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Productivity and the Health Care Workforce

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Executive Summary Since World War II, health care spending in the United States has grown rapidly – faster than in the rest of the developed world, and much faster than the US gross domestic product. With less than 5% of the world's population, the US now accounts for 40% of global health spending, almost twice as much per capita as the next highest-spending country. American health outcomes, however, have not kept pace with spending. The United States compares remarkably poorly to other developed countries on such measures as life expectancy at birth, infant mortality, and other indicators of population health. In other words, we are spending more and more on health care for lower and lower returns in the form of better health. Despite a steady stream of medical innovations, productivity growth in the health care sector has been slow. There are several reasons for this poor productivity. Many tests and treatments (both new and old) are routinely put to use with little or no regard for whether they improve patient outcomes. Treating prostate cancer with proton beam therapy, for example, costs $50,000 per patient – roughly twice the cost of standard radiation treatment. Yet there is no evidence that proton beam therapy is any better for the patient's chances of surviving cancer or avoiding serious side effects. There is also evidence that between one-tenth and one-third of tests and treatments are unnecessary or unwanted by patients. Such overuse consumes real resources and can cause real harm, and is largely the result of two main factors. One is the failure to measure whether treatments such as proton beam therapy are effective. The other is a phenomenon known as " supply-sensitive " care, the tendency of providers to deliver hospitalizations and other medical services simply because resources such as beds and technology are available.
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Next Social Contract Initiative and Economic Growth Program
Productivity and the Health Care
Workforce
Shannon Brownlee, Joseph Colucci, and Thom Walsh
October 2013
Executive Summary
Since World War II, health care spending in the United States has grown rapidly faster than in the rest of the
developed world, and much faster than the US gross domestic product. With less than 5% of the world’s population,
the US now accounts for 40% of global health spending, almost twice as much per capita as the next highest-
spending country. American health outcomes, however, have not kept pace with spending. The United States
compares remarkably poorly to other developed countries on such measures as life expectancy at birth, infant
mortality, and other indicators of population health. In other words, we are spending more and more on health care
for lower and lower returns in the form of better health. Despite a steady stream of medical innovations, productivity
growth in the health care sector has been slow.
There are several reasons for this poor productivity. Many tests and treatments (both new and old) are routinely put
to use with little or no regard for whether they improve patient outcomes. Treating prostate cancer with proton beam
therapy, for example, costs $50,000 per patient roughly twice the cost of standard radiation treatment. Yet there is
no evidence that proton beam therapy is any better for the patient’s chances of surviving cancer or avoiding serious
side effects.
There is also evidence that between one-tenth and one-third of tests and treatments are unnecessary or unwanted by
patients. Such overuse consumes real resources and can cause real harm, and is largely the result of two main
factors. One is the failure to measure whether treatments such as proton beam therapy are effective. The other is a
phenomenon known as “supply-sensitive” care, the tendency of providers to deliver hospitalizations and other
medical services simply because resources such as beds and technology are available.
New America Foundation
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There is one additional reason for health care’s poor productivity: medical institutions are poorly organized. They
waste time, money, labor, and other resources by operating inefficiently. The Dartmouth Atlas, which tracks
Medicare data, shows that inputs (beds, physicians, equipment per capita) vary widely among hospitals, with little if
any discernible difference in the outcomes of statistically similar patient populations.
Studies of provider performance, especially of hospitals, suggest that vast productivity gains are within reach of
American medicine. Some institutions have made dramatic improvements in efficiency by improving their care
delivery processes improvements that have led, for example, to a 30% reduction in the use of MRIs for patients
with back pain, and improvements in workflow patterns that take nurses from spending over 60% of their time
away
from patients to spending 90% of their time
with
patients. For example, if all hospitals achieved the same level
of efficiency for inpatient care as Intermountain Healthcare, a multi-hospital chain based in Salt Lake City, total
hospital spending in the US would fall by an estimated 43 percent.
Making better use of health care labor force is the key to improving productivity in the sector. This paper looks first
at sources of low productivity in health care, and then examines the implications for future health care workforce
needs.
Sources of Low Productivity
There are three principal reasons for poor productivity in health care: measurement challenges, supply-sensitive
care, and poor organization.
Measurement challenges: Measuring productivity requires us to gauge the value of services provided, but this poses
special challenges in health care. The consumption or utilization of health care services and the market price of
those services (two easily-measured quantities) offer only limited information as to the value a patient receives from
medical care. Many services don’t have a clear relationship with improved health, and many expensive treatments
are no more valuable than low-cost care. For example, medical (drug) treatment for stable angina (chest pain) is, for
most patients, just as effective at relieving pain as medical treatment plus angioplasty, a surgical procedure and the
surgery is both invasive and expensive. Using the amount spent as a metric for value when comparing an angina
patient who received medical management alone to one who had medical management plus angioplasty would
incorrectly suggest that the patient who underwent angioplasty enjoyed better health. And even when we measure
patients’ actual health, it’s difficult to attribute improvements to a particular treatment. The problems inherent in
measuring the value of health care have prevented markets from providing effective signals to clinicians or patients
about which treatments to use and which to avoid.
Supply-sensitive care: There is considerable geographic variation in utilization of medical services across the country,
both in terms of dollars spent and the rate of utilization of specific tests and procedures. This variation cannot be
explained entirely by differences in rates of illness, or the quality of care being delivered. Rather, it is explained in
large part by differences in the concentration of such medical resources as hospital beds, CT scanners, and
physicians. Los Angeles has more beds per capita than most other places in the U.S. and not surprisingly, patients
in Los Angeles spend more days in the hospital than most. This effect, known as supply-sensitive care, contributes to
higher utilization in high-supply areas but patients in those areas do not appear to benefit from much of the
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additional utilization. This suggests that much of the care delivered in these high-supply areas is waste. The
phenomenon of supply-sensitive care has significant implications for our discussion of the workforce.
Poor organization and productive inefficiency: Even when the care being provided is valuable, resources are routinely
used ineffectively and wastefully in health care. Examples of this inefficiency abound. Highly-trained and highly-paid
physicians fill out basic paperwork, patients wait hours for answers to simple questions, and nurses and technicians
spend hours each day searching for commonly-needed medical supplies. A few hospital systems have reinvented
their production processes using the waste-elimination philosophy of Lean management and have experienced
significant improvements in efficiency and reductions in cost. Others have used different methods that have
improved efficiency by reducing the delivery of unnecessary services.
Implications for the Workforce
Each of these factors poor measurement, supply-sensitive care, and poor organization has significant
implications for health care workforce planning. Nearly 14 million people are involved in the direct delivery of
medical services. Many of them, including 691,000 practicing physicians, 2.7 million nurses and nurse
practitioners, and 83,000 physician assistants, spend years in training much of which is federally subsidized.
Thus, their numbers are influenced by federal and state policy. For example, the number of new physicians is
determined in large part by the U.S. Department of Health and Human Services, which pays for the majority of
graduate medical (residency) training and sets the number of residency slots. In the past, decisions about the
number of physicians the nation needs has been determined largely on the basis of population growth and historical
patterns of utilization of medical services. Most projections have assumed that as the population grows and ages, the
nation will need more physicians and other health care workers to deliver needed services.
We argue that over-reliance on the workforce of the past as a model for the workforce of the future is likely to
impede productivity growth in the health care sector. Supply and demand are not independent in health care. In
assuming that the quantity of medical services delivered is independent of the size of workforce, policy experts who
predict we need more physicians as the population expands and ages are making a crucial error, which is likely to
exacerbate, not ameliorate, the poor productivity currently seen. Moreover, since increasing supply increases the
provision of low-value services more than high-value services, an unwarranted expansion of the health care
workforce is likely to reduce productivity even further.
This paper draws on a wide range of research, from the Dartmouth Atlas Project on geographic variation in
utilization and efficiency, to the economics literature, and to case studies that have demonstrated it is possible to
lower production costs and increase productive efficiency in hospitals and primary care settings. From this work, we
draw three main conclusions about current workforce allocation and future workforce needs:
By improving primary care, it is possible to reduce the rate of hospitalization, and thus reduce the need for hospital -based labor for a given
population of patients. Organized primary care practices, built around smaller panels (fewer patients per primary care doctor) and
collaboration among clinicians, have successfully reduced utilization of emergency room and inpatient services while improving
patient health, increasing patient and employee satisfaction, and reducing spending.
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Improving productive efficiency within hospitals can also decrease the need for hospital-based labor. By improving skill-task alignment
and reducing unnecessary processes and the delivery of unnecessary services, hospitals can free up high-priced specialists to
serve a larger patient population. In some cases, reorganization of care processes has also boosted efficiency, allowing physical
and staffing resources to serve more patients and decreasing the cost of providing care.
If all hospitals and ambulatory practices increase efficiency and improve care, the workforce of the future is likely to be very different from
the workforce of today. The variation in the allocation of resources in different regions of the country coupled with case studies
suggest that we now have more highly specialized clinicians than we need and too few primary care physicians to handle a larger
patient population. Thus, the nation may not need more physicians, even after taking into account aging of the population and
growth in rates of insurance, but we will surely need to address the ratio of primary care doctors to specialists. Moreover, many
tasks that are currently performed by physicians could be done by other clinicians, such as nurse practitioners, or by trained
non-clinicians such as health coaches and medical assistants.
Recommendations
Residency: In light of the deleterious effects a larger workforce is likely to have on productivity, the federal
government should refrain from expanding funding for graduate medical education slots. If, after further study, an
expanded workforce seems necessary to provide all beneficial medical care, this expansion should aim to encourage
team-based care and strong primary care management, and to discourage a glut of specialists.
Regulation: Scope-of-practice and staffing ratio laws that interfere with skill-task alignment or with necessary
reductions in the hospital workforce should be repealed or relaxed on providers with consistently good outcomes.
Research: Any projections of future utilization or workforce needs should account for supply-sensitive care, and
adjust workforce recommendations to minimize overuse, or the provision of ineffective care. Research is also needed
to determine how best to organize the delivery of health care services and reduce waste.
