145
Chapter 6
The Health Care
Challenge
In May 2012, a fifty-five-year-old man checked into a clinic at the
University of Marburg in Germany. The patient suffered from fever,
an inflamed esophagus, low thyroid hormone levels, and failing vision.
He had visited a series of doctors, all of whom were baffled by
his condition. By the time he arrived at the Marburg clinic, he was
nearly blind and was on the verge of heart failure. Months earlier,
and a continent away, a very similar medical mystery had culminated
with a fifty-nine-year-old woman receiving a heart transplant at the
University of Colorado Medical Center in Denver.
The answer to both mysteries turned out to be the same: cobalt
poisoning.1
Both patients had previously received artificial hips made
from metal. The metal implants had abraded over time, releasing
cobalt particles and exposing the patients to chronic toxicity. In a
remarkable coincidence, papers describing the two cases were published
independently in two leading medical journals on nearly the
same day in February 2014. The report published by the German
doctors came with a fascinating twist: whereas the American team
had resorted to surgery, the German team had managed to solve the
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mystery not because of their training but because one of the doctors
had seen a February 2011 episode of the television show House. In
the episode, the show’s protagonist, Dr. Gregory House, is faced with
the same problem and makes an ingenious diagnosis: cobalt poisoning
resulting from a metal prosthetic hip replacement.
The fact that two teams of doctors can struggle to make the
same diagnosis—and that they can do so even when the answer to
the mystery has been broadcast to millions of prime-time television
viewers—is a testament to the extent to which medical knowledge
and diagnostic skill are compartmentalized in the brains of individual
physicians, even in an age when the Internet has enabled an
unprecedented degree of collaboration and access to information. As
a result, the fundamental process that doctors use to diagnose and
treat illnesses has remained, in important ways, relatively unchanged.
Upending that traditional approach to problem solving, and unleashing
all the information trapped in individual minds or published in
obscure medical journals, likely represents one of the most important
potential benefits of artificial intelligence and big data as applied to
medicine.
In general, the advances in information technology that are disrupting
other areas of the economy have so far made relatively few
inroads into the health care sector. Especially hard to find is any
evidence that technology is resulting in meaningful improvements in
overall efficiency. In 1960, health care represented less than 6 percent
of the US economy.2
By 2013 it had nearly tripled, having grown to
nearly 18 percent, and per capita health care spending in the United
States had soared to a level roughly double that of most other industrialized
countries. One of the greatest risks going forward is that
technology will continue to impact asymmetrically, driving down
wages or creating unemployment across most of the economy, even
as the cost of health care continues to climb. The danger, in a sense,
is not too many health care robots but too few. If technology fails to
rise to the health care challenge, the result is likely to be a soaring,
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The Health Care Challenge 147
and ultimately unsustainable, burden on both individual households
and the economy as a whole.
Artificial Intelligence in Medicine
The total amount of information that could potentially be useful to
a physician attempting to diagnose a particular patient’s condition
or design an optimal treatment strategy is staggering. Physicians are
faced with a continuous torrent of new discoveries, innovative treatments,
and clinical study evaluations published in medical and scientific
journals throughout the world. For example, MEDLINE, an
online database maintained by the US National Library of Medicine,
indexes over 5,600 separate journals—each of which might publish
anywhere from dozens to hundreds of distinct research papers every
year. In addition, there are millions of medical records, patient histories,
and case studies that might offer important insights. According
to one estimate, the total volume of all this data doubles roughly
every five years.3
It would be impossible for any human being to
assimilate more than a tiny fraction of the relevant information even
within highly specific areas of medical practice.
As we saw in Chapter 4, medicine is one of the primary areas
where IBM foresees its Watson technology having a transformative
impact. IBM’s system is capable of churning through vast troves of
information in disparate formats and then almost instantly constructing
inferences that might elude even the most attentive human
researcher. It’s easy to imagine a near-term future where such a diagnostic
tool is considered indispensable, at least for physicians confronting
especially challenging cases.
The MD Anderson Cancer Center at the University of Texas
handles over 100,000 patients at its Houston hospital each year and is
generally regarded as the best cancer treatment facility in the United
States. In 2011, IBM’s Watson team began working with MD Anderson’s
doctors to build a customized version of the system geared
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toward assisting oncologists working with leukemia cases. The goal
is to create an interactive adviser capable of recommending the best
evidence-based treatment options, matching patients with clinical
drug trials, and highlighting possible dangers or side effects that
might threaten specific patients. Initial progress on the project proved
to be somewhat slower than the team expected, largely because of the
challenges associated with designing algorithms capable of taking on
the complexities of cancer diagnosis and treatment. Cancer, it turns
out, is tougher than Jeopardy! Nonetheless, by January 2014, the
Wall Street Journal reported that the Watson-based leukemia system
at MD Anderson was “back on track” toward becoming opera-
tional.4
Researchers hope to expand the system to handle other kinds
of cancer within roughly two years. It’s very likely that the lessons
IBM takes away from this pilot program will enable the company to
streamline future implementations of the Watson technology.
Once the system is operating smoothly, the MD Anderson staff
plans to make it available via the Internet so that it can become a
powerful resource for doctors everywhere. According to Dr. Courtney
DiNardo, a leukemia expert, the Watson technology has the
“potential to democratize cancer care” by allowing any physician to
“access the latest scientific knowledge and MD Anderson’s expertise.”
