Explore the strategies of hospitals facing global budget and
evaluate its effects in Taiwan!
Lih-Lian Hwang
Assistant Professor
Department of Healthcare Information and Management
Ming Chuan University
12F No 82 Chang Gung Yi Hu Hsin Tsun, Gweishan, Taoyuan, Taiwan, R. O. C.
Postcode is 333
Tel: 886-3-3272365
E-mail: lihlian@mcuedutw
Abstract
The Bureau of National Health Insurance (BNHI) fully implemented a global
budget (GB) payment system in 2002. Under GB, the benefit payments were under
control as planned. Since the benefit payments from BNHI are the largest part of the
revenues of hospitals, some hospitals have financial imbalance.
This study uses system dynamics to explore the strategies of hospitals facing GB
and evaluate its effects on hospitals and patients. In order to improve the financial
imbalance of hospitals, four strategies and two scenarios are proposed. Each strategy
is evaluated for each scenario. According to the simulation results, the strategy of
increasing out of pocket plus making a contract with BNHI so each hospital will have
its own budget is the best strategy for improving hospital financial imbalance and.
does not cause the most severe wait problem.
This research reaches two achievements. First, submitting the stock-flow
diagrams can provide the managers of hospitals with a further understanding on their
strategies. Second, building the model can simulate and evaluate the effects of
multiple strategies on hospitals and patients.
Keywords: System Dynamics, Simulation, Global budget, Hospital, Strategy
' Hwang, Lih-Lian, 2005, Explore the strategies of hospitals facing global budget and evaluate its
effects in Taiwan, Full Paper on CD-ROM Proceeding of the 23rd Intemational Conference of the
System Dynamics Society, Boston, Massachusetts USA.
Introduction
The National Health Insurance (NHI) program has been implemented in Taiwan.
since March 1995. The initial balance of revenues and expenditures was stable, but
there has been a deficit since 1998 (Bureau of National Health Insurance, 2004,
pp.94-99).
According to the literature review, the deficit problem is mostly caused by the
Payment system of fee-for-service (FFS). FFS was a major method of the payment
scheme from the time the NHI program was launched. Under FFS, hospitals increased.
the volume of care to make the maximum profits. This is because 1 RVU (relative
value unit) is equal to NT$ 1. The more medical services hospitals provide, the more
medical benefits the Bureau of NHI (BNHI) pays, and the more revenues hospitals
will receive.
As the deficit problem is mostly caused by the payment system of FFS, the BNHI
has implemented the global budget (GB) payment system to limit the payments under
FPS. Under the GB payment system, the BNHI negotiates with hospitals to set a rate
of change of benefit payments per beneficiary before a fiscal year. The payment per
RVU is floating and equal to the quotient of the annual medical benefit budget
divided by the total RVU of medical services. As total RVU of medical services are
over the medical benefit budget, the payment per RVU will be less than one NT$. On
the contrary, when total RVU of medical services are under the medical benefit
budget, the payment per RVU will be more than one NT$.
After the GB payment system has been implemented for dental care, Chinese
medicine, basic Westem medicine and Westem medical center on July 1998, July
2000, July 2001 and July 2002 respectively, the benefit payments were under control
as planned. Since the benefit payments from BNHI are the largest part of revenues of
hospitals, some hospitals have financial imbalance. What strategies will hospitals
adopt to cope with the GB payment system?
Up to now, those researches about strategies for the financial status of hospitals
focused mostly on those influences of the individual strategy. Research rarely studied
the long-term whole influences of multiple strategies. Moreover, those methods of the
researches were mostly questionnaires, data analysis and regression. Researchers
rarely used simulation methods. Hence, this study applies system dynamics to explore
the long-term influences of multiple strategies on the financial status of hospitals.
The system dynamics model
The system dynamics model was developed on the basis of the research of Hwang
(2002, 2004), Wolstenholme et al. (2005) and Rauner and Schaffhauser Linzatti
(2002). The model is constructed using the V ensim software (Ventana Systems Inc,
2004). Figure 1 shows the stock-flow diagram of the system dynamics model. The
Appendix shows the model’s complete equations.
rate of change of
romal demand intial capacity of
effect of out of Serving <tate of change of capacity
pocket on demand <days per year> of service>
\ re \ a _—» Spare capacity
a capacity of service
A i, See
initial demand | <nitial average out of pocket> a
average length of
under GB ~+
Global budget <Time> initial total fixed
wT cost
‘ rate of change of p> Tale of change of
SUSE . GB capacity of service year
Figure 1. The stock-flow diagram of the system dynamics model
At the top of Figure 1, the stocks of await service and receive service in hospital
represent accumulations of patients. The flow of hospital service admission rate is
driven by demand. The flow of admissions into hospital is controlled by the spare
capacity and await service. The flow of discharge rate is determined as those patients
receiving service in hospital divided by a given average length of stay. The patient
process is similar to the research of Wolstenholme et al. (2005).
