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Keywords:

  • health care expenditure;
  • end-of-life patients;
  • private hospitals;
  • public hospitals;
  • national health insurance;
  • Taiwan

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

This paper empirically investigates the relationship between the health care expenditure of end-of-life patients and hospital characteristics in Taiwan where (i) hospitals of different ownership differ in their financial incentives; (ii) patients are free to choose their providers; and (iii) health care services are paid for by a single public payer on a fee-for-services basis with a global budget cap. Utilizing insurance claims for 11 863 individuals who died during 2005–2007, we trace their hospital expenditures over the last 24 months of their lives. We find that end-of-life patients who are treated by private hospitals in general are associated with higher inpatient expenditures than those treated by public hospitals, while there is no significant difference in days of hospital stay. This finding is consistent with the difference in financial incentives between public and private hospitals in Taiwan. Nevertheless, we also find that the public–private differences vary across accreditation levels. Copyright © 2013 John Wiley & Sons, Ltd.

1 INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

In the health care market, a stylized fact is that health care expenditure is positively associated with age (Newhouse, 1992; Dormont et al., 2006). However, a recent debate focuses on whether it is age or proximity to death that can better explain the rapid growth in health care expenditures around the world (see e.g. Zweifel et al., 1999; Seshamani and Gray, 2004a; Seshamani and Gray, 2004b; Felder et al., 2010; van Baal and Wong, 2012). Proximity to death matters because it is often much more costly to treat end-of-life patients, e.g. cancer or end-stage renal disease patients, than less-ill patients, e.g. seasonal influenza patients. Yet end-of-life patients are not necessarily old patients, and less-ill patients are not always young patients either. In other words, proximity to death is not a deterministic function of age.

Following this debate, an increasing number of studies have paid attention to the determinants of health care expenditure in the last months of life (Felder et al., 2000; Levinsky et al., 2001; Stearns and Norton, 2004; Seshamani and Gray, 2004b). Most of them, nevertheless, focus on the demand side factors such as patient characteristics. Relatively little is known about what role the supply side factors such as hospital characteristics play in determining health care expenditure at the end stage of life.

In addition, since the agency problem is another hallmark of the health care market, one would naturally suspect that health care providers may not act in the best interests of the end-of-life patients, leading to a rise in health care expenditure toward the end of life. In fact, there has been a growing concern about the high cost of treating end-of-life patients in recent years (Simoens et al., 2013).

This paper thus aims to increase the understanding of the determinants of health care expenditure toward the end of life by focusing on the role of hospital ownership. We study the case of Taiwan where (i) hospitals of different ownership differ in their financial incentives; (ii) patients are free to choose their providers; and (iii) health care services are paid for by a single public payer on a fee-for-services basis with a global budget cap.

We utilize a large data set of insurance claims that provides detailed information on both patient and hospital characteristics as well as claimed inpatient expenditures. Taiwan launched its universal and compulsory public insurance program in 1995, and this data set includes all the insurance claims of this program for every citizen since then. We extract a sample consisting of 11 863 individuals who died during 2005–2007 and trace their inpatient expenditures over the last 24 months of their lives. Throughout the rest of this paper, we call them the end-of-life patients to reflect the fact that they all ended up with death over our study period.1

In general, we find that the end-of-life patients who are treated by private hospitals are associated with higher inpatient expenditures than those who are treated by their public counterparts. However, there is no significant difference in the length of hospital stay between public and private hospitals. We argue that this finding is consistent with and most likely to have resulted from their differences in financial incentives. In addition, we find that the public–private differences vary across accreditation levels.

The remainder of this paper is organized as follows. Section 2 briefly describes the institutional background of the health care market in Taiwan and provides a conceptual framework to predict the difference in behavior between the public and private hospitals. Section 3 describes the data and methodology used in the analysis. Section 4 presents the results. Section 5 provides additional discussions on empirical concerns, and Section 6 concludes.

