Vol.1, No.2, 93-103 (2009)
doi:10.4236/health.2009.12016
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Health
Openly accessible at
Evaluation of the inclusive payment system based on
the diagnosis procedure combination with respect to
cataract operations in Japan
------A comparison of lengths of hospital stay and medical payments
among hospitals
Kazumitsu Nawata1*, Masako Ii2, Hinako Toyama3, Tai Takahashi4
1Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-kun, Tokyo, Japan;
2School of International and Public Policy, Hitotsubashi University 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan
3Department of Health Service Management, International University of Health and Welfare 2600-1 Kitakanemaru, Ohtawara, Tochigi, Japan
4Department of Medicine and Welfare, International University of Health and Welfare 2600-1 Kitakanemaru, Ohtawara, Tochigi, Japan
Received 26 June 2009; revised 23 July 2009; accepted 25 July 2009.
ABSTRACT
Following the recommendations of a report
submitted by the Central Social Insurance
Medical Council concerning the 2002 revision of
the Medical Service Fee Schedule, a new inclu-
sive payment system, which is based on the
Diagnosis Procedure Combination (DPC) sys-
tem, was introduced in 82 special functioning
hospitals in Japan, effective beginning in April
2003. Since April 2004, the system has been
gradually extended to general hospitals that
satisfy certain prerequisites. In this paper, the
new inclusive payment system is analyzed. Data
pertaining to 1,225 patients, who were hospital-
ized for cataract diseases and underwent lens
operations from July 2004 to September 2005,
are used. The lengths of hospital stay and
medical payments among hospitals are com-
pared. Even after eliminating the influence of
patient characteristics, there are large differ-
ences among hospitals in average lengths of
hospital stay and DPC-based inclusive pay-
ments. The highest average inclusive payment
is 3.5 times as high as the lowest payment. On
the other hand, there are relatively small differ-
ences in non-inclusive payments based on the
conventional fee-for-service system—the larg-
est deviation from the average of all hospitals is
approximately 10%. Thus, although payments
based on the DPC account for only one-third of
the total medical payments for this disease, the
major differences in medical payments among
hospitals are caused by differences in their
DPC-based inclusive payments. The results of
the study strongly suggest that revisions of the
payment system in Japan are necessary for the
efficient use of medical resources in the future.
Keywords: DPC; Inclusive Payment System; Cataract;
Lens Operation; Length of Hospital Stay
1. INTRODUCTION
Lengthy hospitalization is one of the characteristics of
the Japanese health care system. The average length of
stay of a patient in 2005 was 10.2 days in Germany, 13.4
days in France, 7 days in the U.K., and 6.5 days in the
U.S; however, in Japan it was nearly 20 days [1]. With
the rapid increase in medical care expenses, decreasing
the average length of stay in hospitals by reducing the
number of instances of long-term hospitalization has
become an important political issue in Japan.
Following the recommendations of a report submitted
by the Central Social Insurance Medical Council con-
cerning the 2002 revision of the Medical Service Fee
Schedule, a new case-mix payment system [2] was in-
troduced in 82 special functioning hospitals (i.e., univer-
sity hospitals, the National Cancer Center, and the Na-
tional Cardiovascular Center) in Japan, effective begin-
ning in April 2003. Since April 2004, the system has
been gradually extended to general hospitals that satisfy
certain prerequisites. It was the largest and most impor-
tant revision of the payment system since the Second
World War. Under the new payment system, the medical
payments are comprised of inclusive payments based on
the Diagnosis Procedure Combination (DPC) system and

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94
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non-inclusive payments based on the conventional
fee-for-service system. In this paper, the new payment
system is referred as the DPC-based inclusive payment
system, since this is the more commonly used descrip-
tion [3].
The DPC system is unique to Japan. It allows the
classification of diseases, operations, treatments, and
patient conditions using a 14-digit code. The first 6 dig-
its classify principal diseases on the basis of the Interna-
tional Classification of Diseases-10 (ICD-10)1. The re-
maining digits pertain to information on operations,
treatments, and patient conditions such as the presence
of a secondary disease. Initially, the DPC system classi-
fied patients into 1,860 categories2. Currently, the num-
ber of categories is 1,572. Inclusive payments based on
the DPC system cover fees for the following six catego-
ries only: basic hospital stays, medical checkups, image
diagnosis, medication, injections, treatments under 1,000
points3, and medicines used during rehabilitation treat-
ments and related activities. Fees for all other categories,
such as fees for operations, are paid on the basis of the
conventional fee-for-service system.
Unlike the Diagnosis-Related Group/Prospective Pay-
ment System (DRG/PPS) used in the U.S and other
countries [4,5,6,7,8,9,10] the Japanese DPC-based pay-
ment system is a per diem prospective payment system.
More specifically, three periods are established accord-
ing to which per diem payment is applied, Period I, Pe-
riod II, or Specific Hospitalization Period, which is de-
termined for each DPC code. Period I is set as the 25th
percentile of the length of hospital stay of the hospitals.
Period II is set as the average length of hospital stay, that
is, the 50th percentile (although this value is actually the
median, it is called the “average length of hospital stay”
in the DPC-based inclusive payment system). Finally,
the Specific Hospitalization Period is given by the fol-
lowing equation: (average length of hospital stay) + 2

