Health System Reconfiguration
Evaluation of Fully-Integrated
Rural Health Hubs
Investigating the possibility of
system-allocative efficiency gains
PREPARED BY:
Audrey Laporte, BA MA PhD
Associate Professor of Health Economics
Director Canadian Centre for Health Economics
Institute of Health Policy, Management and Evaluation
University of Toronto
Brian Ferguson, BA MA PhD
Department of Economics
University of Guelph
Canadian Centre for Health Economics
JUNE 2015
SPONSORED BY
CCHE
CCES
Canadian Centre for Health Economics
Centre canadien en économie de la santé
The Canadian Centre for Health Economics (CCHE) was engaged by the Ontario Hospital Association
(OHA) to explore the development of a framework for evaluating fully-integrated rural health hubs in
Ontario. After reviewing the OHA’s research on health hubs and performing a scoping review of the
econometric and health services literature, the CCHE proposed to focus the evaluation framework on the
basic idea of efficiency (or productivity) gain. Health services research typically provides frameworks and
methods for assessing health services and health systems based on the objectives of effectiveness, efficiency
and equity. By focusing solely on efficiency, this framework allows us to identify the sources and limits of
productivity within the rural health hub model, and help explain why the hunt for such gains has often
been unsuccessful in previous evaluative research on the integration of health services. Not only can the
concepts discussed be used to evaluate fully-integrated rural health hubs, but they will also be useful in
understanding the efficiency gains made through other integrated models of care.
Although assessing effectiveness and equity are important, it is critical to understand the components of
efficiency so that we can pinpoint attributes that offer healthcare providers value and understand how
integration of services is likely to work in terms of the economic theory. This paper will explain why there
are potential efficiency gains to be realized in this macro context, and where we can expect to see those
gains. So, firstly, we will determine how we should be thinking about the operations of rural hospitals and
other healthcare providers (e.g., home care agencies, long-term care facilities) so that we can predict the
likely impact of integration. Getting at the nature of efficiency lies at the heart of system sustainability because it deals with how we might be able to do more with the healthcare resources at our disposal.
Efficiency Gain
Broadly speaking, we can identify three broad types of efficiency gain: technical efficiency, scale efficiency,
and allocative efficiency. We are proposing that it is conceptually important to separate changes in the
way care is delivered in a region following administrative integration so that we can understand the
relative importance of each of the components of efficiency. By separating these changes, we will obtain
a complete picture of how past health hub mergers have affected the delivery of care. We will consider
all three types of efficiency gains in this paper, but the starting point for any discussion of efficiency is
technical efficiency.
WHAT IS TECHNICAL EFFICIENCY?
Technical efficiency is primarily an engineering concept: it refers to whether the hospital or long-term
care facility is getting as many services or outputs (e.g., patient days of care) as it reasonably can, given the
resources or inputs it is using (e.g., nursing hours, beds, capital equipment, supplies etc.).
The starting point for the analysis of technical efficiency is the notion of ‘production function’. Production
function in economics is a relation (technically, a frontier relation) that shows the maximum amount of
services a hospital or healthcare provider can provide from different quantities of resources or inputs (e.g.,
labour and capital). In general, the more resources or inputs the hospital or healthcare provider uses,
the more services or outputs it can deliver. However, the rate at which output or service levels increases as
input use increases (i.e., the degree of returns to scale) varies across activities. Strictly speaking, a hospital,
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
1
for example, can be operating in a technically efficient manner when it is on its production function
(i.e., when it is not possible for the hospital to produce more services without increasing the quantity of
resources it uses).
Figure 1, below, shows the standard textbook illustration of the production function for a firm. From now
on, this paper will refer to the producer or health service provider as an HSP – which is producing a single
output or service, shown on the vertical axis as Y, using Labour, which is shown on the horizontal axis as L,
and Capital, labelled K, and not shown on an axis on the diagram. Therefore, unless otherwise indicated,
Capital, or K, is assumed to be held constant in the problem being considered. If we ignore K altogether
(which we can get away with at this stage since we are holding the level of K constant), we have the textbook
illustration of a single output/single input production function.
