly rankings of Liveops agents selling TruStage insurance. He pointed out that while Mr. Jones, whom he recognized only by his identification number, 141806, had more sales — 87 to his 82 — he had far fewer paid sales, charged at the time of purchase rather than by invoice.

“The real thing is the paid application rate — they want it around 95 percent,” Mr. Carter said. “He has 87 sales, but only 65 percent paid, compared to my 94 percent.” This, he explained, was why he enjoyed the right to call himself the top agent for the month.

Mr. Carter is what you might call a serial entrepreneur. He once started an art supply website that folded within a few months, and a penny auction site called Bid Tree that foundered for lack of a marketing budget.

He sees Liveops, on which he spends 40 to 50 hours per week, as of a piece with these entrepreneurial efforts. In fact, it is something of a family business. His wife, Lori, handles incoming calls while he’s busy with customers. “I’m a housewife/secretary/receptionist,” she said. Even Mr. Carter’s 9-year-old son, Logan, plays a role. “At nighttime, he says the last part of his prayer based on how many sales I did today,” Mr. Carter said. “If it was a lot of sales, he’ll pray, ‘Dear Lord, help my dad get the same amount of sales tomorrow.’”

Though Liveops agents work from a script, Mr. Carter, like Mr. Jones, adds his own flourishes. Before asking a caller’s gender, as he is required to do, he will say, “Now I already know the answer to this question, but please confirm if you’re male or female.” Upon receiving the answer, he will pause momentarily before saying, “I told you I already knew the answer,” and break into a laugh.

He might make this identical joke, with identical timing, dozens of times in a workday. “It’s like a comedian has a little pause before a joke,” he told me. “It relaxes them right off.”

Even with these touches, results can vary widely. Two days earlier, Mr. Carter had made seven sales, only a few shy of his record. The day I turned up, he managed only one. He said some callers had the impression they could receive $25,000 of insurance for $9.95 per month — the commercial mentions both figures — and begged off when Mr. Carter told them that
Mr. Carter has done research on how to comport himself, including watching an instructional YouTube video by the former stockbroker who was the subject of the movie “The Wolf of Wall Street.” He believes the key is to come off as the alpha presence. “The one that asks the most questions is the one in control,” he said. “If they ask me questions — ‘How are you doing?’ — I’ll come back, ‘The question is how are you doing?’ This is my phone call, as much as I can make it.”

But on this day he repeatedly ran up against the limits of his powers. Even those who remained interested after 10 or 15 minutes of painstaking back-and-forth often demurred when Mr. Carter asked them for payment information. “This one guy was outside in a wheelchair,” Mr. Carter said of a caller who couldn’t produce his credit card. “He didn’t want to go in and get it. I said, ‘I’m fine waiting,’ but I can’t push him.”

These setbacks only seem to make Mr. Carter focus more. At one point, he made a swiping motion between his face and his headset with his index finger and middle finger. “They recommend that you keep the microphone two fingers away,” he said. “I’m always doing that — checking that it’s two fingers. I’ll do that for the rest of my life.”

It seemed, all in all, like a grueling way to make the slightly more than $30,000 that Mr. Carter estimates he takes in before taxes. “The good thing is he can take hours off,” Lori told me. “But then he can lose his spot. It’s always a fight for the top.”

I was reminded of the Alec Baldwin monologue from the movie “Glengarry Glen Ross,” except that the prize for having the most sales wouldn’t be a Cadillac, it would be a set of steak knives, because the Liveops analytics team had calculated that agents would give nearly as much effort for a prize worth a small fraction of the cost.

Of course, unlike the salesmen in that movie, the Liveops agents can’t really be fired — the third prize — because they weren’t employees to begin with.

A while later, Mr. Carter described a recent initiative in which agents were promised a bonus if 95 percent of their collective sales were paid up front. “I knew it wasn’t going to work as soon as they said it,” he told me, because a handful of agents with low paid rates could ruin everyone else’s chances.

“They did do a pullover sweatshirt for the top two,” he added, brightening. “I was second, so that’s coming.”

A version of this article appears in print on November 12, 2017, on Page BU1 of the New York edition with the headline: Paradise at the End of a Phone Line. Order Reprints| Today’s Paper|Subscribe

Virtual Workplace and Virtual Retailer

The virtual workplace is a kissing cousin of the virtual retailer. How are they related?

Both are complex, adaptive systems with the ability to rapidly expand or contract based on demand. Both employ sophisticated methods of train workers fast

– to the characteristics of a new product or service being launched, or;
– to a crisis with particular characteristics.

Example: run a TV ad. Then hire 50 virtual workers from a virtual workplace provider – to take the calls, and respond to the demand.

A virtual workplace is a workplace without a physical home. Employees can work, but not at the employer’s office.

A virtual retailer is a retailer without of physical home. A consumer can shop, but not at the retailer’s physical store.

Both depend on internet and telecommunication platforms.

A virtual call center is an example of a virtual workplace. Companies like LiveOps and Working Solutions create virtual cultures for their employees to thrive, while at the same time creating virtual solutions for companies whose needs for call center services are best met in this way. Co-working facilities are in the business of attracting employees of the virtual workplace.

Amazon is an example of a virtual retailer.

Of course, the world is not generally one or the other.

A virtual workplace can be supplemental to a physical workplace, and provides employment to those who cannot or will not come to the physical place of work. It provides important services to the employer, which the employer gladly pays for.

