Emerging Trends in The Social and Behavioral Sciences

Inefficiencies in Health Care Provision

Item

Title

Inefficiencies in Health Care Provision

Author

Burgess, James F.

Hockenberry, Jason M.

Allen, Lindsay

Research Area

Social Institutions

Topic

Health Care Institutions

Abstract

This essay seeks to elucidate salient issues on the topic of inefficiency in the provision of health care. We begin with a discussion on the definition of efficiency, and the particular forms it can take in health care. From there, we define a useful framework for thinking about ways in which efficiency in the health care system can be improved. We describe cutting edge research being conducted in the field, and the major challenges to furthering the research agenda in this area, not the least of which is the unique nature of health care itself. We conclude with a discussion about key issues for future research, including the importance of incorporating multidisciplinary perspectives into this research.

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Identifier

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extracted text

Inefficiencies in Health Care
Provision
JAMES F. BURGESS, JASON M. HOCKENBERRY, and LINDSAY ALLEN

Abstract
This essay seeks to elucidate salient issues on the topic of inefficiency in the provision
of health care. We begin with a discussion on the definition of efficiency, and the particular forms it can take in health care. From there, we define a useful framework for
thinking about ways in which efficiency in the health care system can be improved.
We describe cutting edge research being conducted in the field, and the major challenges to furthering the research agenda in this area, not the least of which is the
unique nature of health care itself. We conclude with a discussion about key issues
for future research, including the importance of incorporating multidisciplinary perspectives into this research.

INTRODUCTION
Our evolving understanding of what constitutes inefficiency in health care is
challenged by two fundamental questions. The first of these has to do with
the definition of efficiency, a necessary starting point for a discussion of what
represents a lack of efficiency. The second and closely related issue has to do
with the level of efficiency that is feasible and/or desirable in the health care
market (Burgess, 2012).
In the United States, which spent $2.8 trillion on health care in 2012 (Martin, Hartman, Whittle, & Catlin, 2014), any detectable amount of inefficiency
in the health care system represents an opportunity to reallocate resources
in ways that might return sizable benefits to society. However, changes to
the system are unlikely to yield these large societal benefits unless they are
based on research that inherently incorporates the complex, multifactorial
nature of health care delivery. In this sense, moving the health care system
toward better outcomes is likely to require new perceptions and interdisciplinary approaches that accept and take advantage of these inherent system
complexities. Furthermore, these interdisciplinary approaches account for

Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

certain characteristics that are peculiar to the health care industry. These characteristics, which we discuss later, can have a magnifying effect on small
changes to the system, a mitigating effect on large changes, or even pose
fixed, immutable barriers to change. Thus, we will discuss how decoupling
those elements of health care that we want to hold fixed from those that we
want to modify is essential to making progress.
The starting point for this investigation is a brief discussion of how we
can conceptualize efficiency, a term that is often associated with the notion of
value. The value equation can be thought of as quality divided by cost. Cost
is typically expressed in monetary terms, making it relatively easy to measure. As any health economist will attest, however, making broad changes to
health care costs remains a difficult task. Quality, on the other hand, is a rather
amorphous construct, making it more difficult to measure, but also potentially easier to alter. We will discuss, then, that a reasonable framework for
thinking about the rest of this essay is to set costs as fixed and measure inefficiency first as “failed” quality for given effort. At the system level, this can
be thought of as holding the spending on health care relatively fixed while
trying to improve the quality of what we receive for those expenditures.
One particular difficulty in analyzing any aspect of the health care system is its inflexibility, stemming from, among other things, the presence in
the market of sunk costs (i.e., resources spent that cannot be recovered) and
asset specificity (i.e., once clinical providers specialize in a field, they tend
to continue practicing in that field). The health care system protects itself
by making this inflexibility mostly opaque to outside evaluation and fundamental change. This essay illuminates the emerging trends that illustrate
how challenging these problems are to conceptualize and attack, and points
toward the most promising avenues of further investigation and research.
FOUNDATIONAL RESEARCH
In light of the considerable discord that exists between both early and recent
definitions of efficiency found in the literature (Burgess, 2012), a comprehensive review of them here would be counterproductive (see the Further
Reading section for more resources on this topic). Instead, we ground our
discussion in a recently developed, person-centered efficiency framework
developed by Palmer and Torgerson (1999).
First, consider that the inefficiency we seek to address is wrapped up in the
relationship between resource inputs (i.e., costs, in the form of labor, capital, equipment, and supplies) and either intermediate outputs (e.g., numbers
of patients treated, measures of access to care) or final person-level health
outcomes [e.g., QALYs (quality adjusted life years), as noted by Palmer and