Remuneration: Payment reform will be a part of any effective strategy for improving productivity. It’s crucial that
reform efforts diminish or eliminate rewards for delivering large volumes of care that offer little or no benefit to
patients. Clinicians may be salary-based (as they are at many high-quality health care organizations), with hospitals
receiving global budgets to take care of a specified population of patients, or payment may take another form, but the
nation should shift away from the current fee-for-service model toward a system that rewards the delivery of valuable
care, not just more care.
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Introduction
American health care spending as a percentage of GDP has been rising for more than 40 years. The US accounts for
40% of global health care spending, and spends around one and a half times as much per capita on health care as
the next highest-spending country, Norway.
1
,
2
Although the rate of growth in health care spending slowed during the
2007-09 recession, it remains higher than the rate of growth in the economy as a whole (Figure 1).
In isolation, rising health care spending might not be cause for concern. As incomes go up, it makes sense that
people want to allocate more resources to longer lives and better health. However, the reality of escalating health
spending should give us pause because it does not appear to be purchasing better health. In 2008, U.S. per capita
spending was 103% higher than the average per capita spending of Canada, Japan, Australia, and 16 Western
European countries. Yet many U.S. health statistics, such as life expectancy at birth, infant mortality, and maternal
mortality, ranked at or near the bottom of that group.
3
Rising health spending is also crowding out other goods we might wish to purchase. For many of the roughly 170
million Americans with employer-provided health insurance, coverage accounts for a growing percentage of total
worker compensation. As a result, living standards for middle- and lower-income Americans have stagnated or
fallen, even as median total compensation has risen.
4
Finally, escalating spending on health care at both the federal
and state levels is squeezing out other public goods such as education, defense, and infrastructure maintenance,
leaving the federal government and the states either to raise taxes or to accept unprecedented levels of public debt
(Figure 2). We are spending more and more public and private money on health care, while getting less and less for
it.
At the same time, rising health care spending has been accompanied by remarkably low productivity growth in the
sector. In car manufacturing and computers, for example, rising productivity has meant that fewer workers are
Figure 1:
Source: Congressional Budget Office, 2011
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needed to produce a vehicle, and that each new microchip offers more processing power for the same price. Those
are just two examples of the sorts of productivity gains that have helped enrich the U.S. and the world over the last
fifty years. Yet even as health care’s share of GDP grows ever larger, productivity gains in the sector lag far behind
the rest of the economy.
5
While labor-intensive service sector industries in general have lower productivity growth
than manufacturing or agriculture, health care does have room to be more productive. Health care resources,
including the health care workforce, are not being put to the most efficient possible use.
6
This paper focuses on just one of the
inputs needed to deliver health care:
the physicians, physician assistants,
nurse practitioners, nurses, and allied
health professionals who make up the
health care workforce. It draws on a
broad range of literature, including
studies of labor productivity,
organizational improvements in
health care, and regional variation in
medical spending and practice. Our
goal is to understand the role that the
health care workforce plays in the
sector’s low productivity and to make
specific recommendations for
improving productivity through
workforce policy. We first examine the
evidence for poor productivity and
some of the factors that contribute to
it. We then look at the role of excess
supply in driving overuse of medical
services, and at the particular habits
and techniques used by the most
efficient hospitals and primary care groups to determine what contributes to their greater efficiency.
The key ingredient to efficiently-delivered health care, it turns out, is human organization. Different hospitals
allocate resources, including labor, very differently, depending in part upon the degree to which they have organized
their processes for delivering care. Hallmarks of more efficient hospitals include formal methods for examining
their own production processes and efforts to ensure that physicians and other clinicians work together as teams in
caring for patients.
7
More efficient health care providers also tend to emphasize community-based primary care over
(expensive) hospital-based specialty services, and may be better at using clinicians as effectively as possible by
harnessing their most advanced skills and ensuring that highly-trained workers are not performing low-value tasks.
Studies of exemplary hospitals and physician practices, those that are more efficient than most, suggest that vast
productivity gains are being left on the table.
Figure 2:
Source: Congressional Budget Office, 2011
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If the nation could harness the best practices of these relatively efficient organizations and apply them to all health
care providers in all states, the savings would be substantial not just for Medicare and the federal government, but
for state governments and private payers as well. To take a single example, if productivity in the hospital sector rose
and all hospitals achieved the same level of efficiency for inpatient care as Intermountain Healthcare, a multi-
hospital chain based in Salt Lake City, total hospital spending in the US would fall by 43 percent.
8
The final aim of this paper is to examine current workforce policy in light of what we have learned about the causes
of low efficiency and productivity, and about the ingredients necessary to improve them. From this critique, we offer
several workforce policy recommendations.
Part I: Waste and Productivity
How do we measure productivity in health care? Economists and health services researchers often measure it in the
same way they measure productivity in other industries. A systematic review of 265 productivity measures in 172
papers conducted by Hussey, et al. of the RAND Corporation found that the majority of health care productivity
research tracked utilization, or the provision of medical services, either per dollar (of total cost or labor cost) or per
worker (or worker-hour, etc.).
9
Over 97 percent
of productivity studies looked at the utilization of health care services
as the only output. (Not surprisingly, most of those metrics were created by or for hospital managers, physician
practices, and others interested in improving revenue generation.)
The other common method for measuring health care productivity is to look at the role of health care in the national
economy. For example, in a recent paper published in the
New England Journal of Medicine,
Robert Kocher and
Nikhil Sahni
estimate health care labor productivity by measuring the growth rate of total health care spending
(health care’s contribution to GDP), minus the growth rate of the health care labor force.
10
In their calculation,
workers are the input; health care spending is the output.
11
In Kocher and Sahni’s analysis, health care labor has
become less productive over the last two decades, because wages in the sector have risen faster than the sector’s
contribution to GDP.
12
These are reasonably useful ways to measure productivity in most industries, but they fail to measure the real
value
of health care. Writing in the
New England Journal of Medicine
in 2010, Michael Porter defined value as “the health
outcomes achieved per dollar spent. This is what matters to patients and unites all actors in the system.”
13
Individuals don’t purchase medical services because they like to go to the doctor or be admitted to the hospital; they
seek care because they want longer, healthier lives and relief from suffering.
14
Employers and governments invest in
health care for employees and citizens for the same reasons. This means, in the view of four health care economists
interviewed for this paper,
15
that we need a different set of output measures if we want to develop a productivity
metric that incorporates the notion of value. (See the paper by Chernew, McKellar, and Colucci accompanying this
report for more detail on how productivity has been measured.)
Health Care (In)efficiency
Measuring efficiency in health care is also uniquely difficult because purchasers, providers and patients each have
their own idea of what constitutes value, quality, and the right price for care.
16
Prices are often equated with value in
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other industries, but in health care prices for identical services vary widely even within the same hospital. Spending
per patient, similarly, is a poor measure of efficiency because health care providers are generally paid by the unit of
service for each CT scan, physician visit, day in the hospital, surgical procedure without regard for the actual
improvement the service caused in the patient’s health. If we used spending to measure value, it would appear more
valuable to let a patient get diabetes and then treat the disease than to prevent its occurrence altogether but no
patient would choose that outcome. Thus, neither the price per unit of service nor the amount spent per patient is a
sound measure of output or value.
Even without being able to measure efficiency with much precision, we know that some hospitals are more efficient
than others. For example, some hospitals (often in more competitive markets) cannot negotiate favorable rates with
private insurers, so they manage to make a profit on Medicare’s comparatively low reimbursement rates,
17
while
others claim to lose money on their Medicare patients. A more useful metric for the efficiency of different hospitals
has been produced by the Dartmouth Atlas Project, which attempts to quantify variation in rates at which medical
services are delivered to similarly ill patients in different hospitals. Dartmouth researchers studied chronically-ill
Medicare recipients during the last two years or six months of their lives to produce a “relative efficiency” metric,
which reflects the amount of resources used at a hospital to treat similar patients with similar outcomes.
18
The Differences Bet w een Spending, Price, and Cost
Terminology frequently becomes a problem in discussions of health care productivity, because people
use the phrases “health care costs,” “health care spending,” and “the price of health care”
interchangeably, without clarifying which quantity they are referencing. For clarity’s sake, we suggest the
following definitions:
Cost refers to a provider’s total financial outlay for each unit of service. Costs can be measured per
patient, per procedure, per hospital day, or something similar.
Price refers to the dollar amount paid to a provider for a specific service or bundle of services. For
instance, we might refer to the price of an office visit or an MRI, or the price of cardiac bypass surgery,
which includes multiple services.
Spending is the total amount spent to reimburse providers of health care services and equipment.
Spending is affected by both the price of services, and by utilization, or the quantity of services provided.
Thus, a payer might spend less per patient in a hospital with high costs and prices, but where utilization
is low. The payer could also end up paying more per patient at another hospital where utilization is high,
even if prices are lower.
Note that spending is the true variable of interest for most policymakers, and reduced spending can come
as a result of lower costs (allowing lower prices), price controls or competition between providers (again
leading to lower prices), or lower utilization.
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The Dartmouth Atlas methodology takes advantage of the similarity among such patients to eliminate most
explainable variation in how much care patients need. Within the study population, after appropriate adjustments
for differences in rates of different chronic illnesses, age, sex, and race, the patients are substantially identical.
Because patients are comparable in terms of initial and final health status (death), any benefit they gained from
treatment in terms of longevity is essentially the same.
19
This methodology also allows the researchers to measure
resource inputs at different hospitals for the same output.
It turns out there are large differences among hospitals in terms of the amount Medicare spends per patient to treat
the chronically ill in their last few months of life, and thus in the relative efficiency of each hospital. According to the
Dartmouth metric, the Mayo Clinic’s St. Mary’s Hospital, in Rochester, Minnesota, and the Cleveland Clinic, in
Ohio, are among the most efficient academic medical centers. (They are also widely considered to be exemplary in
terms of the technical quality of their care.) At both hospitals, Medicare Part A spent roughly $34,000 on inpatient
services per decedent over the last two years of life. Contrast that to the $90,000 spent per patient at Hahnemann
Different Production Functions
One common interpretation of the Dartmouth findings (of wide variation in health spending and levels
of utilization) is that the marginal benefit of additional tests and treatments in most American hospitals
is small or nonexistent. That interpretation suggests that many hospitals are delivering low-value or even
totally unnecessary care. However, Amitabh Chandra and Jon Skinner have proposed an alternate
interpretation. They contend that different hospitals operate on different production functions, because
they have chosen to specialize in different kinds of treatments which are of varying degrees of cost-
effectiveness.20, 21 Chandra and Skinner offer a typology for different kinds of services.