Y
Unattainable outputs
Efficient outputs
Y(L,K)
Y1
Attainable but inefficent outputs
Y1’
L
L1
Figure 1: Single Output Production
On Figure 1, we have marked regions as Attainable, Efficient and Unattainable. The curve displays
combinations of services that can be produced using resources during a given period and represents the
production possibility frontier. The production function is a frontier, marking a boundary between what
is attainable and what is unattainable, given current technology (and capital stock), and the most efficient
production methods. The production frontier shows the highest level of output, Y, which can be produced
given a particular level of labour input, L. Thus, the level of labour input, which is being used is L1, the
maximum output that can be produced is Y1. Since Y1 is a maximum, we cannot reach any point above
it, but we can reach a point below it: if we were to find that our HSP was producing output level Y1’ with
input level L1, we would class it as technically inefficient.
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Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
We can measure the degree of this HSP’s technical efficiency (or strictly, inefficiency) in either of two
directions, as set out in Figure 2 (below). One possibility, shown as the distance from Y1’ to point A, would
measure the additional output that could be produced using L1 units of labour, if the HSP were to operate
in a fully technically efficient manner. The other, from Y1’ to B, measures the degree to which the HSP
could reduce its labour input and still produce output Y1’ (i.e., not aim to produce Y1 but take Y1’ as its
optimal output level) were it to increase its level of technical efficiency.
Y
Unattainable outputs
Efficient outputs
Y(L,K)
A
Y1
Attainable but inefficent outputs
B
Y1’
L1
L
Figure 2: Options for measuring degree of technical efficiency/inefficiency
Technical efficiency, then, subject to this caveat, deals with whether a hospital or healthcare provider is
actually operating on its production frontier or whether it is employing more inputs than it needs to,
given the level of output it is producing. In health care, even after taking account of managers’ need to be
forward-looking, measuring technical efficiency is not easy, nor can it be done in exactly the same way as it
is in other fields.
In most areas of economics, the firm – the producer – is thought of as having as a considerable degree of
control over the rate at which it produces output, because it is usually thought of as producing a physical
product. The demand for its product is reasonably predictable in the long run, over a period of a year, say,
although day-to-day it may have a significant stochastic element, which is based simply on when customers
happen to appear at the store. To deal with that stochastic element (since it’s not good business to have to
turn customers away too often, lest they turn to other suppliers), the producer will produce (or purchase)
supply for inventory purposes as well as for immediate sale. Holding inventories is one of the essential
parts of its business, and the cost of inventory is a significant part of overall business costs.
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
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While healthcare providers, like hospitals, can hold inventories of inputs (most obviously beds and
materials, or a 24-hour emergency department), they cannot hold inventories of outputs (such as patients
days of care, surgeries).
At the same time, the nature of the mission of a healthcare provider, and the fact that it is likely to be one
of a very limited number of suppliers of its health services in its particular catchment area, means that
it has much less freedom to turn demand away. This is quite common in rural hospitals with very small
number of emergency department visits. The health provider can turn away demand to some degree
– waiting lists are an integral part of any health care system and it can be argued that a health service
provider, which has no waiting list at all, is employing too many inputs.
The essence of the analysis is that it is often more efficient to hold extra inventories of inputs to deal
with unanticipated spikes in demand for care than it is to reduce inventories and try and accommodate
a spike in demand by working the smaller stock of inputs harder. The small rural hospital with a small
intensive care unit (ICU) is a good example of this point. Obviously, any manager has to trade off the cost
of extra inventories (here we are including some personnel as part of the inventories of labour services)
against the need to be able to meet foreseeable unplanned spikes in demand. (Foreseeable in the sense
that experience tells how many unusually busy days the hospital, clinic or home care agency is likely to
experience in, say, a year; unplanned in the sense that the manager cannot be absolutely certain about
that total, nor is he going to be able to pinpoint in advance exactly when the spikes in demand for health
services will occur.)
Beyond the theoretical issues that arise from managerial uncertainty facing healthcare providers about the
time pattern of demand for a health provider’s services are the measurement issues. Even if the healthcare
provider is being parsimonious in its holding of input inventories, it may, in a formal productivity
analysis exercise, look as if it is holding too many, should the data used in the analysis span a period in
which demand is unusually low. This is an argument made vis-a-vis rural and remote hospitals since the
sparse populations they serve mean that demand for some services may be even more subject to service
spikes compared to heavily-populated regions. Again, we will not go into the technical details here, but
uncertainty as to the pattern of demand that a health service provider like a hospital expects to face
(expected uncertainty – known unknowns, as it were) needs to be allowed for in any exercise in measuring
actual efficiency.