2 INSTITUTIONAL BACKGROUND

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

Taiwan has a social insurance system, known as the National Health Insurance (NHI) program, which mandates coverage to all citizens and offers comprehensive benefits, including outpatient services, hospital care, and prescription drugs. NHI is a single-payer fee-for-services system that employs a uniform fee schedule to reimburse providers. The fee schedule lists the regulated nominal prices for all diagnosis services and treatment procedures that are paid by the NHI program. In addition, the government has adopted global budgets to control the total cost of the NHI program. Under the annual fixed budget for each type of service, the real unit price decreases as the aggregate quantity of medical services increases. 2

Taiwan's health care market is dominated by the private providers. By 2007, about two-thirds of inpatient services (in terms of NHI spending) were provided by private hospitals, while only one-third was provided by public hospitals. It is noteworthy that patients are free to choose their own providers.

Moreover, public and private hospitals in Taiwan have different financial incentives for at least the following three reasons. First, physicians in public hospitals are paid on a fixed salary basis with some bonuses that are related to a wide range of indicators, including the seniority, academic achievement, and service provision. By contrast, compensation for physicians in private hospitals is primarily performance-based. Specifically, their compensations are tied to the number of patients they treat and the total profit made by the hospital.3 Second, the government provides regular subsidies to public hospitals but not private hospitals. Third, since the government is the owner of public hospitals, the residual claimant is not as well defined as for private hospitals, implying that seeking profits may not be the sole or even major objective for public hospitals (Duggan, 2000; Horwitz and Nichols, 2009; Bayindir, 2012).

The stark contrast in financial incentives between the private and public hospitals suggests that physicians in private hospitals are more responsive to profits than their public counterparts. We hypothesize that other things being equal, this private–public difference in financial incentives would lead to a difference in inpatient expenditures for end-of-life patients. More specifically, end-of-life patients at private hospitals are likely to have higher inpatient expenditures than their counterparts at public hospitals. We test this hypothesis using a representative sample of end-of-life patients in Taiwan.

3 DATA AND METHOD

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

3.1 Data Source and the Selection of Sample

The data used in this study are obtained from a longitudinal claims data set that is created through a two-stage process. First, this data set randomly selects a sample of one million individuals from the registry of NHI beneficiaries in 2005.4 Second, this sample is then merged with insurance claim files that record all medical utilization claims for all individuals in the sample from 1996 to 2007. Hereafter, we refer to this data set as the NHI sampling claims data. The NHI sampling claims data are publicly available through the National Health Research Institute. This data set contains detailed records on the utilization of personal health care services, including outpatient visits, hospital admissions, and prescription drugs. The advantage of this data set is that all the medical utilization data can be linked together for the same patient. Thus, this data set allows us to trace the same individual's inpatient expenditures between 1996 and 2007. In this data set, each inpatient service claim documents the date of admission and discharge, age and sex of the patient, diagnosis, treatment, hospital characteristics, and, most importantly, the claimed expenditures.5

To identify the end-of-life patients who died during our study period, we utilize the NHI enrollment records that accompany the claims data.6 At any given point in time, an individual is coded as one of the three statuses: “enrolled”, “suspended”, or “withdrawn”. If the individual dies, his/her status will be coded as “withdrawn”. However, death is not the only reason for being coded as “withdrawn”. An individual can also be coded in this way if he/she is (i) incarcerated for longer than two months; (ii) missing for six months or longer; or (iii) ineligible for the NHI due to emigration. Unfortunately, the exact reason for being coded as “withdrawn” is not provided in the data.