"[Category I treatments are] highly cost-effective “home run” innovations with little chance of overuse, such as
anti-retroviral therapy for HIV, [Category II] treatments [are] highly effective for some but not for all (e.g. cardiac
stents), and [Category III] “gray area” treatments [are those] with uncertain clinical value such as ICU days
among chronically ill patients.”22, 23, 24
Hospitals that invest in delivering more or less of each category of care will have different production
functions (see Figure 3). Empirical data suggest this is the case. For example, there is a negative
correlation between the use of relatively high-value, low-tech treatments, such as aspirin and beta-
blockers for secondary prevention in heart attack patients (which would fall under Chandra and Skinner’s
Category I) and expensive high-tech treatments of dubious value, such as angioplasty for asymptomatic
patients (Category III).25, 26 The spending differences among hospitals that lie on different production
function curves can be substantial. Skinner, Staiger, and Fisher found that risk adjusted one-year survival
for a first heart attack was 69.7 per 100 patients in Knoxville, Tennessee, with one-year spending of
$20,720. By contrast, in Manhattan risk-adjusted survival was 65.6 per 100 patients, and one-year
spending was $47,133.27 These studies and others support the argument that there is considerable room
for increasing productive efficiency through shifting investment in how inefficient practices deliver care.
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Hospital, in Philadelphia, and nearly $72,000 at Cedars Sinai Medical Center, in Los Angeles. There was some
variation in what Medicare paid each institution in terms of
price
per unit of service, but most of the difference was
in utilization days in the hospital per patient, days in the intensive care unit, and physician visits for hospitalized
patients. Over the last two years of life, the average patient had 155.8 physician visits at Cedars Sinai, more than
double the 62.8 visits at the Cleveland Clinic.
28
These findings should not be surprising, given the myriad sources of waste and disorganization in health care. A
recent study by PricewaterhouseCoopers estimated that as much as half of health care spending is waste, about one-
third of which can be attributed to the delivery of health services that are either unnecessary or provided
incorrectly.
29
The report estimates that unnecessary or unwanted tests, treatments, hospitalizations, and physician
visits account for $210 billion of that waste. Preventable complications from diabetes lead to $22 billion in waste;
overprescribing of antibiotics creates another $1 billion.
Some other common examples of inefficiency in health care:
Highly skilled providers regularly perform tasks that require a lower level of skill and fewer formal
qualifications than they possess. For example, registered nurses escort patients to examination rooms,
schedule follow-up visits, and discuss whether the patient has had the recommended screenings basic
clinical and administrative tasks that could be performed by a medical assistant, nurse’s aide, or a case
manager.
30
Every industry requires a certain amount of paperwork and administration to function, but the American
health care sector stands out in the amount of time, money, and effort it wastes on administrative work that
In the figure, “Aspirin” represents high-value, low-cost care
that falls into Chandra and Skinner’s Category I;
“Angioplasty” represents Category II care that has value for
some patients but not for others. Hospitals that over-invest
in Category II care, because of reputational or other
incentives, may neglect high-value, low-tech care and achieve
lower health at a given level of spending. If a hospital is on
the lower production function, reducing spending will not
improve outcomes indeed, patients will be harmed.
Improving outcomes will require a concerted effort to
improve organization, reallocate resources, and move to the
higher production function.
Source: Baicker and Chandra, 2011
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provides no value for patients or payers. (For a list of some of the tasks undertaken by health care
administrators, see Cutler & Ly 2011).
31
Estimates of the cost of administrative waste (not total administrative
spending just
waste
) range from $100 billion to almost $400 billion per year.
32
The US spends about three
times as much on paperwork and other administrative activities as does Canada.
33
,
34
Administrative tasks
also waste the time of highly trained providers: primary care physicians, physician’s assistants, and hospital
nurses spend multiple hours a day doing paperwork instead of caring for patients.
Moving hospitalized patients around is another source of waste: it congests hallways, exposes patients to
injuries in transit and while moving in and out of bed, and requires hospitals have enough staff to do the
moving. A patient who needs an X-ray will be transferred from the bed to the gurney in order to be taken to
the machine’s location, rather than moving the machine to the patient. Each aspect of patient movement
creates several forms of waste: overstaffing, defects (injuries to both patients and workers), and forcing
patients to spend extra time in the hospital.
35
Physicians often care for similar patients in different ways, even when the doctors work in the same hospital.
Nurses have talked about carrying notecards to remind themselves of the settings on equipment, which tests
to perform, and the drugs and other treatments each patient should get, based not on evidence but on the
preferences of the different surgeons they work for meaning some patients are not getting optimal care,
and the nurses have to waste time and effort keeping track of the differences.
The Role of Health Care Labor
Labor is the single most expensive input in health care (and the most important one for the quality of patient care),
and as the examples above suggest, it is not being deployed efficiently. That means we could achieve equal (or better)
health outcomes in the U.S. while spending less on health care labor if the processes used to deliver care were more
efficient. A handful of hospitals and ambulatory care practices have made significant gains in efficiency by extracting
waste from their processes and focusing attention on managing the care of chronically ill patients. Whether driven
by a sense of professional responsibility for improving care or by financial pressure to reduce input costs, their
methods are similar and instructive.
Three examples illustrate the vast room for improvement in health care efficiency and productivity, and suggest
some specific changes in the composition of the future workforce.
In 2002, ThedaCare, a four-hospital system in northeastern Wisconsin, implemented a version of “Lean,” a
production improvement system pioneered by American efficiency expert W. Edwards Deming and first put
into practice in Japan by Toyota.
36
At the time, ThedaCare’s hospital-wide mortality rate for coronary bypass
surgery was nearly 4 percent. After several iterations of implementing Lean management methods, mortality
dropped to 1.4 percent in 2008 and was 0 percent through six months of 2009. ThedaCare’s efforts to
streamline its cardiac bypass processes, including elimination of steps that did not contribute to patient
outcomes, led to the average time a bypass patient spent in the hospital to fall 22%, from 6.3 days to 4.9
days.
37
,
38
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Virginia Mason Medical Center in Seattle, Washington, found that patients suffering from acute (recently
developed) low back pain followed different treatment paths depending on how they entered the hospital. If
the patient came through the emergency room, they often underwent an MRI, regardless of whether or not it
was likely to improve diagnosis. If the patient came through a referral from primary care, by contrast, they
were likely to be seen by a neurologist. In the end, most patients were treated with painkillers and physical
therapy regardless of how they came into the system. That treatment could as easily have been prescribed
without taking up so much specialist time and without unnecessary imaging.
Using the Toyota method, the hospital defined a single pathway for all back pain patients. This pathway sent
the vast majority of patients directly to a nurse practitioner (NP), who screened each patient for warning
signs of serious complications (such as a tumor), and then to physical therapy. The NP reduced the number
of patients who unnecessarily saw a neurologist, and restricted MRIs to patients with specific, evidence-
based justifications slashing the number of patients getting an MRI for back pain by 31 percent.
39
Another way to increase productivity is by keeping patients healthier, so they have less need for costly
hospitalizations and surgeries. Many chronically-ill patients find themselves going in and out of the hospital
because their conditions are poorly controlled. Some of these conditions, such as heart failure and diabetes,
are called “ambulatory care-sensitive conditions,” because hospitalization can often be avoided with good
primary care.
40
,
41
,
42
,
43
,
44
Several pilot programs and demonstration projects aimed at intensively managing
chronically ill patients in the primary care setting have shown reductions in rates of unnecessary
hospitalization.
For example, Group Health Cooperative of Puget Sound's “patient-centered medical home” test clinic made
significant progress in keeping patients out of the hospital by reducing the number of patients each primary
care physician was responsible for and matching physicians with inexpensive, relatively low-skilled medical
assistants who could greet patients, escort them to exam rooms, check whether they were up to date on
standard tests, and schedule future tests using evidence-based protocols. Chronically-ill patients were
intensively managed, often with home visits and longer office visits. Compared with patients at other Group
Health clinics, those in the medical home had fewer complications from chronic illness, 29 percent fewer
emergency visits, and 6 percent fewer hospitalizations. The higher initial spending on improved primary
care was offset by lower hospital spending and led to lower total spending per patient.
45
,
46
When taken together, the results of these programs and others like them, as well as the Dartmouth Atlas findings on
relative efficiency, show the profound implications the structure of the health care workforce has for improving
productivity in the sector.
47
We can say with some certainty that health care is not as productive as it could be,
because we know it is not operationally efficient. And we can say that health care labor could be used more
efficiently, perhaps dramatically so, to produce the same or better population outcomes with lower spending. The
following section looks at implications of improving efficiency for the size and composition of the health care
workforce. It examines current workforce projections, most of which predict (we think incorrectly) that the
healthcare system will need significantly more physicians and other clinicians in the future. We make some
predictions about the effect on health outcomes and productivity of the health care sector of carrying out current
new america foundation page 13
workforce estimates and expanding the supply of clinicians. The final section of the paper makes specific policy
recommendations.
Part II: Implications for the Workforce
Seeing the evidence of low productivity in the health care workforce should be a radicalizing moment and for
many policy experts, it has been. There is now widespread recognition that among the many changes that need to
come to pass if we are to improve health care efficiency and productivity are distinct shifts in how we train, allocate,
and use the health care workforce.
The U.S. needs to make these shifts very deliberately. In most industries, competitive pressures force companies to
make an efficient use of labor. In the steel industry, for example, increasing automation allowed fewer, higher-
skilled workers to replace larger numbers of lower-skilled workers; companies that failed to adjust were driven out of
the market. However, because health care providers (such as hospitals) are generally paid for the volume of services
they deliver regardless of the value of those services, health care providers have not had much incentive to shrink
their workforce and to allocate labor in a way that would maximize efficiency. Health care delivery is also
complicated by the effect that the supply of clinicians (particularly physicians) has on the rate at which medical
services are delivered (see box on supply-sensitive care, next page.) Simply put, the more doctors there are in a
geographic region, the more services they will deliver, regardless of whether those services make the population
better off.
There is clearly a role for government in arresting the maldistribution and disorganization of the medical labor force.
