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Citation: Kou, K.; Dou, Y.; Arai, I.
Analysis of the Forces Driving Public
Hospitals’ Operating Costs Using
LMDI Decomposition: The Case of
Japan. Sustainability 2024,16, 853.
https://doi.org/10.3390/
su16020853
Academic Editor: Adam Smoli ´nski
Received: 26 September 2023
Revised: 15 December 2023
Accepted: 11 January 2024
Published: 19 January 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Analysis of the Forces Driving Public Hospitals’ Operating Costs
Using LMDI Decomposition: The Case of Japan
Kiyotoshi Kou 1, * , Yi Dou 2and Ichiro Arai 1
1Department of Pharmaceutical Sciences, Nihon Pharmaceutical University, Saitama 362-0806, Japan;
i-arai@nichiyaku.ac.jp
2Institute for Future Initiatives, The University of Tokyo, Tokyo 113-8656, Japan; ydou@ifi.u-tokyo.ac.jp
*Correspondence: k-kou@toyokou.co.jp
Abstract: The sustainable management of public hospitals is usually threatened by long-term operat-
ing deficit, which was exacerbated during the COVID-19 pandemic. This study aimed to quantita-
tively decompose the historical changes in the annual operating costs of public hospitals in Japan
to identify the main driving forces responsible for a worsening imbalance between operating costs
and income over the past two decades. A dataset of the annual operating costs of public hospitals in
Japan was compiled, in which influencing factors were redefined to make the data amenable to the
application of a decomposition method referred to as the Logarithmic Mean Divisia Index (LMDI).
Using the LMDI method, the contribution of each influencing factor to the changes in public hospital
operating costs was quantitatively determined. The results indicate that, on average, there is an
annual reduction in operating costs by JPY 9 million per hospital, arising out of the national reform
of public hospitals, but the rapid increase in the prices and worsened structure of costs in recent
years resulted in an annual increment of JPY 127 million per hospital to the increasing operating costs.
The pandemic revealed damage to the financial balance of public hospitals, but epidemic prevention
policies brought an offset to the increased operating cost. A more resilient domestic medical supply
chain, the introduction of new technologies, and continuous endeavors in system reform and pricing
policies are required to achieve financial sustainability in public hospitals in Japan.
Keywords: factor decomposition; operating costs; hospital management; COVID-19; Japan
1. Introduction and Background
Public hospitals represent the largest portion of total health spending in most countries,
of which socially inclusive and cost-effective healthcare services with lower environmental
impact are critical targets for a sustainable healthcare system [
1
–
4
]. In most countries
with a rapidly increasing ageing population, public hospitals fall into everlasting financial
deficit, which makes financial sustainability a core issue [
5
–
7
]. The dilemma of whether
to simply shut down the low-cost-effective regional hospitals or lower the quality of the
healthcare service is always present in a sustainable healthcare system [
8
]. Meanwhile,
temporarily, during pandemic periods such as COVID-19, people falling sick in large
numbers has resulted in patients being rushed to public hospitals, immediately causing
overcapacity problems, with serious resource and economic costs as a consequence [
9
–
14
].
To increase the cost effectiveness of public hospitals, national governments have formulated
and implemented various reform plans such as developing a hierarchical medical system,
incorporating public hospitals, introducing private capital and more efficient management
systems, introducing a prospective payment system, implementing public reporting for
price transparency, and even allowing market competition in a bid to suppress operating
costs [15–20].
Japan’s public hospitals reveal a typical case in challenging the sustainability of
healthcare services in an era of ageing and depopulation [
21
,
22
]. Since the 1990s, an
Sustainability 2024,16, 853. https://doi.org/10.3390/su16020853 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 853 2 of 15
increasing number of hospitals have been facing the risks attendant on a continuously
worsening financial balance brought about by an inverted population pyramid, making
these hospitals vulnerable to bankruptcy in the face of socioeconomic crises or pandemic
diseases. From 1996 to the present, the ratio of annual income to operating costs in public
hospitals in Japan has always been below 100%; the worst result (83.2%) was recorded
during the COVID-19 pandemic (refer to Figure S1 in the Supplementary Document).
To reduce the financial deficit ratios, in 2007, the Japanese government formulated a
guideline for the institutional reform of public hospitals that emphasized setting numerical
targets with the aim of increasing operating efficiency; restructuring and networking
public hospitals, including adjusting the numbers of doctors and beds; and resetting
the operational status of public hospitals, such as by privatization. Later, a reform plan
update of 2015 emphasized the flexible setting of targets to improve operating efficiency,
considering the quality of the healthcare service, identifying the role of public hospitals in
local healthcare initiatives, adjusting hospital taxation, which takes into account the actual
bed occupancy rate, and strengthening financial support to restructure and network public
hospitals, among other initiatives [
23
]. According to statistical reports on the operational
status of public hospitals, the annual income/cost ratio jumped to above 92% from 2008
and remained stable for 4 years after the introduction of the 2007 reform plan, but fell again
even after the implementation of the reform plan update of 2015.
The forces that drive increases in operating costs of public hospitals faster than their
incomes, and the means by which these driving forces can be controlled to realize the
status of financial sustainability, need to be understood and methods for evaluating the
institutional reform policies mentioned above need to be devised. Direct statistical data can
only represent the total cost increases, including salaries, materials, medicines, depreciation,
and other costs (research and training costs, commission fees, and the others) [
24
] (refer to
Figure S2 in the Supplementary Document), and fail to explain how the internal and external
factors influence the changes in operating costs. Without the application of quantitative
analysis to identify the driving forces, it is difficult for the government and hospitals to
formulate specific action plans to counter the expanding financial deficit.
Worldwide, various analytical tools such as data envelope analysis, regression models,
and time series analysis have been applied to evaluate the improvements in efficiency at
public hospitals after institutional reform and to identify the key influencing factors that
significantly affect the operating effectiveness of these institutions [
6
,
17
,
25
–
27
]. However,
regarding the application of these analysis tools, previous case studies in Japan had not
reached definitive conclusions on what the key forces driving the public hospitals’ operating
costs are. Some of them identified salary costs, material costs, and the number of outpatients
as the main factors [
28
] (risk factor analysis) [
29
] (regression analysis) [
30
] (cluster analysis),
while some of them denied this result [
31
] (regression analysis). Among tens of indicators,
many are mentioned as key factors, such as inpatient unit price, average daily number
of inpatients, and outpatient unit price [
32
] (regression analysis), and outpatient number
and inpatient unit price [
33
] (regression analysis). Geographically, factors such as the
number of persons per household, the unemployment rate, and the ratio of the elderly
in the population were also mentioned as key factors [
34
]. In conclusion, the results are
inconsistent because of different perspectives and research designs regarding variable
selection, while the approaches have been unable to track the impacts from historical events
and quantitatively evaluate the contribution from driving forces.
Beyond the analysis tools mentioned above, decomposition methods, such as the
Logarithmic Mean Divisia Index (LMDI), make it possible to extract the driving forces
from various internal and external factors to an aggregate indicator [
35
]. Since the LMDI
method can be used to completely decompose the changes in an overall indicator to the
contributions of influencing factors, it has become a commonly used decomposition method
that is often applied to analyses of the driving forces behind carbon
emissions [36–39]
and energy consumption [
40
–
42
] of a city, region, country, or sector [
42
–
44
]. The LMDI
method is also applied to decomposition studies in other fields, but this method had
Sustainability 2024,16, 853 3 of 15
never formerly been used for analyzing changes in the overall financial performance of
a hospital. Compared to regression analysis, the LMDI method may mask some of the
complex interactions between influencing factors, but can immediately reveal the structural
changes between influencing factors before and after institutional reform, even those that
occurred during the COVID-19 pandemic.
