Efisiensi ICU Rumah Sakit Pemerintah Dengan Metode Data Envelopment Analysis (DEA)

DOI: 10.29241/jmk.v10i1.1814


Hilwa Salsabila(1), Irwan Saputra(2), Dedy Syahrizal(3), Nasrul Zaman(4), Said Usman(5),
(1) Program Studi Magister Kesehatan Masyarakat, Universitas Syiah Kuala
(2) Departemen Kesehatan Masyarakat, FK Universitas Syiah Kuala
(3) Departemen Biokimia, FK Universitas Syiah Kuala
(4) Departemen Kesehatan Masyarakat, FK Universitas Syiah Kuala
(5) Departemen Kesehatan Masyarakat, FK Universitas Syiah Kuala
Corresponding Author

Full Text

Full Text: View / Download PDF

Article Metrics

Abstract View : 125 times; PDF Download : 43 times


Efficiency defined by optimizing any resources which used to produce an output in a maximal level. Since INA-CBG was applied to hospital payment system, hospital was forced to keep the resources that used for producing process is less than the cost of National Health Assurance. Intensive Care Unit (ICU) is a ward needed advanced technology. qualified human resources, and high cost. Thus, ICU should be concern about their efficiency. Aim of this study is to measure the efficiency of the ICU at a public hospital. This is a quantitative study with a cross-sectional design. The study was conduct at type A public hospital in Banda Aceh, Aceh Province, Indonesia. We include six units of intensive care such as MICU, CSICU, ICCU, PICU, NICU, RICU. Data Envelopment Analysis (DEA) model is used to measure the efficiency at period 2022. DEA measurement performed by MAXDEA Lite 2.0. Results: ICU, CSICU, ICCU, PICU, and RICU efficiency score was one (efficient), while NICU was 0,87 (inefficient). Conclusion of the study that MICU, CSICU, ICCU, PICU, and RICU are efficient. NICU are not efficient.


DEA, Efficiency, Intensive Care Unit


Antunes, B. B. P., Bastos, L. S. L., Hamacher, S., & Bozza, F. A. (2021). Using data envelopment analysis to perform benchmarking in intensive care units. PLoS ONE, 16(11 November), 1–13. https://doi.org/10.1371/journal.pone.0260025

Bahrami, M. A., Rafiei, S., Abedi, M., & Askari, R. (2018). Data envelopment analysis for estimating efficiency of intensive care units: a case study in Iran. International Journal of Health Care Quality Assurance, 31(4), 276–282. https://doi.org/10.1108/IJHCQA-12-2016-0181

Chasbullah, R. dr. (2021). Persiapan RSUD dr. Chasbullah Abdulmadjid Kota Bekasi Sebagai RS Tipe B Pendidikan. http://www.rsudkotabekasi.net/author/admindcam/

Derienzo, C., Kohler, J. A., Lada, E., Meanor, P., & Tanaka, D. (2016). Demonstrating the relationships of length of stay, cost and clinical outcomes in a simulated NICU. Journal of Perinatology, 36(12), 1128–1131. https://doi.org/10.1038/jp.2016.128

Edwards, E. M., & Horbar, J. D. (2018). Variation in use by NICU types in the United States. Pediatrics, 142(5). https://doi.org/10.1542/peds.2018-0457

Firdaus, I., Lilihata, G., Kristianto, A., Simanjuntak, C. K., Danny, S. S., Irmalita, I., Dharma, S., Juzar, D. A., & Tobing, D. P. L. (2017). Hemodynamic Profiles as a Predictor of Mortality and Length Of Stay in ICCU: Insight from Registry of Acute and Intensive Cardiovascular Care Outcome. Indonesian Journal of Cardiology, 38(3), 160–167. https://doi.org/10.30701/ijc.v38i3.779

Fu, M., Song, W., Yu, G., Yu, Y., & Yang, Q. (2023). Risk Factors For Length Of NICU Stay Of Newborns: A Systematic Review. Frontiers in Pediatrics, 11(March). https://doi.org/10.3389/fped.2023.1121406

Irwandy, & Sjaaf, A. C. (2018). Dampak Kebijakan Jaminan Kesehatan Nasional terhadap Efisiensi Rumah Sakit : Studi Kasus di Provinsi Sulawesi Selatan The Effect of Health Insurance National Reform on Hospital Efficiency in Indonesia : The Case Study of South Sulawesi Province. Media Kesehatan Masyarakat Indonesia, 14(4), 360–367.