Inefficiencies in Health Care Provision

3

Torgerson]. As we described above, this approach permits one to alter efficiency by improving quality while holding these costs and input/output
relationships (in a cost sense) fixed. With this framework in mind, and following Palmer and Torgerson, we can define three foundational economic
measures of inefficiency.
Technical inefficiency is seen as occurring when we fail to use productive
inputs and/or resources to their greatest potential as we maximize health
outcomes. One can attempt to identify which particular resources are at fault
in this type of inefficiency, and, as a result, target a particular resource for
intervention or improvement. This is the most basic and straightforward definition of inefficiency.
Productive inefficiency occurs when we fail to choose the right combinations of productive resources that achieve the maximum quality-adjusted
health benefits for a given cost. This conceptual approach specifically fixes
cost, but quickly becomes complicated, given the multifactorial causes that
could underlie a particular type of productive inefficiency.
Allocative inefficiency results when we fail to seek the right mixture of
health care programs, social programs, and health systems that maximize
the health of society as a whole. This is the loftiest of efficiency goals, and is
also the most complex, not least because many aspects of allocative efficiency
rely on factors that lie outside of the health care system.
CUTTING-EDGE RESEARCH
We have described elsewhere some of the main challenges involved in defining health care inefficiency, both in general (Burgess, 2012) and with respect
to hospital readmissions specifically (Burgess & Hockenberry, 2014). However, additional challenges exist. First, health economists are not alone in raising questions about how provider actions can influence health care services;
providers themselves are often concerned with how their actions compare to
those of their peers (Huesch, 2014). In many cases, providers might be better equipped to handle these questions, as there are some unique aspects of
defining health care expenditures (Aaron, 2013) that might elude economists,
who often are underexposed to nuances of the health care industry (Baumol,
2012). While official Bureau of Labor Statistics figures tend to suggest dramatic health care price increases over time, prices in many key areas (e.g.,
cardiovascular care) have actually fallen, and effectiveness of treatments has
increased (Cutler, 2014).
Second, there are an almost innumerable set of extremely different areas
in which one might focus a research agenda on the topic of health care efficiency. As we note above in the definition of allocative inefficiency, perhaps
our largest problem is in allocating too much of total social spending on

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

health care, and not enough on social services (Bradley & Taylor, 2013). Perhaps our health care organizations, burdened by institutionalism, are not
built to “learn” and incorporate evidence-based strategies to decrease inefficiency (Smith et al., 2013). Further, there exist wide geographic differences in
nearly any measure of the health care system (Newhouse & Garber, 2013). In
our view, some of the most path-breaking recent empirical papers addressing these challenges are by Gutacker et al. (2013), Mutter et al. (2013), and
Wildman and McMeekin (2014). Another challenge has to do with the availability and form of data to be used in this type of research; a particularly
salient question at the moment is what the role of “Big Data” is going to be
in research in the future (Murdoch & Detsky, 2013).
A third challenge characterizes any study of social science, but especially
important to the science of health care delivery: the question of causality
in relationships. Different approaches for addressing questions of causal
inference are frequently debated (Pearl, 2009), but a useful framework for
high-level thinking about causality in health care efficiency comes from
Gelman and Imbens (2013). Their model distinguishes between the “effects
of causes” and the “causes of effects.” The former is concerned with isolating
a single factor (or variable), then estimating its contribution to a given outcome of interest. The latter is a broader construct, emphasizing the relative
contribution of multiple factors to a particular outcome of interest. Our
discussion centers on this second construct, taking the “effect” in question
to be inefficiency in the health care system.
Much of the contemporary literature in medicine is concerned with the
delivery of an intervention to a patient, with a focus on the effectiveness of
“treatment” A versus that of “treatment” B in changing outcomes. Many
research design and statistical methods, whether prospective or observational, are focused on this sort of one-versus-the-other causal reasoning,
which might not be the optimal approach in a field as complex as health
care. Few studies examine the relative contribution of the multitude of
factors that contribute to an outcome, or how these factors might mediate or
moderate the treatment effect.
This approach to causal reasoning has made its way, in varying degrees, to
other disciplines, including health services research, health systems research,
and to health economics. In these fields, the “treatment” being studied is
rarely an actual medical treatment, but rather is often an organizational
feature of the provision system, or how it is financed. At the most macro
level, research in these fields seeks to explain a seemingly paradoxical
phenomenon: the growth in health care spending in the United States far
outpaces that found in other developed nations, but US life expectancy outcomes are poorer than in those countries. Many reasons for this phenomenon