We need to be clear about technical efficiency since as we will show below, the gains to integration may not
accrue in terms of technical efficiency. This is because empirical evidence suggests that non-integrated
rural and remote hospitals and many long-term care facilities (see Hsu et al, 2015 for example) may very
well be operating on or close to their production frontiers. That is, they may already be maximizing the
use of the inputs at their disposal given the budgets and costs that they face. This suggests that we need
to understand in detail the other aspects of efficiency and how these may be impacted by integration
of services of the kind that has been undertaken by some rural hospitals in Ontario and that could be
emulated across other regions in the province.
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Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
WHAT IS SCALE EFFICIENCY?
The second aspect of efficiency is scale efficiency. Scale efficiency refers to the notion that the larger scale
of operation tends to be associated with lower unit (or average) costs of output. It invokes the economic
notion of economies of scale, a concept which refers to the idea that the nature of production is such that
there exists at least some range of operations in which increases in input or resource use result in increases
in output or services that are proportionally larger than the increase in the input use that drove them. In
textbook terms, suppose for example that we increase the level of all of our inputs (labour, capital and
materials) by 10 per cent. When the production process displays Constant Returns to Scale (CRS), output
or services will also increase by 10 per cent. The production process is said to display Decreasing Returns
to Scale (DRS, also referred to as Diseconomies of Scale) if the output increases by less than 10 per cent.
When the production process displays Increasing Returns to Scale, (IRS, also referred to as Economies of
Scale) output will increase by more than 10 per cent. It is important to note that if an increase of 10 per
cent in all of our inputs will increase costs by 10 per cent, if total output increases by more than do total
costs, costs per unit of output – average costs – will decrease. We have drawn Figures 1 and 2 above on the
assumption of decreasing returns to scale input level, since this is the assumption which is maintained in
the CCHE paper.
The concept of increasing returns to scale underlies the notion that bigger is better; that a larger hospital
for example, is automatically going to produce more output than two smaller ones, each of which is half
the size of the larger one. It is a concept that has lain behind many consolidations, and there is no doubt
that many production processes display IRS, at least over some part of their output range.
This last qualifier: “over some part of their output range” is crucial. Even if a production process does
display increasing returns, it will, at some point, revert to having decreasing returns. When that scale of
operation is reached, costs will start to rise faster than output, and average costs will increase. There are
various reasons why decreasing returns will eventually set in – some will be engineering-type reasons, while
others will reflect the increased demands on management of trying to coordinate successively larger units.
In this latter case, the information demands of managing larger and larger healthcare providers mean
that what we might find that managerial information processing and coordination requirements increase
faster than the health care provider’s scale of operation. The difficulty of coordinating larger flows of
information is the result of less efficient information processing, which in turn hinders efficiency of
production of health services. At the very least, managerial information processing capacity requirement
starts to increase faster than the requirements for other types of inputs, and we have reached the point
where it is better to split the production unit in two.
In addition, it is easy to exaggerate the potential gains from increased scale, even when increasing returns
are in operation. In other words, unexploited IRS might be present, but smaller than optimists tend to
assume. Any unexploited increasing returns should certainly be considered as sources of efficiency gains
(although it is important to note in this case that total costs must increase if we are to be able to take
advantage of any unexploited returns to scale), but we should not expect miracles from them. There is
no doubt that many health sector activities do display increasing returns, but how large those returns are
and, more importantly for our purposes, whether there are any unexploited returns in the system, are key
questions here.
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
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In this CCHE paper, we have deliberately not assumed the presence of increasing returns to scale in rural
health care. While this does not mean that there exist no such unexploited returns, we did not want to
make our analysis rely on their presence. If there are unexploited IRS, they should be regarded as a bonus,
not as a key element of the process of efficiency gain.
In other words, some scale efficiency benefits may be realized through integration, but even if we assume
that they don’t exist, there are potential efficiency gains to be realized from hub-style integration.