(standard deviation).
The basic per diem payment is determined according
to the length of hospital stay. For stays below Period I,
the per diem payment to hospitals is 15% more than the
average per diem payment of the patients whose stays
were within the average length of hospital stay. For hos-
pital stays between Periods I and II, the per diem pay-
ment is determined such that (per diem payment in the
Period I – average per diem payments) (number of
days in Period I ) equals (the average per diem payments
– per diem payment between Periods I and II)

(num-
ber of days between Periods I and II). For stays between
Period II and the Specific Hospitalization Period, the per
diem payment is reduced by an additional 15%. Finally,
for stays above the Specific Hospitalization Period, the
per diem payment is determined through the conven-
tional fee-for-service system. Note that the periods and
per diem inclusive payments are affected by the condi-
tions of a patient, such as the presence of a secondary
disease. Furthermore, for each hospital, the actual pay-
ment amount is determined by multiplying the basic
payment by the individual hospital coefficient, which is
the sum of a basic coefficient and an adjustment coeffi-
cient. The adjustment coefficient is determined such that
the hospital’s revenue does not become less than that of
the previous year. This is an incentive for hospitals to
adhere to the new payment system. Since the system was
introduced only recently, thorough evaluations of the
system have yet to be performed. Empirical studies
based on data pertaining to the length of hospital stay
and medical payment amounts for a wide range of hos-
pitals are necessary for an accurate evaluation of the
system. Moreover, for a thorough analysis, a simple
comparison among hospitals in terms of the average
length of stay is not sufficient, and differences in the
types of diseases for which the patients are hospitalized
should also be considered. For each disease, individual
patient characteristics and treatment types must also be
taken into account.
One of the major purposes of the DPC-based payment
system is to reduce the long-term hospitalization cost by
standardizing the medical payments so that the payments
become the same amount for identical treatments, re-
gardless of the hospital that provides them. This means
that if the system works properly, the differences in the
inclusive payment amounts become smaller than those
of the non-inclusive payment amounts among different
hospitals. In this study, this hypothesis is evaluated for
cataract operations (DPC category code: 020110).
Lengths of hospital stay and medical payments among
hospitals are compared. The number of cataract patients
in Japan has been increasing rapidly with the ageing of
the population. According to a survey conducted by the
Ministry of Health, Labour and Welfare, the number of
cataract operations in June 2006 was 61,383 [11]. Thus,
it is estimated that nearly 800,000 cataract operations are
performed annually and nearly 2.5 billion yen are spent
for cataract operations. The overall difficulty level of
surgical and treatment procedures for cataracts is not
high, owing to their standardization, and the outcomes
are generally predictable. Moreover, most cataract op-
erations are scheduled in advance, and the possibility of
postoperative infections or complications is very low.
Fedorowicz, Lawrence and Guttie [12] found no signifi-
cant difference in outcome or risk of postoperative com-
plications between day care and inpatient cataract sur-
geries. Thus cataract cases are considered to be the most
suitable candidate for evaluating the various aspects of
the DPC-based payment system. To accomplish this,
data pertaining to 1,225 patients, who were hospitalized
for cataracts or related diseases and underwent a lens
peration on one eye, are used. o
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95
Table 1. Average medical payments by hospital (in points).
Total
Payment
Inclusive
Payment
Non-inclusive
Payment
Hospital
Mean S.D.* Mean S.D. Mean S.D.
Number of
Patients
Hp1 25,058 3,996 10,482 2,596 14,576 2,431 60
Hp2 25,602 3,981 9,669 2,711 15,933 1,963 177
Hp3 28,492 3,309 12,808 2,789 15,684 1,313 94
Hp4 29,020 3,817 11,694 2,455 17,326 2,419 41
Hp5 25,799 1,206 11,529 1,173 14,270 109 8
Hp6 25,397 3,249 11,504 3,201 13,893 395 9
Hp7 22,617 699 7,702 88 14,914 680 111
Hp8 22,782 1,131 7,638 0 15,144 1,131 28
Hp9 21,171 5,753 4,837 1,510 16,334 4,591 78
Hp10 26,581 2,674 9,829 2,348 16,751 987 88
Hp11 24,163 7,483 8,768 6,025 15,395 1,983 25
Hp12 19,191 2,400 3,813 2,181 15,377 541 226
Hp13 29,892 2,285 15,467 1,847 14,425 783 41
Hp14 20,439 2,670 5,625 2,557 14,814 463 24
Hp15 29,073 3,408 13,311 2,833 15,762 1,497 67
Hp16 25,065 1,834 9,488 1,600 15,577 815 148
All 24,320 4,664 8,716 3,983 15,604 1,851 1,225
*: Standard Deviation.
2. DATA
2.1. Surveyed Hospitals
In this paper, data collected from 16 general hospitals
(denoted as Hp1–Hp16) in Japan are used. The data were
originally collected by the DPC Hospital Conference in
Japan from July 2004 to September 2005 and include the
following details for each patient: DPC code, dates of
hospitalization and discharge from the hospital, date of
birth, sex, placement after hospitalization, principal dis-
ease classification (ICD-10 code for the principal disease
for which the patient was hospitalized), purpose of hos-
pitalization, presence of secondary disease and the at-
tending treatment if any, and medical payment amounts
(including DPC-based, fee-for-service, and total pay-
ments). Since the same data officially submitted to the
Ministry of Health, Labour and Welfare are used, the
reliability of data is considered to be very high.
In our study, the data pertaining to patients classified
under the DPC category code 020110 (ICD-10 code:
H25.0-H26.9) are analyzed. These patients were hospi-
talized for cataract diseases and underwent lens opera-
tions. Furthermore, unlike in other countries, hospitals in
Japan perform two-eye operations (where both eyes of
the patient are operated on in a single period of hospi-
talization) in addition to one-eye operations (where only
one eye of the patient is operated on in a single period of
hospitalization). It is evident that the two-eye operation
will require a patient to remain hospitalized for a longer
period of time than that required following a one-eye
operation. Therefore, we utilize data strictly pertaining to
those patients who underwent cataract operations and
insertion of prosthetic lens on one eye only (DPC codes:
0201103x01x000, 0201103x01x010, and 0201103x01
x1x0)4. The number of patients included in our data set
is 1,225.
2.2. Medical Payments
The average total payment per patient is 24,320 points
(i.e., 243,200 yen). Of the total points, inclusive pay-
ments based on the DPC system (hereafter referred to
simply as “inclusive payments”) account for 8,716
points and non-inclusive payments based on the conven-
tional fee-for-service system (hereafter referred to sim-
ply as “non-inclusive payments”) account for the re-
mainder, that is, 15,604 points (note that pre-adjustment
values are used for the inclusive payments). Thus, the
share of inclusive payments is 35.8%, or approximately
one-third of the total payment.
Table 1 shows the medical payment amount per pa-
tient for Hp1–Hp16. Although in general the share of