This study aimed to quantitatively decompose the historical changes in the annual
operating costs of public hospitals in Japan to identify the main forces responsible for
driving the worsening imbalance between operating costs and income over the preceding
two decades. This will not only provide further evidence that can be used in the evaluation
of the effectiveness of the recent institutional reforms of the public healthcare system, but
will also provide an overarching perspective of the impacts arising from the socio-economic
changes to the operation of public hospitals that will in turn support further policy making.
In addition, as this is the first time the LMDI method has been applied to the field of
healthcare, this study will also provide a reference for the academic community to broaden
the scope of research in this area.
The rest of this paper is organized as follows: Section 2describes model development
and the process of data collection, Section 3summarizes the decomposition results on
operation costs of public hospitals, along with some discussion, and Section 4presents the
main findings from the study and their policy implications.
2. Materials and Methods
2.1. Theoretical Analysis and Indicator Selection
LMDI is one of the many specific decomposition methods developed based on Index
Decomposition Analysis, which is an analytical tool that originated from energy studies [
45
].
Although many other options such as Laspeyres and Divisia can also be applied, they do
not deal with residuals that lead to large error estimation [
46
]. In contrast, LMDI can
fully decompose the residual. Conventionally, an aggregate indicator can be decomposed
into at least 3 factors, namely the overall activity (activity effect), activity mix (structure
effect), and sectoral intensity (intensity effect). This study followed an analytical design
that considered population, income, and consultation rate to represent the overall activity,
while the coverage of public hospitals in terms of the total consultation, visit duration of
a single consultation, and the structure of operating costs represent the activity mix and
sectoral intensity. The definitions of these 6 factors are summarized in Table 1.
Table 1. The aggregate and influencing factors defined in this study.
Factor Definition
CtAverage total operating costs of one public hospital in Japan in year t (JPY)
PoptPopulation of Japan in year t (person)
IntAnnual income per capita in year t (JPY/person) (income level)
NtConsultation rate by annual income in the year t (times/JPY) (health level)
RtCoverage of one public hospital in terms of total annual medical consultations in year t (%) (service scope)
TtVisit duration for a single consultation to a public hospital, including inpatients and outpatients, in year t (days/visit); here, a single
consultation of an outpatient is counted as a one-day stay in a public hospital (length of stay)
Cst
i
Type i operating cost for one patient during his/her one-day stay in a public hospital in year t (JPY/day) (structure of operating costs)
Accordingly, the operating costs of one public hospital can be aggregated as the
product of these factors, using the following Equation (1):
Ct=∑iCt
i=∑iPopt×Int×Nt×Rt
i×Tt×Cst
i(1)
Here, population is a kind of total indicator, while average income, consultation rate,
and visit duration for a single consultation are the factors influencing the level of demand for
medical consultations; the coverage of public hospitals for medical consultations means the
service scale of public hospitals, and each term of the operating cost points to the management
Sustainability 2024,16, 853 4 of 15
costs of public hospitals in providing medical consultations for the public. As hypotheses in
this study, these factors are likely to be affected by several historical events, as follows:
•
The ageing rate in Japan was over 20% in the year 2005, while the population began to
decline from 2010 [47].
•
The subprime mortgage crisis of 2008 led to a temporary decrease in personal income in
Japan [48].
•
In 2013, the Bank of Japan introduced a quantitative
−
qualitative easing policy that
aimed to achieve a target inflation rate of 2%, which continues to the present, but in
fact this has led to a long-term devaluation of the Japanese Yen currency [49,50].
•
The 2007 guideline for the institutional reform of public hospitals and the following
updated 2015 reform plan established by the Japanese government had long-term
comprehensive impacts on the number and scale, costs management, income system,
and the consultation rate of public hospitals [32].
•
The COVID-19 pandemic has shocked the whole healthcare system, from 2020 to the
present [51].
In this study, the definitions of operating costs of public hospitals as defined in the
Yearbook of Local Public Enterprises [
24
] were followed, which includes salaries, materials,
medicines, depreciation, and other costs (research and training costs, commission fees, etc.),
but excludes interest costs, extraordinary losses, and other non-operating costs.
2.2. Data Preparation
To apply LMDI decomposition to the operating costs, some of the necessary data
could be obtained from statistics reports, while the other data were estimated based on
reliable statistics from other sources. In this study, the population and income data were
obtained from the national annual statistics of Japan, while the overall indicators, such
as consultation times of inpatients and outpatients, were obtained from the Survey of
Medical Institutions issued by the Ministry of Health, Labour and Welfare [
52
], and the
data on public hospitals were obtained from the Yearbook of Local Public Enterprises
published by the Ministry of Internal Affairs and Communications [
24
]. As summarized
in Table 2, the data of influencing factors defined in this study were prepared from the
relevant statistical data.
Table 2. Data preparation for LMDI decomposition from the corresponding statistical report.
Factor Variable Definition Formula
Population Population 1
Personal income level Annual income per capita 3
/1
Personal health level Consultation rate by annual income (8
+9
/10
)/3
(4
+5
)/(6
+7
)
Service scope of public hospitals Coverage of one public hospital in terms of yearly medical consultations 4
+5
6
+7
/2
Length of stay in public hospitals Visit duration for a single consultation at a public hospital, including inpatients
and outpatients
8
+9
8
+9
/10
Structure of operating costs of
public hospitals
Material costs for one patient during his/her one-day stay in a public hospital 11
/(8
+9
)
Medicine costs for one patient during his/her one-day stay in a public hospital 12
/(8
+9
)
Salary costs for one patient during his/her one-day stay in a public hospital 13
/(8
+9
)
Depreciation costs for one patient during his/her one-day stay in a public hospital
14
/(8
+9
)
Other costs for one patient during his/her one-day stay in a public hospital 15
/(8
+9
)
Notes: Primary data from the statistics and reports:
1
population, from [
53
],
2
number of public hospitals,
4
number of outpatients per day in public hospitals,
5
number of inpatients per day in public hospitals,
8
annual gross number of outpatients in public hospitals,
9
annual gross number of inpatients in public
hospitals,
11
material costs,
12
medicine costs,
13
salary costs,
14
depreciation costs,
15
other costs, from [
24
].
3
Annual income, from [
48
].
6
Number of outpatients per day in all hospitals,
7
number of inpatients per day
in all hospitals, 10
average visit duration of inpatients in all hospitals, from [52].
Accordingly, the values of influencing factors were obtained and readied for LMDI
decomposition, as shown in Table 3.
Sustainability 2024,16, 853 5 of 15
Table 3. Historical changes in factors influencing the operating costs of a public hospital.