“For physicians who aren’t leukemia experts,” she added, the
system “can function as an expert second opinion, allowing them
to access the same knowledge and information” relied on by the nation’s
top cancer treatment center. DiNardo also believes that, beyond
offering advice for specific patients, the system “will provide
an unparalleled research platform that can be used to generate questions,
explore hypotheses and provide answers to critical research
questions.”5
Watson is currently the most ambitious and prominent application
of artificial intelligence to medicine, but there are other important
success stories as well. In 2009, researchers at the Mayo Clinic in
Rochester, Minnesota, built an artificial neural network designed to
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The Health Care Challenge 149
diagnose cases of endocarditis—an inflammation of the inner layer
of the heart. Endocarditis normally requires that a probe be inserted
into the patient’s esophagus in order to determine whether or not the
inflammation is caused by a potentially deadly infection—a procedure
that is uncomfortable, expensive, and itself carries risks for the
patient. The Mayo doctors instead trained a neural network to make
the diagnosis based on routine tests and observable symptoms alone,
without the need for the invasive technique. A study involving 189
patients found that the system was accurate more than 99 percent of
the time and successfully saved over half of the patients from having
to needlessly undergo the invasive diagnostic procedure.6
One of the most important benefits of artificial intelligence in
medicine is likely to be the avoidance of potentially fatal errors in
both diagnosis and treatment. In November 1994, Betsy Lehman,
a thirty-nine-year-old mother of two and a widely read columnist
who wrote about health-related issues for the Boston Globe, was
scheduled to begin her third round of chemotherapy as she continued
her battle against breast cancer. Lehman was admitted to the
Dana-Farber Cancer Institute in Boston, which, like MD Anderson,
is regarded as one of the country’s preeminent cancer centers. The
treatment plan called for Lehman to be given a powerful dose of
cyclophosphamide—a highly toxic drug intended to wipe out her
cancer cells. The research fellow who wrote the medication order
made a simple numerical error, which meant that the total dosage
Lehman received was about four times what the treatment plan
actually called for. Lehman died from the overdose on December
3, 1994.7
Lehman was just one of as many as 98,000 patients who die in the
United States each year as a direct result of preventable medical er-
rors.8
A 2006 report by the US Institute of Medicine estimated that at
least 1.5 million Americans are harmed by medication errors alone,
and that such mistakes result in more than $3.5 billion in additional
annual treatment costs.9
An AI system with access to detailed patient
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histories, as well as information about medications, including their
associated toxicity and side effects, would potentially be able to prevent
errors even in very complex situations involving the interaction
of multiple drugs. Such a system could act as an interactive adviser to
doctors and nurses, offering instantaneous verification of both safety
and effectiveness before medication is administered, and—especially
in situations where hospital staff are tired or distracted—it would be
very likely to save both lives and needless discomfort and expense.
Once medical applications of artificial intelligence evolve to the
point where the systems can act as true advisers capable of providing
consistently high-quality second opinions, the technology could also
help rein in the high costs associated with malpractice liability. Many
physicians feel the need to practice “defensive medicine” and order
every conceivable test in an attempt to protect themselves against
potential lawsuits. A documented second opinion from an AI system
versed in best practice standards could offer doctors a “safe harbor”
defense against such claims. The result might be less spending on
needless medical tests and scans as well as lower malpractice insurance
premiums.*
Looking even further ahead, we can easily imagine artificial
intelligence having a genuinely transformative impact on the way
medical services are delivered. Once machines demonstrate that they
can offer accurate diagnosis and effective treatment, perhaps it will
not be necessary for a physician to directly oversee every encounter
with every patient.
* This raises the question of whether the liability would simply migrate to the
manufacturer of the AI system. Since such systems might be used to diagnose
tens or even hundreds of thousands of patients, the potential liability for errors
could be daunting. However, the US Supreme Court ruled in the 2008 case Riegel
v. Medtronic, Inc., that medical device manufacturers are protected from some
lawsuits if their products have been approved by the FDA. Perhaps similar reasoning
would be extended to diagnostic systems. Another issue is that previous
attempts to create “safe harbor” laws for doctors have been vigorously opposed
by the trial lawyers, who have a great deal of political influence.
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In an op-ed I wrote for the Washington Post, shortly after Watson’s
2011 triumph at playing Jeopardy!, I suggested that there may
eventually be an opportunity to create a new class of medical professionals:
persons educated with perhaps a four-year college or master’s
degree, and who are trained primarily to interact with and examine
patients—and then to convey that information into a standardized
diagnostic and treatment system.10
These new, lower-cost practitioners
would be able to take on many routine cases, and could be
deployed to help manage the dramatically growing number of patients
with chronic conditions such as obesity and diabetes.
Physicians groups would, of course, be likely to oppose the influx
of these less-educated competitors.*
However, the reality is that
the vast majority of medical school graduates are not especially interested
in entering family practice, and they are even less excited
about serving rural areas of the country. Various studies predict a
shortage of up to 200,000 doctors within the next fifteen years as
older doctors retire, the Affordable Care Act plan brings as many
as 32 million new patients into the health insurance system, and an
aging population requires more care.11
The shortage will be most
acute among primary-care physicians as medical school graduates,
typically burdened by onerous levels of student debt, choose overwhelmingly
to enter more lucrative specialties.
These new practitioners, trained to utilize a standardized AI system
that encapsulates much of the knowledge that doctors acquire
during the course of nearly a decade of intensive training, could handle
routine cases, while referring patients who require more specialized
care to physicians. College graduates would benefit significantly
from the availability of a compelling new career path, especially as
intelligent software increasingly erodes opportunities in other sectors
of the job market.
* Nurse practitioners with advanced degrees have been able to overcome such
political opposition in seventeen US states and are likely to be an important
component of primary care in the future.
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In some areas of medicine, particularly those that don’t require
direct interaction with patients, advances in AI are poised to drive
dramatic productivity increases and perhaps eventually full automation.
Radiologists, for example, are trained to interpret the images
that result from various medical scans. Image processing and
recognition technology is advancing rapidly and may soon be able
to usurp the radiologist’s traditional role. Software can already recognize
people in photos posted on Facebook and even help identify
potential terrorists in airports. In September 2012, the FDA
approved an automated ultrasound system for screening women for
breast cancer. The device, designed by U-Systems, Inc., is designed
to help identify cancer in the roughly 40 percent of women whose
dense breast tissue can render standard mammogram technology
ineffective. Radiologists still need to interpret the images, but doing
so now takes only about three minutes. That compares with twenty
to thirty minutes for images produced using standard handheld
ultrasound technology.12
Automated systems can also provide a viable second opinion. A
very effective—but expensive—way to increase cancer detection rates
is to have two radiologists read every mammogram image separately
and then reach a consensus on any potential anomalies identified
by either doctor. This “double reading” strategy results in significantly
improved cancer detection and also dramatically reduces the
number of patients who have to be recalled for further testing. A
2008 study published in the New England Journal of Medicine found
that a machine can step into the role of the second doctor. When
a radiologist is paired with a computer-aided detection system, the
results are just as good as having two doctors separately interpret
the images.13
Pathology is another area where artificial intelligence is already
encroaching. Each year, over a hundred million women throughout
the world receive a Pap test to screen for cervical cancer. The test requires
that cervical cells be deposited on a glass microscope slide and
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then be examined by a technician or doctor for signs of malignancy.