Inefficiencies in Health Care Provision

5

have been proposed over the years. On the supply side, these include differences in the financing system, the resultant impact on prices (Anderson
et al., 2003), and the American tastes for high technology (Newhouse,
1992). On the demand side, some have argued that systematic differences
in risky behaviors drive the quality gap, and once this is accounted for,
some of the cost-quality disparity evaporates (Ohsfeldt & Schneider, 2006).
Still others point to structural social inequities that translate into poorer
health (Marmot et al., 2008). In general, each of these contributions to the
health care inefficiency literature focuses on the “effects of causes,” while
acknowledging the existence of many “causes of the effect.”
KEY ISSUES FOR FUTURE RESEARCH
One might assume such broad, high-level considerations of what causes
health care inefficiency at the macro level would translate into clearer
thinking among those addressing more granular, ground-level questions
of specific health care inefficiencies. However, this is not always the case.
Consider the illustrative case of hospital readmissions. If one simply looks
at aggregate statistics, expenditures on hospital care, particularly for individuals who are discharged from the hospitals but are readmitted within a
short time frame, are staggering (Jencks, Williams, & Coleman, 2009): about
30% of total US national health care expenditures are on hospital care, and
approximately 1 in 5 patients are rehospitalized within 30 days. Clearly,
hospital readmission is a ground-level issue that has implications for the
broader question of system-wide health care efficiency.
In response to these statistics, the US Medicare program, as specified in the
Affordable Care Act, has implemented payment reductions for hospitals with
higher-than-expected readmission rates among its beneficiaries. The implicit
assumption in such a policy is that hospital quality (in the form of either inpatient care or coordination of care at discharge) is the chief “cause of the effect”
in this context. As such, a multitude of hospital-based interventions that were
developed to improve the quality of hospital care and subsequent care coordination also are now touted as effective “treatments” for the “disease” of
high readmission rates (Axon & Williams, 2011; Coleman, Parry, Chalmers,
& Min, 2006; Kocher & Adashi, 2011). However, there are important questions
remaining regarding the efficiency of these approaches.
For example, one emerging consideration is that even in systems with
lower overall spending than the United States and only recently increasing
growth rates (e.g., NHS in England, or Medicare in Australia), the proportion
of spending on readmissions is just as staggering as that found in the United
States If some dimension of hospital-based quality is at the root of this
perceived readmission inefficiency, then it permeates hospital organizations