Based on One Public Hospital Year 1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Population million 125.9
126.2
126.5
126.7
126.9
127.3
127.5
127.7
127.8
127.8
127.9
128.0
128.1
128.0
128.1
127.8
127.6
127.4
127.2
127.1
127.0
126.9
126.7
126.6
126.1
125.5
Annual income per capita million JPY/capita 3.37
3.35
3.27
3.25
3.34
3.22
3.19
3.24
3.30
3.31
3.35
3.35
3.10
3.00
3.09
3.05
3.06
3.18
3.25
3.41
3.40
3.48
3.50
3.50
3.32
3.51
Consultation rate by
annual income
patient-times/million
JPY 1.43
1.42
1.45
1.45
1.42
1.47
1.43
1.37
1.31
1.29
1.24
1.20
1.25
1.28
1.24
1.24
1.24
1.18
1.15
1.09
1.08
1.06
1.05
1.03
1.01
0.98
Coverage of one public hospital
in terms of yearly medical
consultations
% 0.023
0.023
0.024
0.024
0.024
0.024
0.023
0.023
0.023
0.023
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.021
0.021
Visit duration for a single
consultation at a public hospital,
including inpatients and
outpatients
days/visit 1.50
1.50
1.49
1.49
1.49
1.48
1.51
1.53
1.54
1.55
1.56
1.56
1.57
1.57
1.58
1.58
1.57
1.57
1.57
1.57
1.57
1.58
1.58
1.58
1.58
1.55
Material costs for one patient
during his/her one-day stay in a
public hospital
JPY/patient-day 1759
1808
1870
1926
1940
1981
2132
2247
2293
2426
2449
2420
2527
2603
2529
2583
2617
2756
2785
2874
2907
3040
3096
3190
3476
3712
Medicine costs for one patient
during his/her one-day stay in a
public hospital
JPY/patient-day 3642
3528
3388
3250
3047
2889
2882
2751
2688
2744
2746
2827
2806
2831
2845
2919
2939
3024
3030
3315
3318
3381
3522
3829
4122
4139
Salary costs for one patient
during his/her one-day stay in a
public hospital
JPY/patient-day 8912
9254
9409
9436
9416
9479
9844
10,021
10,253
10,559
10,956
11,451
11,992
12,326
12,432
12,769
13,081
13,335
13,859
14,304
14,941
15,250
15,637
16,096
18,981
18,693
Depreciation costs for one patient
during his/her one-day stay in a
public hospital
JPY/patient-day 955
1007
1078
1168
1180
1195
1272
1363
1401
1473
1560
1648
1734
1739
1735
1751
1785
1869
2284
2368
2468
2517
2571
2603
2894
2790
Other costs for one patient during
his/her one-day stay in a
public hospital
JPY/patient-day 2642
2757
2838
2928
3053
3175
3409
3646
3906
4190
4522
5027
5397
5529
5729
5809
5964
6275
6367
6434
6484
6658
6815
7057
7262
7173
Sustainability 2024,16, 853 6 of 15
2.3. Decomposition by LMDI Method
In this study, the calculation process described in the practical guideline for applying
the LMDI method provided by Ang [
54
] was followed. According to the definitions of
the aggregate indicator and influencing factors mentioned above, the annual changes in
the indicator (operating cost) can be calculated as the sum of the contribution of each
influencing factor (Table 4), as in the following Equation (2):
∆C=∆CPop +∆CIn +∆CN+∆CR+∆CT+∆CCs (2)
Table 4. The contribution from each influencing factor to the total operating cost.
Variable Definition
∆CAnnual changes in total operating cost of one public hospital (JPY)
∆CPop
Annual contribution from population changes in Japan to the operating cost
of one public hospital (JPY)
∆CIn Annual contribution from income level changes to the operating cost of one
public hospital (JPY)
∆CNAnnual contribution from consultation rate changes to the operating cost of
one public hospital (JPY)
∆CTAnnual contribution from the changes in patient visit duration to the
operating cost of one public hospital (JPY)
∆CCs Annual contribution from the structural changes and cost-efficiency
management level to the operating cost of one public hospital (JPY)
Next, the annual contribution of each influencing factor can be calculated using the
following Equations (3)–(8):
∆CPop =∑i∆CPop,i=
∆CPop,i=0, if Popt
i×Pop0
i=0
∆CPop,i=∑iLCt
i,C0
iln Po pt
i
Pop0
i!
, if Popt
i×Pop0
i=0
(3)
∆CIn =∑i∆CI n,i=
∆CIn,i=0, if Int
i×In0
i=0
∆CIn,i=∑iLCt
i,C0
iln Int
i
In0
i!
, if Int
i×In0
i=0
(4)
∆CN=∑i∆CN,i=
∆CN,i=0, if Nt
i×N0
i=0
∆CN,i=∑iLCt
i,C0
iln Nt
i
N0
i!
, if Nt
i×N0
i=0
(5)
∆CR=∑i∆CR,i=
∆CR,i=0, if Rt
i×R0
i=0
∆CR,i=∑iLCt
i,C0
iln Rt
i
R0
i!
, if Rt
i×R0
i=0
(6)
∆CT=∑i∆CT,i=
∆CT,i=0, if Tt
i×T0
i=0
∆CT,i=∑iLCt
i,C0
iln Tt
i
T0
i!
, if Tt
i×T0
i=0
(7)
Sustainability 2024,16, 853 7 of 15
∆CCs =∑i∆CCs,i=
∆CCs,i=0, if Cst
i×Cs0
i=0
∆CCs,i=∑iLCt
i,C0
iln Cst
i
Cs0
i!
, if Cst
i×Cs0
i=0
(8)
where L(a,b)=(a−b)/(Ina−Inb).
3. Results
3.1. Historical Changes in Influencing Factors
As seen in Table 3, first, it is clear that the population of Japan continued to increase,
reaching 128.1 million by 2010, but then began to decrease from that point onward. In con-
trast, annual income per capita showed an overall decreasing trend up until 2009, but then
began to increase after that. Second, the consultation rate by annual income per capita
revealed an amazing 32% decrease from 1996 to 2021, while the coverage of one public
hospital as a proportion of total medical consultations peaked in 2000 and then began to
decrease to some extent. Third, the visit duration for a single medical consultation to a
public hospital revealed a continuous increase from 2000. From these factors, after 2000,
the public hospital system was also found to have become more concentrated on a regional
scale and also became more effective in the area of critical medical care compared to other
hospitals. However, the various costs per one patient during his/her one-day stay in
a public hospital showed a continuous increase in recent years; in particular, the salary
costs doubled during the period from 1996 to 2021. Although the depreciation costs and
other costs were not the largest contributors to the total operating cost, the rate of increase
remained the same as that for salary costs. Interestingly, only the medicine costs initially
kept decreasing until 2009, and then began to increase, until finally reaching the same level
in 2019 that they were in 1996.
3.2. Contribution of Influencing Factors Based on Decomposition Results
The decomposition results for the annual contribution of influencing factors are sum-
marized in Figure 1. In general, the net changes in operating cost at one public hospital
consistently showed an increasing trend from 1996, but the increase declined during the
period from 1996 to 2006, and then suddenly increased again from then onward. From the
decomposition of each influencing factor, it is obvious that the costs for a patient during a
stay in hospital made the greatest contribution to the increase in the total operating cost
of one public hospital. This contribution included two types of effects: one was from the
overall increase in each branch of operating cost, and the other was because of a worsening
cost structure in which the branch with the larger proportion of the total (salary cost)
increased faster than the other branches with smaller proportions. Annually, the rising cost
contributed around JPY 45 million to the total operating cost.
In contrast, the contribution from the changing consultation rate by annual income
generally remained negative, and it fell annually by around JPY 30 million as a proportion
of the total operating cost. However, this reduction was largely offset by the contribution
from the changes in annual income per capita. During the entire period, visit duration for
a single consultation continued to contribute around JPY 10 million annually to the total
operating cost. Additionally, the coverage of one public hospital as a proportion of total
yearly medical consultations contributed to some reduction in total operating cost before
2010, but stopped after that. The smallest contribution came from the changing population
in Japan, so this contribution can be ignored in the short- and medium-term analysis.