At the bottom of Figure 1, the stock of cumulative balance of hospital represents
the financial status of the hospital. The flow of total revenue is the sum of benefit
payments, out of pocket and copayment The flow of total cost equals total fixed cost
plus total variable cost.
The parameters in Figure 1 need to be estimated. The values of approved.
proportion, average claims, average copayment and average length of stay were
obtained from the National Health Insurance Annual Statistical Report (Bureau of
National Health Insurance, 2004). For the patient process to be in equilibrium, the
stocks of await service and receive service in hospital were initialized to 280 and 200
respectively. Since the GB payment system was implemented at the beginning of the
simulation, the stock of cumulative balance of hospital was initialized to zero.
The model has been examined by the author. Structure verification test, parameter
verification test, dimensional-consistency test, extreme-conditions test, behavior
reproduction test, changed behavior prediction test, and behavior-sensitivity test were
used to validate the model (Forrester and Senge 1980; Sterman 2000, pp.843-891;
Ventana Systems Inc, 2004).
The base case
FES was a major method of the payment scheme since the NHI program was
launched in 1995. In order to improve the financial status of NHI, the BNHI gradually
implemented the GB payment system from 1998 and had fully implemented it in 2002.
Since the payment system of FFS is completely different from the GB payment
system, the simulation of the base case starts from 2002 to 2005.
Figure 2 displays the output of the base case simulation. The volume of await
service is increasing because hospital service admission rate is greater than admission
rate into hospital. Although the volume of receive service in hospital has been
increasing, the cumulative balance of hospital has been decreasing since 2004. This is
because the payment per RVU under GB is less than one NT$. The model reproduces
the behavior of interest in the hospital.
Strategies of hospitals facing GB
What are the strategies of hospitals facing GB? Chuang et al. (2004) investigated
127 hospitals in Taiwan by sending them structured questionnaires. The response rate
was 59.1% with 75 hospitals retuming the questionnaires. The results of their study
indicated that most hospitals adopted strategies such as further educating their staff
about the major changes in the GB payment system, enhancing their financial
Management and constructing a better cost accounting system, establishing a referral
system, developing the services which were not included in the NHI program.
Why did hospitals adopt the strategy of developing the services which were not
included in the NHI program? The main reason is that patients must pay the services
themselves and the payments (out of pocket) are another hospital revenue which is
different from benefit payments from the BNHI (see Figure 1).
1,000 patients
4M NID Ss
1.1 NID/RVU
0 NID .
0.9 NID/RVU
2002 2003 2004 2005
year (year)
await service : base +t + + + +t +t + +— patients
receive service in hospital : base 2 2 2 2 eee
Cumulative balance of hospital : base —3 3 3 3 NID
payment per RVU under GB : base. ———-4—_4__4_4—__4—_4— NID/RVU
Figure 2. The output of the base case simulation
Chang and Liao (2002) employed Outpatient Medical Expenditure Data file to
compare the changes in medical claims among primary Westem physician clinics in
the same half year before (i.e., 2000/07) and after (i.e., 2001/ 07) the GB payment
system was inaugurated. The results of their study indicated that six months after
launching the GB payment system, the number of cases claimed from primary
Westem physician clinics dropped sharply with a significant increase in unit price.
However, the results of the study of Chuang et al. (2004) showed that change in
service volume following the implementation of the GB payment system is diversified.
In ambulatory care, the percentage of increasing service volume, maintaining service
volume and decreasing service volume is 56%, 25.3% and 18.7% respectively. The
corresponding percentage for emergency is 48%, 41.3% and 10.7% respectively. The
corresponding percentage for inpatient is 50.7%, 33.3% and 16% respectively.
Young and Hwang (2000) used causal loop diagrams and the prisoners’ dilemma
to explore two providers (A and B) and three strategies (increasing service volume,
maintaining service volume and decreasing service volume). The results of their study
indicated that increasing service volume is dominant for both A and B. However,
adopting the strategy of increasing service volume is related to an increase in costs
because more services are provided. Therefore, hospitals might collapse when the
benefit payments are unable to cover the costs.
To avoid the “tragedy of the commons” as in the above description, a hospital
could make a contract with BNHI to have its own annual budget. In this condition, no
matter what strategies are adopted by other hospitals, the hospital will receive the
fixed benefit payments, if it meets the criteria proposed by BNHI. And, for reducing
the service costs, the hospital will not adopt the strategy of increasing service volume.