It’s a labor-intensive process that can cost up to $100 per test. Many
diagnostic labs, however, are now turning to a powerful automated
imaging system manufactured by BD, a New Jersey–based medical
device company. In a 2011 series of articles about job automation for
Slate, technology columnist Farhad Manjoo called the BD FocalPoint
GS Imaging System “a marvel of medical engineering” whose “imagesearching
software rapidly scans slides in search of more than 100
visual signs of abnormal cells.” The system then “ranks the slides according
to the likelihood they contain disease” and finally “identifies
10 areas on each slide for a human to scrutinize.”14
The machine does
a significantly better job of finding instances of cancer than human
analysts alone, even as it roughly doubles the speed at which the tests
can be processed.
Hospital and Pharmacy Robotics
The pharmacy at the University of California Medical Center in San
Francisco prepares about 10,000 individual doses of medication every
day, and yet a pharmacist never touches a pill or a medicine bottle. A
massive automated system manages thousands of different drugs and
handles everything from storing and retrieving bulk pharmaceutical
supplies to dispensing and packaging individual tablets. A robotic arm
continuously picks pills from an array of bins and places them in small
plastic bags. Every dose goes into a separate bag and is labeled with
a barcode that identifies both the medication and the patient who
should receive it. The machine then arranges each patient’s daily meds
in the order that they need to be taken and binds them together. Later,
the nurse who administers the medication will scan the barcodes on
both the dosage bag and the patient’s wrist band. If they don’t match,
or if the medication is being given at the wrong time, an alarm sounds.
Three other specialized robots automate the preparation of injectable
medicines; one of these robots deals exclusively with highly toxic
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chemotherapy drugs. The system virtually eliminates the possibility
of human error by cutting humans almost entirely out of the loop.
UCSF’s $7 million automated system is just one of the more spectacular
examples of the robotic transformation that’s unfolding in
the pharmacy industry. Far less expensive robots, not much larger
than a vending machine, are invading retail pharmacies located in
drug and grocery stores. Pharmacists in the United States require
extensive training (a four-year doctoral degree) and have to pass a
challenging licensing exam. They are also well paid, earning about
$117,000 on average in 2012. Yet, especially in retail settings, much
of the work is fundamentally routine and repetitive, and the overriding
concern is to avoid a potentially deadly mistake. In other words,
much of what pharmacists do is almost ideally suited to automation.
Once a patient’s medication is ready to leave a hospital pharmacy,
it’s increasingly likely that it will do so in the care of a delivery robot.
Such machines already cruise the hallways in huge medical complexes
delivering drugs, lab samples, patient meals, or fresh linens. The robots
can navigate around obstacles and use elevators. In 2010, El
Camino Hospital in Mountain View, California, leased nineteen delivery
robots from Aethon, Inc., at an annual cost of about $350,000.
According to one hospital administrator, paying people to do the
same work would have cost over a million dollars per year.15
In early
2013, General Electric announced plans to develop a mobile robot
capable of locating, cleaning, sterilizing, and delivering the thousands
of surgical tools used in operating rooms. The tools would be tagged
with radio-frequency identification (RFID) locator chips, making it
easy for the machine to find them.16
Beyond the specific areas of pharmacy and hospital logistics and
delivery, autonomous robots have so far made relatively few inroads.
Surgical robots are in widespread use, but they are designed to extend
the capabilities of surgeons, and robotic surgery actually costs more
than traditional methods. There is some preliminary work being
done on building more ambitious surgical robots; for example, the
I-Sur project is an EU-backed consortium of European researchers
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who are attempting to automate basic procedures like puncturing,
cutting, and suturing.17
Still, for the foreseeable future, it seems inconceivable
that any patient would be allowed to undergo an invasive
procedure without a doctor being present and ready to intervene, so
even if such technology materializes, any cost savings would likely
be marginal at best.
Elder-Care Robots
The populations of all advanced countries, as well as many developing
nations, are aging rapidly. The United States is projected to have
over 70 million senior citizens, making up about 19 percent of the
population, by 2030. That’s up from just 12.4 percent in 2000.18
In
Japan, longevity combined with a low birth rate make the problem
even more extreme; by 2025 fully a third of the population will be
over sixty-five. The Japanese also have a nearly xenophobic aversion
to the increased immigration that might help mitigate the problem.
As a result, Japan already has at least 700,000 fewer elder-care workers
than it needs—and the shortage is expected to become far more
severe in the coming decades.19
This surging global demographic imbalance is creating one of the
greatest opportunities in the field of robotics: the development of
affordable machines that can assist in caring for the elderly. The 2012
movie Robot & Frank, a comedy that tells the story of an elderly man
and his robotic caretaker, offers a very hopeful take on the kind of
progress we’re likely to see. The movie opens by announcing to the
viewer that it is set in the “near future.” The robot then proceeds to
exhibit extraordinary dexterity, carry out intelligent conversations,
and generally act just like a person. At one point, a glass is knocked
off a table, and the robot snatches it out of midair. That, I’m afraid,
is not a “near future” scenario.
Indeed, the main problem with elder-care robots as they exist
today is that they really don’t do a whole lot. Much of the initial
progress has been with therapeutic pets like Paro, a robotic baby
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seal that provides companionship (at a cost of up to $5,000). Other
robots are able to lift and move elderly people, saving a great deal of
wear and tear on human caretakers. However, such machines are expensive
and heavy—they may weigh ten times as much as the person
they are lifting—and will, therefore, probably be deployed primarily
in nursing homes or hospitals. Building a low-cost robot with sufficient
dexterity to assist with personal hygiene or using the bathroom
remains an extraordinary challenge. Experimental machines capable
of specific tasks have appeared. For example, researchers at Georgia
Tech have built a robot with a soft touch that can give patients a
gentle bed bath, but the realization of an affordable, multitasking
elder-care robot that can autonomously assist people who are almost
completely dependent on others probably remains far in the future.
