Assessing the technical efficiency of DOH-retained hospitals from 2009 to 2019
The recent health crises in the Philippines have highlighted the need for an efficient healthcare system, particularly in hospitals, as most Filipinos go directly to such institutions to receive treatment. However, studies on hospital efficiency in developing countries, including the Philippines, ha...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_econ/48 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1049&context=etdb_econ |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | The recent health crises in the Philippines have highlighted the need for an efficient healthcare system, particularly in hospitals, as most Filipinos go directly to such institutions to receive treatment. However, studies on hospital efficiency in developing countries, including the Philippines, have been insufficient. Existing literature primarily focuses on quantitative output measures rather than qualitative measures of health improvement (e.g. mortality rates and infection rates), and there are limited studies on efficiency measurement in the country. For this study, the researchers analyze the technical efficiency of the DOH-retained hospitals from the years 2009 to 2019. In particular, the Data Envelopment Analysis (DEA), paired with the ratio model by You and Yan (2011), estimates the efficiency scores of the hospitals. After which, Tobit Regression determines which specific variables significantly affect the technical efficiency scores. Inputs are the number of implementing beds, medical staff, and non-medical staff, while outputs are the number of net deaths and discharges alive. The results show that, although some hospitals reach technical efficiency for certain years, most DOH-Retained Hospitals are still inefficient. Furthermore, the Tobit Regression results show that the variables that significantly affect the technical efficiency scores are the number of implementing beds, patients discharged alive, and deaths for level 1 hospitals, and only the number of medical and non-medical staff for level 3 hospitals.
JEL Keywords: Public Health, Technical Efficiency, DEA |
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