However, we argue that the “withdrawn” cases due to nondeath reasons mostly occur to younger cohorts under the age of 50. To see this, we calculate the sample age-specific “death rates” in 2005 by treating all the “withdrawn” cases within each 10-year age group as dead and dividing them by their corresponding group size. We then compare them with the population age-specific death rates, which are obtained from the Statistical Yearbook of Interior 2005 published by the Ministry of Interior.7 As shown in Figure 1, the two death rates are almost identical, except for ages 10–49. In particular, the sample “death rate” is higher than the population rate over ages 10–49, implying that some of the “withdrawn” cases over this age range were actually alive in 2005. Therefore, in order to reduce the misidentification of those deceased, we restrict our analysis sample to those who were aged 50–99 in 2005 and turned into “withdrawn” during 2005–2007. Based on this definition, our analysis sample includes 11 863 individuals, whom we define as end-of-life patients and who presumably died during 2005–2007.

image

Figure 1. Sample versus population age-specific death rate in 2005

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In Figure 2, we plot the mean NHI hospital expenditure and death rate by age. As shown, most deaths and hospital expenditures occurred for those aged 50 and older, indicating that the concern for a potential sample selection bias resulting from restricting the age to 50–99 in our study should be minimal.

image

Figure 2. Hospital expenditure and death rate by age in 2005

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We then backwardly trace their end-of-life hospital expenditures for 24 months, which are further grouped into eight quarters. More specifically, we define the day on which an individual's insurance status turns into “withdrawn” as the day of death and then trace his/her hospital expenditures backwardly.8 In the end, we have 48 971 observations of hospital stays during the last eight quarters before death for the 11 863 end-of-life patients.

3.2 Descriptive Analysis

Table 1 provides summary statistics for our sample. Among the 11 863 end-of-life patients, 60% are male and 40% are female. Their mean age is 74 in 2005. Of the 48 971 observations for hospital stays, the average expenditure is 41 357 New Taiwan Dollars (NTD), and the average length of stay is 9.58 days. 67% of stays are in private hospitals, and only 33% are in public hospitals. The market shares are consistent with the nationally aggregate statistics, suggesting that our sample is a good representation for the whole population.

Table 1. Summary statistics of the end-of-life patients
 (1)(2)(3)(4)
 MeanStd. dev.MinMax
  1. Notes: The sample consists of 11 863 patients and 48 971 hospital admission records. The mean values of age and sex are based on the 11 863 patients, while other means are based on all admission records. All hospital stays in the last eight quarters before death for the 11 863 individuals are included. A hospital stay can last for multiple quarters; quarter 0 denotes the quarter ending in death, quarter 1 refers to the quarter before quarter 0, and so forth.

Age in 200574.1411.045099
Male0.600.4901
Log(Expenditure)10.631.111.4914.77
Log(Days)2.26106.58
Comorbidity index3.363.61025
Hospital characteristics    
Private0.670.4701
Public0.330.4701
Medical center0.340.4701
Metropolitan hospital0.400.4901
Community hospital0.260.4401
Discharge quarter
00.400.4901
10.140.3501
20.110.3101
30.090.2801
40.080.2701
50.070.2501
60.060.2401
70.060.2301

In Taiwan, the regulatory agency classifies hospitals into three accreditation levels: (i) medical centers; (ii) metropolitan hospitals; and (iii) community hospitals. The accreditation level is closely correlated with the size of the hospitals. In our sample, 34% of the hospital stays are in medical centers while metropolitan and community hospitals account for 40% and 26% of the hospital stays respectively. If we look at the distribution of the hospital stays over the last eight quarters, we find that the closer the individual is to death, the more hospital stays that occur. More specifically, only 6% of the stays occur in the seventh quarter prior to death, and the percentage increases all the way up to 40% in the last quarter.9

Before we move to our empirical test, it is helpful to briefly examine the dynamic patterns of the end-of-life hospital expenditures and related hospital characteristics. Figure 3 illustrates the average quarterly hospital expenditure (the solid line) for the last eight quarters prior to death.10 (Note that quarter “0” denotes the quarter that ended in death, quarter “–1” is the first quarter before the death quarter, and so forth.) Most of the time, the end-of-life hospital expenditure increases gradually and remains under 50 000 NTD until the terminal quarter when it suddenly soars more than threefold.11 One of the plausible explanations for this dramatic rise is the rapidly deteriorating health in the terminal quarter, because the average Charlson's comorbidity index (the dashed line; the higher the index, the more serious the morbidity), which measures the intensity of illness based on the ICD-9 code, parallels the hospital expenditure, and also jumps more than three times in the last quarter.12

image

Figure 3. Mean quarterly hospital expenditure and comorbidity index in the final stage of life