Keels, E. L., & Goldsmith, J. P. (2019). Neonatal provider workforce. Pediatrics, 144(6). https://doi.org/10.1542/peds.2019-3147

Lee, H. C., Bennett, M. V., Schulman, J., Gould, J. B., & Profit, J. (2016). Estimating Length of Stay by Patient Type in the Neonatal Intensive Care Unit. American Journal of Perinatology, 33(8), 751–757. https://doi.org/10.1055/s-0036-1572433

Maienza, F., Ewan, T., Mucia, M., Rizzo, S., Sireci, F., & Gargano, R. (2017). Standards For Levels Of Neonatal Care. Neonatal Intensive Care Units (NICUs): Clinical and Patient Perspectives, Levels of Care and Emerging Challenges, 151(6), 27–40.

Mufarrihah, Andayani, T. M., & Suparniati, E. (2016). Biaya Perawatan Pasien Neonatal Jkn Rawat Inap Di Rumah Sakit Umum Pusat. Jurnal Manajemen Dan Pelayanan Farmasi, 6(2), 101–114.

Shroff, A., Kupfer, J., Gilchrist, I. C., Caputo, R., Speiser, B., Bertrand, O. F., Pancholy, S. B., & Rao, S. V. (2016). Same-day discharge after percutaneous coronary intervention: Current perspectives and strategies for implementation. JAMA Cardiology, 1(2), 216–223. https://doi.org/10.1001/jamacardio.2016.0148

Singh, H., Cho, S. J., Gupta, S., Kaur, R., Sunidhi, S., Saluja, S., Pandey, A. K., Bennett, M. V., Lee, H. C., Das, R., Palma, J., McAdams, R. M., Kaur, A., Yadav, G., & Sun, Y. (2021). Designing A Bed-Side System For Predicting Length Of Stay In A Neonatal Intensive Care Unit. Scientific Reports, 11(1), 1–13. https://doi.org/10.1038/s41598-021-82957-z

Smith, J. R., Donze, A., Wolf, M., Smyser, C. D., Mathur, A., & Proctor, E. K. (2015). Ensuring quality in the NICU: Translating research into appropriate clinical care. Journal of Perinatal and Neonatal Nursing, 29(3), 255–261. https://doi.org/10.1097/JPN.0000000000000122

Wortel, S. A., de Keizer, N. F., Abu-Hanna, A., Dongelmans, D. A., & Bakhshi-Raiez, F. (2021). Number of intensivists per bed is associated with efficiency of Dutch intensive care units. Journal of Critical Care, 62, 223–229. https://doi.org/10.1016/j.jcrc.2020.12.008

Zampieri, F. G., Salluh, J. I. F., Azevedo, L. C. P., Kahn, J. M., Damiani, L. P., Borges, L. P., Viana, W. N., Costa, R., Corrêa, T. D., Araya, D. E. S., Maia, M. O., Ferez, M. A., Carvalho, A. G. R., Knibel, M. F., Melo, U. O., Santino, M. S., Lisboa, T., Caser, E. B., Besen, B. A. M. P., … Soares, M. (2019). ICU staffing feature phenotypes and their relationship with patients’ outcomes: an unsupervised machine learning analysis. Intensive Care Medicine, 45(11), 1599–1607. https://doi.org/10.1007/s00134-019-05790-z

Zheng, W., Sun, H., Zhang, P., Zhou, G., Jin, Q., & Lu, X. (2018). A four-stage DEA-based efficiency evaluation of public hospitals in China after the implementation of new medical reforms. PLoS ONE, 13(10), 1–17. https://doi.org/10.1371/journal.pone.0203780


  • There are currently no refbacks.

Copyright (c) 2024 Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.