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

across borders. If one steps back and considers the “causes of effects” in
this context, he or she might reach a different conclusion regarding which
policies would be effective at addressing this inefficiency, or whether high
rates of readmission represent inefficiency at all (Burgess & Hockenberry,
2014).
First, one must consider whether readmission itself even represents a
“cause of the effect,” or whether it is simply one component of a complex,
multifaceted pathway from multiple causes to the effect. If readmission is a
cause of inefficiency, then tackling it can reduce resource use, and assuming
patients do not prefer to be in the hospital, improve quality as defined by
patient satisfaction. On the other hand, if excess readmission is a symptom
of a wider set of causes, such as social inequality, low health literacy, then
targeting strong incentives may reap short-term cost savings with regard to
hospital care, but could exacerbate inefficiency across the system, including
increasing inefficiency in hospital care in the long run.
Even if readmission itself is a cause, there exist other considerations for anyone seeking to measure the efficiency of the approaches intended to address
the problem. First, targeted treatments for readmission rely on whether the
system can identify and “treat” only those patients most likely to be readmitted. If this proves unfeasible, the intervention has to be applied to everyone
(like a vaccine) to gain reductions in readmission. Thus, the cost-effectiveness
(and therefore, the efficiency) of a particular readmissions intervention relies
on whether the sum cost of delivering the intervention to every patient is
satisfactorily offset by the cost reduction gained by avoided readmissions.
Second, this thinking relies on the somewhat unlikely assumption that hospitals, in response to loss of revenue from readmissions, will not simply replace
that revenue by raising prices on other services provided to patients from
other payment sources. Third, from a social welfare perspective, a more desirable target for reducing readmission might be items of broader social concern: health literacy, self-efficacy, and social inequality, all of which might be
thought of as “causes of the effect.”
Readmission policy is, of course, just one of many granular issues related
to macro-level health care inefficiency, but we use it here as a rich example to
demonstrate the importance of thinking deeply about causal questions when
addressing inefficiency, writ large, in these social policy contexts.
Modern health care delivery also suffers from all three forms of inefficiency
related to both the nature of demand for health care and industrial engineering issues characterizing its manufacture. Medical systems in developed
nations are laden with complex technology, not unlike modern manufacturing in other industries. However, unlike other manufacturing industries,
much of the demand in the health care sector is unpredictable in its timing.
Furthermore, technology in other industries is often designed to reduce costs,

Inefficiencies in Health Care Provision

7

whereas technology in the health care sector is often associated with greater
costs (Cutler & Huckman, 2003; Weisbrod, 1991).
To understand the timing issue, contrast heath care with automobile manufacturing. An auto manufacturer can organize a production line, staff the
production line, and time the delivery of inputs with supply chain management, all of which can be controlled or anticipated through contracting. Once
the automobiles are manufactured, output can be stored to meet customer
demand when necessary, albeit with some financing and storage costs. Similarly, a health care administrator can organize a production line and staff,
but in the case of many illnesses, he or she cannot control the timing of the
arrival of the key input to production: the patient. Furthermore, medical care
providers cannot produce health and simply store it in a warehouse for ready
purchase by the customer. As such, health care has to build out capital capacity and staff it with labor that will often sit partially, or completely, idle for
long periods. This creates the somewhat perverse observation that the least
costly and most general provider types (such as floor nurses and primary
care providers) are most pressed to high effort, whereas the most costly and
most specialized resources often sit idle and do not have to work as hard to
generate high rents.
Further compounding this inefficiency are agency issues inherent in health
care delivery and finance systems that align incentives in a way that promotes induced demand. Physicians, who control much of the utilization of
care either directly or indirectly, act as agents for patients and hospitals. The
payment system is designed in such a manner that providing more care is
personally beneficial to physicians. Absent an altruistic motive to conserve
social resources, there is a strong incentive to physicians, possibly mitigated
by altruism, to provide more care, even when the incremental value of care
in terms of increased health is smaller than its benefit. In the context of the
queuing problems noted above, this is compounded by physician agency
on behalf of hospitals. Indeed, evidence of short-term excess capacity (as
opposed to medical necessity) driving utilization is found in such high cost
health care services as neonatal intensive care units (Freedman, 2012) and
cardiac surgery (Hockenberry et al., 2011).
Given these characteristics of the health care system, it may be tempting
to address inefficiency by simply reducing capacity through regulation.
However, in this regard also, health care poses some unique challenges.
Unlike customers of auto manufacturers, patients have the potential to die
if immediate access to health care is reduced. Restated another way, while
short-term excess capacity in health care leads to demand inducement,
reducing quality of and/or access to health can lead to (in extreme cases)
socially undesirable loss of life, an arguably inefficient outcome. Indeed,
an emerging body of literature (Almond & Doyle, 2011; Almond, Doyle,