According to the analysis described above, four strategies and two scenarios are
proposed. These strategies are currently adopted by hospitals. Each strategy is
evaluated for each scenario. These are:
Strategy 1: nothing will be changed. As in the base case, the rate of change of
capacity of service is supposed to be 120% of the rate of change of GB.
Tt means increasing service volume as in the payment system of
fee-for-service.
Strategy 2: a 5% increase in out of pocket will begin in 2005.
Strategy 3: making a contract with BNHI to have the hospital’s own budget will
begin in 2006. It means the rate of change of capacity of service is the
same as the rate of change of GB.
Strategy 4: Strategy 2 plus Strategy 3.
Scenario 1: nothing will change. As in the base case, the rate of change of GB is
4% each year.
Scenario 2: the rate of change of GB is 5% each year will begin in 2006.
Figure 3 and Figure 4 display the cumulative balance of hospital under each
strategy against Scenario 1 and Scenario 2 respectively. Figure 5 and Figure 6 show
the volume of await service under each strategy against Scenario 1 and Scenario 2
respectively. According to the simulation results, Strategy 4 is the best strategy for
improving hospital’s financial imbalance (Figure 3 and Figure 4). Besides, Strategy 4
does not cause the most severe wait problem (Figure 5 and Figure 6). This is because
Strategy 4 is Strategy 2 plus Strategy 3. By increasing out of pocket, Strategy 2 can
increase hospital’s revenue and decrease the service demand (Figure 5 and Figure 6).
Adopting Strategy 3, a hospital can receive the fixed benefit payments and plan the
service volume provided to control service costs and improve financial imbalance
(Figure 3 and Figure 4).
10M a]
a <—
-15M —
-27.5M
-40M
2002 2003 2004 2005 2006 2007 2008
year (year)
Cumulative balance of hospital : strategy 1. —+ a zs 4 1: zt NID
Cunulative balance of hospital : strategy 2 2 2 2 NID
Cumulative balance of hospital : strategy 3 3 3 3 3- NID
Cumulative balance of hospital : strategy 4 a yg NID
Figure 3. The cumulative balance of hospital under each strategy against scenario 1
10M
-25M Shy
-15M ~
-27.5M
N\
-40M
2002 2003 2004 2005 2006 2007 2008
year (year)
Cunulative balance of hospital : strategy 1. —t + + + + + NID
Cumulative balance of hospital : strategy 2 NID
Cumulative balance of hospital : strategy 3 3 3 3- NID
Cunulative balance of hospital : strategy 4 + + + 4 4 4 NID
Figure 4. The cumulative balance of hospital under each strategy against scenario 2
1,250 eee
23 ere aed
0 44
2002 2003 2004 2005 2006 2007 2008
year (year)
await service : strategy 1 + + + + + + + +— patients
await service : strategy 2 2 2 2 2 2 2 patients
await service : strategy 3 3 3 3 3 3 3 3 patients
await service : strategy 4 -4 4 4 4 4 4 4 4 patients
Figure 5. The volume of await service under each strategy against scenario 1
year (year)
await service : strategy 1 t i: r x E t t +— patients
await service : strategy 2 2 2 2 2 2 2 2 2 patients
await service : strategy 3 3 3 =; 3 3 3 3 patients
await service : strategy 4 + + 4+ + + 4 + 4 patients
Figure 6. The volume of await service under each strategy against scenario 2
Summary and future prospects
This study used system dynamics to explore the strategies of hospitals facing
GB and to evaluate its effects on hospitals and patients. In order to improve the
financial imbalance of the hospital, four strategies and two scenarios were proposed.
Each strategy was evaluated for each scenario. According to the simulation results,
the strategy of increasing out of pocket plus making a contract with BNHI to have
hospital’s own budget is the best strategy for improving hospital’s financial imbalance
and does not cause the most severe wait problem.
This research reaches two achievements. First, submitting the stock-flow
diagrams can provide the managers of hospitals with a further understanding on their
strategies. Second, building the model can simulate and evaluate the effects of
multiple strategies on hospitals and patients.
This research had two reservations. First, it only explored the financial problem
and those strategies of hospitals. Second, the cumulative balance of the hospital and
the volume of await service were the only two variables to evaluate the effects of
strategies. However, different criteria could be used for judging the priority of
strategies and could have different results.
Hence, future research could consider extending the model boundary and could
consider selecting the criteria used for judging the priority of strategies under multiple
objectives.