WHAT IS ALLOCATIVE EFFICIENCY AT THE LEVEL OF THE
HOSPITAL OR PROVIDER?
The third category of efficiency, and the one which is the primary focus of the CCHE paper, is allocative
efficiency. In the productivity literature, there are a number of aspects of allocative efficiency; but in the
CCHE paper, we focus on the issue of which outputs or services are produced where.
For this focus to make sense in the context of rural hub integration, we must extend the analysis in two
directions – we must be able to talk about a health service provider, such as a hospital, producing more
than just a single output or service, for example emergency care, and we must be able to discuss the case
where there is more than one health service provider in a (potential) catchment area (i.e., a hospital and
long-term care facility for example).
To be able to talk about multiple outputs of health services, we extend the notion of the production
function to what is termed a Production Possibility Frontier (PPF).
Y1
Unattainable output levels
Attainable, inefficent
output levels
Efficient outputs
Y2
Figure 3: Production Possibility Frontier
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Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
Figure 3 shows a PPF for the case of a health service provider, such as a hospital, producing two outputs,
labelled Y1 and Y2, each of which is assigned an axis. In drawing the PPF we take the health provider’s
levels (labour, capital and materials) as given, since we only have two axes to work with. The PPF shows in
a sense a menu of possible output mixes, given the levels of inputs the health service provider has available
to it. Because the health service provider’s stock of inputs is fixed, there is an upper limit to what it can
produce. The horizontal intercept of the curve shows the maximum level of Y2 which the health service
provider could produce if it were to devote all of its inputs to the production of Y2 (and hence none to
producing Y1, resulting in a zero level of output of Y1) while the vertical intercept shows the maximum
amount of Y1 which it could produce if it were to apply all of its (fixed level of) inputs to producing Y1 and
hence produce no Y2. The negative slope of the curve shows that if the health service provider decides,
for example, that it wants to increase its output of Y2, the only way it can do this (since its input levels are
fixed) would be by shifting inputs away from the production of Y1 and into the production of Y2. Since
shifting outputs out of Y1 reduces the amount of Y1 that the health service provider is producing, the
opportunity cost (in economic terms) of an additional unit of Y2, which is shown by the slope of the PPF,
is the amount of Y1 which must be given up to increase the production of Y2 by one unit, starting from
whatever output mix the health service provider happens to be at when the decision to change the output
mix is taken. The actual slope of the PPF reflects the production functions for the two outputs and will
differ across health service providers depending on the mix of inputs (mix of labour skills, for example)
which each happens to have.
Y1
B
E
A
C
Y2
Figure 4: Efficiency Measurement with a Production Possibility Frontier
To see how we conceive of efficiency measurement in the multi-output case, consider Figure 4, and assume
that the health service provider is observed to be producing at point A. Since A is inside the PPF, in the
inefficient region, the health service provider is producing in a technically inefficient manner, whether
looked at in terms of input efficiency or output inefficiency.
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
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Because we have outputs on the axes of the PPF diagram, we will discuss health service provider A in terms
of output efficiency. In this case, however, the direction in which to measure output efficiency is not as
intuitive as it is in the case of a health service provider producing a single output. Our health service
provider could choose to move from point A to point B, holding its Y2 output unchanged and increasing its
Y1 output, for example, or it might prefer to move from A to C, holding Y1 unchanged and increasing Y2.
Or it might prefer to move to some other point on the PPF, most probably, but by no means necessarily, on
the arc between B and C.
This is where we first encounter the concept of output allocative efficiency. Broadly defined, output
allocative efficiency refers to the question of the particular output mix being produced. Because this
might differ between otherwise identical health service providers on the basis of differences in their social
objectives, we need a measure of technical efficiency which will not penalize one unit for choosing an
output mix which differs from the mix being chosen by other, otherwise identical, units.
We accomplish this by introducing the concept of a radial expansion of output. This basically means that
we hold the mix, in the sense of the relative (but not absolute) amounts of the two outputs unchanged and
look at how much more of both a hospital could produce if it were to move to a fully technically efficient
point – i.e. to a point on its PPF. In terms of Figure 4, above, the slope of the ray from the origin of the
diagram to point A shows us the ratio, or relative amounts, of Y1 and Y2, which the hospital is producing
while the length of that ray gives us the health service provider’s output level. A radial expansion of
output involves extending the ray through point A to point E on the PPF. The extent to which the ray can
be stretched out, without changing its slope, gives us our measure of the health service provider’s output
technical efficiency (on Figure 4, the hospital is technically inefficient because it is not operating on the
frontier). Then, we can look at the distance from point E to whatever its preferred output mix on the PPF
might be, to get at the individual health service provider’s, or hospital’s output allocative efficiency.