Indeed, the federal government is already heavily involved in the training of physicians because the Centers for
Medicare and Medicaid Services (CMS) fund the majority of post-graduate medical education (residencies). By
changing how CMS funds residency and the other qualification requirements for physicians and medical schools, it
is possible create significant savings and improve productivity.
With that in mind, here are four conclusions about the health care workforce that can be drawn.
Existing projections of future workforce needs are probably overestimates.
Existing projections for health care workforce needs assume that the current health care system is efficient, and ignore the
possibility that hospitals can move to higher production functions. Such projections may grossly overestimate the need for
additional physician capacity in a more organized health care system.
There is a large body of literature in medical journals and policy publications laying out what kinds of medical
services and providers Americans will want and need in the future. The most cited (and credible) projections come
from the Center for Workforce Studies of the Association of American Medical Colleges (AAMC), a trade group
representing medical schools and teaching hospitals across the U.S. and Canada. The Center’s most recent study,
published in 2008, argued that the U.S. faces a shortage of around 124,000 physicians by 2025.
48
As the population
ages and more citizens are covered under the Patient Protection and Affordable Care Act of 2010, the Center predicts
we will need more than 72,000 additional physicians
49
and 581,000 more nurses by 2020.
50
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Their projections are based on current levels and trends of the number of working physicians, how much service
those doctors can provide, and the general level of demand for medical services. Their projection of the number of
available physicians is relatively uncontroversial (especially given the restrictions on physicians entering the
workforce imposed by residency requirements). But the authors of the AAMC study admit that predicting physician
behavior and patients' future desires and needs for physician services is difficult, and requires making some strong
assumptions. Their headline prediction assumes, for example, that doctors won’t retire later or work more hours in
response to any emerging shortage of physicians, and that as the patient population expands and ages, the need for
medical services per capita will grow. In the scenario the authors consider most likely, their assumptions lead to an
estimate of a significant physician shortage. But if those assumptions aren’t borne out, the prediction loses validity.
Supply-Sensitive Care
Supply-sensitive care is one of the most important explanations for why health care spending and
treatment patterns vary across the country.51 Essentially, the hypothesis is that physicians use the
availability of medical resources (like hospital beds and consulting specialists) as a heuristic when
making decisions about how to treat patients. Thus, the larger the supply of hospital beds in a market, the
more likely a patient with a given set of symptoms will be admitted, and the more days patients in the
area will spend in the hospital. Similarly, the greater the supply of primary care physicians, the more
primary care visits patients will have. Researchers at the Dartmouth Atlas Project and elsewhere have
documented this phenomenon for the amount of end-of-life care dying patients receive, days infants
spend in Neonatal Intensive Care Unit (NICU) beds, the rate of neonatologist consultations, and a variety
of other treatment patterns. Patients in resource-dense areas (areas with numerous doctors and hospital
beds available, compared to the predicted needs of the population) receive more care than those in areas
with lower supply.52, 53, 54
Not all clinical decisions are subject to influence by supply. A patient with a hip fracture, for instance, will
nearly always be hospitalized, regardless of the supply of orthopedic surgeons or hospital beds. Rather,
the supply factor appears to have the greatest influence on physician’s decisions when good scientific
evidence is lacking about which treatment is most likely to be effective, or when consensus about the best
course of treatment is lacking. This category of care includes a wide range of decisions, such as whether
to admit a patient with chest pain to the hospital, or place an elderly patient in a regular hospital bed or
the Intensive Care Unit.55, 56, 57, 58
For example, an otherwise healthy low-birth weight infant can be taken care of in the regular nursery, or
they can be placed in the neonatal intensive care unit (NICU). This decision appears to be influenced by a
combination of bed availability and the habitual pattern of care at the hospital. Likewise, the decision to
call in a neonatologist for a consultation about a newborn who is having a little difficulty breathing is
influenced by the availability of such specialists. The same is true of office-based specialists. Imagine two
similarly-sized cities with similar populations of people with heart disease. Because there are no clear,
evidence-based clinical guidelines that determine how often cardiology patients should have follow-up
appointments, doctors use their available time as a heuristic. If city A has twice as many cardiologists as
City B, the time between follow-up appointments for patients with heart disease will be roughly half as
new america foundation page 15
The authors do offer a variety of projections under alternate assumptions, including the possibility that physicians
will be able to serve more patients, work longer careers, or work more hours. The authors even suggest in their text
that some of the medical care that patients currently receive is not worth providing. However, all of their quantitative
projections assume that the medical workforce will continue to deliver those services. In so doing, they both accept
that overuse will continue unabated, and assume that the level of unnecessary utilization is unrelated to the
workforce available to provide it.
While the AAMC study focuses on the physician workforce, it makes some predictions based on a larger primary
care role for NPs and PAs. If more NPs and PAs enter the workforce, they may alleviate some of the burden on
primary care doctors and may increase access for patients who have trouble getting a primary care appointment.
There is some evidence to support the notion that NPs can free up time for higher-level providers as noted above,
Virginia Mason successfully used them to reduce the unnecessary workload on neurologists. However, primary care
studies haven’t looked at long time horizons to see if there’s a difference over a lifetime between patients who get
primary care from a physician and those who get it from an NP or PA. Studies have focused instead on measuring
the marginal productivity of different types of provider (usually by measuring patient throughput), in order to
determine whether hospitals could save money by replacing higher-skilled workers.
64
,
65
,
66
,
67
,
68
The lack of such
long as in city B.
It’s important to recognize the distinction between the terms supplier-induced demand” and “supply-
sensitive care.” Both are attempts to explain the provision of excess services that don’t benefit the patient.
However, the term “supplier” refers to an individual – typically a clinician and “supplier-induced
demand” refers to individual behavior. “Supply” is a feature of a city, a market, or a region. The proposed
causal mechanism is the most important difference between the two. In supplier-induced demand,
clinicians are motivated by the fee-for-service payment system to prescribe more care than their patients
need or is actually beneficial. While there is evidence that physicians respond to financial incentives, the
claim that they systematically recommend services that they know to be unnecessary is controversial and
not well-established by research.59 The mechanism behind supply-sensitive care, by contrast, involves the
fact that clinicians face many decisions which are not clearly informed by research, because the relevant
clinical trials don’t exist. In those cases, physicians often lean on their training, and a number of human
cognitive biases and heuristics come into play including the availability of resources. The existence of
supply-sensitive care has been well-documented by observational studies.60
There are a couple of other important points regarding supply-sensitive care. One, the per capita supply
of such resources as NICU beds and neonatologists to staff them does not correlate with the prevalence
of illness in the population.61 Indeed, physician supply tends to be lower in communities with high rates
of minority and low-income patients, the very people who need more care.62 (This is sometimes called
the “inverse care law.”) Two, more is not always better when it comes to improving patient outcomes, and
a greater supply of many resources does not necessarily lead to better health.63 Those both reinforce the
fact that an excess supply of resources can lead to harmful overuse and reduced efficiency.
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long-time-horizon studies is not a reason to avoid reassigning primary care to NPs and PAs, but it is an area for
future research.
Expanding the health care workforce is likely to worsen current inefficiencies.
If the U.S. increases the physician workforce to fill AAMC projections, we will continue providing unnecessary care and
further entrench inefficient production patterns. Continued overuse is antithetical to the goal of improving productivity, and
will remain a problem even if we succeed in reducing the waste in production processes.
Given the flaws in projections of workforce need, we must be careful not to over-expand physician capacity. If we
overestimate the effects of aging and rising rates of obesity on future medical need, we may be left with a workforce
that provides even
more
unnecessary care than it does now, and further reduces productivity. By contrast, the
problems associated with underestimating growth in health care needs seem less worrisome our existing
workforce has large amounts of excess capacity that could, if a shortage became apparent, be used more effectively to
care for more and sicker patients.
Increasing the size of the workforce also makes it harder to exert political control over health care spending, because
it creates an ever-larger constituency with a vested economic interest in keeping health care spending high. The
interests of medical workers are concentrated, giving them a strong incentive to participate in political discussions
where their incomes are at risk, while the harm from unnecessary medical treatment and spending is broadly
distributed across the population, and therefore not as powerful a motivator for political action.
69
The same patterns
exist within institutions. The internal resistance of administrative workers to change (especially change that would
put them out of work) may explain why administrative waste is so prevalent in health care, while companies like
Amazon have been able to drastically reduce the administrative waste in industries like retail.
Better primary care can decrease utilization of expensive emergency department and hospital services, and thereby
reduce the need for hospital-based specialist labor.
More intensive primary care for chronically ill patients appears to lead to fewer emergency room visits and lower hospital-
based utilization. While we may need a larger primary care workforce with more nursing and allied health professionals such
as case managers, better primary care will probably mean we need fewer hospital-based specialists.
The Group Health primary care medical home and other similar efforts to reduce utilization of hospital and
emergency room services have several implications for workforce projections.
70
First, the results suggest that
expanding the medical home model across the country could significantly reduce the workforce needed in hospitals
for the current patient population. Increasing the number of primary care physicians is a recommendation that has
reached near-consensus in the policy arena.
71
It’s possible that we may need to expand the primary care workforce in
order to serve more people while simultaneously reducing panel sizes to match the levels in Group Health’s medical
new america foundation page 17
home and other demonstrations. But simply adding primary care physicians to the system will not improve
productivity without changes in the way primary care is delivered.
There is also widespread agreement that the ratio of primary care physicians to specialists needs to increase.
72
Most
other developed countries have much higher ratios of primary care to specialists than we do in the U.S. In most
developed nations, less than half of physicians are specialists;
73
in the U.S., specialists make up more than 60
percent of the physician workforce. Rebalancing that ratio poses a significant challenge, since the number of young
physicians going into primary care (family practice, general internal medicine, and pediatrics) has been dropping
steadily for more than a decade.
74
Boosting the ratio of primary care physicians to specialists, and deploying primary
care resources to regions of the country where they are needed most, will require targeted policies by the Centers for
Medicare and Medicaid Services.
Projections based on the experiences of efficient hospitals suggest a need for fewer hospital-based specialists.