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inclusive payments is approximately one-third of the
total payment amount, its dispersion is rather large. For
all patients, the standard deviation for non-inclusive
payments is 1,851 points. On the other hand, the stan-
dard deviation for inclusive payments is 3,983 points,
which is significantly higher than that in the case of non-
inclusive payments. The coefficient of variation (= stan-
dard deviation/mean) of inclusive and non-inclusive
payments is 45.7% and 11.7%, respectively. As is evi-
dent, the former is four times larger than the latter. Fur-
thermore, the maximum and minimum average pay-
ments are, respectively, equivalent to 3,813 (Hp12) and
15,467 (Hp13) points for the inclusive payments and
13,893 (Hp6) and 16,751 (Hp10) points for the
non-inclusive payments. Thus, the range is 11,254 points
for the inclusive payments and 2,852 points for the
non-inclusive payments. These facts suggest that varia-
tions in the inclusive payment amounts are the main
cause of the differences in the medical payment amount
per patient.
2.3. Medical Payments and Lengths of
Hospital Stay
As expected, there exists a strong linear relationship
between length of hospital stay (in number of days) and
the inclusive payment amount. The correlation coeffi-
cient is particularly high at 0.9932 for the patients who
were hospitalized for 10 days or less. This implies that
for this period, almost all inclusive payment amounts are
determined by the length of hospital stay (note that if the
length of hospital stay is more than 10 days, the payment
amounts in some cases are determined through the
fee-for-service system). From the above, it is clear that a
strong relationship exists between the length of hospital
stay and the total amount of payment (with a correlation
coefficient of 0.9101) and that the total payment amount
increases as the length of hospital stay becomes longer.
This, however, does not hold true for non-inclusive
payments, which increase little as the length of hospital
stay becomes longer (with a correlation coefficient of
0.1742).
The per diem inclusive payment is affected by various
factors such as hospitalization period, the presence of a
secondary disease, and the individual hospital coefficient.
As a result, even if two patients undergo identical opera-
tions and treatments at two different hospitals, their
payment amounts will differ. Since the length of hospital
stay is an important factor in the inclusive payment
amount determined for a patient, we analyze the length
of hospital stay rather than the inclusive payment
amount.
Table 2 shows the distribution of the average lengths
of stay by hospital. Large differences can be seen among
the hospitals. Hp12 has the shortest length of hospital
stay, with an average of only 1.50 days, while the length
Table 2. Lengths of stay by hospital (in days).
Hospital MeanS/D.* Skewness Kurtosis**
Hp1 4.47 1.47 3.57 14.29
Hp2 4.09 1.40 1.20 3.78
Hp3 5.64 1.38 -0.47 4.68
Hp4 5.07 1.46 3.19 13.42
Hp5 5.00 0.53 0.00 3.50
Hp6 4.89 1.62 0.68 0.28
Hp7 3.00 0.00 - -
Hp8 3.00 0.00 - -
Hp9 1.88 0.58 0.01 -0.04
Hp10 4.20 1.42 3.41 18.21
Hp11 4.16 3.87 2.02 4.81
Hp12 1.50 0.85 1.16 -0.60
Hp13 7.22 1.13 1.62 3.55
Hp14 2.21 1.02 -0.19 -1.65
Hp15 5.99 1.64 1.20 4.21
Hp16 3.85 0.79 1.84 12.88
All 3.68 1.96 1.06 3.21
*: Standard Deviation.
**: The kurtosis value is set as 0 for the normal distribution.
of hospital stay was the longest in Hp 13, with an aver-
age of 7.22 days, which is 5.72 days longer than that of
Hp12. Two hospitals, Hp7 and Hp8, have a standard
deviation of zero, that is, all the patients at these hospi-
tals were hospitalized for exactly three days during the
survey period. This reflects the fact that the length of
hospital stay at these hospitals is determined by the hos-
pital’s clinical paths. Finally, the skewness and kurtosis
values are large for some of the hospitals. In other words,
the distributions for these hospitals are different from the
normal distribution: the large skewness and kurtosis
values for certain hospitals imply that some patients re-
mained in the hospital for a long period of time.
3. MODELS
3.1. Length of Hospital Stay
The length of hospital stay is a discrete-type variable
taking positive integers (1,2,3,…). Moreover, the skew-
ness and kurtosis values for some of the hospitals are
large. Therefore, the use of ordinary methods such as the
least-squares method would not be suitable for analyzing
the length of hospital stay (the results of the least
-squares estimation are available from the authors upon
request). Therefore, the length of hospital stay is ana-
lyzed by applying the model of Nawata et al. [13] to
hospital profits.
First, let us consider the procedure that hospitals use
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to decide when to discharge patients, which determines
the length of hospital stay. For cataract operations, the
length of hospital stay is typically short. Therefore, we
assume that the hospital can decide when to discharge
the patient. However, the hospital must also consider its
reputation, which can be affected by the length of hospi-
tal stay. A hospital’s reputation has asset value because it
can affect the hospital’s revenue; for example, a highly
reputed hospital would be the first choice for people
when they become ill.
Suppose that the revenue and cost of the hospital are
given by
),,( 11iii uxtbb  and , (1)
),,(22 iii uxtcc 
where x1i and x2i are vectors of explanatory variables
affecting the hospital’s revenue and cost, respectively.
The revenue includes not only direct monetary payments
but also improvements in its asset value owing to
high-quality medical services, and the cost also includes
an opportunity cost arising from the loss of revenue that
the hospital suffers because of the unavailability of beds
for new patients.
Next, let
12
(, ,,)i
ii i
cb
gtxuutt