Notably, the COVID-19 pandemic, beginning from the year 2020, caused a significant
shock to the financial balances of public hospitals, where the average cost for treating a
patient immediately shot up, but this increase was substantially offset. Comparing the
contributions among all influencing factors, it can be concluded that the increasing cost
levels and worsening cost structure were the main forces driving the long-term increase in
Sustainability 2024,16, 853 8 of 15
total operating cost in a public hospital, while the other influencing factors were almost all
yearly variables that affected the total operating cost in a single fiscal year.
Sustainability 2024, 16, x FOR PEER REVIEW 9 of 17
Figure 1. Decomposition results of various factors influencing the total operating cost in a public
hospital.
In contrast, the contribution from the changing consultation rate by annual income
generally remained negative, and it fell annually by around JPY 30 million as a proportion
of the total operating cost. However, this reduction was largely offset by the contribution
from the changes in annual income per capita. During the entire period, visit duration for
a single consultation continued to contribute around JPY 10 million annually to the total
operating cost. Additionally, the coverage of one public hospital as a proportion of total
yearly medical consultations contributed to some reduction in total operating cost before
2010, but stopped after that. The smallest contribution came from the changing population
in Japan, so this contribution can be ignored in the short- and medium-term analysis.
Notably, the COVID-19 pandemic, beginning from the year 2020, caused a significant
shock to the financial balances of public hospitals, where the average cost for treating a
patient immediately shot up, but this increase was substantially offset. Comparing the
contributions among all influencing factors, it can be concluded that the increasing cost
levels and worsening cost structure were the main forces driving the long-term increase
in total operating cost in a public hospital, while the other influencing factors were almost
all yearly variables that affected the total operating cost in a single fiscal year.
3.3. Facts beyond the Decomposition Results
3.3.1. Impact from the Institutional Integration of Public Hospitals
A broad institutional reconfiguration of public hospitals in Japan arose out of the
national systemic reform of public hospitals in 2007. As shown in Figure 2, the number of
public hospitals in Japan rapidly decreased during the decades, from 1000 hospitals at the
peak to 753 at the end of the study period. Many of the hospitals were disestablished,
incorporated, restructured, or assigned to private owners [24]. In particular, the
institutional reform of public hospitals of 2007 redefined the importance of public
hospitals to support regional medical systems in cooperation with private hospitals and
Figure 1. Decomposition results of various factors influencing the total operating cost in a public hospital.
3.3. Facts beyond the Decomposition Results
3.3.1. Impact from the Institutional Integration of Public Hospitals
A broad institutional reconfiguration of public hospitals in Japan arose out of the
national systemic reform of public hospitals in 2007. As shown in Figure 2, the number of
public hospitals in Japan rapidly decreased during the decades, from 1000 hospitals at the
peak to 753 at the end of the study period. Many of the hospitals were disestablished, in-
corporated, restructured, or assigned to private owners [
24
]. In particular, the institutional
reform of public hospitals of 2007 redefined the importance of public hospitals to support
regional medical systems in cooperation with private hospitals and clinics. Public hospitals
with serious human resource shortages were downgraded to regional clinics, while some
others with financial problems were transferred into local incorporated administrative
agencies, which enabled them to source their own funds to allow ongoing operation [
55
].
An improved hierarchical medical system not only reduced the consultation rate to public
hospitals because they were more focused on treating critical patients [
56
], but also reduced
the coverage of one public hospital as a proportion of total yearly medical consultations
because of the integration [
57
]. As shown in Table 3, one public hospital used to perform
on an annual basis 0.024% of the total number of medical consultations for all hospitals in
2000, but the coverage began to decrease during the period of institutional reform of public
hospitals. Furthermore, it also brought about a continuous increase in the charge income
level by 87.6% during 1996–2021, which is a positive outcome, and evidence shows the
success of the institutional reforms [
24
]. However, such activities conversely led to a contin-
uous increase in the visit duration for a single consultation and the operating cost for one
patient per one-day stay, which substantially offset the positive effects mentioned above.
Sustainability 2024,16, 853 9 of 15
Sustainability 2024, 16, x FOR PEER REVIEW 10 of 17
clinics. Public hospitals with serious human resource shortages were downgraded to
regional clinics, while some others with financial problems were transferred into local
incorporated administrative agencies, which enabled them to source their own funds to
allow ongoing operation [55]. An improved hierarchical medical system not only reduced
the consultation rate to public hospitals because they were more focused on treating
critical patients [56], but also reduced the coverage of one public hospital as a proportion
of total yearly medical consultations because of the integration [57]. As shown in Table 3,
one public hospital used to perform on an annual basis 0.024% of the total number of
medical consultations for all hospitals in 2000, but the coverage began to decrease during
the period of institutional reform of public hospitals. Furthermore, it also brought about
a continuous increase in the charge income level by 87.6% during 1996–2021, which is a
positive outcome, and evidence shows the success of the institutional reforms [24].
However, such activities conversely led to a continuous increase in the visit duration for
a single consultation and the operating cost for one patient per one-day stay, which
substantially offset the positive effects mentioned above.
Figure 2. Changes in the number of public hospitals in Japan (details of the change in 2021 were
not recorded in the statistics at the time of this study).
3.3.2. Operating Cost Level Changes Compared with Charge Income Level
Next, the question as to whether the institutional integration of public hospitals failed
in controlling the operating cost level was addressed. One simple test is to compare the
change rate of charge income to that of operating costs. As can be seen from Figure 3, the
changes in charge income level are closely correlated with the increases in salary costs.
According to the statistics, the number of staff remained at 220,000 in the past, and the
patient number per staff gradually decreased, but the average annual salary increased
from JPY 8.33 to 9.24 million [24,56]. In addition, material costs, depreciation costs, and
Figure 2. Changes in the number of public hospitals in Japan (details of the change in 2021 were not
recorded in the statistics at the time of this study).
3.3.2. Operating Cost Level Changes Compared with Charge Income Level
Next, the question as to whether the institutional integration of public hospitals failed
in controlling the operating cost level was addressed. One simple test is to compare the
change rate of charge income to that of operating costs. As can be seen from Figure 3, the
changes in charge income level are closely correlated with the increases in salary costs.
According to the statistics, the number of staff remained at 220,000 in the past, and the
patient number per staff gradually decreased, but the average annual salary increased
from JPY 8.33 to 9.24 million [
24
,
56
]. In addition, material costs, depreciation costs, and
other incidental costs also generally followed the same path of annual changes as charge
income. The only mismatch was with the medicine costs, which apparently increased more
slowly than the charge income level before 2010, but gradually increased more rapidly
than the latter from that point onward. In fact, the consumer price index was always
around 95% during 1996–2013, and then slightly recovered to 100% afterwards as a result
of monetary easing policy [
58
]. By contrast, the exchange rate of USD to JPY increased from
approximately 80 to 120 during 2011–2021 [
59
]. Accordingly, the changes in unit material
and medicine costs matched with the trend of JPY exchange rate very well. The medicine
prices seemed to play an external role by affecting changes in the total operating cost of
Japan’s public hospitals. It is not clear why the depreciation costs suddenly increased in
year 2014, but generally they were prevented from contributing to the increase in total
operating cost.