Along with the reduced utilization of hospital services resulting from implementing primary care medical homes, improving
the productive efficiency at hospitals should lead to adjustments in their workforce size and mix. As some tasks are shifted to
lower-skill clinicians or to less expensive specialties, more expensive specialists in individual hospitals will be able to use their
extra time to serve a larger population of patients. Improving hospital efficiency across markets, however, will lead to a surplus
of specialists.
Implementing efficiency improvements such as those seen at Virginia Mason, ThedaCare, and other hospitals across
all hospitals in a region or market would have significant implications for the local health care workforce size and
mix.
75
These efficiency demonstration projects have often shifted tasks down the skill ladder, while still providing
care that is as effective or better with lower immediate labor costs.
76
Implemented across a market, these efforts
would lead to a reduced need for at least some hospital-based specialists. This possibility is supported by the
Dartmouth Atlas, which shows lower specialist inputs at the most efficient hospitals (many of which are also
considered some of the nation’s highest quality medical centers).
Interestingly, while case studies of primary care medical homes and Toyota efficiency methods often produced
reduced hospital utilization, none made explicit projections for reductions in workforce as a result of greater
productive efficiency. Many hospitals that have used Toyota and other efficiency-improvement methods have had no-
layoff policies as a way to gain cooperation from employees and avoid the aforementioned political resistance to
improved efficiency.
77
They have relied instead on workforce attrition, and on recruiting new patients from
competing hospitals, to ensure that physician labor is fully used. If all hospitals in a region or market improve
efficiency, however, they will run out of patients to recruit and could find themselves with more physicians than they
need given the current patient population. What their needs will be as the U.S. population grows and ages, and as 30
million newly insured Americans begin to access health care under the provisions of the 2010 Patient Protection and
Affordable Care Act, is simply uncertain.
Many policymakers have suggested that money will be saved by shifting care to less-skilled, less-expensive clinicians
like nurse practitioners and licensed practical nurses, rather than physicians and registered nurses, respectively.
78
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However, immediate labor costs are only part of the spending equation. Caring for the chronically ill makes up
nearly half of spending, and that proportion will likely increase as the population gets older and more obese, so
short-term spending measurements are increasingly insufficient. The best way to measure the effect of cost-cutting
interventions like better primary care and making better use of lower-level clinicians in hospitals would be to
examine lifetime inpatient utilization, particularly the number of lifetime hospital admissions and readmissions. As
we mentioned, there are no studies looking at long-term utilization of medical services based on whether patients
see a physician or nurse practitioner for primary care. That lack of research leaves open the possibility that one
provider or the other tends to miss important indicators of future health problems, and fails to prevent an expensive
medical condition later in life. A case manager who focuses on diabetic care may produce equivalent diabetes
outcomes to a physician, but it’s possible they will miss early signs of preventable heart disease. Conversely, one sort
of provider may tend to use more unnecessary or low-value tests, leading to higher spending down the road with no
discernible benefit.
79
In general, the evidence does not support the idea that we can reach sustainable levels of health care spending, or
greatly increase productivity, by simply task-shifting or relying on other small, discrete interventions. The most
impressive results in reducing spending, improving outcomes, and boosting efficiency and productivity have come
from concerted efforts to organize care, eliminate waste in production processes, and communicate with patients
in short, to develop coherent systems that provide only the tests and treatments that patients value.
80
Conclusion
Some policy makers have looked to the health care sector as a way out of our current unemployment problem, and
indeed health care is one of the few sectors that continued hiring throughout the 2007-09 recession.
81
Looking to the
future, the Bureau of Labor Statistics predicts that health care will add 3,600,000 jobs by the end of the decade.
82
But simply adding jobs to an already inefficient sector will have the perverse effect of further driving up spending,
decreasing sector productivity, and depressing job growth in the rest of the economy while worsening the federal
government’s prospects of resolving our long-term fiscal imbalance.
Policy analysts have long been pointing to human organization as the key to providing better health care. It is also
the hallmark of greater efficiency. Indeed, most of the hospitals in the top 10 percent of Dartmouth’s relative
efficiency metric are organized, salaried, multi-specialty group practices.
83
,
84
This suggests that one of the secrets to
improving efficiency across the health care sector, and thus increasing productivity, is finding incentives for
hospitals and physicians to emulate the habits of organized, salaried group practices. This is not a new idea: it is
implicit in several aspects of the 2010 Patient Protection and Affordable Care Act, particularly in the provisions for
Accountable Care Organizations.
85
,
86
Also needed are ways of disseminating usable information to patients about
the value of seeking more organized, efficient care.
Improving productive efficiency in health care will also have profound implications for the workforce of the future.
Adopted broadly, medical homes similar to the Group Health experience, Lean improvement programs for
eliminating waste in hospitals, and lower levels of supply-sensitive care will likely lead to reduced demand for
hospital-based specialists and other hospital-based workers such as nurses and administrative personnel. At the
new america foundation page 19
same time, we may need more clinicians in primary care, including more primary care physicians, physician
assistants, physical therapists, nurses and nurse’s aides, nutritionists, pharmacists, and case managers.
87
,
88
Finally, we note these conclusions are based on observations of relatively few highly efficient systems. We believe the
findings from these case studies have a degree of generalizability, but there are undoubtedly site-specific attributes
that do not generalize. For this reason, a rigorous assessment of health care delivery and efficiency is badly needed.
The science of health care delivery is a burgeoning field that seeks to determine the best ways to deliver care. The
most effective use of the health care workforce should be included in this research.
The potential to cut waste from health care is vast, and the key to tapping that potential lies in organizing systems of
care. The health care workforce of the future cannot simply expand the current mix of specialties and types of
workers. As providers become more organized, they will begin to rationalize their workforces, so that the supply of
clinicians and other workers matches the needs of the patient population they serve. Organized systems of care will
likely use fewer physicians in specialties and more in primary care, along with fewer nurses in hospitals and more in
primary care. This shift will offer a more equitable distribution of health care incomes, as the need for highly paid
specialists declines and demand for mid-level clinicians and allied health professionals increases. The transition
towards organized care and this new workforce represents a challenge in the short term, because it is difficult to
predict how quickly we will achieve greater efficiency and thus whom to train. But failing to improve productive
efficiency in health care is not an option. The system we have is a drag on the rest of the economy. Barring a major
overhaul in the health care workforce and how it is organized, the effects of an overbuilt, oversupplied health care
industry will be felt by future generations.
Shannon Brownlee is a senior fellow in the Health Policy Program at the New America Foundation, a senior vice president
at the Lown Institute, and an instructor at the Dartmouth Institute for Health Policy and Clinical Practice. She can be
reached at brownlee@newamerica.org.
Joseph Colucci is the assistant director of communications at the Lown Institute and former program associate at the New
America Foundation. He can be reached at jcolucci@lowninstitute.org.
Thom Walsh is a post-doctoral research fellow at The Dartmouth Center for Health Care Delivery Science. He can be
reached at thom.walsh@dartmouth.edu.
new america foundation page 20
Summary of Policy Recommendations
The findings of this paper suggest key policy recommendations in four areas: the training of medical residents;
regulation of health care, particularly with regard to scope of practice and health information technology; research;
and payment.
Residency
For the moment, CMS should not expand the number of residency slots that it supports. The physician shortages projected by
the AAMC ignore the effects of supply-sensitive care and the possibility of improved efficiency. These shortages may not appear,
and expanding the physician workforce will likely exacerbate low productivity seen at many health care institutions.
At the same time, CMS should establish clear standards to increase the proportion of residents trained in delivering primary
care, including chronic disease management and communicating with patients.
CMS should shift its allocation of residency funds to encourage/reward teaching hospitals and academic medical centers with
strong group practice norms, organized systems of care, protocols that encourage shared decision making, and other hallmarks
of low-cost, high-value care.
89
That inducement might take the form of higher resident salaries at efficient institutions, or
simply moving slots from relatively inefficient hospitals, such as New York University Medical Center in Manhattan, to
outpatient training for primary care in more efficient systems, such as Intermountain Healthcare in Salt Lake City.
Regulation
Congress and state legislatures should create exemptions from scope-of-practice and staff ratio regulations, and provide legal
liability protection, for provider groups that a) are actively experimenting and collecting data on the most cost -effective provider
mixes or b) have consistently excellent quality and patient outcomes.
Private insurance, with each company’s individual requirements, fees schedules and restrictions on coverage, contribute to the
extraordinary degree of administrative waste at the provider level. States should examine their insurance regulations and find
ways to harmonize administrative requirements for providers.
Research
New projections of workforce shortages/surpluses and recommendations for workforce policy must either account for the
effects of supply-sensitive care, or provide a compelling alternative explanation for the huge variation seen among providers in
the use of medical resources without commensurate variation in outcomes.
Federal funds should be directed towards developing a science of health care delivery. The bulk of federal research money now
goes toward seeking new treatments and comparing existing ones. The next frontier is determining the best ways to deliver care
as efficiently as possible, and the most effective use of the health care workforce should be included in this research agenda.
new america foundation page 21
Remuneration
Payers must begin implementing methods of paying hospitals and physician practices that offer incentives to become more
organized and efficient. There is growing recognition among policymakers that fee-for-service should be mostly phased out. The
majority of providers that rank high in Dartmouth’s metric for relative efficiency are salaried practices, which suggests that a
salary, as opposed to fee for service, offers incentives for physicians to avoid waste. (At the same time, salaried group practices
often provide incentives to maintain quality of care and throughput.) However, salaries alone will not create human
organization. Fee-for-service as a means of paying hospitals should also be scaled back in favor of bundled payments and global
budgets, which would push hospitals to extract waste from their processes and rationalize their investment in labor and capital.
new america foundation page 22
Appendix I: Overview of Today’s Health Care Workforce
There are approximately 691,000 physicians working in the U.S. right now about one for every 450 people.
90
In
addition to physicians, there are about 275,000 pharmacists dispensing medications in drugstores and hospitals
nationwide, as well as 156,000 dentists and oral surgeons.
Only around thirty percent of physicians in the US practice in primary care specialties (general internal medicine,
general and family medicine, and general pediatrics). The remaining physicians are specialists, including surgeons,
OB/GYNs, psychiatrists, cardiologists, pulmonologists, and many others. In most other developed countries, there
are about two primary care physicians for each specialist. In the US that ratio is much lower, and the number of
specialists compared to primary care physicians has been increasing over the last two decades. The change has not
been the result simply of growth in the number of specialists while primary care stayed constant, but instead has
represented an exchange of primary care for specialists.