i

1
(2)
where xi is a vector of the explanatory variables con-
tained in x1i, and x2i Moreover, g(t) is assumed to be an
increasing function of t. This is because if g(t) is not an
increasing function of t, it implies that the patient never
leaves the hospital. While this may be applicable for
patients with fatal diseases such as heart disease, brain
disease, and cancer, cataract patients rarely have pro-
longed hospital stays. Therefore, for the cases included
in our data set, we can reasonably assume that all the
patients left the hospital at some point. We assume that
)'(),,()( 2
1iiiiivxtvxtgtz 


(3)
where a1, a2≥0 and vi=h(u1i, u2i).
Since the model is the same if we put
it is considered as
the Box-Cox transformation [14] of t, which is widely
used in various fields. Here, vi is an error term that fol-
lows the standard normal distribution. We have made the
term negative so that the length of hospital
stay increases as the value of becomes larger. Fur-
ther, to remove the influence of a small number of pa-
tients who remained in the hospital over a long period of
time, we limit the maximum number of days that pa-
tients could stay at the hospital to T. For patients staying
more than T days, we just use the information such that
they stay in the hospital more than T days.
),'(}/)1{()( *
2
*
1
2
iii vxttz 


)'( ii vx  
 '
i
x
The length of hospital stay is a discrete variable taking
positive integers. Therefore, the condition for the i-th
patient to leave the hospital on the ti-th day is given by
()0,if 1
ii i
zt t
(4)
(1)0,()0,if
iiii i
ztzt t


Note that if the error term follows a normal distribu-
tion, the probability of a patient leaving the hospital be-
comes positive for any positive t. To maintain consis-
tency in the model, we treat zi(ti)≥0 if ti=1. Thus, the
probability of the i-th patient leaving the hospital on the
ti -th day (ti ≤T) is given by
2
22
1
11
[()'], 1
[( 1)''],1
iiii
i
iiiiii
Ptx vt
PPtxvt xtT




 



 


(5)
Let

be a distribution function of the standard
normal distribution. Then,
2
22
1
11
('),1
(')[(1)'],1
iii
i
iiii i
txt
Ptxtx t



 


T

 


(6)
The probability of the i-th patient staying in the hos-
pital for a period longer than T0+T is given by
22
11
[(')0]1( '
ii i
PTxvT x

)



  (7)
From Eq.5–7, we obtain the following likelihood
function:
2
22
2
12 1
1
11
1
1
(, ,)(')
('){(1)'
1( ')
i
i
i
ii
t
iii i
tT
ii
tT
Ltx
txt x
Tx



 
}

 





 


  



 




(8)
We obtain the maximum likelihood estimator (MLE),
21ˆ
,
ˆ


, and

ˆ by maximizing the likelihood function. A
program that was specifically developed for this study is
employed to estimate the model.
3.2. Non-Inclusive Payments
Let yi be the non-inclusive payment. Since yi can be
treated as a continuous variable, it is analyzed using the
regression model given by
'
ii
yx i



 (9)
As in the previous model, xi is a vector of explanatory
variables affecting the effectiveness of treatment and ε1
is the error term with mean 0 and variance 2 