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In terms of hospital characteristics, we are mainly interested in the distribution of hospital expenditure across the ownership form and the accreditation level of the hospital. Figure 4 shows the quarterly hospital choices in the final stage of life based on these two characteristics. We define the quarterly choice in terms of the percentage of days spent in a certain type of hospital in each quarter.13 The left panel shows that metropolitan and community hospitals account for 30% of the hospital choices at the beginning of our observation period (the eighth quarter prior to death), while medical centers account for 40%. Then, the share of community hospitals decreases over time, while the share of higher-level hospitals increases. This suggests that patients tend to move to larger hospitals (in terms of the accreditation level) as they approach death. By contrast, the choice between public and private hospitals does not seem to respond to health deterioration. As shown in the right panel, private hospitals constantly account for roughly two-thirds of the total hospital days over the last eight quarters, while the market share of hospital days in public hospitals remains fairly stable in the range of one-third.

image

Figure 4. The distribution of hospital days in the final stage of life across the accreditation level and ownership form

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3.3 Empirical Specification

Our benchmark empirical model is specified as follows:

  • display math(1)

where yijq is hospital expenditure or days of hospital stay for individual i's jth hospital admission in the qth quarter relative to death; Private is a dummy variable denoting a private hospital; Metropolitan is a dummy variable denoting a metropolitan hospital; Medicalcenter is a dummy variable denoting a medical center; X is a vector of control variables, including the patient's age, sex, Charlson's comorbidity index, and a full set of regional dummies and quarter dummies, which indicate the discharge quarter of each hospital admission; B is a vector of coefficients corresponding to the variables in X; and εijq is an error term.

Equation (1) is estimated by both the ordinary least squares (OLS) method and quantile regression method. The former method estimates the effect on the mean of the outcomes, while the latter estimates the effect on different quantiles of the outcomes. In our data, an individual may have multiple hospital admissions even within the same quarter. We thus use robust standard errors clustered at the individual level for statistical inference. The standard errors in the quantile regressions are calculated by using bootstrapping method. In addition to the benchmark model, we also use an alternative specification that adds to Equation (1) the interaction terms between the ownership and accreditation level. This alternative specification allows us to test if the private–public differences vary across accreditation levels.

We recognize the fact that the patient's choice of hospital is highly correlated with the severity and complexity of his/her medical condition. For example, magnetic resonance imaging (MRI) is often used to detect structural anomalies of the body for patients with severe medical conditions, such as a brain tumor. However, MRI machines are only available at large hospitals, such as metropolitan hospitals and medical centers in Taiwan, and MRI scans are rather expensive. To control for the severity and complexity of medical conditions, we calculate Charlson's comorbidity index for each admission and add it as a control variable in our regressions.

It is possible that the patient's choice of hospital is also associated with proximity to a certain type of hospital. For example, if sicker people tend to live closer to larger hospitals, failing to control for such proximity could bias our estimates. Although our regressions include regional dummies, which divide Taiwan into six regions, they may be too broad to fully control for the proximity to hospitals. Unfortunately, we do not have more detailed information about patients' residence.

4 EMPIRICAL RESULTS

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

Columns (1) and (2) in Table 2 report the OLS estimates of our benchmark specification. The dependent variable in columns (1) and (2) are logarithmic expenditure and logarithmic days of hospital stay respectively. The coefficient of the Private dummy in column (1) suggests that the hospital expenditure for end-of-life patients in private hospitals is 6% higher than that in public hospitals. On the contrary, the Private dummy in column (2) shows that there is little difference in their length of stay between private and public hospitals. These two findings together suggest that, other things being equal, the hospital expenditure per day is higher in private hospitals than in their public counterparts. This is consistent with our hypothesis, which is driven by the difference in financial incentives between private and public hospitals.