Of the doctors working at any given time, over 100,000 are residents recent graduates of medical school,
participating in their post-graduate medical training.
91
They work in teaching hospitals across the country, under the
supervision of attending physicians, for between three and eight years depending on their specialty. Residencies
have traditionally involved working long hours. Residents are permitted to work up to 80-hour weeks, and may be
responsible for more medical labor than their numbers indicate.
The Centers for Medicare and Medicaid Services (CMS) pay teaching hospitals to run residencies through their
graduate medical education (GME) programs. The number of slots Medicare funds has been set at around 25,000
for several years,
92
,
93
meaning that regardless of the number of physicians matriculating in U.S. medical schools,
the number of new doctors produced each year is capped. Although physicians make up only a small portion of the
clinical workforce, their importance and the long lead-time involved in training them has led most workforce
planning discussions to focus on physician supply when projecting future health care needs.
Physicians (including residents) supervise a variety of skilled providers, including physician assistants (PAs),
physical therapists, nurse practitioners (NPs), and registered nurses (RNs). There are approximately 2,737,000 RNs
and advanced practice nurses working in the US, making it the fifth largest occupation in the country (after retail
sales, cashiers, office clerks, and food prep/service).
94
Other mid-level providers are significantly less common
there are about 83,000 PAs and 198,000 physical therapists nationwide.
Lower-level clinical workers include medical assistants, licensed practical and vocational nurses, home health aides,
emergency medical technicians (EMTs) and paramedics, and others who work directly with patients under the
direction and supervision of nurses, physicians, and other medical personnel. The precise number of low-level
clinical workers is difficult to establish, but it’s well over four million, including about 750,000 lower-level nurses,
226,000 EMTs and paramedics, 528,000 medical assistants, 219,000 radiology technicians, 330,000 laboratory
techs, 334,000 pharmacy techs, 94,000 surgical techs, and 1,879,000 home health and personal care aides.
In addition to clinical workers, the health care industry employs a vast army of administrative and management
personnel. Many of those workers spend their time communicating with payers, including private insurers, the
federal Centers for Medicare and Medicaid Services (CMS), and state Medicaid programs. These administrators
make sure hospitals and physician practices get paid and determine how much patients are responsible for paying
new america foundation page 23
out of pocket. (We have chosen to exclude payers, both private insurance and public, from our discussion of
productivity; while the insurance industry has an important role to play in shaping the future of the medical system
by reforming payment systems, we do not see the composition of the insurance workforce as a particularly
important driver of productivity.) Just as it is for low-level clinical workers, establishing a complete picture of the
administrative workforce is difficult because of the wide range of job titles. However, at a minimum this workforce
includes 508,000 medical secretaries, 179,000 medical records & health IT workers, and 95,000 medical
transcriptionists, led by 303,000 medical and health services managers. The work performed by those million
administrators is supplemented by the significant paperwork and other clerical duties performed by clinicians,
including highly trained (and expensive) physicians and nurses.
While we have not enumerated all of them here, the health care industry as a whole employs over 14 million people
11 percent of the US civilian workforce. That proportion will increase over the next decade: several of the occupations
projected to grow the fastest over the current decade (both in percentage terms, and in the absolute number of new
workers) are health-related. They include RNs (711,900 new jobs), home health and personal care aides (1,313,200
new jobs), physician assistants (24,700 new jobs),
95
medical assistants (162,900 new jobs), and nursing aides and
orderlies (302,000 new jobs).
96
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Appendix II: Challenges to the Dartmouth Atlas Findings
The Dartmouth Atlas researchers’ claim that more treatment at the end of life doesn’t help patients is not universally
accepted. There are two central counterarguments: that hospitals which deliver more care are treating sicker
patients, or that patients who receive more care live longer.
97
Both arguments rest on the claim that the Dartmouth
analysis improperly accounts for how sick patients are. In the Dartmouth analysis, adjustments are made for the age,
sex, and racial characteristics of different hospitals’ patient populations. Those adjustments flatten out most of the
differences between regions (since age, sex, and race are strongly predictive of how sick someone is and how much
medical care they’ll need), so regions with older populations don’t look less effective because their patients are more
likely to die.
However, after those basic risk adjustments, there are still some variations across regions in how sick patients
appear, based on their medical records. Using the Medical Services Hierarchical Condition Category (HCC) to adjust
for illness, some researchers have found a small but statistically significant benefit to patients in higher-spending
hospitals. If the HCC offers the most accurate picture of a population’s health, then there are some benefits to the
very intense treatment offered at hospitals like Cedars-Sinai and Hahneman Hospital.
However, there is reason to doubt the accuracy of the HCC, which relies on counting diagnoses and assuming that
patients with more diagnoses are at a higher risk of death.
98
The number of diagnoses is not a perfect measurement
tool, though, and it has an important source of bias: the same factors that lead patients to get more intense treatment
can also lead doctors to record more diagnoses for a patient (see the box on supply-sensitive care). Seeing more
specialists can lead to a patient receiving extra diagnoses and therefore appearing sicker in the HCC.
99
,
100
In technical language, a patient’s HCC score is an endogenous outcome of the supply of resources in a hospital or
region, as is the intensity of treatment. Using one endogenous variable to predict another is a major source of
potential bias. In this case, risk-adjusting based on the HCC makes patients in resource-dense areas look sicker than
they are, and thus provides an erroneous justification for the extra medical care they receive.
We do not have data showing the size of the bias created by using the HCC, so we cannot prove that there are no
benefits to high-intensity treatment. However, even the studies that have shown benefit have demonstrated only
minimal improvements in mortality.
101
Therefore, the question of whether extra high-intensity medical treatment
offers patients any substantial benefit or is cost-effective is still open for debate.
Finally, other evidence weighs against the idea that more intense care results in greater longevity for chronically ill
patients and those who are approaching the end of life. Several randomized controlled trials suggest that aggressive
care for frail, elderly patients may be of limited value.
102
A study published in the
New England Journal of Medicine
in 2011 found that terminal lung cancer patients who received palliative care lived on average two months
longer
than lung cancer patients who received usual and more aggressive care.
103
new america foundation page 25
Notes
1
US Centers for Medicare and Medicaid Services, "National Health Expenditures 2011 Highlights."
See also: “WHO Global Health Expenditure Atlas,” World Health Organization, 2012.
2
Organisation for Economic Co-Operation and Development, "Health Data 2013."
3
Aaron Carroll, "How Do We Rate the Quality of the US Health Care System -- Population Statistics,"
The Incidental
Economist
, 2010.
4
Steven Nyce and Sylvester Schieber, "How Rising Health Costs Slow Wage Growth," Progressive Policy Institute, March 2012.
5
Katherine Baicker and Amitabh Chandra, "Aspirin, Angioplasty, and Proton Beam Therapy: The Economics of Smarter Health
Care Spending,"
Jackson Hole Economic Policy Symposium
, 2011.
6
Donald Berwick and Andrew Hackbarth, "Eliminating Waste in US Health Care,"
JAMA
, 2012.
7
Randall Cebul, James Rebitzer, Lowell Taylor, and Mark Votruba, "Organizational Fragmentation and Care Quality in the U.S
Healthcare System, "Journal of Economic Perspectives, 2008.
8
John Wennberg, Shannon Brownlee, Elliott Fisher, Jonathan Skinner, and James Weinstein, "An Agenda for Change:
Improving Quality and Curbing Health Care Spending: Opportunities for the Congress and the Obama Administration,"
Dartmouth Atlas White Paper
, 2008.
9
Peter S Hussey, Han de Vries, John Romley, Margaret Wang, Susan Chen, Paul Shekelle, and Elizabeth McGlynn, "A
Systematic Review of Health Care Efficiency Measures,"
Health Services Research
, 2009.
10
Robert Kocher and Nikhil Sahni, "Rethinking Health Care Labor,"
The New England Journal of Medicine
, 2011.
11
It is worth pointing out that the units of “productivity” are essentially ignored, or at least left implicit, in their analysis –
especially since they discuss productivity
growth
, rather than the absolute value of productivity. For the record, their units are
GDP dollars per health care worker.
12
Their analysis is somewhat confused by the Bureau of Economic Analysis’s (BEA) categorization scheme: Kocher and Sahni
use BEA-generated data on “Health care and social assistance,” a category which includes activities like emergency relief
services, childcare, and other social services. The growth rate and level of labor productivity of those services may be markedly
different than those of the medical services included in the category nursing homes, ambulatory care, hospitals, etc. Even
including that sector does not entirely explain their numbers, but the example of productivity measurement stands.
13
Michael Porter, "What Is Value in Health Care?"
The New England Journal of Medicine
, 2010.
new america foundation page 26
14
Michael Chernew, Michael McKellar, Joseph Colucci, "The Nature and Challenges of Health Care Productivity Measurement,"
New America Foundation, 2013.
15
Economists interviewed were: Katherine Baicker, PhD, Professor of Health Economics, Department of Health Policy and
Management, Harvard School of Public Health; Amitabh Chandra, PhD, Professor and Director of Health Policy Research,
Harvard University, Kennedy School of Government; Michael Chernew, PhD, Professor, Department of Health Care Policy,
Harvard Medical School; and David Cutler, PhD, Otto Eckstein Professor of Applied Economics, Department of Economics and
Kennedy School of Government, Harvard University. Interviews took place June 29-30, 2011 and were conducted by Vanessa
Hurley, MPH and Sam Wainwright.
16
Liza Greenberg, "Efficiency in Health Care: What Does It Mean? How Is It Measured? How Can It Be Used for Value-Based
Purchasing?" 2006.
17
Jeffrey Stensland, Zachary Gaumer, and Mark Miller, "Private-Payer Profits Can Induce Negative Medicare Margins,"
Health
Affairs,
2010.
18
John Wennberg, Elliott Fisher, David Goodman, Jonathan Skinner, "Tracking the Care of Patients with Severe Chronic
Illness: The Dartmouth Atlas of Health Care 2008."