, respec-
tively.
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4. ESTIMATION RESULTS
4.1. Length of Hospital Stay
In this paper, we employ variables that represent 1) the
characteristics of patients, 2) the principal disease classi-
fication based on ICD-10, and 3) influence of hospitals
as explanatory variables. The variables that represented
the characteristics of the patient are sex, age, usage of an
ambulance, hospital’s own outpatient or not, place of
hospital stay post-hospitalization, and information about
the secondary disease and treatment. The Female
dummy (= 1 if the patient was female and 0 if the patient
was male) is used to indicate the sex of the patient. The
numbers of male and female patients are 518 and 707,
respectively. Since the length of hospital stay tends to
increase with patient age and the number of young pa-
tients under 30 is small, the Below 30 dummy (= 1 if the
patient was below 30 years and 0 if otherwise), the Age
30 dummy (= 1 if the patient was between 30 and 40
years of age and 0 if otherwise), and the Age 40 dummy
(= 1 if the patient was over 40 years old and 0 if other-
wise) are used. The numbers of patents by age in the Age
40 group is further subdivided into 546 in their seventies,
289 in their eighties, and 253 in their sixties. The total
number of patients in the Below 30 and Age 30 groups is
13. For the other patient characteristics, the Ambulance
dummy (= 1 if the patient used an ambulance and 0 if
otherwise), the Own Outpatient dummy (= 1 if the pa-
tient is an outpatient of the hospital where they under-
went surgery and 0 if otherwise), and the Home dummy
(= 1 if the patient returned home post- hospitalization
and 0 if otherwise) are used. Since the outpatient care is
exempt from the prospective payment, hospitals may
choose to shift necessary medical checkups, medication,
injections, and treatments to outpatient settings, as hap-
pened in the U.S. when prospective payment was intro-
duced into the Medicare program system [15]. This may
affect the length of hospital stay. The Own Outpatient
dummy evaluates this effect.
For secondary diseases and treatments, we use the
Secondary Disease dummy (= 1 if the patient had a sec-
ondary disease and 0 if otherwise) and the Secondary
Treatment dummy (= 1 if the patient underwent secon-
dary treatment and 0 if otherwise). Although all hospi-
talizations were planned in advance, five patients used
ambulances. A total of 985 patients went directly to the
hospital where they were treated, while 240 were re-
ferred there by other hospitals. Post-hospitalization,
1,088 patients returned home, whereas 137 went to an-
other hospital or facility. Of the total, 766 patients did
not have any secondary diseases and treatments, 499
patients had secondary diseases but did not undergo any
secondary treatments, and 10 patients had secondary
diseases for which they underwent treatment.
For principal disease classifications, dummy variables
based on the H25.0 (Senile incipient cataract) category
are used. For classification, 173 patients had diseases
classified under H25.0, 555 had diseases under H25.9
(Senile cataract, unspecified), and 382 had diseases un-
der H26.9 (Cataract, unspecified). The number of pa-
tients with diseases under other categories is relatively
small: 90, 6, and 19 patients with diseases classified un-
der H25.1 (Senile nuclear cataract), H25.2 (Senile cata-
ract, morgagnian type), and H25.8 (Other senile cataract),
respectively. Since the average length of hospital stay is
the shortest for Hp12, dummy variables based on Hp12
are used to represent the influence of hospitals.
'
i
x

of Eq.3, becomes








012
34
56
78
9
'FemaledummyBelow30 dummy
Age30 dummyAge40 dummyAge-40
AmbulancedummyOwn Outpatient dummy)
+Home dummySecondaryDisease dummy
SecondaryTreatment dummy
-th Principal
i
j
j
x
j
 





 
 




(

Disease dummy
(-th Hospitaldummy)
k
k
k  


(10)
We select T = 10. Note that a total of 7 patients—less
than 1% of all patients—stayed at the hospital for more
than 10 days.
Table 3 presents the estimates for α and β. The esti-
mate for α2 is significantly smaller than 1.0, which im-
plies that certain patients remained at the hospital for a
long period of time. The estimate for the Female dummy
is positive and significant at the 5% level. Moreover, the
estimates for the Below 30 dummy, Age 30 dummy, and
Age 40 dummy are positive and significant at the 5%
level, negative and significant at the 1% level, and posi-
tive and significant at the 1% level, respectively. This
implies that sex and age affect the length of hospital stay.
The estimates for the Ambulance and Own Outpatient
dummies are negative but not significant at the 5% level.
We could not find an evidence that the length of stay
depends on whether the patient is an outpatient of the
hospital. The estimate for the Secondary Disease dummy
is positive and significant at the 1% level and exerts a
strong influence on the length of hospital stay. The esti-
mates for the Secondary Treatment and Home dummies
are negative but not significant at the 5% level. None of
the estimates for the Principal Disease dummies are sig-
nificant at the 5% level. In other words, differences in
the principal disease that patients suffer from do not sig-
nificantly affect the length of hospital stay. This may
support the suitability of the DPC groups with respect to
cataract patients.
All values for the Hospital dummies are positive, with
a maximum value of 5.290. This implies that the length
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Table 3. Estimation results for length of hospital stay.
Variable EstimateStandard
Error t-value
Constant 2.1310 0.6044 3.5256**
Female dummy 0.1588 0.0695 2.2843*
Below 30 dummy 1.4054 0.6979 2.0137*
Age 30 dummy -1.3652 0.2580 -5.2920**
Age 40 dummy 0.0174 0.0037 4.7029**
 (Age – 40)
Ambulance dummy -0.1051 0.7809 -0.1346
Own Outpatient
du
mm
y
-0.0808 0.0914 -0.8842
Secondary Disease
du
mm
y
0.3001 0.0769 3.9009**
Secondary Treat-
m
e
n
t
du
mm
y
0.1581 0.4822 0.3279
Home dummy -0.1173 0.1528 -0.7678
Principal Disease Dummies
H25.1 0.0286 0.6069 0.0471
H25.2 0.4881 0.6197 0.7875
H25.8 -0.0316 0.5090 -0.0621
H25.9 -0.1577 0.4230 -0.3728
H26.9 -0.0565 0.4167 -0.1356
Hospital Dummies
Hp1 3.2520 0.4535 7.1709**
Hp2 2.9639 0.1484 19.9733**
Hp3 4.3442 0.1727 25.1563**
Hp4 3.8194 0.2109 18.1121**
Hp5 3.7617 1.3073 2.8775**
Hp6 3.6674 0.3619 10.1326**
Hp7 2.0452 0.4871 4.1983**
Hp8 2.0845 1.2731 1.6373**
Hp9 0.9070 0.1843 4.9222**
Hp10 3.1095 0.4354 7.1414**
Hp11 2.5642 0.1213 21.1454**
Hp13 5.2897 0.4018 13.1664**
Hp14 1.0896 0.4695 2.3208*
Hp15 4.3771 0.4742 9.2311**
Hp16 2.7421 0.1645 16.6733**
1