Sustainability 2024,16, 853 10 of 15
Sustainability 2024, 16, x FOR PEER REVIEW 11 of 17
other incidental costs also generally followed the same path of annual changes as charge
income. The only mismatch was with the medicine costs, which apparently increased
more slowly than the charge income level before 2010, but gradually increased more
rapidly than the laer from that point onward. In fact, the consumer price index was
always around 95% during 1996–2013, and then slightly recovered to 100% afterwards as
a result of monetary easing policy [58]. By contrast, the exchange rate of USD to JPY
increased from approximately 80 to 120 during 2011–2021 [59]. Accordingly, the changes
in unit material and medicine costs matched with the trend of JPY exchange rate very well.
The medicine prices seemed to play an external role by affecting changes in the total
operating cost of Japan’s public hospitals. It is not clear why the depreciation costs
suddenly increased in year 2014, but generally they were prevented from contributing to
the increase in total operating cost.
Figure 3. Relative change rate of various costs for a patient during a one-day stay in a public
hospital against the change rate of charge income.
3.3.3. Impacts from the COVID-19 Pandemic
As shown in Figures 1 and 3, the COVID-19 pandemic caused a great shock to the
cost structure of public hospitals, but the overall impacts on operating costs were limited.
In the year 2020, salary costs suddenly increased because of the serious shortage of
medical staff, which contributed substantially to the increase in operating costs. However,
the consultation rate and the coverage of one public hospital in terms of yearly medical
consultations recorded the lowest rate, because public hospitals became locations of
cluster infection and many patients tried to avoid visiting them, or could not visit public
hospitals due to city-wide lockdowns and temporary regulations for public hospitals [60].
The decline in annual income caused by unemployment also greatly offset the increase in
operating costs. In the next year, 2021, although annual income recovered and patients
returning to public hospitals contributed to an increase in operating costs, this increase
was offset by the suppressed salary costs and reduced consultation rates and visit
durations, when medical staff returned and the patients strengthened their self-protection
from nosocomial infection [61]. During the pandemic, material and medicine costs
continuously increased because of the serious price increases in Japan caused by the
devaluation of the JPY and worldwide inflation. According to the Trade Statistics of Japan
Figure 3. Relative change rate of various costs for a patient during a one-day stay in a public hospital
against the change rate of charge income.
3.3.3. Impacts from the COVID-19 Pandemic
As shown in Figures 1and 3, the COVID-19 pandemic caused a great shock to the cost
structure of public hospitals, but the overall impacts on operating costs were limited. In the
year 2020, salary costs suddenly increased because of the serious shortage of medical staff,
which contributed substantially to the increase in operating costs. However, the consulta-
tion rate and the coverage of one public hospital in terms of yearly medical consultations
recorded the lowest rate, because public hospitals became locations of cluster infection
and many patients tried to avoid visiting them, or could not visit public hospitals due to
city-wide lockdowns and temporary regulations for public hospitals [
60
]. The decline in
annual income caused by unemployment also greatly offset the increase in operating costs.
In the next year, 2021, although annual income recovered and patients returning to public
hospitals contributed to an increase in operating costs, this increase was offset by the sup-
pressed salary costs and reduced consultation rates and visit durations, when medical staff
returned and the patients strengthened their self-protection from nosocomial infection [
61
].
During the pandemic, material and medicine costs continuously increased because of
the serious price increases in Japan caused by the devaluation of the JPY and worldwide
inflation. According to the Trade Statistics of Japan issued by the Ministry of Finance,
Japan’s importation of medical materials and medicines had surpassed exports for decades,
particularly after the COVID-19 pandemic [
47
]. In 2021, the value of Japan’s imported
medicines reached USD 31 billion, which is five times the export value. Notably, many
reports revealed a crucial laboratory diagnostics cost input due to anti-COVID-19 cam-
paigns [
62
–
64
]; however, this could be excluded from the general accounting, and therefore
the other costs for a patient per one-day stay singularly decreased during 2020–2021.
4. Discussion
The trial case study using LMDI decomposition on the changes to operating costs
of public hospitals in Japan not only revealed the feasibility of the methodology, but also
provided several referrable findings for policymaking towards financial sustainability.
Sustainability 2024,16, 853 11 of 15
4.1. Main Findings and Implications
By applying LMDI method to decompose the annual changes during 1996–2021 in
operating costs of public hospitals in Japan, this study indicates the following: the recent
institutional reform of public hospitals in Japan succeeded in reducing needless medical
demands on public hospitals and raised the charge income, which on average led to
an annual reduction of operating costs by JPY 9 million per hospital during 1996–2021.
However, the reform failed to suppress the rapid increments in salary costs, material costs,
and medicine costs, which added an annual increment of JPY 127 million per hospital to
the increasing operating costs. Beyond the institutional reform, increased material and
medicine costs are also thought to have resulted from currency devaluation and worldwide
inflation. In addition, historical changes in population and people’s overall consultation
rates contributed an annual reduction of JPY 73 million per hospital. The COVID-19
pandemic shocked the healthcare system supported by public hospitals, but the impacts
were internally offset among the factors.
These findings suggest further directions in system reform and policy making. First,
it is necessary to establish and perfect an independent domestic medical supply chain to
stabilize medical prices. Based on the empirical analysis, although the recent monetary
easing policy led to the devaluation of the JPY and imported inflation, it contributed
much to the export economy while the domestic consumer prices were kept low [
50
,
65
–
69
].
Second, information technologies such as 5G, big data analytic engines, remote medical
consultation, and applications of Artificial Intelligence (AI) are expected to suppress the
operating costs [
70
–
75
]. These measures help in downsizing the scale of public hospitals
and improve the cost effectiveness [
76
,
77
]. Furthermore, policies such as cost-sharing can be
proliferated with an aim to divert some of the inpatients from long-term stays in hospitals to
community-based care facilities for conditions that do not necessitate medical intervention,
especially in cases of elderly and chronic patients [
78
]. These policies may reduce the
visit duration of single consultations and help in minimizing the overall investment for
hospitals. Finally, it is necessary to improve the literacy and communications for citizens
towards new technology protocols and health regulations, especially during a pandemic,
to maximize the efficiency in reducing the operating costs of hospitals [79,80].
4.2. Methodological Considerations and Limitations
As a trial case study, here, a comprehensive decomposition of the annual changes
in operating costs at public hospitals in Japan using LMDI is presented. Through the de-
composition analysis, both historical influencing factors such as depopulation, hierarchical
medical system reform, patient demand, and cost level changes, and temporary events such
as the pandemic period, are quantitatively evaluated to determine the important driving
forces that contributed the most to the increased hospital operating costs. However, the de-
composition was still simple in comparison with similar studies in energy or transportation
research fields. For example, costs for one patient per day can be further decomposed into
the product of element input changes and price level changes, but we failed to access these
data during the study. Further, compared to previous studies using correlation analysis, the
number of influencing factors taken into account in this study was very limited because of
the increasing difficulty of decomposing in a more detailed scale. This study revealed that
decomposition using LMDI has advantages in quantitatively evaluating the contributions
of influencing factors to the operating costs of hospitals, but is weak in selecting influencing
factors, as many other theories support.