One of the ramifications of that daunting technical hurdle is that,
despite the theoretically huge market opportunity, there are relatively
few start-up companies focused on designing elder-care robots and
little venture capital flowing into the field. The best hope almost
certainly comes from Japan, which is on the brink of a national crisis
and which, unlike the United States, has little aversion to direct collaboration
between industry and government. In 2013, the Japanese
government initiated a program in which it will pay two-thirds of
the costs associated with developing inexpensive, single-task robotic
devices that can assist the elderly or their caretakers.20
Perhaps the most remarkable elder-care innovation developed in
Japan so far is the Hybrid Assistive Limb (HAL)—a powered exoskeleton
suit straight out of science fiction. Developed by Professor
Yoshiyuki Sankai of the University of Tsukuba, the HAL suit is
the result of twenty years of research and development. Sensors
in the suit are able to detect and interpret signals from the brain.
When the person wearing the battery-powered suit thinks about
standing up or walking, powerful motors instantly spring into action,
providing mechanical assistance. A version is also available
for the upper body and could assist caretakers in lifting the elderly.
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Wheelchair-bound seniors have been able to stand up and walk with
the help of HAL. Sankai’s company, Cyberdyne, has also designed a
more robust version of the exoskeleton for use by workers cleaning up
the Fukushima Daiichi nuclear plant in the wake of the 2011 disaster.
The company says the suit will almost completely offset the burden
of over 130 pounds of tungsten radiation shielding worn by workers.*
HAL is the first elder-care robotic device to be certified by Japan’s
Ministry of Economy, Trade, and Industry. The suits lease for just
under $2,000 per year and are already in use at over three hundred
Japanese hospitals and nursing homes.21
Other near-term developments will probably include robotic
walkers to assist in mobility and inexpensive robots capable of bringing
medicine, providing a glass of water, or retrieving commonly misplaced
items like eyeglasses. (This would likely be done by attaching
RFID tags to the items.) Robots that can help track and monitor
people with dementia are also appearing. Telepresence robots that
allow doctors or caretakers to interact with patients remotely are already
in use in some hospitals and care facilities. Devices of this type
are relatively easy to develop because they skirt around the challenge
of dexterity. The near-term nursing-care robotics story is primarily
going to be about machines that assist, monitor, or enable communication.
Affordable robots that can independently perform genuinely
useful tasks will be slower to arrive.
Given that truly capable and autonomous elder-care robots are
unlikely to emerge in the near future, it might seem reasonable to
expect that the looming shortage of nursing home workers and home
health aids will, to a significant extent, offset any technology-driven
* The names selected by Sankai seem a bit odd for a company focused primarily
on elder care. HAL, of course, was the unfriendly computer that wouldn’t
open the pod bay doors in 2001: A Space Odyssey. Cyberdyne was the fictional
corporation that built Skynet in the Terminator movies. Perhaps the company
is eying other markets.
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job losses that occur in other sectors of the economy. Maybe employment
will simply migrate to the health and elder care sector. The US
Bureau of Labor Statistics (BLS) projects that by 2022, there will be
580,000 new jobs for personal-care aids and 527,000 for registered
nurses (those are the two fastest-growing occupations in the United
States), as well as 424,000 home heath aids and 312,000 nursing
aids.22
That adds up to about 1.8 million jobs.
This sounds like a big number. But now consider that the Economic
Policy Institute estimates that, as of January 2014, the United
States was still short 7.9 million jobs as a result of the Great Recession.
That includes 1.3 million jobs that were lost during the downturn
and hadn’t yet been recovered as well as another 6.6 million jobs
that were never created.23
In other words, if those 1.8 million jobs all
appeared today, they would fill only about a quarter of the hole.
Another factor, of course, is that these jobs are low-paying and
not particularly suitable for a large fraction of the population. According
to the BLS, home health aids and personal aids both provided
a medium 2012 income of under $21,000 and require an education
level of “less than high school.” Large numbers of workers are likely
to lack the temperament necessary to thrive in these jobs. If a worker
hates his job stamping out widgets, that’s one thing. If he despises
his job caring for a dependent older person, that’s a major problem.
Assuming the BLS’s projections are correct and these jobs do materialize
in large numbers, there is also the question of who will actually
pay for these workers. Decades of stagnant wages, together with
the transition from defined benefit pensions to often under-funded
401k plans, will leave a large fraction of Americans in relatively insecure
retirement situations. By the time the majority of older people
reach the point where they need personal, daily assistance, relatively
few are likely to have the private means to hire home health aids,
even if the wages for these jobs continue to be very low. As a result,
these will probably be quasi-government jobs funded by programs
like Medicare or Medicaid and will therefore be viewed as more of
a problem than a solution.
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Unleashing the Power of Data
As we saw in Chapter 4, the big data revolution offers the promise
of new management insights and significantly improved efficiency.
In fact, the increasing importance of all this data may be a powerful
argument for consolidation in the health insurance sector, or
alternatively creating some mechanism for sharing data among insurance
companies, hospitals, and other providers. Access to more
data could well mean more innovation. Just as Target, Inc., was
able to predict pregnancy based on customer purchasing patterns,
hospitals or insurance companies with access to large datasets will
potentially discover correlations between specific factors that can
be controlled and the likelihood of a positive patient outcome. The
original AT&T was famous for sponsoring Bell Labs, where many
of the twentieth century’s most important advances in information
technology took place. Perhaps one or more health insurance companies
with sufficient scale could play a somewhat similar role—
except that the innovations would come not from tinkering in a
lab but from continuously analyzing reams of detailed patient and
hospital operational data.
Medical sensors either implanted or attached to patients will provide
another important source of data. These devices will produce
a continuous stream of biometric information that can be used in
both diagnosis and in the management of chronic diseases. One of
the most promising areas of research is the design of sensors capable
of monitoring glucose in people with diabetes. The sensors could
communicate with a smart phone or other external device, instantly
alerting patients if their glucose level falls outside the safe range and
avoiding the need for uncomfortable blood tests. A number of companies
already manufacture glucose monitors that can be embedded
under a patient’s skin. In January 2014, Google announced that it is
working on a contact lens that would contain a tiny glucose detector
and wireless chip. The lenses would continuously monitor glucose
levels by analyzing tears; if the wearer’s blood sugar is too high or too
low, a tiny LED light would illuminate, providing an instant alert.