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Kowalski, & Williams, 2010; Chandra & Staiger, 2007; Cutler & Huckman,
2003; Doyle, 2011; Doyle, Graves, Gruber, & Kleiner, 2012; Hockenberry &
Helmchen, 2014) suggests that increases in medical treatment intensity on
the margin lead to the cost-effective preservation of life and/or reduction
of severe symptoms. From this perspective, reducing capacity could itself
prove socially inefficient.
In light of the issues described thus far, it is clear that more research is
warranted in the area of inefficiency in health care delivery. Here, key considerations for prioritizing this research agenda are outlined.
One vital area of research that remains underdeveloped is the body of work
addressing how we measure trade-offs within and among different types of
resources within the health care system. Recall from earlier in the essay that
most commonly-accepted definitions of efficiency rely on the construct of
quality, yet an agreed-upon definition of quality remains elusive. Thus, it has
been difficult to conduct studies that uncover the degree to which different
health care inputs affect health care quality, making this field a potentially
fruitful research area.
For example, under the “right patient, right time, right intensity of care”
quality definition, questions remain about what constitutes “right intensity
of care.” If two inputs, A and B, produce the same patient outcome, but
input B is less resource-intensive, most would agree that input B represents
the “right” care intensity. Although this thinking originally developed
around medical interventions, the framework can easily be extended to
questions regarding the mix of labor inputs to health care. The case of nurse
practitioners (NPs) and physician’s assistants (PAs) provides one illustrative
example. Studies have suggested that these nonphysician practitioners can
reduce labor costs (Roblin, Howard, Becker, Kathleen Adams, & Roberts,
2004; Venning, Durie, Roland, Roberts, & Leese, 2000), whereas other studies
have shown the quality of care they can provide is on par with that provided
by physicians (Horrocks, Anderson, & Salisbury, 2002; Sox, 1979). Taken
together, these results suggest that nonphysician practitioners might offer
a cost-effective alternative to medical doctors in at least some areas of
care delivery. However, the respective contribution of such providers to
quality outcomes depends to some extent on scope of practice regulations.
These regulations, which vary by state, dictate the degree to which NPs
and PAs may practice independently, without physician supervision. At
least one study has offered evidence that allowing NPs to practice and
prescribe medications independently offers cost savings over models that
require physician oversight (Spetz, Parente, Town, & Bazarko, 2013), but
questions still remain about the optimal scope of practice for these providers.
Furthermore, as an accepted definition of quality evolves in the literature,

Inefficiencies in Health Care Provision

9

the relative importance of nonphysician providers in achieving that quality
will become an important area for research.
The debate surrounding what constitutes an optimal health care labor
mix is not limited to medical providers. Nurses have long been considered
an integral input to health care outcomes, yet few studies exist examining
whether some nurses are “better” than others at improving care quality, and
what might constitute an appropriate nursing-type balance in the clinical
setting. One recent study has found that hospitals with higher proportions
of care delivered by contract nurses (as opposed to nurses employed
full-time by the hospital) have lower patient satisfaction scores (Becker &
Hockenberry, submitted). Given the growing role of patient satisfaction as
an indicator of quality, this issue should warrant considerable attention in
the literature in the near future.
AREAS OF RESEARCH IN OTHER FIELDS
Our approach in discussing these emerging trends has been drawn primarily
from the field of health economics. However, similar important insights
are emerging from other fields, such as sociology, anthropology (e.g., Hoff,
2013), medicine (e.g., Srinivasan & Schwartz, 2014), and organization [issues
of teamwork (Pullon, McKinlay, & Dew, 2009), power of staff such as nurses
(Sabiston & Lascbinger, 1995), and identity and control (Doolin, 2002)].
The overriding theme of these efforts in other fields is synchronous with
our approach, in that they emphasize the complexity of the health care
systems and the resulting difficulties in defining successful interventions
for improvement. For example, Doolin (2002) emphasizes the challenges in
trying to control, curtail, or influence professional autonomy of clinicians
which is a likely outcome of any system-level interventions. Hoff (2013)
identifies a difference between hard implementation practices that represent
structural interventions as distinct from soft relational practices that play out
in day-to-day interactions. Srinivasan and Schwartz outline the approaches
that medicine, in particular the Society for General Internal Medicine,
is recommending attacking these issues. Successful efforts in the future
are likely to employ multidisciplinary methods which account for these
complexities that are resistant to standard modeling.
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FURTHER READING
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Cutler, D. (2014). The quality cure: How focusing on health care quality can save your life
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Economic Perspectives, 3–21.
Ohsfeldt, R. L., & Schneider, J. E. (2006). The business of health: The role of competition,
markets, and regulation. Washington, DC: AEI Press.
Palmer, S., & Torgerson, D. J. (1999). Economic notes: Definitions of efficiency. BMJ
(Clinical research ed.), 318(7191), 1136.
Pearl, J. (2009). Causality: Models, reasoning and inference (2nd ed.). Cambridge, England: Cambridge University Press.
Smith, M., Saunders, R., Stuckhardt, L., & McGinnis, J. M. (2013). Best care at lower
cost: The path to continuously learning health care in America. Washington, DC: The
National Academies Press.
Wildman, J. & McMeekin, P. (2014). Health care and social care: Complements, substitutes and attributes. MPRA Paper. MPRA Paper No. 544252014. Available at
MPRA: http://mpra.ub.uni-muenchen.de/54425/1/MPRA_paper_54425.pdf