4.3. Future Research and Recommendations
According to the practice of LMDI decomposition in this study, future research can
extend the decomposition in more detail, such as by decomposing the changes of the cost
of one patient per day into the products of element input (the amount of labor forces,
medicines, materials, building construction, and equipment that are input and the improve-
ment in its structure) and price level (price changes of each element input in the medical
Sustainability 2024,16, 853 12 of 15
treatment). It is then possible to quantitatively check whether the efficiency of the element
input has been improved and whether the price level is under control or not, and the degree
of contribution of each element. Furthermore, future predictions of the operating costs of
a hospital can be carried out if the data due to the future scenarios are input; this will be
of great help in the quantitative assessment of the efficiency of the policies and measures
against the increase in hospital operating costs.
5. Conclusions
The increase in operating costs against charge income is a critical problem for the
financial management of public hospitals in Japan. To quantitatively identify the forces
driving the increase in operating costs in recent decades, in this study, a complete decom-
position analysis of public hospital operating costs was conducted using the LMDI method
with corresponding definitions of the influencing factors based on statistical data covering
the period from 1996 to 2021. The results indicate that the institutional reform of public
hospitals was effective in increasing the efficiency of operating costs to charge income to
some extent, while the continuously increasing operating cost level and a worsening cost
structure played key roles in increasing the total operating costs rather than the charge
income. In particular, the continuous rise in medicine costs caused by currency devaluation
and worldwide inflation was a danger signal for the financial status of public hospitals.
The COVID-19 pandemic caused a short-term shock to public hospital financial balances
in the beginning of the year, but the situation seems to have returned to normal later on.
In addition, historic changes in population, personal income levels, and health levels were
found to have contributed significantly to a reduction in the total operating costs of public
hospitals in Japan.
Learning from these findings, policymakers should pay more attention to material
and medicine cost management during medical treatment. Particularly, establishing and
perfecting independent domestic medicine production can contribute to stabilizing the
variations in material and medicine prices arising from external factors. As experienced
during the COVID-19 pandemic, new technologies, healthcare regulations, and policies
should be quickly proliferated to society to bring major improvements in the financial
balances of public hospitals during and after a pandemic. Short-term subsidies can fill part
of the gap between a hospital’s income and costs, but cannot essentially solve the long-term
imbalance between these factors. With the ongoing institutional reform of public hospitals,
a strategic national plan for building a stable material and medicine supply chain will be
indispensable.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/su16020853/s1.
Author Contributions: Conceptualization, K.K. and Y.D.; methodology, K.K.; software, K.K.; vali-
dation, K.K.; formal analysis, K.K.; investigation, K.K.; resources, K.K. and Y.D.; data curation, K.K.
and Y.D.; writing—original draft preparation, K.K.; writing—review and editing, Y.D.; visualization,
K.K.; supervision, I.A. and Y.D.; project administration, I.A.; funding acquisition, I.A. All authors
have read and agreed to the published version of the manuscript.
Funding: This work was financially supported by MEXT/JSPS KAKENHI, Grant Number 21K14276.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: This work was conducted in collaboration with Japan Kampo Inc., PHT Inc., and
TOYOKOU INC., all of which provided great support.
Conflicts of Interest: The authors declare no conflicts of interest.
Sustainability 2024,16, 853 13 of 15
References
1.
Carnero, M.C. Assessment of Environmental Sustainability in Health Care Organizations. Sustainability 2015,7, 8270–8291.
[CrossRef]
2.
Borgonovi, E.; Adinolfi, P.; Palumbo, R.; Piscopo, G. Framing the Shades of Sustainability in Health Care: Pitfalls and Perspectives
from Western EU Countries. Sustainability 2018,10, 4439. [CrossRef]
3.
McKenzie, S. Social Sustainability: Towards Some Definitions; Hawke Research Institute University of South Australia Magill:
Underdale, South Australia, 2004.
4.
Dubas-Jakóbczyk, K.; Kozieł, A. Towards Financial Sustainability of the Hospital Sector in Poland—A Post Hoc Evaluation of
Policy Approaches. Sustainability 2020,12, 4801. [CrossRef]
5.
Rattan, T.K.; Joshi, M.; Vesty, G.; Sharma, S. Sustainability indicators in public healthcare: A factor analysis approach. J. Clean.
Prod. 2022,370, 133253. [CrossRef]
6.
Fragkiadakis, G.; Doumpos, M.; Zopounidis, C.; Germain, C. Operational and economic efficiency analysis of public hospitals in
Greece. Ann. Oper. Res. 2016,247, 787–806. [CrossRef]
7.
Dubas-Jakóbczyk, K.; Kocot, E.; Kozieł, A. Financial Performance of Public Hospitals: A Cross-Sectional Study among Polish
Providers. Int. J. Environ. Res. Public Health 2020,17, 2188. [CrossRef]
8.
Akinleye, D.D.; McNutt, L.-A.; Lazariu, V.; McLaughlin, C.C. Correlation between hospital finances and quality and safety of
patient care. PLoS ONE 2019,14, e0219124. [CrossRef]
9.
Cai, Y.; Kwek, S.; Tang, S.S.L.; Saffari, S.E.; Lum, E.; Yoon, S.; Ansah, J.P.; Matchar, D.B.; Kwa, A.L.; Ang, K.A.; et al. Impact of the
COVID-19 pandemic on a tertiary care public hospital in Singapore: Resources and economic costs. J. Hosp. Infect. 2022,121, 1–8.
[CrossRef]
10.
Chen, Y.Q.; Cai, M.; Li, Z.P.; Lin, X.J.; Wang, L.A. Impacts of the COVID-19 Pandemic on Public Hospitals of Different Levels:
Six-Month Evidence from Shanghai, China. Risk Manag. Healthc. Policy 2021,14, 3635–3651. [CrossRef]
11.
Gidwani, R.; Damberg, C.L. Changes in US Hospital Financial Performance During the COVID-19 Public Health Emergency.
JAMA Health Forum 2023,4, e231928. [CrossRef]
12.
Bastías, G.; Poblete, F. Improving the performance of hospitals and the health system in Latin America and the Caribbean. Lancet
Glob. Health 2021,9, e1045–e1046. [CrossRef] [PubMed]
13.
Stylianidi, M.; Stamatopoulou, E.; Kontodimopoulos, N. Worldwide responses of health systems to the financial challenges of the
COVID-19 pandemic. Arch. Hell. Med. Arheia Ellenikes Iatr. 2023,40, 184–191.
14.
Association, A.H. Hospitals and Health Systems Face Unprecedented Financial Pressures Due to COVID-19; American Hospital
Association: Chicago, IL, USA, 2020.
15.
Barber, S.L.; Borowitz, M.; Bekedam, H.; Ma, J. The hospital of the future in China: China’s reform of public hospitals and trends
from industrialized countries. Health Policy Plan. 2014,29, 367–378. [CrossRef] [PubMed]
16.
Fidler, A.H.; Haslinger, R.R.; Hofmarcher, M.M.; Jesse, M.; Palu, T. Incorporation of public hospitals: A “silver bullet” against
overcapacity, managerial bottlenecks and resource constraints? Case studies from Austria and Estonia. Health Policy 2007,81,
328–338. [CrossRef] [PubMed]
17.
Besstremyannaya, G. The Impact of Japanese Hospital Financing Reform on Hospital Efficiency: A Difference-in-Difference
Approach. Jpn. Econ. Rev. 2013,64, 337–362. [CrossRef]
18.
Han, A.; Lee, K.H.; Park, J. The impact of price transparency and competition on hospital costs: A research on all-payer claims
databases. BMC Health Serv. Res. 2022,22, 1321. [CrossRef] [PubMed]
19.