Table 2. OLS estimation of hospital expenditure and duration
 (1) Log(Expenditure)(2) Log(Days)(3) Log(Expenditure)(4) Log(Days)
  • Notes: Robust t statistics clustered in individuals are reported in parentheses. Regressions include a full set of regional dummies, which are not reported.

  • *

    denotes significance at the 10 percent level;

  • **

    denotes the 5 percent level;

  • ***

    denotes the 1 percent level.

Private0.060***–0.0200.106***–0.205***
 (5.30)(–1.25)(4.63)(–7.16)
Metropolitan0.394***–0.039*0.389***–0.149***
 (33.79)(–2.41)(16.51)(–4.86)
Medicalcenter0.563***0.0250.663***–0.229***
 (42.95)(1.44)(24.85)(–6.56)
Private × Metropolitan  0.0160.132***
   (0.60)(3.67)
Private × Medicalcenter  –0.147***0.362***
   (–4.88)(8.82)
Male–0.0020.044**–0.0030.041**
 (–0.23)(2.91)(–0.25)(2.75)
Age–0.005***0.008***–0.005***0.008***
 (–9.66)(11.98)(–9.75)(12.11)
Comorbidity index–0.006***–0.020***–0.007***–0.018***
 (–3.65)(–8.13)(–4.36)(–7.80)
Log(Days)0.797*** 0.799*** 
 (129.10) (130.31) 
Log(Days)2–0.000*** –0.000*** 
 (–26.63) (–26.75) 
Discharge quarter    
00.362***0.445***0.363***0.443***
 (20.81)(23.16)(20.96)(23.30)
10.128***0.257***0.128***0.257***
 (7.06)(12.38)(7.09)(12.47)
20.137***0.186***0.137***0.186***
 (7.27)(8.49)(7.30)(8.55)
30.118***0.150***0.118***0.149***
 (5.97)(6.85)(5.99)(6.87)
40.115***0.115***0.114***0.116***
 (5.83)(5.09)(5.82)(5.16)
50.078***0.110***0.079***0.108***
 (3.87)(4.76)(3.92)(4.72)
60.045*0.059**0.044*0.060**
 (2.36)(2.60)(2.31)(2.69)
Constant8.657***1.515***8.610***1.670***
 (180.63)(23.88)(169.62)(25.61)
Observations48 97148 97148 97148 971

In columns (3) and (4), we have the Private dummy interact with accreditation levels to test if the private–public difference varies across accreditation levels. The reference accreditation level is community hospitals. In column (3), the results show that the private–public difference in expenditure is 10.6% at the community hospital level, 12.2% (0.106 + 0.016 = 0.122) at the metropolitan hospital level, and –4.1% (0.106 – 0.147 = –0.041) at the medical center level. On the other hand, the results in column (4) show that the private–public difference in days of hospital stay is –20.5% at the community hospital level, –7.3% (–0.205 + 0.132 = –0.073) at metropolitan hospital level and 15.7% (–0.205 + 0.362 = 0.157) at medical center level. Overall, above results suggest that private community and metropolitan hospitals have higher expenditures but shorter stays than their public counterparts. However, this pattern is reversed at the medical center levels.

With regard to other control variables, we find that gender does not have a significant effect on hospital expenditure, but males tend to have longer hospital stays than females. The patient's age is negatively associated with hospital expenditure but is positively associated with the length of hospital stays. This result is consistent with the finding obtained from the U.S. Medicare beneficiaries in their last year of life, indicating that the aggressiveness of medical care at the end stage of life decreases with increasing age (Levinsky et al., 2001). We also find that the comorbidity index has a significantly negative effect on both hospital expenditure and length of stay. A plausible explanation is that physicians tend to adopt a less aggressive treatment strategy for patients associated with higher comorbidities after accounting for the timing to death.