19
There is conflicting evidence regarding the quality of care delivered at the hospitals examined by the Dartmouth Atlas. For
more, see: Wennberg, Fisher, Goodman, and Skinner, "Tracking the Care of Patients with Severe Chronic Illness: The
Dartmouth Atlas of Health Care 2008”; and Peter Bach, "A Map to Bad Policy--Hospital Efficiency Measures in the Dartmouth
Atlas,"
The New England Journal of Medicine,
2010.
In terms of mortality, the evidence is also conflicting, with some studies showing more care is associated with slightly lower
mortality and other studies showing the opposite. For studies showing lower mortality, see:
Joshua Fenton, Anthony Jerant, Klea Bertakis, Peter Franks, " The Cost of Satisfaction: A National Study of Patient Satisfaction,
Health Care Utilization, Expenditures, and Mortality,"
Archives of Internal Medicine
, 2012;
Elliott Fisher, John Wennberg, Therese Stukel, and Daniel Gottlieb, "Variations in the Longitudinal Efficiency of Academic
Medical Centers,"
Health Affairs,
2004;
Elliott Fisher, John Wennberg, Therese Stukel, and Daniel Gottlieb, F.L. Lucas, and Etoile Pinder, "The Implications of Regional
Variations in Medicare Spending. Part 2: Health Outcomes and Satisfaction with Care,"
Annals of Internal Medicine
, 2003;
and Elliott Fisher, John Wennberg, Therese Stukel, and Daniel Gottlieb, F.L. Lucas, and Etoile Pinder, "The Implications of
Regional Variations in Medicare Spending. Part 1: The Content, Quality, and Accessibility of Care,"
Annals of Internal Medicine
,
2003.
new america foundation page 27
For studies showing higher mortality, see:
Dierdre McCaughey and Anthony Stanowski, "Efficiency Drives Value: The Relationship between Hcahps and Expense
Control," 2012;
Michael Ong, Carol Mangione, Patrick Romano, Qiong Zhou, Andrew Auerbach, Alein Chun, Bruce Davidson, Theodore
Ganiats, Sheldon Greenfield, Michael Gropper, Shaista Malik, Thomas Rosenthal, and Jose Escarce, " Looking Forward, Looking
Back: Assessing Variations in Hospital Resource Use and Outcomes for Elderly Patients with Heart Failure,"
Cardiovascular
Quality and Outcomes
, 2009;
John Romley, Anupam Jena, and Dana Goldman, "Hospital Spending and Inpatient Mortality: Evidence from California: An
Observational Study,"
Annals of Internal Medicine,
2011;
Jeffrey Silber, Robert Kaestner, Orit Even-Shoshan, Yanli Wang, Laura Bressler, "Aggressive Treatment Style and Surgical
Outcomes,"
Health Services Research
, 2010;
and Jonathan Skinner, Douglas Staiger, and Elliott Fisher, "Is Technological Change in Medicine Always Worth It? The Case of
Acute Myocardial Infarction,"
Health Affairs,
2006.
20
Amitabh Chandra and Jonathan Skinner, "Technology Growth and Expenditure Growth in Health Care." In
National Bureau
of Economic Research Working Papers
, 2011.
21
Amitabh Chandra and Douglas Staiger, "Productivity Spillovers in Healthcare: Evidence from the Treatment of Heart
Attacks,"
The Journal of Political Economy
, 2007.
22
In order for all three types of treatment to contribute to health, they must be shown to be effective, they must be matched to
the right patient, and they must be delivered correctly. See paper by Chernew, McKellar, and Colucci.
23
Chandra and Skinner, 2011.
24
Chernew, McKellar, and Colucci, 2013.
25
Laura Yasaitis, Elliott Fisher, Jonathan Skinner, and Amitabh Chandra, "Hospital Quality and Intensity of Spending: Is There
an Association?"
Health Affairs,
2009.
26
Katherine Baicker and Amitabh Chandra, " Medicare Spending, the Physician Workforce, and Beneficiaries' Quality of Care,"
Health Affairs,
2004.
27
Skinner, Staiger, and Fisher, 2006.
new america foundation page 28
28
"The Dartmouth Atlas of Health Care." The Dartmouth Institute for Health Care and Clinical Practice.
29
"The Price of Excess: Identifying Waste in Healthcare Spending," PricewaterhouseCoopers Health Research Institute, 2010.
30
Chantale LeClerc, Julie Doyon, Debbie Gravelle, Bonnie hall, and Josette Roussel, "The Autonomous-Collaborative Care
Model: Meeting the Future Head On,"
Nursing Leadership,
2008.
31
David Cutler and Dan Ly, "The (Paper) Work of Medicine: Understanding International Medical Costs,"
Journal of Economic
Perspectives
, 2011.
32
Harvey Fineberg, "Shattuck Lecture. A Successful and Sustainable Health System--How to Get There from Here,"
The New
England Journal of Medicine
, 2012.
See also: "The Price of Excess: Identifying Waste in Healthcare Spending,” 2010; and Berwick and Hackbarth, 2012.
33
Cutler and Ly, 2011.
34
Steffie Woolhandler, Terry Campbell, and David Himmelstein, " Costs of Health Care Administration in the United States
and Canada,"
The New England Journal of Medicine
, 2003.
35
David Belson,"How to Reduce Hospital Health Care Costs," Huffington Post, 2010.
36
Christopher Kim, David Spahlinger, J.M. Kin, and J.E. Billi, " Lean Health Care: What Can Hospitals Learn from a World-
Class Automaker?"
Journal of Hospital Medicine,
2006.
37
Kim Barnas, "Thedacare's Business Performance System: Sustaining Continuous Daily Improvement through Hospital
Management in a Lean Environment,"
Joint Commission Journal on Quality and Patient Safety
, 2011.
38
John Toussaint, "Writing the New Playbook for U.S. Health Care: Lessons from Wisconsin,"
Health Affairs
, 2009.
39
Charles Kenney,
Transforming Health Care: Virginia Mason Medical Center's Pursuit of the Perfect Patient Experience
, 2011.
40
Not surprisingly, some primary care providers are better at keeping patients out of the hospital than others. The Dartmouth
Atlas has found a more than fourfold difference in the rate of ambulatory care-sensitive discharges among Medicare
beneficiaries from 2003-2007, ranging from 30.7 per 1,000 beneficiaries in Honolulu to 135.0 per 1,000 in Monroe, Louisiana.
(The national average was 76.0 per 1,000.)
41
Jayasree Basu, Bernard Friedman, Helen Burstin, "Primary Care, Hmo Enrollment, and Hospitalization for Ambulatory Care
Sensitive Conditions: A New Approach,"
Medical Care
, 2002.
new america foundation page 29
42
Michael Parchman and Steven Culler, " Primary Care Physicians and Avoidable Hospitalizations,"
The Journal of Family
Practice,
1994.
43
Michael Parchman and Steven Culler, "Preventable Hospitalizations in Primary Care Shortage Areas. An Analysis of
Vulnerable Medicare Beneficiaries,"
Archives of Family Medicine,
1999.
44
David Goodman, Shannon Brownlee, Ching-Hua Chang, Elliott Fisher, "Regional and Racial Variation in Primary Care and
the Quality of Care among Medicare Beneficiaries," Dartmouth Atlas Project, 2010.
45
Robert Reid, Katie Coleman, Eric Johnson, Paul Fishman, Clarissa Hsu, Michael Soman, Claire Trescott, Michael Erikson,
and Eric Larson, "The Group Health Medical Home at Year Two: Cost Savings, Higher Patient Satisfaction, and Less Burnout
for Providers,"
Health Affairs
, 2010.
46
Robert Reid, Paul Fishman, Onchee Yu, Tyler Ross, James Tufano, Michael Soman, Eric Larson, " Patient-Centered Medical
Home Demonstration: A Prospective, Quasi-Experimental, before and after Evaluation,"
The American Journal of Managed
Care
, 2009.
47
Barnas, 2011; Touissant, 2009; and Kenney, 2011.
48
Michael Dill and Edward Salsberg, "The Complexities of Physician Supply and Demand: Projections through 2025," AAMC,
2008.
49
"Occupational Outlook Handbook, 2012-2013 Edition, Physicians and Surgeons," Bureau of Labor Statistics.
50
Yevgeniy Goryakin, Peter Griffiths, and Jill Maben, "Economic Evaluation of Nurse Staffing and Nurse Substitution in Health
Care: A Scoping Review,"
International Journal of Nursing Studies
, 2011.
51
John Wennberg and Alan Gittelsohn, "Small Area Variations in Health Care Delivery,"
Science
, 1973.
52
David Goodman, Elliott Fisher, George Little, Therese Stukel, and Chiang-Hua Chang, " Are Neonatal Intensive Care
Resources Located According to Need? Regional Variation in Neonatologists, Beds, and Low Birth Weight Newborns,"
Pediatrics
, 2001.
53
David Goodman, Elliott Fisher, George Little, Therese Stukel, and Chiang-Hua Chang, "The Uneven Landscape of Newborn
Intensive Care Services: Variation in the Neonatology Workforce,"
Effective Clinical Practice
, 2001.
54
David Goodman, Elliott Fisher, George Little, Therese Stukel, Chiang-Hua Chang, and Kenneth Schoendorf, "The Relation
between the Availability of Neonatal Intensive Care and Neonatal Mortality,"
The New England Journal of Medicine
, 2002.
new america foundation page 30
See also: Wennberg, Fisher, Goodman, and Skinner, “Tracking the Care of Patients with Severe Chronic Illness: The Dartmouth
Atlas of Health Care 2008."
55
David Goodman, Elliott Fisher, George Little, Therese Stukel, and Chiang-Hua Chang, "Are Neonatal Intensive Care
Resources Located According to Need? Regional Variation in Neonatologists, Beds, and Low Birth Weight Newborns,"
Pediatrics
, 2001.
56
David Goodman, Elliott Fisher, George Little, Therese Stukel, and Chiang-Hua Chang, "The Uneven Landscape of Newborn
Intensive Care Services: Variation in the Neonatology Workforce,"
Effective Clinical Practice
, 2001.
57
David Goodman, Elliott Fisher, George Little, Therese Stukel, Chiang-Hua Chang, and Kenneth Schoendorf, "The Relation
between the Availability of Neonatal Intensive Care and Neonatal Mortality,"
The New England Journal of Medicine
, 2002.