Liu, P.C.; Gong, X.; Yao, Q.H.; Liu, Q. Impacts of the medical arms race on medical expenses: A public hospital-based study in
Shenzhen, China, during 2009–2013. Cost Eff. Resour. Alloc. 2022,20, 73. [CrossRef]
20.
Ramamonjiarivelo, Z.; Weech-Maldonado, R.; Hearld, L.; Menachemi, N.; Epane, J.P.; O’Connor, S. Public hospitals in financial
distress: Is privatization a strategic choice? Health Care Manag. Rev. 2015,40, 337–347. [CrossRef]
21.
Hashimoto, H.; Ikegami, N.; Shibuya, K.; Izumida, N.; Noguchi, H.; Yasunaga, H.; Miyata, H.; Acuin, J.M.; Reich, M.R. Japan:
Universal Health Care at 50 years 3 Cost containment and quality of care in Japan: Is there a trade-off? Lancet 2011,378, 1174–1182.
[CrossRef]
22. MHLWJ. National Health Insurance Annual Report 2021; Ministry of Health, Labour and Welfare (MHLWJ): Tokyo, Japan, 2021.
23.
Iseki, T. Recent Municipal Hospital Policy Transition and Municipal Hospital Reform Guidelines (in Japanese). J. Soc. Secur. Res.
2017,1, 778–796.
24. MICJ. Yearbook of Local Public Enterprises 1996–2021; The Ministry of Internal Affairs and Communications (MICJ): Tokyo, Japan,
2021.
25.
Hunt, D.J.; Link, C.R. Better outcomes at lower costs? The effect of public health expenditures on hospital efficiency. Appl. Econ.
2020,52, 400–414. [CrossRef]
26.
Kawaguchi, H.; Tone, K.; Tsutsui, M. Estimation of the efficiency of Japanese hospitals using a dynamic and network data
envelopment analysis model. Health Care Manag. Sci. 2014,17, 101–112. [CrossRef] [PubMed]
27.
Liu, M.L.; Jia, M.Y.; Lin, Q.; Zhu, J.W.; Wang, D. Effects of Chinese medical pricing reform on the structure of hospital revenue and
healthcare expenditure in county hospital: An interrupted time series analysis. BMC Health Serv. Res. 2021,21, 385. [CrossRef]
[PubMed]
Sustainability 2024,16, 853 14 of 15
28.
Taniguchi, K.; Nozawa, R.; Koike, D.; Ninomiya, T.; Ueda, S. Risk factors analysis of hospital management which points to extract
part system. Kawasaki J. Med. Welf. 2004,14, 109–123.
29.
Shimomura, K.; Kubo, R. Quantitative Analysis of Cost Structures in Hospital Management—Comparison between groups of
surplus and deficit hospitals belonging to the National Hospital Organization. J. Jpn. Soc. Healthc. Adm. 2011,48, 129–136.
[CrossRef]
30.
Kawaguchi, H. Study for Development of a Benchmarking Method in Hospital Management Using a Multivariate Statistical
Technique subtitle in Japanese. Iryo Shakai 2005,15, 23–37. [CrossRef]
31.
Ishikawa, M. Factor Analyses Regarding Transition in Hospital Profit before and after Public Hospital Reform at Public Hospitals
Mainly Providing Acute Medical Services. J. Jpn. Assoc. Health Care Adm. 2019,13, 11–17. [CrossRef]
32.
Ishibashi, K. Factors influencing the optimization of management in the public hospital reform plan—Focusing on hospitals
directly managed by local governments. J. Jpn. Soc. Healthc. Adm. 2016,53, 7–18. [CrossRef]
33.
Otsubo, T.; Imanaka, Y. Determinants of change in the revenue to cost ratio of municipal hospitals by scale in Japan. Nihon Koshu
Eisei Zasshi (Jpn. J. Public Health) 2008,55, 761–767. [CrossRef]
34.
Seo, Y.; Takikawa, T. Regional Variation in National Healthcare Expenditure and Health System Performance in Central Cities
and Suburbs in Japan. Healthcare 2022,10, 968. [CrossRef]
35. Ang, B.W. LMDI decomposition approach: A guide for implementation. Energ Policy 2015,86, 233–238. [CrossRef]
36.
Xu, S.C.; He, Z.X.; Long, R.Y. Factors that influence carbon emissions due to energy consumption in China: Decomposition
analysis using LMDI. Appl. Energy 2014,127, 182–193. [CrossRef]
37.
Luo, X.; Dong, L.; Dou, Y.; Liang, H.W.; Ren, J.Z.; Fang, K. Regional disparity analysis of Chinese freight transport CO
2
emissions
from 1990 to 2007: Driving forces and policy challenges. J. Transp. Geogr. 2016,56, 1–14. [CrossRef]
38.
Yu, Y.; Kong, Q.Y. Analysis on the influencing factors of carbon emissions from energy consumption in China based on LMDI
method. Nat. Hazards 2017,88, 1691–1707. [CrossRef]
39.
Moutinho, V.; Moreira, A.C.; Silva, P.M. The driving forces of change in energy-related CO
2
emissions in Eastern, Western,
Northern and Southern Europe: The LMDI approach to decomposition analysis. Renew. Sustain. Energy Rev. 2015,50, 1485–1499.
[CrossRef]
40.
Zhang, M.; Guo, F.Y. Analysis of rural residential commercial energy consumption in China. Energy 2013,52, 222–229. [CrossRef]
41.
Gonzalez, P.F.; Landajo, M.; Presno, M.J. Multilevel LMDI decomposition of changes in aggregate energy consumption. A cross
country analysis in the EU-27. Energy Policy 2014,68, 576–584. [CrossRef]
42.
Goh, T.; Ang, B.W. Tracking economy-wide energy efficiency using LMDI: Approach and practices. Energy Effic. 2018,12, 829–847.
[CrossRef]
43.
Zhang, M.; Li, H.A.; Zhou, M.; Mu, H.L. Decomposition analysis of energy consumption in Chinese transportation sector. Appl.
Energy 2011,88, 2279–2285. [CrossRef]
44.
Luo, X.; Dong, L.; Dou, Y.; Li, Y.; Liu, K.; Ren, J.Z.; Liang, H.W.; Mai, X.M. Factor decomposition analysis and causal mechanism
investigation on urban transport CO
2
emissions: Comparative study on Shanghai and Tokyo. Energy Policy 2017,107, 658–668.
[CrossRef]
45. Ang, B.W. A Simple Guide to LMDI Decomposition Analysis; National University of Singapore: Singapore, 2012.
46.
Bissai, F.D.; Fouda Mbanga, B.G.; Adiang Mezoue, C.; Nguiya, S. An Analysis of the Driving Factors Related to Energy
Consumption in the Road Transport Sector of the City of Douala, Cameroon. Sustainability 2023,15, 11743. [CrossRef]
47. MOF. Trade Statistics of Japan; Ministry of Finance (MOF): Tokyo, Japan, 2022.
48. Japan, C.O.O. Annual Report on National Accounts; Cabinet Office: Tokyo, Japan, 2022.
49.
Ferreira-Lopes, A.; Linhares, P.; Martins, L.F.; Sequeira, T.N. Quantitative easing and economic growth in Japan: A meta-analysis.
J. Econ. Surv. 2022,36, 235–268. [CrossRef]
50.