Admowledgements
The author would like to thank three anonymous referees for constructive and.
thorough comments. This research is partly based on a project that is funded by a
grant from the National Science Council of Taiwan (NSC 93-2416-H- 130-013).
References
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Zhong-Ren Wei. 2004. Factors associated with strategic choices of hospitals in
response to the global budget payment system. Taiwan J Public Health
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Appendix: Equations of the model
(01) admit into hospital = MIN ( await service , spare capacity ) / time unit
Units: patients/day
(02) Approved medical benefit payments =NHI medical claims * Approved.
proportion
Units: RVU/day
(03) Approved proportion =0.975
Units: Dnml
(04) average claims =4500
Units: RVU/(patients*day)
(05) average copayment =250
Units: NT'D/(patients*day)
(06) average length of stay = 10
Units: day
(07) average out of pocket =IF THEN ELSE ( strategy =2 :OR: strategy =4, STEP
(initial average out of pocket * 0.05, 1095) , 0) + initial average out of pocket
Units: NT'D/(patients*day)
registration fee, bed fee gap
(08) average variable cost =1500
Units: NT'D/(patients*day)
(09) await service =INTEG( hospital service admission rate - admit into hospital ,
280)
Units: patients
(10) Benefit payments =Approved medical benefit payments * payment per RVU
under GB
Units: NTD/day
(11) capacity of service =initial capacity of service * ( 1 +-rate of change of capacity
of service ) “INTEGER ( Time/ days per year )
Units: patients
(12) Copayment = average copayment * receive service in hospital
Units: NTD/day
(13) Cumulative balance of hospital = INTEG( total revenue - total cost , 0)
Units: NTD
(14) days per year =365
Units: day
(15) demand =initial demand * ( 1 +rate of change of nonmal demand ) “INTEGER
( Time / days per year ) * effect of out of pocket on demand ( average out of
pocket / initial average out of pocket )
Units: patients/day
(16) discharge rate =receive service in hospital / average length of stay
Units: patients/day
(17) effect of out of pocket on demand
([(0,0)-(4,2)],(0,1.25),(0.25,1.25),(0.5,1.2),(0.75,1.1),(1, 1), (1.25,0.93),(1.5,0.88)
/(1.75,0.85),(2,0.82),(3,0.8),(4,0.78) )
Units: Dol
(18) FINAL TIME =2190
Units: day
The final time for the simulation.
(19) Global budget =initial global budget * ( 1 +rate of change of GB ) “INTEGER
((Time- 1) / days per year)
Units: NTD/day
(20) hospital service admission rate =demand
Units: patients/day
(21) initial average out of pocket =550
Units: NTD/(patients*day)
(22) initial capacity of service =200
Units: patients
(23) initial demand =20
Units: patients/day
(24) initial global budget =877500
Units: NTD/day
(25) INITIAL TIME =0
Units: day
The initial time for the simulation.
(26) initial total fixed cost =730000
Units: NTD/day
(27) NHI medical claims =receive service in hospital * average claims
Units: RVU/day
(28) Out of pocket = average out of pocket * receive service in hospital
Units: NTD/day
(29) payment per RVU under GB =Global budget / Approved medical benefit
payments
Units: NTD/RVU
(30) rate of change of capacity of service = IF THEN ELSE ( Time < 1460, 1.2, IF
THEN ELSE ( strategy =3 :OR: strategy =4, 1, 1.2) ) * rate of change of GB
Units: Drm
(31) rate of change of GB =IF THEN ELSE ( Time <1460, 0.04, IF THEN ELSE
(scenario =1, 0.04, IF THEN ELSE ( scenario =2, 0.05, 0.06) ) )
Units: Dnml
(32) rate of change of normal demand =0.08
Units: Dnml
(33) receive service in hospital = INTEG( admit into hospital - discharge rate , 200)
Units: patients
(34) SAVEPER =TIME STEP
Units: day
The frequency with which output is stored.
(35) scenario =1
Units: Dmnl
(36) spare capacity =( capacity of service - receive service in hospital ) + discharge
rate * time unit
Units: patients
(37) strategy =4
Units: Dmml
(38) TIME STEP =1
Units: day
The time step for the simulation.
(39) time unit =1
Units: day
(40) total cost =total fixed cost + total variable cost
Units: NTD/day
(41) total fixed cost =initial total fixed cost * ( 1 +rate of change of capacity of
service ) “INTEGER ( Time/ days per year )
Units: NTD/day
(42) total revenue = Benefit payments +Copayment + Out of pocket
Units: NTD/day
(43) total variable cost = average variable cost * receive service in hospital
Units: NTD/day
(44) year =TIME BASE ( 2002, 0.00273973)
Units: year [2002,0.00273973]
