1. Kanhom Kan Shu-Fen Li Wei-Der Tsai 1

2. Objective of this study Investigate the impact of global budgeting on treatment outcome. Motivation: 1. The rapid increase in health care expenditure since the 1960s has become a great concern to policy makers in most developed countries. 2. Global Budgeting is effective in controlling medical expenditures and it was adopted in OECD countries (see Docteur and Oxley, 2004, and Wolfe and Moran, 1993). 2

3. Figure 1 Comparison of per capita NHE between OECD Countries and Taiwan Data Source ： OECD Health Data 2009 3

4. 資料來源：中央健保局 Figure 2 The Growth of NHI Revenues and Expenditures 4

5. Literature Review Most relative research focus on the provider’s behavior responses (quantity and quality) to global budgeting: A. Theoretical Prediction: 1. Phelps (1997) and Fan, et al. (1998) show that medical service providers will increase the quantity of services supplied. 2. Benstetter and Wambach’s (2006) suggest that there is likely to be a coordination failure such that medical service providers will supply a high quantity of services in order to achieve a target income and prevent bankruptcy (the so- called “treadmill effect”). 5

6. Literature Review (Cont) A. Theoretical Predictions: 3. Based on the assumption of monopolist and Cournot competitive market, Mougeot and Naegelen (2005) suggest that compared with FFS, an expenditure cap results in a lower level of service quantity and quality. 4. Feldman and Lobo’s (1997) assume that medical service providers’ utility is a function of services quantity and quality. Their model indicates that the excess demand which is prevalence under global budget systems is due to the high level of resource intensity chosen by service providers. 6

7. Literature Review (Cont) B. Empirical Evidence: 1. Rochaix (1993) show in response to an expenditure cap, physicians in Québec increase their activity levels, and provide more complex and high-priced procedures. 1. Similar results found by Hurley et al. (1997) [cases of Alberta and Scotia Nova in Canada] and Lee and Jones (2004) [case of Taiwan’s dentists]. 3. Chen et al. (2007) and Cheng, et al. (2009) show that hospitals in Taiwan are more likely to hospitalize patients under global budgeting. 7

8. The literature is silent on the issue that whether the implement of global budgeting has an impact on quality or treatment outcome. Using the data of Taiwan’s National Health Insurance claim records in 1998-2007, we examine the effect of global budgeting on treatment outcomes of AMI (acute myocardial infraction), ischemic stroke and hemorrhagic stroke patients. The treatment outcome is measured by inpatient readmission within 30 days, and the rate of 7, 14, 30, 60 and 90 days post-discharge mortality. 8

9. Background of Taiwan’s NHI National Health Insurance (NHI) was implemented in March of 1995. NHI provides patients with comprehensive care, but only requests low out of pocket expenditures. Payment system started from FFS in 1995, but changed to global budget system sector by sector. 1998/7Dental services 2000/7Chinese Medicine 2001/7Community clinics in 2001 2002/7Hospital services 2010/1DRG for hospital inpatient services. 9

10. Background of Taiwan’s NHI (cont) Under FFS, a providers is credited a certain point for each treatment procedure offered and each point is worth NT$ 1. Under global budgeting system, there is a regional level expenditure cap. Taiwan was divided into six medical regions. The point value for a given region is determined as follows 10

11. Figure 3 Medical Region in Taiwan 11

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13. Figure 4 Health Care Expenditures Funded by NHI 13

14. Figure 5 Point Value 14

15. Figure 6 Treatment Intensity by Average Number of Points per in-patients 15

16. Figure 7 Treatment of AMI Patients 16

17. Data Description Claim record form the 1998-2007 Claim File of Taiwan’s NHI. The claim record contain information both on hospitalized patients’ and hospitals’ characteristics. We use the claim data to construct three samples, including AMI (acute myocardial infraction, ICD 410), ischemic stroke ( ICD 434) and hemorrhagic stroke (ICD 430 & 431)patients. 17

18. Data Description Some criteria are imposed to exclude observations. (a) admitted to hospitals due to same ICD code in previous year; (b) hospitalized for more than 30 days; (c) admitted to a hospital, which treated less than 300 cases in the past 10 year; (d) younger than 30 or older than 80. (e) hospitalized during June, July and August of 2002. There are total 63,142, 238,810 and 99,907 patients, respectively, for AMI, Ischemic stroke and hemorrhagic stroke in 1998-2007. 18