Kawamoto, T.; Nakazawa, T.; Kishaba, Y.; Matsumura, K.; Nakajima, J. Estimating the macroeconomic effects of Japan’s
expansionary monetary policy under Quantitative and Qualitative Monetary Easing during 2013–2020. Econ. Anal. Policy 2023,
78, 208–224. [CrossRef]
51. Karako, K.; Song, P.; Chen, Y.; Karako, T. COVID-19 in Japan during 2020–2022: Characteristics, responses, and implications for
the health care system. J. Glob. Health 2022,12, 03073. [CrossRef] [PubMed]
52.
MHLWJ. Survey of Medical Institutions 1996–2021; The Ministry of Health, Labor, and Welfare of Japan (MHLWJ): Tokyo, Japan,
2021.
53. Statistics Bureau of Japan. Japan Statistical Yearbook 1996–2021; Statistics Bureau: Tokyo, Japan, 2021.
54. Ang, B.W. The LMDI approach to decomposition analysis: A practical guide. Energy Policy 2005,33, 867–871. [CrossRef]
55.
Nakagawa, Y.; Irisa, K.; Nakagawa, Y.; Kanatani, Y. Hospital Management and Public Health Role of National Hospitals after
Transformation into Independent Administrative Agencies. Healthcare 2022,10, 2084. [CrossRef] [PubMed]
56.
Shiomi, T. Current Status and Problems of Health Care System Reform. The Issues Surrounding the Reform of the Medical Care
Provision System, such as those found in Reform Plan of Local Government Hospitals and Public Medical Institution. J. Health
Welf. Policy 2019,2, 47–56. (In Japanese) [CrossRef]
57.
Otani, Y.; Fukuda, H. The effects of public hospital restructuring on management performance. J. Jpn. Soc. Healthc. Adm. 2019,56,
17–27. (In Japanese) [CrossRef]
58. Japan, S.B.O. Statistics of Japan: Annual Average Consumer Price Index 1996–2022; Statistics Bureau of Japan: Tokyo, Japan, 2022.
Sustainability 2024,16, 853 15 of 15
59. BOJ. Bank of Japan Time-Series Data Search; BOJ Time-Series Data Search: Tokyo, Japan, 2023.
60.
Tashiro, A.; Shaw, R. COVID-19 Pandemic Response in Japan: What Is behind the Initial Flattening of the Curve? Sustainability
2020,12, 5250. [CrossRef]
61.
Hibiya, K.; Iwata, H.; Kinjo, T.; Shinzato, A.; Tateyama, M.; Ueda, S.; Fujita, J. Incidence of common infectious diseases in Japan
during the COVID-19 pandemic. PLoS ONE 2022,17, e0261332. [CrossRef]
62.
Dobrijevi´c, D.; Anti´c, J.; Raki´c, G.; Andrijevi´c, L.; Katani´c, J.; Pastor, K. Could platelet indices have diagnostic properties in
children with COVID-19? J. Clin. Lab. Anal. 2022,36, e24749. [CrossRef] [PubMed]
63.
Dobrijevi´c, D.; Andrijevi ´c, L.; Anti´c, J.; Raki´c, G.; Pastor, K. Hemogram-based decision tree models for discriminating COVID-19
from RSV in infants. J. Clin. Lab. Anal. 2023,37, e24862. [CrossRef] [PubMed]
64.
Dobrijevi´c, D.; Katani ´c, J.; Todorovi´c, M.; Vuˇckovi ´c, B. Baseline laboratory parameters for preliminary diagnosis of COVID-19
among children: A cross-sectional study. Sao Paulo Med. J. 2022,140, 691–696. [CrossRef] [PubMed]
65.
Lau, W.-Y.; Yip, T.-M. Are monetary transmission channels effective under the quantitative and qualitative easing policy? Acta
Oeconomica 2023,73, 231–249. [CrossRef]
66.
Bialas, C.; Bechtsis, D.; Aivazidou, E.; Achillas, C.; Aidonis, D. Digitalization of the Healthcare Supply Chain through the
Adoption of Enterprise Resource Planning (ERP) Systems in Hospitals: An Empirical Study on Influencing Factors and Cost
Performance. Sustainability 2023,15, 3163. [CrossRef]
67.
Ding, B. Pharma Industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains. Process
Saf. Environ. Prot. 2018,119, 115–130. [CrossRef]
68.
Zahiri, B.; Zhuang, J.; Mohammadi, M. Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study.
Transp. Res. Part E Logist. Transp. Rev. 2017,103, 109–142. [CrossRef]
69.
Kwon, I.-W.G.; Kim, S.-H.; Martin, D.G. Healthcare supply chain management; strategic areas for quality and financial improve-
ment. Technol. Forecast. Soc. Chang. 2016,113, 422–428. [CrossRef]
70.
Abraham, C.; Nishihara, E.; Akiyama, M. Transforming healthcare with information technology in Japan: A review of policy,
people, and progress. Int. J. Med. Inform. 2011,80, 157–170. [CrossRef]
71.
Ramírez-Saltos, D.; Acosta-Vargas, P.; Acosta-Vargas, G.; Santórum, M.; Carrion-Toro, M.; Ayala-Chauvin, M.; Ortiz-Prado,
E.; Maldonado-Garcés, V.; González-Rodríguez, M. Enhancing Sustainability through Accessible Health Platforms: A Scoping
Review. Sustainability 2023,15, 15916. [CrossRef]
72.
Obi, T.; Ishmatova, D.; Iwasaki, N. Promoting ICT innovations for the ageing population in Japan. Int. J. Med. Inform. 2013,82,
e47–e62. [CrossRef] [PubMed]
73.
Kaliappan, V.K.; Gnanamurthy, S.; Yahya, A.; Samikannu, R.; Babar, M.; Qureshi, B.; Koubaa, A. Machine Learning Based
Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities. Sustainability 2023,15,
5457. [CrossRef]
74.
Cabanillas-Carbonell, M.; Pérez-Martínez, J.; Yáñez, J.A. 5G Technology in the Digital Transformation of Healthcare, a Systematic
Review. Sustainability 2023,15, 3178. [CrossRef]
75.
Akiyama, M.; Yoo, B.K. A Systematic Review of the Economic Evaluation of Telemedicine in Japan. J. Prev. Med. Public Health
2016,49, 183–196. [CrossRef] [PubMed]
76.
Dobrijevi´c, D.; Anti´c, J.; Raki´c, G.; Katani´c, J.; Andrijevi´c, L.; Pastor, K. Clinical Hematochemical Parameters in Differential
Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach. Children
2023,10, 761. [CrossRef]
77.
Dobrijevi´c, D.; Vilotijevi´c-Dautovi´c, G.; Katani´c, J.; Horvat, M.; Horvat, Z.; Pastor, K. Rapid Triage of Children with Suspected
COVID-19 Using Laboratory-Based Machine-Learning Algorithms. Viruses 2023,15, 1522. [CrossRef]
78.
Li, Y.; Babazono, A.; Jamal, A.; Jiang, P.; Fujita, T. Cost-Sharing Effects on Hospital Service Utilization Among Older People in
Fukuoka Prefecture, Japan. Int. J. Health Policy Manag. 2022,11, 489–497. [CrossRef]
79.
Wahyuni, H.I. Trust, pandemic and communication: An analysis of the COVID-19 pandemic from an autopoietic systems
perspective. Kybernetes 2023,ahead-of-print. [CrossRef]
80.
Ausaf, A.; Yuan, H.; Nasir, S.A. Technology protocols and new health regulations for pandemic severity control: An S-O-R
theoretical risk reduction approach. Kybernetes 2023,ahead-of-print. [CrossRef]
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