JAMES F. BURGESS SHORT BIOGRAPHY
James F. Burgess, Jr., PhD, is a VA Center for Healthcare Organization
and Implementation Research (CHOIR) Senior Investigator and Professor
of Health Policy and Management, Boston University School of Public
Health, where he is the director of the health economics program. As a
Health Economist, he is a founding Coeditor of Health Economics Letters
and an Associate Editor of its parent journal, Health Economics. He also

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

serves as a Senior Associate Editor of Health Services Research, a journal of
Academy Health, where he is on the steering committee for the Quality and
Value Interest Group, is an HSR Methods Council member, and chairs the
Academy Health Education Council. Within CHOIR, he is a Codirector of
the Postdoctoral Research Program and a member of the steering committee
for the Medication Optimization Program. He has research expertise in
productivity of health care organizations, diffusion of knowledge, strategic
alliances, and quality evaluation, including physician and organizational
profiling. In VA, his funded studies with numerous VA collaborators across
more than a dozen VA centers has included work on qualitative approaches
to develop and validate a measure of perceived access, implementation
of care management programs in primary care/mental health integration,
spatiotemporal spread of second generation antipsychotics, timely access
to colonoscopy services after positive screening tests, roles of hospitalists
in impacting inpatient quality and inefficiency of care, composite quality
measurements in VA nursing homes, evaluating cost savings for veterans
receiving palliative care consults, cohort evaluation of Medicare and VA
service utilization over time, and studying the impact of information
technology on nurses and patient quality of care. He also has done contract
research for the Centers for Medicare and Medicaid Services on the Hospital
Value Based Purchasing system and is the Treasurer of the International
Health Economics Association.
JASON M. HOCKENBERRY SHORT BIOGRAPHY
Jason M. Hockenberry, PhD, is an Assistant Professor in the Department
of Health Policy and Management at Emory University’s Rollins School of
Public Health. He was formerly an Assistant Professor in the Department
of Health Management and Policy at the University of Iowa, and an investigator in the VA Center for Comprehensive Access and Delivery Research
and Evaluation. An economist by training, Dr. Hockenberry’s main research
interests are in the market forces and policies impacting the human capital of
the health care workforce, the organizational economics of the hospital, and
how both of these relate to medical technology diffusion and services use. He
is also engaged in collaborative work related to behavioral health policy and
services as a core faculty member in the Center for Behavioral Health Policy
Studies at Emory University.
LINDSAY ALLEN SHORT BIOGRAPHY
Lindsay Allen, MA, is a doctoral student in health economics and health
services research at Emory’s Rollins School of Public Health. She earned her

Inefficiencies in Health Care Provision

15

bachelor’s degree in Cognitive Science from the Johns Hopkins University
and her master’s degree in Health Administration and Policy from the University of Chicago. She spent several years working in the pharmaceutical
industry before transitioning to the nonprofit sector, where she worked
as a senior health care technology analyst at ECRI Institute. Her research
interests center on supply and demand factors related to hospital emergency
departments.
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