A virtual retailer can be supplemental to a physical retailer, and provides a shopping experience to those who cannot of will not come to the physical retailer. It provides an important service to the consumer, which the consumer gladly pays for.

The post on on-line work (http://johncreid.com/2017/11/on-demand-work/) raises a fundamental question: is the virtual workplace replacing the physical workplace?

Think about it. Amazon passed &100 billion in revenue in 2016. Apple’s market cap just passed $900 billion. Independent contractors now outnumber employees. Co-working facilities are exploding while company workplaces are static. Telecommuting was a phenomenon touching a few in the eighties, while today 4 million Americans telecommute, up 115% since 2005 (https://2017-State-of-Telecommuting US/)

Its too early to say for sure, but the trends suggest that the virtual retailer and the virtual workplace will grow faster than the physical retailer and physical workplace.

These trends will accelerate as excellent broadband services because the rule rather then the exception (broadband is the sine qua non of these two mega-trends).

They also will accelerate as companies learn to trust the specialized skill set of virtual employers like LiveOps. They will trust more and more their ability to jump in on behalf of a client, to solve their problem, to design solutions that can rapidly expand and contract based on demand – deploying virtual workers as needed to solve the problem.

Primary Care Best Practice

This post is about two important articles related to Primary Care Best Practice: One by Atul Gawande called “Big Med” and the other from Harvard Medical School about Physician Burnout.

As usual, Atul tells stories. His stories begin with his positive experience at the Cheesecake Factory and with his mother’s knee replacement surgery.

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Article by Atul Gawande Big Med and the Cheesecake Factory
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JCR NOTES

Article explores the potential for transferring some of the operational excellence of the Cheesecake Factory to aspects of health care.

He finds it tempting to look for 95% standardization and 5% customization.
He sees lessons in rolling out innovations through test kitchens and training that includes how to train others.
He sees heroes in doctors that push to articulate a standard of care, or technology, or equipment, or pharmaceutical.

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CREDIT: New Yorker Article by Atul Gawande “Big Med”

Annals of Health Care
August 13, 2012 Issue
Big Med
Restaurant chains have managed to combine quality control, cost control, and innovation. Can health care?

By Atul Gawande

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

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. ♦

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Article on Physician Burnout and Best Practice
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JCR Notes:

A primary care physician’s work includes vaccinations, screenings, chronic disease prevention and treatment, relationship building, family planning, behavioral health, counseling, and other vital but time-consuming work.

To be in full compliance with the U.S. Preventive Services Task Force recommendations, primary care physicians with average-sized patient populations need to dedicate 7.4 hours per day to preventative care alone. Taken in conjunction with the other primary care services, namely acute and chronic care, the estimated total working hours per primary care physician comes to 21.7 hours per day, or 108.5 hours per week.

“Complete Care” across 8500 physicians and 4.4 million members at SCPMG has four elements:

1. Share accountability:
share accountability for preventative and chronic care services (e.g., treating people with hypertension or women in need of a mammogram) with high-volume specialties.

2. Delegation:
One fundamental move was to transfer tasks from physicians — not just those in primary care — to non-physicians

3. Information technology
“Outreach team” manages information technologies that allowed patients to schedule visits from mobile apps, access online personalized health care plans (e.g., customized weight-loss calendars and healthy recipes), and manage complex schedules (e.g., the steps prior to a kidney transplant).

4. Standardized Care Process (see Atul Gawande Big Med)
“Proactive Office Encounter” (POE), ensures consistent evidence-based care at every encounter across the organization. At its core, the POE is an agreement of process and delegation of tasks between physicians and their administrative supports.

Glossary:
Medical assistants (MAs)
Licensed vocational nurses (LVNs)

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CREDIT HBR Case Study on SCPMG Primary Care Best Practice

How One California Medical Group Is Decreasing Physician Burnout
Sophia Arabadjis
Erin E. Sullivan
JUNE 07, 2017

Physician burnout is a growing problem for all health care systems in the United States. Burned-out physicians deliver lower quality care, reduce their hours, or stop practicing, reducing access to care around the country. Primary care physicians are particularly vulnerable: They have some of the highest burnout rates of any medical discipline.

As part of our work researching high-performing primary care systems, we discovered a system-wide approach launched by Southern California Permanente Medical Group (SCPMG) in 2004 that unburdens primary care physicians. We believe the program — Complete Care — may be a viable model for other institutions looking to decrease burnout or increase physician satisfaction. (While burnout can easily be measured, institutions often don’t publicly report their own rates and the associated turnover they experience. Consequently, we used physician satisfaction as a proxy for burnout in our research.)

In most health care systems, primary care physicians are the first stop for patients needing care. As a result, their patients’ needs — and their own tasks — vary immensely. A primary care physician’s work includes vaccinations, screenings, chronic disease prevention and treatment, relationship building, family planning, behavioral health, counseling, and other vital but time-consuming work.

Some studies have examined just how much time a primary care physician needs to do all of these tasks and the results are staggering. To be in full compliance with the U.S. Preventive Services Task Force recommendations, primary care physicians with average-sized patient populations need to dedicate 7.4 hours per day to preventative care alone. Taken in conjunction with the other primary care services, namely acute and chronic care, the estimated total working hours per primary care physician comes to 21.7 hours per day, or 108.5 hours per week. Given such workloads, the high burnout rate is hardly surprising.

