| Title |
Hospital efficiency under prospective reimbursement schemes: an empirical assessment for the case of Germany
|
|---|---|
| Published in |
HEPAC Health Economics in Prevention and Care, March 2013
|
| DOI | 10.1007/s10198-013-0464-5 |
| Pubmed ID | |
| Authors |
Helmut Herwartz, Christoph Strumann |
| Abstract |
The introduction of prospective hospital reimbursement based on diagnosis-related groups (DRG) has been a conspicuous attempt to decelerate the steady increase of hospital expenditures in the German health sector. In this work, the effect of the financial reform on hospital efficiency is subjected to empirical testing by means of two complementary testing approaches. On the one hand, we apply a two-stage procedure based on non-parametric efficiency measurement. On the other hand, a stochastic frontier model is employed that allows a one-step estimation of both production frontier parameters and inefficiency effects. To identify efficiency gains as a consequence of changes in the hospital incentive structure, we account for technological progress, spatial dependence and hospital heterogeneity. The results of both approaches do not reveal any increase in overall efficiency after the DRG reform. In contrast, a significant decline in overall hospital efficiency over time is observed. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 2% |
| Australia | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 94 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 19 | 19% |
| Researcher | 16 | 16% |
| Student > Ph. D. Student | 14 | 14% |
| Student > Doctoral Student | 7 | 7% |
| Lecturer | 5 | 5% |
| Other | 15 | 15% |
| Unknown | 22 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Economics, Econometrics and Finance | 21 | 21% |
| Business, Management and Accounting | 13 | 13% |
| Medicine and Dentistry | 12 | 12% |
| Social Sciences | 12 | 12% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 8 | 8% |
| Unknown | 30 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 April 2014.
All research outputs
#16,047,334
of 25,374,647 outputs
Outputs from HEPAC Health Economics in Prevention and Care
#856
of 1,303 outputs
Outputs of similar age
#123,827
of 209,901 outputs
Outputs of similar age from HEPAC Health Economics in Prevention and Care
#18
of 25 outputs
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