2.9848 0.4021 7.4237**
2

0.5245 0.0394 13.3112**
LogL -1743.192
*: Significant at the 5% level. **: Significant at the 1% level.
of hospital stay is the shortest for Hp12 even if the in-
fluence of factors such as patient characteristics is
eliminated. Thus, despite the exclusion of the effects of
patient characteristics and treatment types, large differ-
ences remain among hospitals.
4.2. Non-Inclusive Payments
The non-inclusive payment variable zi is estimated by
the least-squares method. xi is chosen such that xi'γ of
Eq.9 becomes






01 2
34
56
78
910
11
'Femaledummy(Below 30 dummy)
Age30 dummy+Age50 dummy
Age60 dummyAge70 dummy
+Age80 dummyAmbulancedummy
OwnOutpatient dummySecondaryDisease dummy
SecondaryTreatment
i
x  





 







12
dummyHome dummy
th Principal Disease dummy
(thHospital dummy)
j
j
k
k
j
k









(11)

Since there is no clear trend with respect to patient age,
the dummy variables based on the age 40’s (the Age 80
dummy includes all patients over 80 years old) are used.
However, with the exception of the variables for age, the
definitions of all the variables are the same as those in
the previous section.
The estimation results are presented in Table 4. The
Female dummy is positive but not significant at the 5%
level. While the estimate for the Age 30 dummy is nega-
tive and significant at the 5% level, the other estimates
are not significant. The estimates for the Ambulance,
Own Hospital Outpatient, and Secondary Disease dum-
mies are positive but not significant at the 5% level.
Again, we could not find an evidence that the hospitals’
medical treatments depend on whether the patient is an
outpatient of the hospital. The estimate for the Secon-
dary Treatment dummy is positive and significant at the
1% level. In fact, the value of this variable is estimated
at 5,999 points, which implies that there is a large in-
crease in the non-inclusive payment amount when sec-
ondary treatment is carried out. The estimate of the
Home dummy is negative but not significant at the 5%
level. With respect to the principal disease classifications,
the estimate for the H25.8 dummy is negative and sig-
nificant at the 5% level, but none of the other estimates
is significant at this level. The maximum value for the
Hospital dummies is 1,535, while the minimum value is
–1,708; thus, the difference between the maximum and
minimum values is 3,243. This implies that although
there exist significant differences among the hospitals,
they are not very large as compared to the estimates for
the other variables such as those for secondary treat-
ment.
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Table 4. Estimation results for non-inclusive payments.
Variable Estimate
Standard
Error t-value
Constant 15,340 509 30.1517*
Female dummy 106 94 1.1206
Below 30 dummy 232 805 0.2876
Age 30 dummy -1,004 508 -1.9776*
Age 50 dummy -218 459 -0.4750
Age 60 dummy -229 421 -0.5441
Age 70 dummy -378 406 -0.9309
Age 80 dummy -198 417 -0.4749
Ambulance dummy 1,785 2,611 0.6837
Own Outpatient
dummy 94 99 0.9479
Secondary Disease
dummy 246 128 1.9319
Secondary Treat-
ment dummy 5,999 478 12.5625**
Home dummy -98 119 -0.8266
Principal Disease Dummies
H25.1 401 263 1.5252
H25.2 572 441 1.2977
H25.8 783 372 2.1037*
H25.9 172 245 0.7030
H26.9 42 219 0.1924
Hospital Dummies
Hp1 -684 393 -1.7388
Hp2 554 169 3.2817**
Hp3 232 167 1.3927
Hp4 847 224 3.7858**
Hp5 -1,429 232 -6.1556**
Hp6 -1,708 201 -8.4847**
Hp7 -421 123 -3.4213**
Hp8 -43 340 -0.1276
Hp9 839 520 1.6113
Hp10 1,535 261 5.8827**
Hp11 -507 410 -1.2371
Hp13 -1,237 217 -5.6959**
Hp14 -433 259 -1.6730
Hp15 -28 246 -0.1138
Hp16 -29 131 -0.2222
R2 0.231551
*: Significant at the 5% level. **: Significant at the 1% level.
5. COMPARISON OF LENGTHS OF
HOSPITAL STAY AND MEDICAL
PAYMENTS AMONG HOSPITALS
In this section, we compare lengths of hospital stay and
non-inclusive payments, taking into consideration pa-
tient characteristics and principal disease classifications
for each hospital. Let us consider a 70-year-old male
patient whose DPC code is 0201103x01x000 (cataract
operations and insertion of prosthetic lens, no secondary
disease or treatment) and who does not use an ambu-
lance, is an outpatient and returns home after his hospital
stay, and has a principal disease classified under the
ICD-10 code H25.0. Table 5 presents the patient’s esti-
mated average length of hospital stay, inclusive payment
amount, non-inclusive payment amount, and total pay-
ment amount at each of the surveyed hospitals. The av-
erage length of hospital stay is estimated as 3.99 days for
all the hospitals, with a standard deviation of 1.49 days.
The shortest length of hospital stay is estimated as 1.41
days in Hp12. On the other hand, the longest average
length of hospital stay is estimated as 6.97 days in Hp13,
Table 5. Lengths of hospital stay and medical payments after
eliminating the influence of patient characteristics by hospital*
Payments (in points)
Hospital Length of
Stay(days) Inclusive Non-
inclusive Total
Hp1 4.18 9,808 14,278 24,086