While designed with the intent to improve quality of care, SCPMG’s Complete Care program also alleviates some of the identified drivers of physician burnout by following a systematic approach to care delivery. Comprised of 8,500 physicians, SCPMG consistently provides the highest quality care to the region’s 4.4 million plan members. And a recent study of SCPMG physician satisfaction suggests that regardless of discipline, physicians feel high levels of satisfaction in three key areas: their compensation, their perceived ability to deliver high-quality care, and their day-to-day professional lives.

Complete Care has four core elements:

Share Accountability with Specialists
A few years ago, SCPMG’s regional medical director of quality and clinical analysis noticed a plateauing effect in some preventative screenings where screenings rates failed to increase after a certain percentage. He asked his team to analyze how certain patient populations — for example, women in need of a mammogram — accessed the health care system. As approximately one in eight women will develop invasive breast cancer over the course of their lifetimes, a failure to receive the recommended preventative screening could have serious health repercussions.
What the team found was startling: Over the course of a year, nearly two-thirds of women clinically eligible for a mammogram never set foot in their primary care physician’s office. Instead they showed up in specialty care or urgent care.

While this discovery spurred more research into patient access, the outcome remained the same: To achieve better rates of preventative and chronic care compliance, specialists had to be brought into the fold.
SCPMG slowly started to share accountability for preventative and chronic care services (e.g., treating people with hypertension or women in need of a mammogram) with high-volume specialties. In order to bring the specialists on board, SCPMG identified and enlisted physician champions across the medical group to promote the program throughout the region; carefully timed the rollouts of different elements of the program pieces so increased demands wouldn’t overwhelm specialists; and crafted incentive programs whose payout was tied to their performance of preventative and chronic-care activities.

This reallocation of traditional primary care responsibilities has allowed SCPMG to achieve a high level of care integration and challenge traditional notions of roles and systems. Its specialists now have to respond to patients’ needs outside their immediate expertise: For example, a podiatrist will inquire whether a diabetic patient has had his or her regular eye examination, and an emergency room doctor will stitch up a cut and give immunizations in the same visit. And the whole system, not just primary care, is responsible for quality metrics related to prevention and chronic care (e.g., the percentage of eligible patients who received a mammogram).

In addition, SCPMG revamped the way it provided care to match how patients accessed and used their system. For example, it began promoting the idea of the comprehensive visit, where patients could see their primary care provider, get blood drawn, and pick up prescribed medications in the same building.

Ultimately, the burden on primary care physicians started to ease. Even more important, SCPMG estimates that Complete Care has saved over 17,000 lives.

Delegate Responsibility
“Right work, right people,” a guiding principle, helped shape the revamping of the organization’s infrastructure. One fundamental move was to transfer tasks from physicians — not just those in primary care — to non-physicians so physicians could spend their time doing tasks only they could do and everyone was working at the top of his or her license. For example, embedded nurse managers of diabetic patients help coordinate care visits, regularly communicate directly with patients about meeting their health goals (such as weekly calls about lower HbA1c levels), and track metrics on diabetic populations across the entire organization. At the same time, dedicated prescribing nurse practitioners work closely with physicians to monitor medication use, which in the case of blood thinners, is very time intensive and requires careful titration.

Leverage Technology

SCPMG invested in information technologies that allowed patients to schedule visits from mobile apps, access online personalized health care plans (e.g., customized weight-loss calendars and healthy recipes), and manage complex schedules (e.g., the steps prior to a kidney transplant). It also established a small outreach team (about four people) that uses large automated registries of patients to mail seasonal reminders (e.g., “it’s time for your flu vaccine shot”) and alerts about routine checkups (e.g., “you are due for a mammogram”) and handle other duties (e.g., coordinating mail-order, at-home fecal tests for colon cancer). In addition, the outreach team manages automated calls and e-mail reminders for the regions 4.4 million members.

Thanks to this reorganization of responsibilities and use of new technology, traditional primary care tasks such as monitoring blood thinners, managing diabetic care, and tracking patients eligibility for cancer screenings have been transferred to other people and processes within the SCPMG system.

Standardize Care Processes
The final element of Complete Care is the kind of process standardization advocated by Atul Gawande’s in his New Yorker article “Big Med.” Standardizing processes — and in particular, workflows — removes duplicative work, strengthens working relationships, and results in higher-functioning teams, reliable routines and higher-quality outcomes. In primary care, standardized workflows help create consistent communications between providers and staff and providers and patients, which allows physicians to spend more time during visits on patients’ pressing needs.
One such process, the “Proactive Office Encounter” (POE), ensures consistent evidence-based care at every encounter across the organization. At its core, the POE is an agreement of process and delegation of tasks between physicians and their administrative supports. It was originally developed to improve communications between support staff and physicians after SCPMG’s electronic medical record was introduced.
Medical assistants (MAs) and licensed vocational nurses (LVNs) are key players. A series of checklists embedded into the medical record guide their work both before and after the visit. These checklists contain symptoms, actions, and questions that are timely and specific to each patient based on age, disease status, and reason for his or her visit. Prior to the visit, MAs or LVNs contact patients with pre-visit instructions or to schedule necessary lab work. During the visit, they use the same checklists to follow up pre-visit instructions, take vitals, conduct medication reconciliation and prep the patient for the provider.