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Consumer devices like the Apple Watch, formally announced in September
2014, will likewise result in a torrent of health-related data.
Health Care Costs and a Dysfunctional Market
The March 4, 2013, issue of Time magazine featured a cover story
by Steven Brill entitled “Bitter Pill.” The article delved into the forces
underlying ever-escalating health care costs in the United States and
highlighted case after case of what can only be categorized as
outright price gouging—including, for example, a 10,000 percent
markup on the same over-the-counter acetaminophen tablets you
could buy at your local drug store or Walmart. Routine blood tests
for which Medicare would pay about $14 were marked up to $200
and beyond. CT scans that Medicare prices at about $800 were inflated
to over $6,500. A feared heart attack that turned out to be a
case of heartburn resulted in a $17,000 charge—not including fees
for the doctor.24
A few months later, Elisabeth Rosenthal of the New York Times
wrote a series of articles telling essentially the same story: a laceration
requiring three simple stitches came in at well over $2,000. A
dab of skin glue on a toddler’s forehead cost over $1,600. One patient
was charged nearly $80 for a small bottle of local anesthetic that
can be purchased for $5 on the Internet. Rosenthal noted that the
hospital, which buys such supplies in bulk, would likely pay far less.25
Both reporters found that these inflated charges generally originate
with a massive, obscure—and often secretive—list of prices
known as the “chargemaster.” The prices listed in the chargemaster
seemingly have no rhyme or reason and no meaningful relationship
to actual costs. The only thing one can say with consistent certainty
about the chargemaster is that its prices are very, very high. Both Brill
and Rosenthal found that the most egregious cases of chargemaster
abuse occurred with uninsured patients. Hospitals typically expected
these people to pay full list price and often were quick to hire bill
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collectors or even file lawsuits if patients couldn’t or wouldn’t pay.
Even major health insurance companies, however, are increasingly
billed at rates based on a discount from chargemaster prices. In other
words, the costs are first inflated—in many cases by a factor of ten
or even a hundred—and then a discount of perhaps 30, or even 50,
percent is applied, depending on how effectively the insurer negotiates.
Imagine buying a gallon of milk for $20 after negotiating a
50 percent discount from the $40 list price. Given this, it should come
as no surprise that hospital charges are the most important single
driver of consistently soaring health care costs in the United States.
One of the most important lessons of history is that there is a
powerful symbiosis between technological progress and a wellfunctioning
market economy. Healthy markets create the incentives
that lead to meaningful innovation and ever-increasing productivity,
and this has been the driving force behind our prosperity.*
Most intelligent
people understand this (and are very likely to bring up Steve
Jobs and the iPhone when discussing it). The problem is that health
care is a broken market and no amount of technology is likely to bring
down costs unless the structural problems in the industry are resolved.
There is also, I think, a great deal of confusion about the nature of
the health care market and exactly where an effective market pricing
mechanism should come into play. Many people would like to believe
that health care is a normal consumer market: if only we could get
insurance companies, and especially the government, out of the way
and instead push decisions and costs onto the consumer (or patient),
then we’d get innovations and outcomes similar to what we’ve seen in
other industries (Steve Jobs might be mentioned again here).
* Consider, for example, the Soviet Union, which by all accounts had some of
the best scientists and engineers in the world. The Soviets were able to achieve
solid results in military and space technology, but they were never able to scale
the benefits of innovation across the civilian economy. The reason certainly has
a lot to do with the absence of working markets.
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The reality, however, is that health care is simply not comparable
to other markets for consumer products and services, and this has
been well understood for over half a century. In 1963, the Nobel
laureate economist Kenneth Arrow wrote a paper detailing the ways
in which medical care stands apart from other goods and services.
Among other things, Arrow’s paper highlighted the fact that medical
costs are extremely unpredictable and often very high, so that
consumers can neither pay for them out of ongoing income nor effectively
plan ahead as they might for other major purchases. Medical
care can’t be tested before you buy it; it’s not like visiting the
wireless store and trying out all the smart phones. In emergencies,
of course, the patient may be unconscious or about to die. And, in
any case, the whole business is so complex and requires so much
specialized knowledge that a normal person can’t reasonably be expected
to make such decisions. Health care providers and patients
simply don’t come to the table as anything approaching equals, and
as Arrow pointed out, “both parties are aware of this informational
inequality, and their relation is colored by this knowledge.”26
The
bottom line is that the high cost, unpredictability, and complexity
of major medical and hospitalization services make some kind of
insurance model essential for the health care industry.
It is also critical to understand that health care spending is highly
concentrated among a tiny number of very sick people. A 2012 report
by the National Institute for Health Care Management found
that just 1 percent of the population—the very sickest people—
accounted for over 20 percent of total national health care spending.
Nearly half of all spending, about $623 billion in 2009, went to the
sickest 5 percent of the population.27
In fact, health care spending is
subject to the same kind of inequality as income in the United States.
If you draw a graph, it will look very much like the winner-take-all/
long-tail distribution I described in Chapter 3.
The importance of this intense concentration of spending cannot
be overemphasized. The small population of very ill people on
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whom we are spending all this money are obviously not in a position
to negotiate prices with providers; nor would we want to place such
a staggering fiscal responsibility in these people’s hands. The “market”
that we need to make work exists between the providers and
the insurance companies—not between providers and patients. The
essential lesson of the articles written by Brill and Rosenthal is that
this market is dysfunctional because of a fundamental power imbalance
between insurers and providers. While individual consumers
may rightly perceive health insurance companies as powerful and
domineering, the reality is that—relative to providers like hospitals,
doctors, and the pharmaceutical industry—they are, in a great many
cases, too weak. That imbalance is being steadily worsened by an
ongoing wave of consolidations among providers. Brill’s article notes
that as hospitals increasingly snap up “doctor’s practices and competing
hospitals, their leverage over insurance companies is increasing.”28
Imagine a near future where a physician wields a powerful tablet
computer that allows her to order a range of medical tests and
scans with just a few presses on her touch screen. Once a test is
completed, the results are instantly routed to her device. If a patient
needs a CT scan, or perhaps an MRI, the results are accompanied
by a detailed analysis performed by an artificial intelligence application.