In other words, even if a hospital were operating at a point such as E, which is on the frontier (i.e. was fully
technically efficient), this may not be the best place for it to be operating; not because it isn’t using its staff
and other inputs to their full extent, but because it is not using them to produce the best mix of outputs
(i.e., mix of patient services or treatments, or serving the right combination of patient types). This may
because of local circumstances, such as higher prices for certain inputs like labour in some regions, or the
higher travel costs associated with the delivery of care as can be experienced in rural communities. So these
providers might like to produce on the frontier at a point like C, but they can’t because of the types of
constraints on input costs and their budgets.
WHAT IS SYSTEM-ALLOCATIVE EFFICIENCY?
In this paper, we are interested in a different concept of allocative efficiency – what we might term systemallocative efficiency as opposed to the allocative efficiency of a single health service provider discussed
previously. This is a critical distinction in the context of rural hub integration. System-allocative efficiency
gets at the question of whether the health care system’s total output can be increased with no change in its
total input or resource use by reallocating output or health service across health service providers, such as
hospitals, primary care practices and long-term care facilities, etc.
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Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
To see how system-allocative efficiency can be conceived, we need to be able to introduce another health
service provider into our problem. Clearly, for purposes of illustration, we can do this by considering a
second health service provider, producing the same two outputs as the first health service provider, though
not necessarily in the same quantities. Making this assumption allows us to add a second PPF to our
diagram:
Y1
PPFa
PPFb
Y2
Figure 5: A system with two health service providers’ Production Possibility Frontiers
Here, we have two health service providers. Because their PPFs are roughly equally far out from the origin,
they are presumably of roughly the same size (for example, one health service provider could be a small
rural hospital and the other a long-term care facility). Both can produce the same two outputs, Y1 and Y2
(i.e., care for two similar types of individuals). The crucial point here is that the shapes of the two PPFs
differ. System-allocative efficiency gains are not possible when all of the health service providers in the
system have identical PPFs. The differences can be due to differences in the particular capital that the
health service providers happen to own, and/or differences in the particular skill mix of their labour
forces, and can have arisen simply for historical reasons. What is important is that the shapes (the slopes)
of the PPFs differ.
To see why this matters, suppose that, as a result of historical accident, the two health service providers
happen each to be producing the output mix associated with the point at which their PPFs cross. Again, we
emphasize that the only reason we are picking this point is because it is an easy one to spot on the diagram
– it has no other significance than that.
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
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Y1
PPFa
Ya
Yb
PPFb
Y2
Figure 6: System Allocative Efficiency Gain possibilities
Now suppose we shift health service provider A from point A to point Ya on PPFa, and health service
provider B from point A to point Yb on PPFb. This involves health service provider A increasing its
production of Y1 and reducing its production of Y2 while health service provider B reduces its production
of Y2. As we have drawn the figure, health service provider A’s increased output of Y1 cancels out health
service provider B’s reduced Y1, so total system output of Y1 is unchanged from the level it had attained at
point A, but health service provider B’s increase in Y2 output is much larger than health service provider
A’s reduction, so system output of Y2 is increased relative to its level when both health service providers
were operating at A.
The result is that, by reallocating output between the two health service providers (hence the term systemallocative output efficiency), we have been able to increase total system output of Y2 with no reduction in
system output of Y1 and, importantly, with no change in the total input use of the system (and hence no
change in system input costs), and without even reallocating inputs between health service providers. We
have simply reassigned responsibility for output production between the two health service providers. So,
in other words, allocating care differently across the providers yields an increase in overall service volume –
even if we assume no change in the total amount of inputs used (i.e., the total output is greater than the
sum of the output produced by each provider in the pre-integration period).