Pop-ups within the medical record indicate a patient’s eligibility for a new screening or regular test based on new literature, prompting the MAs or LVNs to ask patients for additional information. During the visit, physicians have access to the same checklists and data collected by the MAs or LVNs. This enables them to review the work quickly and efficiently and follow up on any flagged issues. After the visit with the physician, patients see an MA or LVN again and receive a summary of topics discussed with the provider and specific instructions or health education resources.

Contemporary physicians face many challenges: an aging population, rising rates of chronic conditions, workforce shortages, technological uncertainty, changing governmental policies, and greater disparities in health outcomes across populations. All of this, it could be argued, disproportionately affect primary care specialties. These factors promise to increase physician burnout unless something is done by health care organizations to ease their burden. SCPMG’s Complete Care initiative offers a viable blueprint to do just that.

Sophia Arabadjis is a researcher and case writer at the Harvard Medical School Center for Primary Care and a research assistant at the University of Colorado. She has investigated health systems in Europe and the United States.

Erin E. Sullivan is the research director of the Harvard Medical School Center for Primary Care. Her research focuses on high-performing primary care systems.

Apple’s HomeKit

From Business Insider

Apple’s plan to take over your entire home will start in these two categories
EUGENE KIM JUN. 2, 2015, 3:31 PM 202
The first batch of products built on top of Apple’s HomeKit — a framework that helps develop iPhone-controlled home appliances — are finally out. The products range from a lighting dimmer and an air quality monitor to an energy consumption tracker and a door locks controller.

The first HomeKit-based products show which categories will lead the way for the broader shift to a connected-home: home-energy equipment and home safety and security systems. According to BI Intelligence, most of the connected-home devices will first be built in these two areas, as they are fairly cheap and easy to install – making them more accessible for average homeowners.

Smart home-energy devices, such as the Nest thermostat, are expected to grow at a compound annual rate of 74% between 2014 and 2019, while home safety and security systems, led by companies like Dropcam, are set to see a 77% compound annual growth rate by 2019. And with the number of households with broadband internet connections expected to reach 1.2 billion globally, connected-home devices will only continue to grow.

General Systems Theory

Here is why I love the Bertalanffy…..

Ludwig von Bertalanffy, a distinguished biologist, occupies an important position in the intellectual history of the twentieth century. His contributions went beyond biology, and ex- tended to psychology, psychiatry, sociology, cybernetics, history and philosophy. Some of his admirers even believe that von Bertalanffy’s general systems theory could provide a conceptual framework for all these disciplines.

Here are some references!

buy the book here: http://product.half.ebay.com/General-System-Theory-Foundations-Development-Applications-by-Ludwig-Von-Bertalanffy-1969-Paperback-Revised/578858&tg=info

General Systems Theory: The Book

Wikipedia Article on Bertalannfy

More Insight from an Admirer

History of Computing


Www.computerhistory.org/timeline/

See also 2001 and 2007 posts (this is a more extensive more current update):

And

History of Computing

http://us.penguingroup.com/static/packages/us/kurzweil/excerpts/timeline/timeline2.htm