Hp2 3.93 9,332 15,516 24,848
Hp3 5.65 12,523 15,194 27,717
Hp4 4.95 11,243 15,809 27,052
Hp5 4.88 11,123 13,533 24,656
Hp6 4.78 10,949 13,254 24,203
Hp7 2.99 7,374 14,541 21,915
Hp8 2.98 7,339 14,919 22,258
Hp9 2.01 5,085 15,800 20,886
Hp10 4.07 9,587 16,497 26,084
Hp11 3.54 8,525 14,455 22,980
HP12 1.41 3,590 14,962 18,552
Hp13 6.97 14,772 13,725 28,497
Hp14 2.12 5,365 14,529 19,894
Hp15 5.74 12,673 14,934 27,607
Hp16 3.66 8,765 14,933 23,698
All
Mean 3.99 9,253 14,805 24,058
Standard
Deviation 1.49 3,013 869 2,905
*: Considering a 70-year-old male patient whose DPC code is
0201103x01x000 (cataract operations and insertion of prosthetic lens,
no secondary disease or treatment) and who does not use an ambulance,
is an outpatient and returns home, and has a principal disease classified
under the ICD-10 code H25.0.
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which is approximately 5 times that of Hp12. The aver-
age inclusive payment for all the hospitals is 9,253
points, with a standard deviation of 3,013 points. The
lowest and highest inclusive payments are 3,590 points
(Hp12) and 14,772 points (Hp13), respectively, thus ex-
hibiting a range of 11,183 points.
The average non-inclusive payment for all the hospi-
tals is 14,805 points, and the standard deviation is 869
points. The lowest and highest payments are 13,254
points (Hp6) and 16,497 points (Hp10), respectively,
thus exhibiting a range of 3,243 points. The coefficient
of variation among the hospitals is 5.9%, and the range
among the hospitals is 21.9% of the overall average.
Thus, the variation is much smaller than that among the
hospitals’ inclusive payment amounts.
The average total payment is 24,058 points for all the
hospitals. The share of inclusive payments in the total
payments is 38.5%. Although the share of inclusive
payments is small, both the standard deviation and range
for inclusive payments are 3.5 times larger than those for
non-inclusive payments. We thus conclude that the dif-
ferences in the total medical payment amounts among
hospitals are largely due to the differences in the inclu-
sive payment amounts, which are determined by the
length of hospital stay.
6. EVALUATION OF THE NEW PAYMENT
SYSTEM
As mentioned before, one of the major purposes of the
DPC-based payment system is to reduce the long-term
hospitalization cost by standardizing the medical pay-
ments. However, this study found that for cataract pa-
tients, the differences in the non-inclusive payment
amounts—which are conventional fee-for-service reim-
bursements—are relatively small, whereas those in the
inclusive payment amounts are quite large among six-
teen different hospitals. This result shows that the DPC
system, in fact, works in reverse of its intended purpose
to standardize the medical payments. The relatively
small difference in non-inclusive payment amounts
among the hospitals can be explained as follows: 1)
since for the patients in our data set, other opera-
tions—such as ones for glaucoma and vitreous—are
performed in addition to the cataract operations, the dis-
eases are classified into other DPC categories and thus
the homogeneity of the patients is high (for example,
operations for glaucoma and vitreous are classified un-
der the DPC code 020340); and 2) the operation and
treatment procedures for cataracts are standardized, and
therefore, the difficulty levels for cataract operations are
not high.
The correlation coefficient between non-inclusive
payments per diem and total payments is –0.872. To re-
duce the total medical payment for this disease, it may
be effective to shorten the length of hospital stay and
spend medical resources intensively within a short pe-
riod of time. However, since the current system is a per
diem prospective payment system, hospitals may not
have a strong incentive to reduce the length of hospital
stay. For example, since the probability of postoperative
infections or complications is very small in the case of
cataract operations, few medical resources for medical
treatment and examination are necessary after the opera-
tion. In other words, the direct cost to hospitals is a de-
creasing function of time. Moreover, even if the payment
per diem is reduced, empty beds may be worse for hos-
pital managers so long as the marginal revenue exceeds
the marginal cost. Further, for cataract operations, the
length of hospital stay is generally a few days, which is
not a very long period of time. Since the patient does not
change hospitals unless the benefit of reducing the
length of hospital stay exceeds the cost of finding a new
hospital, the hospital can usually make the final decision
with respect to the patient’s length of hospital stay. In-
deed, the hospital may even choose to keep the patient in
the hospital until a new patient is admitted to fill the bed.
If so, the new payment system may not offer hospitals a
sufficiently strong incentive to reduce their patients’
length of hospital stay.
To make the new payment system work effectively in
the case of cataract operations, it may be necessary to