An example of this system-allocative efficiency is the Espanola Regional Hospital and Health Centre, in
which the long-term care home does not have to retain separate 24-hour on-site Registered Nurse (RN)
services since they can draw from the RN resources working in the hospital. There was no change in
overall nurse labour input, but the treatment of some patients was shifted to the long-term care facility
rather than the patients being moved to the hospital for treatment. This case appears to involve colocated organizations, but this need not be the case. The Manitouwadge General Hospital – Family Health
Team (FHT) Collaboration is also a case where by shifting some hospital-based nurse time (in the form
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Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
of providing training in intravenous (IV) therapy to the community-based nurse), the treatment of the
patient could be shifted to the community setting, without changing the overall level of nursing inputs.
Another example of this is Dryden Regional Health Centre, where funding of home care is controlled
by the hospital and patients are discharged from hospital to the community sooner to receive follow-up
care in their homes.1 Again, the opportunity to shift the output this way stems in large measure from the
differences in the slopes of the PPFs across the providers/settings;they face different trade-offs in their
ability to provide care to a given set of patients or in the provision of certain services.
This type of system-allocative output efficiency gain is the starting point for a formal assessment of the
rural hub integration project. It is not the endpoint of the analysis. An analysis would firstly involve an
evaluation of the sort of system-allocative efficiency gain which we have been considering here and then
proceed to consider the possible implications of reallocating inputs along with outputs, a process which
would not reduce the scope for system-allocative efficiency gain and might well enhance it. A full analysis
would also need to consider the possible limitations on the scope of system-allocative efficiency gains to
avoid looking for gains where they would be unlikely to accrue.
WHAT ARE THE SYSTEM-ALLOCATIVE GAINS LIKELY TO BE?
When we are looking at the question of possible gains from an administrative re-organization of a particular
set of health care units, the basic economic approach can be thought of as providing a logical framework
within which to think about possibilities, and also as providing a guide to evaluating the information which
comes out of particular re-organization pilot projects. Clearly the unique features of each particular
instance have to be built into the actual application of the approach, but taking the economic model as a
starting place provides some guidance as to just how to do that.
Undoubtedly, each of the individual units being considered for administrative coordination can be thought
of as having its own production possibility frontier, which simply reflects the mix of outputs the unit is
expected to be able, to some degree or other, to provide, and whose position depends on both the total
quantity and the detailed mix of the inputs it has available to it. Some units will be more capital intensive
and others more labour intensive, and the set of inputs they have will restrict their activities in the sense of
determining whether it is physically possible for a particular unit to supply any of some particular output,
but this variation doesn’t invalidate the framework, it just means that there are particular constraints which
have to be taken account of in each specific instance.
The starting point for our discussion is the proposition that by now there is very little technical inefficiency
left in the healthcare system, and that managers are doing the best they can with the resources available to
them, subject to the constraints imposed by their local circumstances and given whatever their particular
mission has been defined to be.
1
It is worth noting that some of the integration projects might involve re-allocating labour between units and that the formal, technical analysis should endeavor to separate
the various aspects of the administrative merger, in order to try and evaluate their relative importance. That is important in part because different pilot projects will involve
different types of service co-ordination, and not taking account of the differences in the nature of the various experiments will cloud the analysis of the potential benefits
from other administrative mergers.
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
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We take the view that past efforts aimed at productivity improvement have confused technical with
allocative efficiency issues – i.e. that they have assumed that any hypothesized system-level inefficiency
was a result of the individual components of the system operating below their production possibility
frontiers rather than a result of where the particular units were operating on their individual frontiers.
Combined with a tendency to mistake allocative for technical inefficiency was a focus on the input-oriented
perspective, which we referred to in the context of Figure 2 above. The input-oriented perspective looks
at the degree to which inputs could be reduced while continuing to produce the same level of output. If
the problem is indeed one of overall technical inefficiency, in the sense that one or more of the health
service providers is operating below its frontier, then the input and output oriented perspectives are valid
alternatives. The input-oriented perspective appeals to those responsible for overall system budgets as
it suggests that there is scope for cutting costs without cutting the quantity or quality of care provided.
If, however, the reason the system seems to be producing inefficiently is because of a less than optimal
allocation of output responsibilities between units, then each unit is on its frontier and reducing input
levels must necessarily result in a reduction in output.