The software points out any anomalies in the scan and makes
recommendations for further care by accessing a massive database
of patient records and identifying similar cases. The doctor can see
exactly how comparable patients were treated, any issues that arose,
and how things ultimately turned out. All this would, of course, be
efficient and convenient and ought to lead to a better outcome for
the patient. This is the kind of scenario that gets techno-optimists
excited about the revolution soon to unfold in the health care arena.
Now assume that the doctor has a financial interest in the diagnostic
company that performs the tests or scans. Or, then again,
maybe the hospital has acquired the doctor’s practice and also owns
the testing facility. The prices for the tests and scans bear little
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relation to the actual costs of these services—after all, they’re listed
in the chargemaster—and they are highly profitable. Every time our
doctor presses her touch screen, she essentially mints money.
While this example is, at the moment, imaginary, there is an
abundance of evidence demonstrating that new health care technologies
very often lead to more spending rather than improved productivity.
The primary reason is that there is no effective market
pricing mechanism to drive increased efficiency. In the absence of
market pressure, providers often invest in technologies designed to
increase revenue rather than efficiency, or where they do achieve increased
productivity they simply retain the profits rather than lowering
prices.
The poster child for technology investment as a driver of health
care inflation may well be the “proton beam” facilities that are being
built to treat prostate cancer. A May 2013 article by Jenny Gold of
Kaiser Health News noted that “despite efforts to get health care
spending under control, hospitals are still racing to build expensive
new technology—even when the devices don’t necessarily work better
than the cheaper kind.”29
The article describes one proton beam
facility as “a giant cement-encased building the size of a football
field, with a price tag of more than $200 million.” The idea behind
this expensive new technology is that it delivers less radiation to
patients, and yet, studies have found no evidence that protein beam
technology results in better patient outcomes than far less expensive
approaches.30
Health care expert Dr. Ezekiel Emanuel says, “We
don’t have evidence that there’s a need for them in terms of medical
care. They’re simply done to generate profits.”31
To me, it seems evident that the American people could in principle
be made much better off by a massive technological disruption
of the health care sector than of, say, the fast food industry. After
all, lower prices and improved productivity in health care will likely
lead directly to better and longer lives. Cheaper fast food may well
do the opposite. Yet, the fast food industry has well-functioning
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The Health Care Challenge 165
markets—and the health care sector does not. As long as that situation
is allowed to persist, there are few reasons to be optimistic
that accelerating technology alone will succeed in reining in soaring
health care costs. Given this reality, I’d like to take a brief detour
from our technology narrative in order to suggest two alternate
strategies that might help to correct the power imbalance between
insurers and providers, and hopefully enable the kind of synergy between
markets and technology that might bring the transformation
we hope for.
Consolidate the Industry and
Treat Health Insurance as a Utility
One of the primary messages that leaps out from an analysis of the
prices charged by providers is that Medicare—the government-run
program for people aged sixty-five and over—is by far the most efficient
portion of our health care system. As Brill writes, “Unless you
are protected by Medicare, the health care market is not a market at
all. It’s a crapshoot.” The implementation of the Affordable Care Act
(Obamacare) will certainly improve the situation as far as individuals
who previously lacked insurance are concerned, but it does relatively
little to actively rein in hospital costs; instead, the inflated costs will
be shifted to insurers and then ultimately to taxpayers in the form of
the subsidies that were put in place to make health insurance affordable
to people with moderate incomes.
The fact that Medicare is relatively effective at controlling most
patient-related costs, while spending far less than private insurers
on administration and overhead, underlies the argument for simply
expanding the program to include everyone and, in effect, creating
a single-payer system. This has been the path followed by a number
of other advanced countries—all of which spend far less on health
care than the United States and typically have better outcomes according
to metrics like life expectancy and infant mortality. While
a single-payer system, managed by the government, has both logic
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RISE OF THE ROBOTS166
and evidence to support it, there is no escaping the reality that in the
United States the whole idea is ideologically toxic to roughly half
the population. Putting such a system in place would also presumably
result in the demise of nearly the entire private health insurance sector;
that does not seem likely given the enormous political influence
wielded by the industry.
A single-payer system is, in practice, always assumed to be run
by the government, but in theory this does not have to be the case.
Another approach might be to merge all private insurance companies
into a single national corporation, which would then be heavily
regulated. The model would be the original AT&T before it was
broken up in the 1980s. The central idea here is that health care is in
many ways akin to the telecommunications system: it is, in essence,
a utility. Like water and sanitation systems or the nation’s electrical
infrastructure, the health care system does not stand alone—it is a
systemic industry whose efficient operation is critical to both the
economy and society. In many cases, the provision of a utility service
leads to natural monopoly scenarios. In other words, it is most
efficient if only a single firm operates in the market.
An even more effective variation on this theme might be to allow
a small number of large competing insurance companies—in effect,
a sanctioned oligopoly. This would inject an element of competition
into the system. The companies would still be large enough to
have significant market power when negotiating with providers, and
they would have little choice but to compete on the basis of enabling
high-quality care since their reputations would determine their success.
Tight regulation of the industry would limit price increases and
prevent the companies from engaging in undesirable practices like,
for example, designing insurance plans geared specifically toward
“cherry-picking” younger, healthier patients or offering plans with
substandard protection. Instead, they would have to focus on genuine
innovation and efficiency.
Consolidating existing insurance companies into one or more regulated
“health care utilities” might provide many of the advantages
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The Health Care Challenge 167
of a single-payer system while preserving the industry. Rather than
being wiped out, the shareholders of private insurance companies
might conceivably see gains as a result of an industry-wide merger.
