“Efficiency” from the traditional neo-classical optimising methods, as

“Efficiency” can be considered a subjective
term, spanning a variety of different interpretations, but in summary it
essentially means producing the highest possible amount of output, given a
restricted amount of input (Shone, 1981). Efficiency, theoretically, is the
underlying factor for any firm or economy as it gives a great advantage to help
obtain profit-maximisation.  In today’s
societies healthcare systems tend to drift away from the traditional
neo-classical optimising methods, as it generally confides on whether the
industry is publicly or privately-funded (R. Evans, 1971). In an economy that
places a large amount of priority on health and welfare, it is crucial for the
industry to be as efficient and systematic as possible. The way healthcare
systems are operated are generally highly debated and criticized as people tend
to argue over private and public healthcare systems. This literature review
will analyse the different problems and challenges that arise when trying to
compare how these healthcare systems can be differentiated by looking at
various input and output variables.

It will also compare and contrast
these different sources of research and how their approaches and conclusions
have differed. It will essentially involve a summary depicting; how different
authors have defined efficiency within the healthcare industry, what countries
are being compared and how many, what variables are being considered (input and
output), what types of data and research has been collected/ analysed
(methodology), and what have they concluded.

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The UK’s healthcare industry is
predominantly occupied by the National Health Service (NHS), which is the largest
government/publicly-funded health service in the world and is free to all
residents within the country, regardless of their wealth however it’s primarily
financed through taxation (NHS, 2017). Additionally, the UK also has an
alternative offer whereby people can pay for their healthcare and attain
various benefits, known as private healthcare (J. Chang et al., 2017). There is
an ongoing argument, primarily within the UK, as to whether or not the healthcare
system (NHS) should be privatised because some feel that the service is inadequate
and somewhat inefficient. Pro-privatisation residents believe there will be
drastic improvement in care if it’s a privately-run industry as there will be
more competition, similar to a normal competitive market model (Wheeler, 2013).

Germany on the other hand takes a
slightly different approach as to how their healthcare system is operated.
Firstly, it’s different in the way it is provided; a third is charity-run, a
third is run by the government, and the last third is by for-profit companies
(Wheeler, 2013). Secondly, the public sector of the industry is actually
financed through residents in work (employees and employers), and these are
known as social insurance contributions, but it’s only available to residents who
are insured. There are numerous ways to approach an investigation in relation
to this topic area; however there are still research gaps.


Efficiency is a term that is used
vastly within economics and generally refers to producing a good or service and
using the resources to the best of their ability during the course of
production (Shone, 1981).

Defining efficiency within the
healthcare industry can often be proven difficult. Palmer et. al (1999) claimed
that within the healthcare industry, efficiency measures whether the health
industry resources are being utilized to get best value for money. This in turn
means that it’s essentially concerned with the relation between resource inputs
(such as costs, capital, labor costs etc.) and either final health outcomes
(lives saved, life expectancy etc.) or more intermediate outputs (waiting time,
number of patients treated etc.). 
However they feel that for economic evaluations it’s more ideal to focus
on final health outcomes.    


There are various different ways
of determining how to interpret efficiency within the healthcare industry. The
approach can be divided into two ways; a micro and macro perspective. The micro
approach is essentially concerned with assessing the performance of individual
healthcare clinics and hospitals.  Dimas
et al. (2010) analysed the productive performance of Greece’s health system;
particularly focusing on 22 Greek public general hospitals between 2003 and
2005. Additionally, the macro approach is concerned with evaluating the overall
economies healthcare system’s productive performance. Charalampos and Claude
(2009) look at Greece, but in more of a macro perspective.  They look at factors such as life expectancy
and infant mortality rate in a more overall view of the healthcare system in
Greece, in comparison to Dimas et al. (2010), where they looked at individual
public hospitals.


Additionally, between a few
studies there have been differences in not only the output variables, but also
the input variables regarding what influences the productivity and efficiency
of these healthcare systems. One particular study ” Cross-country
comparisons of technical efficiency of health production: a demonstration of
pitfalls” actually aimed to evaluate the effects that socioeconomic
determinants have on the efficiency outcomes. The types of determinants that
were included involve; education attainment, the economy’s unemployment rate,
and GDP per capita. However, after using data from OECD (Organisation for
Economic Co-operation and Development) they concluded that after using these
socioeconomic factors of health, it was difficult and unquantifiable due to the
vast amount of limitations and uncertainty of using these particular