For the expenditure equation in columns (1) and (3), we also control for the effect of the length of hospital stays. The result shows that the hospital expenditure in general increases with the number of hospital days but at a decreasing rate, indicating that the marginal effect of an additional hospital day on hospital expenditure decreases as the length of the hospital stay increases. At last, the estimated coefficients of the discharge quarter clearly indicate that, in general, both the expenditure and length of stay increase as an individual approaches death. For example, relative to quarter 7 (the reference quarter), the expenditure increases from 4% in quarter 6 to 36% in quarter 0, and the length of stay increases from 6% in quarter 6 to 44% in quarter 0.

In Table 3, we report the quantile regression estimation results. Table 3 yields two important findings. First, as shown in Panel A of Table 3, the private–public difference in hospital expenditures increases from 3.1% in the 25th quantile to 7.4% in the 75th quantile. This suggests that the higher the expenditure, the larger the difference between private and public hospitals. Second, to the contrary, the private–public difference in days of hospital stay decreases across quantiles, implying that, among the patients with long stays, patients at private hospitals actually have relatively shorter stays than their counterparts at public hospitals.

Table 3. Quantile regression estimation results
 (1)(2)(3)
  • Notes: Regressions include the same sets of variables as in Table 2. Estimates of other variables are not reported. Robust t statistics clustered in individuals are reported in parentheses. Clustered robust standard errors are calculated using the bootstrapping method with 100 replications.

  • *

    denotes significance at the 5 percent level;

  • **

    denotes the 1 percent level.

 Panel A: Dependent variable = Log(Expenditure)
 0.25 quantileMedian0.75 quantile
Private0.031***0.058***0.074***
(2.86)(4.22)(4.53)
Metropolitan0.325***0.372***0.437***
(25.37)(26.58)(27.68)
Medicalcenter0.496***0.559***0.614***
(34.51)(35.81)(34.54)
 Panel B: Dependent variable = Log(Days)
 0.25 quantileMedian0.75 quantile
Private0.004–0.039**–0.067***
(0.18)(–2.09)(–3.44)
Metropolitan–0.0170.006–0.007
(–0.79)(0.35)(–0.34)
Medicalcenter0.0070.058***0.071***
(0.29)(3.07)(2.89)

5 DISCUSSIONS

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

While we mainly attribute the difference in inpatient expenditures per day between end-of-life patients treated by private and public hospitals to the difference in financial incentives of hospitals, we discuss two empirical concerns below.

One may argue that some end-of-life patients may prefer intensive treatments and thus choose to go to private hospitals, provided that they do know that private hospitals tend to offer intensive treatments. If this is the case, there could be a selection bias because it could be that it is the patient who asks for intensive treatments and the Private dummy in our regressions could have just picked up the difference in unobserved patient preference. (Note that, in our regressions, the bias cannot be caused by the severity of illness or other personal characteristics that we have included as controls.)

However, we argue that such selection bias would not be significant in our case for two reasons. First, it is not common knowledge to patients that private hospitals tend to provide intensive treatment. Second, even if such self-selection does exist, whether it would cause a bias really depends on whether the physician or the patient has more power in determining treatments. If the end-of-life patient has little power in the treatment decision, our finding of the private–public difference is more likely to reflect the behavioral difference in physicians rather than patients. Given the unilateral possession of knowledge by the physician and the severity of illness of the patient, it is more likely that the physician would have the upper hand in the treatment decisions.

Another empirical concern arises with regard to how well we measure the severity of illness of the end-of-life patients. Although the Charlson comorbidity index adopted in this paper is popular and well accepted, it is still plausible that it may not pick up all the differences in illness that are relevant to expenditures and days of hospital stay. Holding the classic errors-in-variables assumption, this measurement error could attenuate our estimates. In other words, the true private–public differences could have been much larger than our estimates. If the other unmeasured differences in illness go in the same direction as the Charlson comorbidity index, then the direction of the private–public difference would still be the same. In this case, our main conclusion still holds. However, it is plausible that there are other confounding variables along a different dimension that may change the direction of the private–public difference. Therefore, a better control of patient disease severity is a possible focus for a future study.