The mechanism by which such a consolidation might be brought
about is, of course, far from obvious. Perhaps the government could
issue a small number of operating licenses, and it might even hold an
auction as it does for the electromagnetic communications spectrum.*
Set “All-Payer” Rates
An alternate, and perhaps more feasible, strategy is the implementation
of an “all-payer” system. In this scenario, the government essentially
sets the schedule of prices that can be charged by health
care providers. Just as Medicare dictates the prices it will pay, an
all-payer system would do the same for all patients receiving care
from any given provider. An all-payer approach is used in the health
care systems of a number of countries, including France, Germany,
and Switzerland. In the United States, Maryland also has such a system
for hospitals, and the state has seen relatively slow growth in
hospitalization costs.32
All-payer systems vary in the specifics of their
implementation; the rates may be set through collective negotiation
between providers and payers, or they might be established by a regulating
commission after an analysis of actual costs at particular
hospitals.
* In the United States, the constitutional authority to create a single-payer
system—regardless of whether it is run by the government or by private
corporations—probably derives from the government’s ability to levy a tax on
everyone to pay for the system. Therefore, all or a portion of the premiums
would be paid by the government. This is already the case with the insurance
subsidies associated with the Affordable Care Act. In other words, the federal
government can force everyone to pay for a single-payer system through taxes,
but it cannot prohibit a parallel private system. So there still would likely be
additional services available to those willing and able to pay out of pocket, just
as there are private schools. This is different from the system in Canada, where
most private health care services are prohibited—leading some Canadians to
seek health care services in the United States.
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Since an all-payer system enforces the same prices for all patients,
it has important implications for the cost shifting that goes on between
private patients and those covered by the public systems in the
United States (Medicaid for low-income people and Medicare for
those over sixty-five). When a single rate is set, the public prices have
to rise considerably, putting more of a burden on taxpayers. Privately
insured patients, and especially those who are uninsured, will typically
benefit from lower prices as they are no longer subsidizing the
public programs. This has been the case with Maryland’s program.*
It seems to me that a much simpler approach that might produce
immediate savings would be to set an all-payer ceiling rather
than a specific price. For instance, suppose the ceiling were set at the
Medicare rate plus 50 percent. In one example from Brill’s article, a
blood test that Medicare says is worth $14 might then be priced at
any amount up to $21—but it could never reach anything like $200.
Insurance companies with sufficient market power would still be
free to negotiate a price lower than the ceiling. This strategy would
immediately eliminate the worst excesses, and as long as the ceiling
was set high enough, it would still provide sufficient revenue to providers.
A 2010 fact sheet published by the American Hospital Association
claims that Medicare paid “90 cents for every dollar spent by
hospitals caring for Medicare patients in 2009.”33
If the industry’s
own lobbying organization says Medicare is covering 90 percent of
hospital costs, then a ceiling somewhat higher than the Medicare
rate should be sufficient to allow enough cost shifting to make up
for that missing 10 percent.**
An all-payer ceiling would also be very
* Maryland has a special waiver that has been in place for over thirty years and
allows it to pay higher Medicare rates. As of 2014, Maryland has moved to a
new experimental system that is allowed under the Affordable Care Act. In addition
to setting all-payer rates, the new program will enforce explicit caps on
per capita hospital spending. The state expects to save $330 million in Medicare
costs over a five-year period.
** The same fact sheet says that Medicaid (the program for the poor) paid 89
percent of actual hospital costs.
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The Health Care Challenge 169
easy to implement since it is based directly on the already-published
Medicare rates.
One of the most hopeful approaches to controlling health costs,
which is gaining some traction in the current environment, is to transition
away from a fee-for-service model and toward an “accountable
care” system in which doctors and hospitals are paid a set fee to
manage the overall health of patients. One of the primary advantages
of this approach is that it would reorient the incentives regarding
innovation. Rather than simply offering a new way to hoover up
even higher fees according to a fixed schedule, emerging technologies
would be viewed in terms of their potential to reduce costs and
make care more efficient. The key to making that happen, however,
is to push more of the financial risk associated with patient care
away from insurers (or the government) and onto hospitals, doctors,
and other providers. Needless to say, the latter are unlikely to accept
that increased risk willingly. In other words, in order to drive a successful
transition toward accountable care, we still need to address
the market power imbalance that often exists between insurers and
providers.
In order to bring relentlessly increasing health care costs in the
United States under control, I think it will probably be necessary to
pursue one of the two general strategies I’ve outlined. We will have
to move toward a single-payer system where either the government
or one or more large private firms exercise more bargaining power
in the health insurance market, or alternatively we will need to have
regulators exercise direct control over the rates paid to providers.
In either scenario, moving aggressively toward an accountable care
model might be a vital part of the solution. Both of these approaches,
in various combinations, are used successfully by other advanced
countries. The bottom line is that a pure “free market” approach in
which we cut government out of the loop and expect patients to operate
like consumers shopping for groceries or smart phones is never
going to work. As Kenneth Arrow pointed out over fifty years ago,
health care is simply different.
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This is not to say that there are no significant dangers associated
with either approach. Both strategies rely on regulators to either
control premiums or set the prices paid to providers. There is an
obvious risk of regulatory capture; powerful companies or industries
may exert influence that bends government policy in their favor.
Attempts at such influence have already been successfully directed
at Medicare, which is specifically prohibited from using its market
power to negotiate drug prices. The United States is virtually the
only country in the world where this is the case; every other national
government negotiates prices with the drug companies. The
result is that Americans, in effect, subsidize lower drug prices in
the rest of the world. The three years between 2006 and 2009 saw
a 68 percent increase in the rate of “prescription abandonment” in
the United States.34
This happens when patients request that a prescription
be filled, but then walk away when they find out the cost.
It’s something of a mystery to me why this is not more disturbing to
Americans, and to grassroots conservatives in particular. The Tea
Party, after all, got started after a famous rant by CNBC personality
Rick Santelli, who decried the fact that people with mortgages they
couldn’t afford might be subsidized by taxpayers. Why aren’t average
Americans more upset about the fact that they are paying the
pharmaceutical freight for the rest of the world—including a number
of countries that have significantly higher per capita incomes
than the United States?