58
Lindsay Thompson, David Goodman, and George Little, "Is More Neonatal Intensive Care Always Better? Insights from a
Cross-National Comparison of Reproductive Care,"
Pediatrics,
2002.
59
James Ramsey, "An Analysis of Competing Hypotheses of the Demand for and Supply of Physician Services," 1980.
60
John Wennberg,
Tracking Medicine: A Researcher's Quest to Understand Health Care
, 2010.
61
Elliott Fisher and John Wennberg, "Health Care Quality, Geographic Variations, and the Challenge of Supply-Sensitive Care,"
Perspectives in Biology and Medicine
, 2003.
See also: Goodman, Fisher, Little, Stukel, and Chang, 2001; Goodman, Fisher, Little, Stukel, Chang, and Schoendorf, 2002; and
Thompson, Goodman, and Little, 2002.
62
Miriam Komaromy, Kevin Grumbach, Michael Drake, Karen Vranizan, Nicole Lurie, Dennis Keane, Andrew Bindman, "The
Role of Black and Hispanic Physicians in Providing Health Care for Underserved Populations,"
The New England Journal of
Medicine
, 1996.
63
Wennberg, Fisher, Goodman, and Skinner, “Tracking the Care of Patients with Severe Chronic Illness: The Dartmouth Atlas
of Health Care 2008."
64
Goryakin, Griffiths, and Maben, 2011.
65
Sue Horrocks, Elizabeth Anderson, and Christopher Salisbury, "Systematic Review of Whether Nurse Practitioners Working
in Primary Care Can Provide Equivalent Care to Doctors,"
BMJ
, 2002.
66
Miranda Laurant, David Reeves, Rosella Hermens, Jose Braspenning, Richard Grol, Bonnie Sibbald, "Substitution of Doctors
by Nurses in Primary Care," Cochrane Effective Practice and Organisation of Care Group, 2009.
new america foundation page 31
67
Gerald Richardson, Alan Maynard, Nicky Cullum, and David Kindig, "Skill Mix Changes: Substitution or Service
Development?"
Health Policy
, 1998.
68
Lynn Unruh, "The Effect of LPN Reductions on RN Patient Load,"
The Journal of Nursing Administration
, 2003.
69
Aaron Carroll, "Why Is This So Hard to Understand? (Part 2),"
The Incidental Economist
, 2012.
70
Reid et al., 2010; Reid et al., 2009.
71
Michael Whitcomb and Jordan Cohen, "The Future of Primary Care Medicine,"
The New England Journal of Medicine
, 2004.
72
James Reschovsky, Arkadipta Ghosh, Kate Steward and Deborah Chollet, "Paying More for Primary Care: Can It Help Bend
the Medicare Cost Curve?" The Commonwealth Fund, 2012.
73
Lewis Sandy, Thomas Bodenheimer, Gregory Pawlson, and Barbara Starfield, "The Political Economy of U.S. Primary Care,"
Health Affairs
, 2009.
74
Thomas Bodenheimer, "Primary Care--Will It Survive?"
The New England Journal of Medicine,
2006.
75
Kenney, 2011; Kim, Spahlinger, Kin, and Billi, 2006; and Toussaint, 2009.
76
Goryakin, Griffiths, and Maben, 2011; Horrocks, Anderson, and Salisbury, 2002; Laurant et al., 2009; Richardson, Maynard,
Cullum, and Kindig, 1998; Unruh, 2003; Toussaint, 2009; Kenney, 2011.
77
Guy Boulton, "Thedacare's 'No Layoffs' Practice May Improve Firm,"
Milwaukee Journal Sentinel
, 2008. See also: Barnas,
2011; and Kenney, 2011.
78
"Prognosis Worsens for Shortages in Primary Care," NPR, 2012.
79
Miranda Laurant, David Reeves, Rosella Hermens, Jose Braspenning, Richard Grol, Bonnie Sibbald, "Substitution of Doctors
by Nurses in Primary Care,"
Cochrane Database of Systematic Reviews
, 2005. See also: Horrocks, Anderson, and Salisbury,
2002.
80
Shannon Brownlee, Vanessa Hurley, and Ben Moulton, "Patient Decision Aids and Shared Decision Making," New America
Foundation, 2011.
81
Parija Kavilanz, "Health Care Jobs a Bright Spot for Hiring," CNN.com, 2011.
82
"Employment Statistics: Industry-Occupation Matrix Data by Industry," Bureau of Labor Statistics, 2012.
new america foundation page 32
83
A group practice is an independent physicians' group that is organized to contract with a managed care plan to provide
medical services to enrollees. The physicians are not employees of the HMO, but are employed by the group practice.
84
"Consumer Information -- Glossary of Terms," National Association of Health Underwriters.
85
Donald Berwick, "Making Good on Acos' Promise--the Final Rule for the Medicare Shared Savings Program,"
The New
England Journal of Medicine
, 2011.
86
Elliott Fisher, Mark McClellan, and Dana Safran, "Building the Path to Accountable Care,"
The New England Journal of
Medicine
, 2011.
87
All of our estimates of how the workforce will change are presented
ceteris paribus
all else being equal. That means when
we say we will need fewer nurses, physicians, or specialists, we mean we will need fewer to serve an equivalent patient
population. We have not factored in the expected demographic changes coming in the US over the next few decades partially
because we believe it obscures our point, and partially because we don't think it's possible to accurately assess what those
demographic changes will mean in an organized health care environment. We do not, therefore, mean to suggest that under
organized care, we will need fewer physicians and nurses per capita when the population is larger, older, and fatter we only
mean that such an outcome is possible, but it is certain that we will need fewer than we would if we applied current staffing
patterns to that future population.
88
Michael Chernew, Lindsay Sabik, Amitabh Chandra, and Joseph Newhouse, "Would Having More Primary Care Doctors Cut
Health Spending Growth?"
Health Affairs
, 2009.
89
Brownlee, Hurley, and Moulton, 2011.
90
All workforce numbers in this Appendix, unless otherwise noted, are from the Bureau of Labor Statistics Occupational
Outlook Handbook, based on 2010 - 2020 projections (published 2012).
91
Approximately 25,000 doctors enter residency each year; residencies range from three to eight years. Number of students in
residency derived from authors’ calculations, confirmed by personal communication with the AAMC.
92
There is a long history of debate over the number of GME slots and thus the number of physicians the nation will have
available. The Council on Graduate Medical Education, an expert advisory committee to the Department of Health and Human
Services, regularly weighs in on this topic, most recently in 2005 in its 16th report: “Physician Workforce Policy Guidelines for
the United States, 2000-2020.”
93
COGME, "Physician Workforce Policy Guidelines for the United States, 2000-2020," 2005.
94
"Occupations with the Largest Employment," CareerOneStop, U.S. Department of Labor, 2013.
new america foundation page 33
95
The number of new PAs is small, but those 24,700 new positions represent 30% growth in the total number of PAs. The
number of home health and personal care aides is projected to increase by an astounding 70 percent.
96
"Occupational Outlook Handbook, 2012-2013 Edition, Physicians and Surgeons," Bureau of Labor Statistics.
97
Ong et al., 2009; Romley, Jena, and Goldman, 2011.
98
Gregory Pope, John Kautter, Melvin Ingber, Sara Freeman, Rishi Sekar, Cordon Newhart, "Evaulation of the CMS -HCC Risk
Adjustment Model (Final Report)," 2011.
99
Yunjie Song, Jonathan Skinner, Julie Bynum, Jason Sutherland, John Wennberg, and Elliott Fisher, "Regional Variations in
Diagnostic Practices,"
The New England Journal of Medicine
, 2010.
100
Gilbert Welch, Sandra Sharp, Dan Gottlieb, Jonathan Skinner, and John Wennberg, "Geographic Variation in Diagnosis
Frequency and Risk of Death among Medicare Beneficiaries,"
JAMA
, 2011.
101
Ong et al., 2009.
102
Bruce Pyenson, Stephen Connor, Kathryn Fitch, Barry Kinzbrunner, "Medicare Cost in Matched Hospice and Non-Hospice
Cohorts,"
Journal of Pain and Symptom Management
, 2004.
103
Jennifer Temel, Joseph Greer, Alona Muzikansky, Emily Gallagher, Sonal Admane, Vicki Jackson, Constance Dahlin, Craig
Blinderman, Juliet Jacobsen, William F. Pirl, Andrew Billings, and Thomas Lynch, “Early Palliative Care for Patients with
Metastatic Non-Small-Cell Lung Cancer,”
The New England Journal of Medicine
, 2010.
new america foundation page 34
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About the Project
The Next Social Contract Initiative aims to rethink our inherited social contract, the system of institutions and policies designed to
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Acknowledgements
This work was funded by a grant from the Rockefeller Foundation. We are grateful to our reviewers, whose comments and
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... Brownlee et al. [15] analysed labour productivity in the United States of America. Because of supply-sensitive tendencies among providers, too many hospitalisations and tests are ordered simply because these resources are available. ...
... These authors advocate assessing the value of services and tests and reducing the poor organisation which may include physicians being excessively burdened by paperwork. Because of these deficiencies in the US health care services, increasing the number of specialist physicians would lead to even more inefficiency [15]. ...
... This phenomenon arises because some input measures can be manipulated that lead to biased outcome assessment. Note the similarity between input bias as the supply sensitivity referred to by Brownlee et al. [15]. ...
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... Brownlee et al. (15) analysed labour productivity in the USA. Because of supply-sensitive tendencies among providers, too many hospitalisations and tests are ordered simply because these resources are available. ...
... These authors advocate assessing the value of services and tests and reducing the poor organisation which may include physicians being excessively burdened by paperwork. Because of these deficiencies in the USA health care services, increasing the number of specialist physicians would lead to even more inefficiency (15). ...
... This phenomenon arises because some input measures can be manipulated that lead to biased outcome assessment. Note the similarity between input bias as the supply sensitivity referred to by Brownlee et al. (15). ...
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... These authors advocate assessing the value of services and tests and reducing the poor organisation which may include physicians being excessively burdened by paperwork. Because of these deficiencies in the USA health care services, increasing the number of specialist physicians would lead to even more inefficiency (16). (1) that due to an aging population and its concomitant disease burden, more rather than fewer specialists will be needed to provide high-quality and effective care. ...
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