TIME LINE
1950
Eckert and Mauchley develop UNIVAC, the first commercially marketed computer. It is used to compile the results of the U.S. census, marking the first time this census is handled by a programmable computer.
1950
In his paper “Computing Machinery and Intelligence,” Alan Turing presents the Turing Test, a means for determining whether a machine is intelligent.
1950
Commercial color television is first broadcast in the United States, and transcontinental black-and-white television is available within the next year.
1950
Claude Elwood Shannon writes “Programming a Computer for Playing Chess,” published in Philosophical Magazine.
1951
Eckert and Mauchley build EDVAC, which is the first computer to use the stored-program concept. The work takes place at the Moore School at the University of Pennsylvania.
1951
Paris is the host to a Cybernetics Congress.
1952
UNIVAC, used by the Columbia Broadcasting System (CBS) television network, successfully predicts the election of Dwight D. Eisenhower as president of the United States.
1952
Pocket-sized transistor radios are introduced.
1952
Nathaniel Rochester designs the 701, IBM’s first production-line electronic digital computer. It is marketed for scientific use.
1953
The chemical structure of the DNA molecule is discovered by James D. Watson and Francis H. C. Crick.
1953
Philosophical Investigations by Ludwig Wittgenstein and Waiting for Godot, a play by Samuel Beckett, are published. Both documents are considered of major importance to modern existentialism.
1953
Marvin Minsky and John McCarthy get summer jobs at Bell Laboratories.
1955
William Shockley’s Semiconductor Laboratory is founded, thereby starting Silicon Valley.
1955
The Remington Rand Corporation and Sperry Gyroscope join forces and become the Sperry-Rand Corporation. For a time, it presents serious competition to IBM.
1955
IBM introduces its first transistor calculator. It uses 2,200 transistors instead of the 1,200 vacuum tubes that would otherwise be required for equivalent computing power.
1955
A U.S. company develops the first design for a robotlike machine to be used in industry.
1955
IPL-II, the first artificial intelligence language, is created by Allen Newell, J. C. Shaw, and Herbert Simon.
1955
The new space program and the U.S. military recognize the importance of having computers with enough power to launch rockets to the moon and missiles through the stratosphere. Both organizations supply major funding for research.
1956
The Logic Theorist, which uses recursive search techniques to solve mathematical problems, is developed by Allen Newell, J. C. Shaw, and Herbert Simon.
1956
John Backus and a team at IBM invent FORTRAN, the first scientific computer-programming language.
1956
Stanislaw Ulam develops MANIAC I, the first computer program to beat a human being in a chess game.
1956
The first commercial watch to run on electric batteries is presented by the Lip company of France.
1956
The term Artificial Intelligence is coined at a computer conference at Dartmouth College.
1957
Kenneth H. Olsen founds Digital Equipment Corporation.
1957
The General Problem Solver, which uses recursive search to solve problems, is developed by Allen Newell, J. C. Shaw, and Herbert Simon.
1957
Noam Chomsky writes Syntactic Structures, in which he seriously considers the computation required for natural-language understanding. This is the first of the many important works that will earn him the title Father of Modern Linguistics.
1958
An integrated circuit is created by Texas Instruments’ Jack St. Clair Kilby.
1958
The Artificial Intelligence Laboratory at the Massachusetts Institute of Technology is founded by John McCarthy and Marvin Minsky.
1958
Allen Newell and Herbert Simon make the prediction that a digital computer will be the world’s chess champion within ten years.
1958
LISP, an early AI language, is developed by John McCarthy.
1958
The Defense Advanced Research Projects Agency, which will fund important computer-science research for years in the future, is established.
1958
Seymour Cray builds the Control Data Corporation 1604, the first fully transistorized supercomputer.
1958-1959
Jack Kilby and Robert Noyce each develop the computer chip independently. The computer chip leads to the development of much cheaper and smaller computers.
1959
Arthur Samuel completes his study in machine learning. The project, a checkers-playing program, performs as well as some of the best players of the time.
1959
Electronic document preparation increases the consumption of paper in the United States. This year, the nation will consume 7 million tons of paper. In 1986, 22 million tons will be used. American businesses alone will use 850 billion pages in 1981, 2.5 trillion pages in 1986, and 4 trillion in 1990.
1959
COBOL, a computer language designed for business use, is developed by Grace Murray Hopper, who was also one of the first programmers of the Mark I.
1959
Xerox introduces the first commercial copier.
1960
Theodore Harold Maimen develops the first laser. It uses a ruby cylinder.
1960
The recently established Defense Department’s Advanced Research Projects Agency substantially increases its funding for computer research.
1960
There are now about six thousand computers in operation in the United States.
1960s
Neural-net machines are quite simple and incorporate a small number of neurons organized in only one or two layers. These models are shown to be limited in their capabilities.
1961
The first time-sharing computer is developed at MIT.
1961
President John F. Kennedy provides the support for space project Apollo and inspiration for important research in computer science when he addresses a joint session of Congress, saying, “I believe we should go to the moon.”
1962
The world’s first industrial robots are marketed by a U.S. company.
1962
Frank Rosenblatt defines the Perceptron in his Principles of Neurodynamics. Rosenblatt first introduced the Perceptron, a simple processing element for neural networks, at a conference in 1959.
1963
The Artificial Intelligence Laboratory at Stanford University is founded by John McCarthy.
1963
The influential Steps Toward Artificial Intelligence by Marvin Minsky is published.
1963
Digital Equipment Corporation announces the PDP-8, which is the first successful minicomputer.
1964
IBM introduces its 360 series, thereby further strengthening its leadership in the computer industry.
1964
Thomas E. Kurtz and John G. Kenny of Dartmouth College invent BASIC (Beginner’s All-purpose Symbolic Instruction Code).
1964
Daniel Bobrow completes his doctoral work on Student, a natural-language program that can solve high-school-level word problems in algebra.
1964
Gordon Moore’s prediction, made this year, says integrated circuits will double in complexity each year. This will become known as Moore’s Law and prove true (with later revisions) for decades to come.
1964
Marshall McLuhan, via his Understanding Media, foresees the potential for electronic media, especially television, to create a “global village” in which “the medium is the message.”
1965
The Robotics Institute at Carnegie Mellon University, which will become a leading research center for AI, is founded by Raj Reddy.
1965
Hubert Dreyfus presents a set of philosophical arguments against the possibility of artificial intelligence in a RAND corporate memo entitled “Alchemy and Artificial Intelligence.”
1965
Herbert Simon predicts that by 1985 “machines will be capable of doing any work a man can do.”
1966
The Amateur Computer Society, possibly the first personal computer club, is founded by Stephen B. Gray. The Amateur Computer Society Newsletter is one of the first magazines about computers.
1967
The first internal pacemaker is developed by Medtronics. It uses integrated circuits.
1968
Gordon Moore and Robert Noyce found Intel (Integrated Electronics) Corporation.
1968
The idea of a computer that can see, speak, hear, and think sparks imaginations when HAL is presented in the film 2001: A Space Odyssey, by Arthur C. Clarke and Stanley Kubrick.
1969
Marvin Minsky and Seymour Papert present the limitation of single-layer neural nets in their book Perceptrons. The book’s pivotal theorem shows that a Perceptron is unable to determine if a line drawing is fully connected. The book essentially halts funding for neural-net research.
1970
The GNP, on a per capita basis and in constant 1958 dollars, is $3,500, or more than six times as much as a century before.
1970
The floppy disc is introduced for storing data in computers.
c. 1970
Researchers at the Xerox Palo Alto Research Center (PARC) develop the first personal computer, called Alto. PARC’s Alto pioneers the use of bit-mapped graphics, windows, icons, and mouse pointing devices.
1970
Terry Winograd completes his landmark thesis on SHRDLU, a natural-language system that exhibits diverse intelligent behavior in the small world of children’s blocks. SHRDLU is criticized, however, for its lack of generality.
1971
The Intel 4004, the first microprocessor, is introduced by Intel.
1971
The first pocket calculator is introduced. It can add, subtract, multiply, and divide.
1972
