THE IMPACT OF GLOBAL BUDGETING ON TREATMENT OUTCOMES. Kanhom Kan Shu -Fen Li Wei- Der Tsai. Objective of this study Investigate the impact of global budgeting on treatment outcome. Motivation:
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1. The rapid increase in health care expenditure since the 1960s has become a great concern to policy makers in most developed countries.
Figure 1 Comparison of per capita NHE between OECD Countries and Taiwan
A. Theoretical Prediction:
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”).
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.
B. Empirical Evidence:
3. Chen et al. (2007) and Cheng, et al. (2009) show that hospitals in Taiwan are more likely to hospitalize patients under global budgeting.
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.
1998/7 Dental services
2000/7 Chinese Medicine
2001/7 Community clinics in 2001
2002/7 Hospital services
2010/1 DRG for hospital inpatient services.
Figure 6 Treatment Intensity by Average Number of Points per in-patients
There are total 63,142, 238,810 and 99,907 patients, respectively, for AMI, Ischemic stroke and hemorrhagic stroke in 1998-2007.
Where subscribe d index calendar dates, h andiindex, respectively, the hospital and the patient;
yhid an outcome of interest;
GBd global budgeting indicator;
trendd year trend,
Xhid a vector of patient characteristics
(i.e., CCI score, age, gender);
ηh hospital fixed effect;
Where g = 0, 1 indicating the pre- and post-global budgeting periods.