Some studies have tried analysing from a slightly different
view, by looking at more environmental factors additionally to health
expenditure levels. Retzlaff-Roberts at al. (2004) initiated a model containing
various environment inputs, including healthcare resources and social
environment factors (population characteristics, education attainment etc.).
The outputs of the model involved using infant mortality and life expectancy at
birth. A third variable (premature mortality from all causes below age
70) was going to be included; however the results of the two initial variables
were consistent enough. The results for all countries were fairly consistent
between the input and output variables, however the USA exhibits a conflict
between the input and output oriented findings. They claimed that the USA’s
results were misleading and inaccurate compared to the other OECD countries due
their large amount of inputs trying to help prolong life near the end of lives.

et al. (2001) aims at looking at different techniques as to how to measure
efficiency within the healthcare industry as a whole. They also agree with
Palmer et al. (1999) in the fact that to formally assess the efficiency in this
industry, it is required that health outcomes are the primary focus in terms of
output variables. This particular study appraised two types of techniques used
to measure this – economic evaluation (measuring health outcomes for a range of
different health conditions) as it shows the cost effectiveness, and data
envelopment analysis as it incorporates the overall nature of health services
and the influence of various external factors on the productive efficiency.

et. al (2006) developed a model of the organisational performance within the
healthcare industry, with mainly costs being the inputs and benefits being the
outputs. It contributes to the argument that many other researchers have found
which is that determining the outputs of the healthcare sector can be
significantly challenging. It also claims that two critical issues need to be
discussed; How to define the outputs of the healthcare sector? And what exactly
should be attached to these particular outputs? The output of healthcare can be
categorised into two extensive sectors; the additional health on a patient, and
patient satisfaction related to the health effect. There is a few criteria that
should be met in order to be fully able to develop a conclusion; measurable
inputs and outputs, outputs can be combined into a single measure, organisation
relies on only its own inputs to achieve these outputs. However in practice
there can be different issues that arise when achieving results for these
outputs, for example if population sample is too large then it can in fact
incur high levels of variation.


analysing how efficient different healthcare systems are, authors tend to look
at various factors which indicate some level of efficiency. One particular
study “The efficiency of healthcare systems in Europe: a Data envelopment
analysis approach” essentially looks at different variables that affect how efficiently
numerous healthcare systems operate. Asanduluia et. al (2014) used a data
envelopment analysis approach (non-parametric method) to help investigate how
three specific input variables (number of doctors, number of hospital beds and
public health expenditures as percentage of GDP) affect output variables (life
expectancy at birth, health adjusted life expectancy and infant mortality rate),
looking over 30 European states in particular. There are a few important findings
portrayed in this paper.  Most of the
more developed countries are productive and efficient in using inputs for their
respective healthcare systems; however there are still some developing
economies that are also seen to be on the efficiency frontier. However the
efficiency scores showed that there were differences between the two models in
countries distribution, meaning both models illustrated that the vast majority
of countries have a medium level of efficiency. Therefore, Asanduluia et al.
(2014) concluded and proposed that the research needed more work, with the
primary motive of creating additional input and output variables.

Wasniewski (2012) researched into the productivity of various different
healthcare systems in relation as to how they’re financed, through creating a
model of change in healthcare quality in direct response to changes in
healthcare expenditure. In order to approach this, he had to perform
econometric analysis linking total healthcare expenditure per capita and life
expectancy, whilst also looking at qualitative case studies of those countries
(189 countries altogether). According to the WHO (World Health Organization)
the comparative efficiency of national healthcare systems is strongly linked to
the magnitude of changes and overall mechanism of healthcare expenditures per
capita.  However
Wasniewski found that in the majority of the recorded countries, healthcare
expenditures have increased vastly (primarily public sector), with no correlation
to the quality of health in the respective countries. The model involved in
this paper actually shows us that the majority of national healthcare
industries are primarily Keynesian, and the healthcare quality being deemed as

C. Kumbhakar (2010) aimed to estimate the efficiency of world health systems
using panel data on World Health Organization (WHO) member countries. The
Stochastic Frontier (SF) approach is used for this purpose. Kumbhakar evaluated
absolute efficiency as well as rankings and their sensitivity across
alternative model specifications using both output-maximizing and
cost-minimizing frameworks. When the two efficiency levels are compared for
each country, it was actually found that the developed countries were ranked at
the top for the output-maximising model; however are virtually bottom of the
rankings when the cost-minimizing model was used. Also when the countries are
compared in terms of productivity of the expenses of their healthcare, he found
that in actuality,

effects of public health spending on self-assessed health status: an ordered
pro-bit model” is an overall analysis of public health expenditure and its
relationship with self-assessed health status. In this study, Rivera (2010)
firstly states the hypothesis that an increase in self-assessed health status
would actually stem directly from an increase in public healthcare expenditure
by determining the correlation between the different estimated levels of health
and the physical resources in each health system as a whole. The paper found
that the estimations indicate that increases in public health expenditure do
actually have a positive impact on both physical and mental health, and these
results persist to be constant when the model is re-estimated.