Continuing his criticism of the capabilities of AI, Hubert Dreyfus publishes What Computers Can’t Do, in which he argues that symbol manipulation cannot be the basis of human intelligence.
1973
Stanley H. Cohen and Herbert W. Boyer show that DNA strands can be cut, joined, and then reproduced by inserting them into the bacterium Escherichia coli. This work creates the foundation for genetic engineering.
1974
Creative Computing starts publication. It is the first magazine for home computer hobbyists.
1974
The 8-bit 8080, which is the first general-purpose microprocessor, is announced by Intel.
1975
Sales of microcomputers in the United States reach more than five thousand, and the first personal computer, the Altair 8800, is introduced. It has 256 bytes of memory.
1975
BYTE, the first widely distributed computer magazine, is published.
1975
Gordon Moore revises his observation on the doubling rate of transistors on an integrated circuit from twelve months to twenty-four months.
1976
Kurzweil Computer Products introduces the Kurzweil Reading Machine (KRM), the first print-to-speech reading machine for the blind. Based on the first omni-font (any font) optical character recognition (OCR) technology, the KRM scans and reads aloud any printed materials (books, magazines, typed documents).
1976
Stephen G. Wozniak and Steven P. Jobs found Apple Computer Corporation.
1977
The concept of true-to-life robots with convincing human emotions is imaginatively portrayed in the film Star Wars.
1977
For the first time, a telephone company conducts large-scale experiments with fiber optics in a telephone system.
1977
The Apple II, the first personal computer to be sold in assembled form and the first with color graphics capability, is introduced and successfully marketed. (JCR buys first Apple II at KO in 1978
J1978
Speak & Spell, a computerized learning aid for young children, is introduced by Texas Instruments. This is the first product that electronically duplicates the human vocal tract on a chip.
1979
In a landmark study by nine researchers published in the Journal of the American Medical Association, the performance of the computer program MYCIN is compared with that of doctors in diagnosing ten test cases of meningitis. MYCIN does at least as well as the medical experts. The potential of expert systems in medicine becomes widely recognized.
1979
Dan Bricklin and Bob Frankston establish the personal computer as a serious business tool when they develop VisiCalc, the first electronic spreadsheet.
1980
AI industry revenue is a few million dollars this year.
1980s
As neuron models are becoming potentially more sophisticated, the neural network paradigm begins to make a comeback, and networks with multiple layers are commonly used.
1981
Xerox introduces the Star Computer, thus launching the concept of Desktop Publishing. Apple’s Laserwriter, available in 1985, will further increase the viability of this inexpensive and efficient way for writers and artists to create their own finished documents.
1981
IBM introduces its Personal Computer (PC).
1981
The prototype of the Bubble Jet printer is presented by Canon.
1982
Compact disc players are marketed for the first time.
1982
Mitch Kapor presents Lotus 1-2-3, an enormously popular spreadsheet program.
1983
Fax machines are fast becoming a necessity in the business world.
1983
The Musical Instrument Digital Interface (MIDI) is presented in Los Angeles at the first North American Music Manufacturers show.
1983
Six million personal computers are sold in the United States.
1984
The Apple Macintosh introduces the “desktop metaphor,” pioneered at Xerox, including bit-mapped graphics, icons, and the mouse.
1984
William Gibson uses the term cyberspace in his book Neuromancer.
1984
The Kurzweil 250 (K250) synthesizer, considered to be the first electronic instrument to successfully emulate the sounds of acoustic instruments, is introduced to the market.
1985
Marvin Minsky publishes The Society of Mind, in which he presents a theory of the mind where intelligence is seen to be the result of proper organization of a hierarchy of minds with simple mechanisms at the lowest level of the hierarchy.
1985
MIT’s Media Laboratory is founded by Jerome Weisner and Nicholas Negroponte. The lab is dedicated to researching possible applications and interactions of computer science, sociology, and artificial intelligence in the context of media technology.
1985
There are 116 million jobs in the United States, compared to 12 million in 1870. In the same period, the number of those employed has grown from 31 percent to 48 percent, and the per capita GNP in constant dollars has increased by 600 percent. These trends show no signs of abating.
1986
Electronic keyboards account for 55.2 percent of the American musical keyboard market, up from 9.5 percent in 1980.
1986
Life expectancy is about 74 years in the United States. Only 3 percent of the American workforce is involved in the production of food. Fully 76 percent of American adults have high-school diplomas, and 7.3 million U.S. students are enrolled in college.
1987
NYSE stocks have their greatest single-day loss due, in part, to computerized trading.
1987
Current speech systems can provide any one of the following: a large vocabulary, continuous speech recognition, or speaker independence.
1987
Robotic-vision systems are now a $300 million industry and will grow to $800 million by 1990.
1988
Computer memory today costs only one hundred millionth of what it did in 1950.
1988
Marvin Minsky and Seymour Papert publish a revised edition of Perceptrons in which they discuss recent developments in neural network machinery for intelligence.
1988
In the United States, 4,700,000 microcomputers, 120,000 minicomputers, and 11,500 mainframes are sold this year.
1988
W. Daniel Hillis’s Connection Machine is capable of 65,536 computations at the same time.
1988
Notebook computers are replacing the bigger laptops in popularity.
1989
Intel introduces the 16-megahertz (MHz) 80386SX, 2.5 MIPS microprocessor.
1990
Nautilus, the first CD-ROM magazine, is published.
1990
The development of HypterText Markup Language by researcher Tim Berners-Lee and its release by CERN, the high-energy physics laboratory in Geneva, Switzerland, leads to the conception of the World Wide Web.
1991
Cell phones and e-mail are increasing in popularity as business and personal communication tools.
1992
The first double-speed CD-ROM drive becomes available from NEC.
1992
The first personal digital assistant (PDA), a hand-held computer, is introduced at the Consumer Electronics Show in Chicago. The developer is Apple Computer.
1993
The Pentium 32-bit microprocessor is launched by Intel. This chip has 3.1 million transistors.
1994
The World Wide Web emerges.
1994
America Online now has more than 1 million subscribers.
1994
Scanners and CD-ROMs are becoming widely used.
1994
Digital Equipment Corporation introduces a 300-MHz version of the Alpha AXP processor that executes 1 billion instructions per second.
1996
Compaq Computer and NEC Computer Systems ship hand-held computers running Windows CE.
1996
NEC Electronics ships the R4101 processor for personal digital assistants. It includes a touch-screen interface.
1997
Deep Blue defeats Gary Kasparov, the world chess champion, in a regulation tournament.
1997
Dragon Systems introduces Naturally Speaking, the first continuous-speech dictation software product.
1997
Video phones are being used in business settings.
1997
Face-recognition systems are beginning to be used in payroll check-cashing machines.
1998
The Dictation Division of Lernout & Hauspie Speech Products (formerly Kurzweil Applied Intelligence) introduces Voice Xpress Plus, the first continuous-speech-recognition program with the ability to understand natural-language commands.
1998
Routine business transactions over the phone are beginning to be conducted between a human customer and an automated system that engages in a verbal dialogue with the customer (e.g., United Airlines reservations).
1998
Investment funds are emerging that use evolutionary algorithms and neural nets to make investment decisions (e.g., Advanced Investment Technologies).
1998
The World Wide Web is ubiquitous. It is routine for high-school students and local grocery stores to have web sites.
1998
Automated personalities, which appear as animated faces that speak with realistic mouth movements and facial expressions, are working in laboratories. These personalities respond to the spoken statements and facial expressions of their human users. They are being developed to be used in future user interfaces for products and services, as personalized research and business assistants, and to conduct transactions.
1998
Microvision’s Virtual Retina Display (VRD) projects images directly onto the user’s retinas. Although expensive, consumer versions are projected for 1999.
1998
“Bluetooth” technology is being developed for “body” local area networks (LANs) and for wireless communication between personal computers and associated peripherals. Wireless communication is being developed for high-bandwidth connection to the Web.
1999
Ray Kurzweil’s The Age of Spiritual Machines: When Computers Exceed Human Intelligence is published, available at your local bookstore!