19. YearReadmission Within 30 days discharge Sample size Mortality within 7 days after discharge Mortality within 14 days after discharge Mortality within 30 days after discharge Mortality within 60 days after discharge Mortality within 90 days after discharge Sample Size 1998.0934,779.186.197.210.222.2306,054 1999.0915,500.188.199.209.221.2336,950 2000.0926,045.169.177.189.202.2127,455 2001.0926,582.177.184.194.207.2178,171 2002.0737,167.160.167.178.192.2028,723 2003.0657,496.158.166.177.190.2009,105 2004.0718,319.158.163.173.187.19710,057 2005.0638,749.150.155.166.176.18610,487 2006.0549,120.143.149.158.171.17910,827 2007.0489,904.122.127.134.144.15111,431 Mean value of treatment outcome for AMI patients 19

20. YearReadmission Within 30 days discharge Sample size Mortality within 7 days after discharge Mortality within 14 days after discharge Mortality within 30 days after discharge Mortality within 60 days after discharge Mortality within 90 days after discharge Sample Size 1998.06920,325.054.061.070.084.09421,907 1999.06922,008.054.059.069.081.09123,684 2000.06823,916.048.054.062.075.08525,581 2001.06926,019.047.051.060.072.08127,779 2002.06727,352.043.048.056.067.07729,069 2003.05626,550.043.048.056.068.07728,247 2004.06527,947.041.045.052.064.07329,700 2005.05828,513.042.046.054.064.07330,349 2006.05930,235.040.043.051.061.06832,054 2007.05728,814.036.039.044.050.05730,352 Mean value of treatment outcome for Ischemic Stroke patients 20

21. YearReadmission Within 30 days discharge Sample size Mortality within 7 days after discharge Mortality within 14 days after discharge Mortality within 30 days after discharge Mortality within 60 days after discharge Mortality within 90 days after discharge Sample Size 1998.1164,729.276.290.306.317.3246,857 1999.1295,040.286.298.310.321.3267,340 2000.1315,593.280.290.300.311.3178,044 2001.1335,749.279.289.299.309.3168,258 2002.1355,769.276.285.293.304.3108,225 2003.1095,813.276.284.293.302.3088,280 2004.1276,141.277.283.291.300.3058,781 2005.1166,294.278.282.289.296.3028,979 2006.1136,104.270.275.281.288.2938,627 2007.1085,884.248.253.259.265.2678,044 Mean value of treatment outcome for hemorrhagic stroke patients 21

22. Empirical Strategy Linear probability model: Where subscribe d index calendar dates, h and i index, respectively, the hospital and the patient; y hid an outcome of interest; GB d global budgeting indicator; trend d year trend, X hid a vector of patient characteristics (i.e., CCI score, age, gender); η h hospital fixed effect; ε hid residuals. 22

23. Empirical Strategy (cont) To have a preliminary examination of the effect of GB on patient outcome, we first estimate a fixed effects model without GB d : We employ a nonparametric smoothing method, call local polynomial (Fan and Gijbels, 1996), to display the predicted residual Where g = 0, 1 indicating the pre- and post-global budgeting periods. 23

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27. Empirical Strategy (cont) The graphs indicate that the pattern of the time trend is the residuals before and after the implementation of GB are different. To incorporate the effect of time on the readmission and mortality of AMI and stroke patients, we estimate the following specification model 27

28. Empirical Results 28

29. Empirical Results (cont) 29

30. Empirical Results (cont) 30

31. Conclusion Our estimation results suggest that global budgeting has some effects on post-discharge readmission and mortality for for-profit hospitals. Our empirical results suggest that global budgeting leads to an improvement in treatment outcomes for for-profit hospitals. For AMI patients, GB reduces post-discharge readmission by 1.67%, and 7 and 14 days post-discharge mortality by approximately 2%. For hemorrhagic stroke patients, GB reduces the 14, 30 and 60 days post-discharge mortality by 0.0195-0.0233. 31