We suggest, therefore, that any formal statistical evaluation of existing pilot projects be based on the
economic model that we have sketched out here and discussed in more detail in the CCHE paper, and
make use of the conceptual difference between technical and allocative efficiency. Such an evaluation
would complement evaluations being done from a managerial/administrative perspective, and, combined,
the two approaches would provide a comprehensive picture of the outcomes of various pilot projects.
In general terms, what the economic approach is looking for in the first instance of an evaluation is what
the economic framework characterizes as differences in the slopes of the production possibility frontiers,
starting from the candidate units present output points. In Figure 6, above, our starting point was the
point of intersection of a pair of PPFs, a point at which the two units were producing the same service mix.
This was simply for purposes of illustration.
In an actual evaluation, we would take the accrual output mixes of the candidate units as the starting
points for evaluation, and then look at the slopes of the two units PPFs. In practical terms, this means that
we should be looking at the output substitution opportunities that are available to each of the units. We
are assuming here that we do not want, at least in the first instance, to transfer labour between the units.
There may be a variety of practical reasons for such a restriction – we adopt it because we want to consider
the scope for allocative efficiency gains as a dynamic process, rather than trying to completely upend
the existing arrangements and then re-build them from scratch. When an evaluation is being done of
relatively long-established pilot projects, it would be desirable to try and distinguish short- from longer-run
outcomes, the short run being defined as a period within which not only are total input levels fixed but so
also is the allocation of inputs between units, and the longer run being defined as a period which allows for
a reallocation of labour. If there are pilot projects in which total input employment has been increased, we
would want to separate out the effects of the increase and the allocation of the net new inputs across units.
Thus, our discussion assumes that we start by looking at whether it is (or has been in the case of
longstanding integration exercises) possible to re-assign the responsibility for producing particular outputs
between (say) two units in a manner which results in an overall increase in the production of at least one
service (and hopefully both) with no reduction in the level of the other service provided. Economists
conceptualize this, as we have said, in terms of the difference in the slopes of the PPF of the two units, and
talk about reallocating responsibility for outputs in the directions of the units comparative advantages: in
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Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
less formal terms, this simply comes down to figuring out what each individual unit is particularly good
at and shifting its responsibilities towards that. Note again that we are assuming no technical inefficiency
– i.e. that each unit is on its PPF at the beginning of the exercise. Note also that we are at this point
discussing a statistical exercise – as we noted above, we conceive of this as complementing, rather than
substituting for, a management assessment.
Any formal statistical analysis has to take into account any constraints that might be specific to individual
pilot projects. For instance, labour contracts, accountability arrangements, proprietary ownership of some
facilities (i.e., long term care). Given the approach being discussed here, this means factoring constraints
into the framework. In the case of rural health hubs, there are two considerations that are immediately
obvious – the fact that a significant portion of the population may be older and its rural geography. Both
of these can be introduced as constraints affecting (for example) labour productivity. An older population
may demand more services – that is easy enough to allow for when the unit of output is total services, but
has to be handled more carefully when the unit is patients served. One essential aspect of any evaluation
exercise is to decide on the output measures being used and to adapt the formal statistical analysis so that
the measurement of output is consistent with the type of efficiency measure being applied. The fact of
rurality may well imply that providing services to patients involves significant travel time. That will tend to
reduce the measured productivity of labour, and again must be factored into the analysis. Conceptually
this is not difficult – if we think in terms of a home visit nurse treating first those patients closest to her
starting point, and gradually working her way out over a wider geographic radius, measured productivity
will tend to decline as number of patients increases simply because of the additional travel time required
for the treatment of more distant patients. One advantage of the OHA pilot projects is the range of data
which is likely to be available for use in working out the best way to handle these issues - from the point
of view of honing the statistical approach to efficiency measurement, and developing tools which can be
applied not only to evaluate existing and imminent projects, but also to identify promising sites for future
administrative co-ordination projects.
Reference
Hsu A, Berta W, PC Coyte, Rohit Dass A, Laporte A (2015) “Efficiency estimation with quantile regressions:
An application using longitudinal data from nursing homes in Ontario, Canada” , manuscript under
journal review.
Evaluation of Fully-Integrated Rural Health Hubs: Investigating the possibility of system-allocative efficiency gains
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Centre canadien en économie de la santé