FORECASTS:

2009
A $1,000 personal computer can perform about a trillion calculations per second.
Personal computers with high-resolution visual displays come in a range of sizes, from those small enough to be embedded in clothing and jewelry up to the size of a thin book.
Cables are disappearing. Communication between components uses short-distance wireless technology. High-speed wireless communication provides access to the Web.
The majority of text is created using continuous speech recognition. Also ubiquitous are language user interfaces (LUIs).
Most routine business transactions (purchases, travel, reservations) take place between a human and a virtual personality. Often, the virtual personality includes an animated visual presence that looks like a human face.
Although traditional classroom organization is still common, intelligent courseware has emerged as a common means of learning.
Pocket-sized reading machines for the blind and visually impaired, “listening machines” (speech- to- text conversion) for the deaf, and computer- controlled orthotic devices for paraplegic individuals result in a growing perception that primary disabilities do not necessarily impart handicaps.
Translating telephones (speech-to-speech language translation) are commonly used for many language pairs.< Accelerating returns from the advance of computer technology have resulted in continued economic expansion. Price deflation, which had been a reality in the computer field during the twentieth century, is now occurring outside the computer field. The reason for this is that virtually all economic sectors are deeply affected by the accelerating improvement in the price performance of computing. Human musicians routinely jam with cybernetic musicians. Bioengineered treatments for cancer and heart disease have greatly reduced the mortality from these diseases. The neo-Luddite movement is growing. 2019 A $1,000 computing device (in 1999 dollars) is now approximately equal to the computational ability of the human brain. Computers are now largely invisible and are embedded everywhere -- in walls, tables, chairs, desks, clothing, jewelry, and bodies. Three-dimensional virtual reality displays, embedded in glasses and contact lenses, as well as auditory "lenses," are used routinely as primary interfaces for communication with other persons, computers, the Web, and virtual reality. Most interaction with computing is through gestures and two-way natural-language spoken communication. Nanoengineered machines are beginning to be applied to manufacturing and process-control applications. High-resolution, three-dimensional visual and auditory virtual reality and realistic all-encompassing tactile environments enable people to do virtually anything with anybody, regardless of physical proximity. Paper books or documents are rarely used and most learning is conducted through intelligent, simulated software-based teachers. Blind persons routinely use eyeglass-mounted reading-navigation systems. Deaf persons read what other people are saying through their lens displays. Paraplegic and some quadriplegic persons routinely walk and climb stairs through a combination of computer-controlled nerve stimulation and exoskeletal robotic devices. The vast majority of transactions include a simulated person. Automated driving systems are now installed in most roads. People are beginning to have relationships with automated personalities and use them as companions, teachers, caretakers, and lovers. Virtual artists, with their own reputations, are emerging in all of the arts. There are widespread reports of computers passing the Turing Test, although these tests do not meet the criteria established by knowledgeable observers. 2029 A $1,000 (in 1999 dollars) unit of computation has the computing capacity of approximately 1,000 human brains. Permanent or removable implants (similar to contact lenses) for the eyes as well as cochlear implants are now used to provide input and output between the human user and the worldwide computing network. Direct neural pathways have been perfected for high-bandwidth connection to the human brain. A range of neural implants is becoming available to enhance visual and auditory perception and interpretation, memory, and reasoning. Automated agents are now learning on their own, and significant knowledge is being created by machines with little or no human intervention. Computers have read all available human- and machine-generated literature and multimedia material. There is widespread use of all-encompassing visual, auditory, and tactile communication using direct neural connections, allowing virtual reality to take place without having to be in a "total touch enclosure." The majority of communication does not involve a human. The majority of communication involving a human is between a human and a machine. There is almost no human employment in production, agriculture, or transportation. Basic life needs are available for the vast majority of the human race. There is a growing discussion about the legal rights of computers and what constitutes being "human." Although computers routinely pass apparently valid forms of the Turing Test, controversy persists about whether or not machine intelligence equals human intelligence in all of its diversity. Machines claim to be conscious. These claims are largely accepted. 2049 The common use of nanoproduced food, which has the correct nutritional composition and the same taste and texture of organically produced food, means that the availability of food is no longer affected by limited resources, bad crop weather, or spoilage.< Nanobot swarm projections are used to create visual-auditory-tactile projections of people and objects in real reality. 2072 Picoengineering (developing technology at the scale of picometers or trillionths of a meter) becomes practical.1 By the year 2099 There is a strong trend toward a merger of human thinking with the world of machine intelligence that the human species initially created. There is no longer any clear distinction between humans and computers. Most conscious entities do not have a permanent physical presence. Machine-based intelligences derived from extended models of human intelligence claim to be human, although their brains are not based on carbon-based cellular processes, but rather electronic and photonic equivalents. Most of these intelligences are not tied to a specific computational processing unit. The number of software-based humans vastly exceeds those still using native neuron-cell-based computation. Even among those human intelligences still using carbon-based neurons, there is ubiquitous use of neural-implant technology, which provides enormous augmentation of human perceptual and cognitive abilities. Humans who do not utilize such implants are unable to meaningfully participate in dialogues with those who do. Because most information is published using standard assimilated knowledge protocols, information can be instantly understood. The goal of education, and of intelligent beings, is discovering new knowledge to learn. Femtoengineering (engineering at the scale of femtometers or one thousandth of a trillionth of a meter) proposals are controversial.2 Life expectancy is no longer a viable term in relation to intelligent beings. Some many millenniums hence . . . Intelligent beings consider the fate of the Universe.