6 CONCLUSIONS

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES

This paper empirically investigates the relationship between hospital characteristics and the inpatient expenditure for end-of-life patients. We find that end-of-life patients who are treated by private hospitals in general are associated with higher inpatient expenditures than those who are treated by public hospitals, while there is no significant difference in days of hospital stay between private and public hospitals. This finding is consistent with the difference in financial incentives between private and public hospitals in Taiwan. In particular, private hospitals are more profit-oriented.

Our study is based on a setting where the treatment for end-of-life patients is reimbursed by the fee-for-services payment system. An important avenue for future research would be to compare health care expenditures for end-of-life patients under alternative payment systems such as a prospective payment system or payments for hospice care. In addition, our study focuses on the end-of-life patients who are less likely to bridge the information gap through learning. Whether our results also apply to other types of patients with an opportunity to learn is another avenue for future research.

At last, our finding also suggests that the private–public difference varies across the accreditation level (or size) of the hospital. The real cause behind the nonlinearity of the private–public difference across the accreditation level is not clear and is worth future research endeavors.

  1. 1

    There is a difference between the terminally ill patients and the end-of-life patients. The former usually refers to patients with such illness that death is unavoidable, while the latter is more outcome-based. In our sample, about 5% of the patients actually died of injury and poisoning, so it is better to refer to our sample as end-of-life patients. We thank one anonymous reviewer for pointing this out.

  2. 2

    The global budgets are divided into four sub-budgets by type of services: (i) dental services; (ii) Chinese traditional medicine; (iii) outpatient clinic services; and (iv) the medical services provided by the hospital sector.

  3. 3

    The difference in payment schemes to physicians between private and public hospitals is common in many OECD countries, especially for specialist physicians. For details, see Wright (2007).

  4. 4

    More specifically, the one million individuals are randomly drawn from a pool of 22 717 053 beneficiaries who enrolled in the NHI program for at least one day in 2005. The entire population in Taiwan at the end of 2005 was 22 770 383. So, this sample serves as a good representation of the whole population.

  5. 5

    Throughout the rest of this paper, “hospital expenditures” refers to claimed expenditures.

  6. 6

    In fact, inpatient service claims can also be used to identify the deceased. However, the claims only record those who die in hospitals. By contrast, using NHI enrollment records should give us a representative sample of the deceased, because the NHI program provides universal coverage and is mandatory.

  7. 7

    The data are available online at http://sowf.moi.gov.tw/stat/year/elist.htm. Age groups above the age of 100 are not reported because of the small number of observations.

  8. 8

    In reality, the date on which an individual turns into “withdrawn” may be different from the date of death. However, since there are no hospital claims after one becomes “withdrawn”, and it is unlikely that an individual would pay hospital expenditures completely out-of-pocket after being “withdrawn” from the NHI; this difference does not matter practically.

  9. 9

    Since a hospital stay can last for multiple quarters and in order to keep hospital stays that started two years before death but ended during the last two years, we use the discharge date to decide whether a record should be included in our sample.

  10. 10

    If a hospital stay lasts for multiple quarters, we divide the total expenditure proportionately across quarters in order to calculate quarterly expenditures.

  11. 11

    The US dollar (USD) to New Taiwan Dollar (NTD) exchange rate is roughly 1:30.

  12. 12

    Specifically, each diagnosed medical condition (corresponding to an ICD-9 code) is assigned a number. A more severe condition is assigned a higher number. The Charlson's comorbidity index is constructed by summing the numbers for all medical conditions in a hospital admission record (Charlson et al., 1987).

  13. 13

    A person may move from one type of hospital to another within the same quarter. We thus calculate the percentage of days in a quarter in which an individual stays in a certain type of hospital. We also plot the hospital choices in terms of the average percentage of expenditures. Although not reported in the text, the patterns are almost identical to those in Figure 4.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. 1 INTRODUCTION
  4. 2 INSTITUTIONAL BACKGROUND
  5. 3 DATA AND METHOD
  6. 4 EMPIRICAL RESULTS
  7. 5 DISCUSSIONS
  8. 6 CONCLUSIONS
  9. REFERENCES