Inspiteofthisproblem,Medicareconsistentlyprovideshigh-quality
care at a cost significantly lower than in the highly fragmented private
insurance sector. In other words, we should not make the perfect
the enemy of the good. Nonetheless, Medicare’s prohibition
against negotiating with the pharmaceutical industry deserves to be
subjected to a great deal more public scrutiny. The industry argues
that inflated drug prices in the United States are necessary in order
to fund further research. However, there are likely more efficient
and certainly more equitable ways to ensure that drug research gets
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The Health Care Challenge 171
funded.35*
The potential to reform or streamline the Federal Drug
Administration’s procedures for testing and approving new drugs
also surely exists.
Another issue with Medicare, and one that touches directly on
the subject of this book, is that waste can easily be driven by the
direct advertisement of products to senior citizens who are told explicitly
to pressure their physicians for a prescription and that Medicare
will then pick up nearly the entire cost. One government audit
found that up to 80 percent of the motorized scooters paid for by
Medicare were not really needed by the elderly patients who received
them and may actually be harmful to their health. The two largest
scooter manufacturers spent over $180 million on advertisements
directed at Medicare recipients in 2011.36
This is another issue that
deserves close scrutiny because, as we’ve seen, there is soon likely to
be a profusion of robotic equipment geared toward providing homebased
assistance to senior citizens. Such advances have great potential
to improve quality of life for the elderly while reducing the cost
of their care—but not if we pay for technology in cases where it is
unneeded or perhaps even detrimental. The specter of millions of
comfortably seated senior citizens watching advertisements telling
them that Medicare will happily pay for a robot capable of retrieving
their television remote should give us pause.**
* A related issue has to do with the patents granted to drug manufacturers.
These prevent the introduction of cheaper, generic drugs for long periods. Many
economists believe that the pharmaceutical patent system is very inefficient.
Other countries can also potentially threaten to void drug patents as a price
negotiating mechanism—putting a still higher burden on Americans. The Center
for Economic and Policy Research published a briefing in 2004 that outlines
these issues and presents some more efficient alternatives for funding drug research.
Please see the corresponding endnote for details.
** The whole idea behind requiring prescriptions is that patients are not able
(or cannot be trusted) to make these decisions for themselves. Why, then, do we
allow drug companies or medical equipment manufacturers to advertise directly
to patients?
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WHILE RECENT APPLICATIONS OF AI and robotics to the health care field
are impressive and advancing rapidly, they are, for the most part, just
beginning to nibble at the edges of the hospital cost problem. With
the exception of pharmacists, and possibly doctors or technicians who
specialize in analyzing images or lab specimens, automating even a
significant portion of the jobs done by most skilled health care workers
remains a daunting challenge. For those seeking a career that is
likely to be relatively safe from automation, a skilled health care profession
that requires direct interaction with patients remains an excellent
bet. That calculus could, of course, change in the more distant
future. Twenty or thirty years from now, I think, it’s impossible to
say with any real confidence what might be technologically possible.
Technology is not the only consideration, of course. Health care,
more than any other sector of the economy, is subject to a complex
web of rules and regulations imposed by governments, agencies like
the FDA, and licensing authorities. Every action and every decision
are also colored by the looming threat of litigation if an error—or
perhaps just an unlucky outcome—should occur. Even among retail
pharmacists, the specific impact of automation on employment isn’t
easily discernible. The reason is likely regulation. Farhad Manjoo interviewed
one pharmacist who said, “Most pharmacists are employed
only because the law says that there has to be a pharmacist present to
dispense drugs.”37
That, at least for the moment, is probably something
of an exaggeration. Job prospects for newly minted pharmacists
have worsened significantly over the past decade, and things may well
get worse. A 2012 analysis identifies a “looming joblessness crisis for
new pharmacy graduates” and suggests that the unemployment rate
could reach 20 percent.38
However, this is likely due largely to an
explosion in the number of new graduates entering the job market as
pharmacy schools have dramatically increased enrollments.*
Relative
* One could also speculate that technology is indirectly contributing to diminished
prospects for pharmacy graduates by driving more people into the
profession. In the first decade of the new millennium, nearly fifty new pharmacy
graduate schools opened their doors (a 60 percent increase), and existing
programs also dramatically increased enrollments. The number of newly
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The Health Care Challenge 173
to most other occupations, there’s little doubt that health care professionals
enjoy an extraordinary degree of employment security as
a result of factors completely unrelated to the technical challenges
associated with automating their jobs.
This may be good news for health care workers, but if technology
has only a muted impact on health care costs even as it disrupts other
employment sectors, the economic risks we face will be amplified.
In that scenario, the burden of soaring health care costs will become
even more unsustainable as advancing technology continues to produce
unemployment and ever-increasing inequality, as well as stagnant,
or even falling, incomes for most workers in other industries.
This prospect makes it even more critical to introduce meaningful
reforms that will correct the market power imbalance between insurers
and providers so that advancing technology can be fully leveraged
as a mechanism for increased efficiency across the health care sector.
Without that, we run the risk that our market economy will eventually
come to be dominated by a sector that is inefficient and, indeed,
not an especially well-functioning market at all.
Controlling the health care cost burden is especially critical because,
as we’ll see in Chapter 8, the last thing American households
need is an ever-increasing drain on their discretionary income. Indeed,
stagnant incomes and growing inequality are already undermining
the broad-based consumer demand that is vital to continued
economic growth.
graduated pharmacists could hit 15,000 per year by 2016; that’s over twice the
number of degrees granted in 2000. Something very similar (and perhaps even
more extreme) happened with law schools, and the law school enrollment bubble
is now famously bursting. Law school has always been a well-traveled path
toward monetizing a liberal arts degree. Pharmacy offers similar potential for
an undergraduate biology degree. It may be that soaring demand for these professional
degrees results, at least in part, from the evaporation of other good
opportunities for college graduates. With relatively few other attractive alternatives,
college graduates have clamored to get into law or pharmacy school, and
the industry has responded by expanding enrollment and ultimately producing
far more graduates than the market could absorb. The fact that both pharmacy
and law are also impacted by direct automation makes things even more unsustainable.
My prediction for the next professional school bubble: MBA degrees.
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So far, we have focused primarily on the ways in which technology
is likely to transform existing employment sectors. In the next
chapter, we’ll leap a decade or more ahead in time and imagine how
things might look in a future economy populated with entirely new
technologies and industries.
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