Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals
The continuous operation and the specialized conditions needed for safely delivering healthcare services make hospitals among the most expensive buildings. Several studies in different countries have investigated the potential role and contribution of macroscopic indices of hospitals in total energy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/1/381 |
| _version_ | 1797499089575739392 |
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| author | Paraskevi N. Zaza Anastasios Sepetis Pantelis G. Bagos |
| author_facet | Paraskevi N. Zaza Anastasios Sepetis Pantelis G. Bagos |
| author_sort | Paraskevi N. Zaza |
| collection | DOAJ |
| description | The continuous operation and the specialized conditions needed for safely delivering healthcare services make hospitals among the most expensive buildings. Several studies in different countries have investigated the potential role and contribution of macroscopic indices of hospitals in total energy requirements. In this work, we tried to investigate the energy requirements of Greek hospitals in terms of cost. We collected data from all public hospitals in Greece over a 2 year period (2018–2019) and evaluated the contribution of various factors in the total energy cost. The data revealed large variability by region and by hospital, even regarding structures of the same category and size. The analysis also showed that structural and operational data of each hospital differently influence the hospitals’ energy requirements. Using regression methods, we developed two models for calculating annual energy costs. One only contains hospital structural data (number of beds, type of hospital, number of employees, and the non/use of alternative energy sources such as natural gas), and it reached an R² of 0.84. The second model contains not only structural but also operational data from each hospital (number of the internal patients, number of surgeries and number of medical imaging tests), and it reached an R² of 0.87. The former model is easier to compute since it only relies on data that can be easily gathered, but the latter has slightly better performance. These tools can help the Ministry of Health and hospitals’ management to identify the factors that contribute to the energy cost in order to plan targeted interventions, be well-prepared regarding budgeting, and be able to progressively measure, monitor, and improve the environmental footprint of hospitals by investing in renewable energy resources. |
| first_indexed | 2024-03-10T03:42:27Z |
| format | Article |
| id | doaj.art-6171984891474362a80deb9c31b8f4f4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T03:42:27Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6171984891474362a80deb9c31b8f4f42023-11-23T11:29:46ZengMDPI AGEnergies1996-10732022-01-0115138110.3390/en15010381Prediction and Optimization of the Cost of Energy Resources in Greek Public HospitalsParaskevi N. Zaza0Anastasios Sepetis1Pantelis G. Bagos2Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, GreeceBusiness Administration Department, University of West Attica, 12244 Egaleo, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, GreeceThe continuous operation and the specialized conditions needed for safely delivering healthcare services make hospitals among the most expensive buildings. Several studies in different countries have investigated the potential role and contribution of macroscopic indices of hospitals in total energy requirements. In this work, we tried to investigate the energy requirements of Greek hospitals in terms of cost. We collected data from all public hospitals in Greece over a 2 year period (2018–2019) and evaluated the contribution of various factors in the total energy cost. The data revealed large variability by region and by hospital, even regarding structures of the same category and size. The analysis also showed that structural and operational data of each hospital differently influence the hospitals’ energy requirements. Using regression methods, we developed two models for calculating annual energy costs. One only contains hospital structural data (number of beds, type of hospital, number of employees, and the non/use of alternative energy sources such as natural gas), and it reached an R² of 0.84. The second model contains not only structural but also operational data from each hospital (number of the internal patients, number of surgeries and number of medical imaging tests), and it reached an R² of 0.87. The former model is easier to compute since it only relies on data that can be easily gathered, but the latter has slightly better performance. These tools can help the Ministry of Health and hospitals’ management to identify the factors that contribute to the energy cost in order to plan targeted interventions, be well-prepared regarding budgeting, and be able to progressively measure, monitor, and improve the environmental footprint of hospitals by investing in renewable energy resources.https://www.mdpi.com/1996-1073/15/1/381hospitalsenergy costsustainable developmentregression analysislongitudinal data |
| spellingShingle | Paraskevi N. Zaza Anastasios Sepetis Pantelis G. Bagos Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals Energies hospitals energy cost sustainable development regression analysis longitudinal data |
| title | Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals |
| title_full | Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals |
| title_fullStr | Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals |
| title_full_unstemmed | Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals |
| title_short | Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals |
| title_sort | prediction and optimization of the cost of energy resources in greek public hospitals |
| topic | hospitals energy cost sustainable development regression analysis longitudinal data |
| url | https://www.mdpi.com/1996-1073/15/1/381 |
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