Complex, adaptive systems

Note date! JCR authored this.

December 2, 2000

Applications and their Enabling Technologies for Adaptive Dynamic Systems

Adaptation to a dynamically changing world is essential. Look at computer science, economics, political science, communication, and hundreds of other fields: Wherever you look, no matter what the field or endeavor, there are applications and then there are underlying enabling technologies that enable applications. Applications can be adaptive and dynamic, and so can the underlying enabling technology.

In computer science, the theory of operating systems is the theory of enabling technologies.

Windows is an operating system on which applications run. It is also an enabling technology. It enables Word, Excel, Powerpoint, Outlook, and hundreds of other applications to run in the complex multi-tasking, multi-device environment of a desktop computer.

In political science, the theory of democracy is a theory of enabling technologies. A constitution is a fundamental enabling technology. The process of legislating is an enabling technology as well. Legislation is provided for in constitutions, and thus a constitution is a more fundamental enabling technology than legislation. When is comes to adaptation, one would expect the more fundamental enabling technology (in this case constitutions) to adapt more slowly than the higher order technology, legislating.

In economics, the theory of capitalism is a theory of enabling technologies. Take a market, for example. A market for wheat futures at the Chicago Board of Trade is an enabling technology. CBOT abides by the laws of Illinois and the U.S. in setting up this marketplace. It provides the underlying infrastructure that makes it possible for millions of buyers of wheat to find millions of sellers. A buyer finds a seller through a market, and thus a market is a fundamental enabling technology that makes it possible to bring a buyer and seller together. In a market, the notion of a price provides a second enabling technology. As in other cases below, a market is more fundamental technology than a price. And – as such – one expects prices to adapt quickly to the dynamically changing world outside.

In communications, the theory of language is a theory of enabling technologies. Syntax and grammar, nouns and verbs are the language of democracies everywhere.

In the internet, it is not unusual to see even more dynamic and adaptive systems, There have been a proliferation of underlying enabling technologies that make there systems possible. Take book selling for example. Amazon.com routinely provides book reviews, provided by everyday people. One enabling technology is simply the technical capability of storing these reviews as they are written. But a very exciting additional enabling technology provides for other readers to grade the quality of the review.