reduce the per diem payment by a large amount for
long-term hospitalizations and encourage hospitals to
spend medical resources intensively within a short pe-
riod of time. Furthermore, the introduction of the
DRG/PPS may merit serious reconsideration in Japan. In
the DRG/PPS, a hospital is paid a fixed fee on the basis
of the classification of the DRG, regardless of the length
of hospital stay. Although ten hospitals in Japan had
adopted the DRG/PPS on a trial basis in 1998, the
medical society expressed strong disapproval with the
DRG/PPS. Moreover, since the Japanese medical system
has been following the fee-for-service payment system
for over half a century, it would have been extremely
difficult to adopt the DRG/PPS system nationwide
without any modifications. Therefore, the DPC inclusive
payment system was introduced. Thus, although the
current system essentially employs the same method
nationwide to classify diseases, it is necessary to revise
the system, taking into consideration the characteristics
of diseases and hospital specialties to facilitate the effec-
tive use of medical resources. Furthermore, individual
hospitals must improve their medical systems by intro-
ducing clinical paths, efficiently managing hospitaliza-
tion schedules [16], and adopting proper medical tech-
nologies [17] to reduce the length of hospital stay.
7. CONCLUSIONS
In this paper, the Japanese DPC-based inclusive payment
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system, which was introduced in 2003, was evaluated.
We utilized data pertaining to 1,225 patients who were
hospitalized for cataract diseases and underwent lens
operations from July 2004 to September 2005. The
lengths of hospital stay and medical payments among
hospitals were compared. The variables found to affect
the length of hospital stay were those pertaining to the
patients’ sex and age and the presence of secondary dis-
eases. We found large differences in the length of hospi-
tal stay among hospitals, even after eliminating the in-
fluence of patient characteristics and principal disease
classifications. The highest average inclusive payment
for the hospitals was 3.5 times as high as the lowest
payment.
Next, non-inclusive payments were analyzed. The
variables affecting the non-inclusive payment were the
Age 30 dummy, Secondary Disease dummy, and H25.8
dummy. The differences among hospitals in terms of
non-inclusive payments based on the conventional
fee-for-service system were relatively small. The largest
deviation from the average of all hospitals was approxi-
mately 10%. Thus, we can conclude that the major dif-
ferences among hospitals with respect to medical pay-
ments are caused by differences in their DPC-based in-
clusive payments, which account for only one-third of
the total medical payments for cataract patients. The
results of the study strongly suggest that in future revi-
sions of the payment system, the characteristics of dis-
eases must be considered when determining the efficient
use of medical resources.
In the present study, only cataract operations were
analyzed. To evaluate the DPC-based inclusive payment
system more precisely, it is necessary to analyze other
important cases such as cancer, cardiac infarction and
stroke, and compare the results of the cataract operations
with the other cases. These are subjects to be analyzed in
future studies.
8. ACKNOWLEDGEMENT
The data used in this study were originally collected by the DPC Hos-
pital Conference of Japan and are used with the approval of the con-
ference’s Data Analysis Division. We are grateful to the anonymous
referee for his/her helpful comments. We also thank the research par-
ticipants from various hospitals for their sincere cooperation. This
study is supported by the Grant-in-Aid for Scientific Research
“Analyses of the Japanese Medical Information and Policy using the
Large Scale Individual-Level Survey Data (Grant Number: 20243016)”
of the Japan Society of Science.
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103
Notes
1. The ICD-10 comprises a wide range of catego-
ries—from general categories to very specific ones—and
uses codes consisting of one alphabet and three-digit
numbers.
2. The initial classification was done based on data
obtained from about 300,000 patients hospitalized in the
special functioning hospitals from July 2002 and October
2002. The revisions were done in April 2004, April 2006,
and April 2008, based on the renewed data.
3. In Japan, medical care fees are measured in points.
This system was first launched in 1943. Under the sys-
tem, each point corresponds to 10 yen, which has been
effective since 1958.
4. In the case of 0201103x01x000 (cataract operations
and insertion of prosthetic lens, no secondary disease and
no secondary treatment), the per diem inclusive payment
during the sample period was 2.536 points for up to 3
days, 1,882 points for 4–6 days, and 1,600 points for
7–10 days. After 10 days, the payment was determined
by the conventional fee-for-service system.