THE LEGAL PROTECTION OF HOSPITALS IN THE CASE OF NON-BPJS PATIENTS WHO ABSCOND AS A FORM OF BAD FAITH DUE TO THEIR INABILITY TO PAY
DOI:
https://doi.org/10.46799/ijssr.v4i11.1117Keywords:
Artificial Intelligence, Hospital, Legal Liability, Legal Protection, PatientAbstract
Hospitals often encounter financial difficulties when non-BPJS patients evade payment due to financial incapacity, with some institutions unfairly shifting the financial burden onto doctors. This situation raises serious legal, ethical, and employment concerns, especially in light of Law No. 17 of 2023 on Health, which mandates institutional responsibility for patient management. Meanwhile, Artificial Intelligence (AI) offers promising solutions for predicting and mitigating financial risks by identifying high-risk patients, though it introduces new challenges regarding data privacy, accountability, and liability. This study aims to explore the legal protections available to doctors when patients abscond without paying, and to propose a legal framework for integrating AI in healthcare to prevent such incidents. Using a normative legal approach, the research analyzes key provisions of Law No. 17/2023, employment regulations, and relevant case studies involving financial disputes between doctors and hospitals. The results show that imposing financial liability on doctors not only breaches employment principles but also contradicts healthcare regulations. Furthermore, AI can improve financial risk management by helping hospitals predict and prevent non-payment cases, though its implementation requires clear legal guidelines to avoid unintended consequences for medical staff. In conclusion, hospitals must bear financial responsibility for unpaid patient bills to protect doctors' legal rights. Additionally, a comprehensive regulatory framework for AI is essential to ensure that the technology is implemented fairly, safeguarding both healthcare professionals and patient interests.
References
Aji, S. B. (2024). Wanprestasi Pasien Terhadap General Consent Di Rumah Sakit Umum Daerah Kemayoran Jakarta Pusat Berdasarkan Undang-Undang Nomor 44 Tahun 2009 Tentang Rumah Sakit. Constitutum: Jurnal Ilmiah Hukum, 2(2).
Al-Shaqi, R., Mourshed, M., & Rezgui, Y. (2016). Progress in ambient assisted systems for independent living by the elderly. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-2272-8
Amato, F., Bianchi, S., Comai, S., Crovari, P., Pasquarelli, M. G. G., Imtiaz, A., Masciadri, A., Toldo, M., & Yuyar, E. (2018). CLONE. Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good, 255–260. https://doi.org/10.1145/3284869.3284906
Ambagtsheer, R. C., Shafiabady, N., Dent, E., Seiboth, C., & Beilby, J. (2020). The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics, 136. https://doi.org/10.1016/j.ijmedinf.2020.104094
ANA, C. for E. and H. R. (2022). The Ethical Use of Artificial Intelligence in Nursing Practice. American Nurses Association.
Beam, A. L., & Kohane, I. S. (2018). Big Data and Machine Learning in Health Care. JAMA, 319(13), 1317. https://doi.org/10.1001/jama.2017.18391
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2020). Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review. JMIR Nursing, 3(1), e23939. https://doi.org/10.2196/23939
Cho, I., Park, I., Kim, E., Lee, E., & Bates, D. W. (2013). Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model. International Journal of Medical Informatics, 82(11), 1059–1067. https://doi.org/10.1016/j.ijmedinf.2013.06.012
European Commission. (2018). High-Level Expert Group on Artificial Intelligence. AI HLEG.
Gerich, H. von, Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L. M. (2022). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 127. https://doi.org/10.1016/j.ijnurstu.2021.104153
Hernanto, T. S., & Amelia, T. (2024). Omnibus Law Penegak Hukum Di Indonesia. PT Kaya Ilmu Bermanfaat.
HS, S., & Nurbani, E. S. (2022). Penerapan Teori Hukum Pada Penelitian Tesis dan Disertasi. Rajawalil Pers.
Karimian, G., Petelos, E., & Evers, S. M. A. A. (2022). The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI and Ethics, 2(4), 539–551. https://doi.org/10.1007/s43681-021-00131-7
Kitula, A. A., Lamberg, K. T., Tyrvainen, P., & Silvennoinen, M. (2018). Developing Solutions for Healthcare - Deploying Artificial Intelligence to an Evolving Target. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, 1637–1642. https://doi.org/10.1109/CSCI.2017.285
Kluge, E. H. W. (2020). Artificial intelligence in healthcare: Ethical considerations. Healthcare Management Forum, 33(1), 47–49. https://doi.org/10.1177/0840470419850438
Krishnan, R. H., & Pugazhenthi, S. (2014). Mobility assistive devices and self-transfer robotic systems for elderly, a review. Intelligent Service Robotics, 7(1), 37–49. https://doi.org/10.1007/s11370-013-0142-6
Lee, D. H., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 1–18. https://doi.org/10.3390/ijerph18010271
Matheny, M. E., Whicher, D., & Thadaney Israni, S. (2020). Artificial Intelligence in Health Care. JAMA, 323(6), 509. https://doi.org/10.1001/jama.2019.21579
McCarthy, M. K. (2019). Artificial Intelligence in Health: Ethical Considerations for Research and Practice. HIMSS.
McGrow K. (2019). Artificial intelligence: essentials for nursing. Nursing, 49(9), 46–49.
Mulyono, M., Setyowati, D., & Kamarudin, K. (2019). Tanggung Jawab Hukum Atas Pasien Gangguan Jiwa Yang Melarikan Diri Dari Ruang Rawat Inap Rumah Sakit. Al-Adalah: Jurnal Hukum Dan Politik Islam, 3(1), 56–65. https://doi.org/10.35673/ajmpi.v3i1.191
Parthasarathy, R., Steinbach, T., Knight, J., & Knight, L. (2018). Framework to Enhance Nurses’ Use of EMR. Hospital Topics, 96(3), 85–93. https://doi.org/10.1080/00185868.2018.1488545
Rahardjo, S. (2014). Ilmu Hukum. PT Citra Aditya Bakti.
Rigby, M. J. (2019). Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA Journal of Ethics, 21(2), E121-124. https://doi.org/10.1001/amajethics.2019.121
Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30–39. https://doi.org/10.1097/01.NUMA.0000578988.56622.21
Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021). Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. Journal of Medical Internet Research, 23(11). https://doi.org/10.2196/26522
Stokes, F., & Palmer, A. (2020). Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans. Nursing Philosophy, 21(4). https://doi.org/10.1111/nup.12306
Susatya, D. H. (2023). Arbitrase Sebagai Alternatif Penyelesaian Sengketa Medis. PT Kaya Ilmu Bermanfaat.
Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q.-H., Ho, M.-T., Vuong, T.-T., La, V.-P., Ho, M.-T., Nghiem, K.-C. P., Nguyen, H. L. T., Latkin, C. A., Tam, W. W. S., Cheung, N.-M., Nguyen, H.-K. T., Ho, C. S. H., & Ho, R. C. M. (2019). Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. Journal of Clinical Medicine, 8(3), 360. https://doi.org/10.3390/jcm8030360
Undang-Undang Kesehatan, Pub. L. No. 17 (2023).
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Global Health, 3(4), e000798. https://doi.org/10.1136/bmjgh-2018-000798
Ye, C., Li, J., Hao, S., Liu, M., Jin, H., Zheng, L., Xia, M., Jin, B., Zhu, C., Alfreds, S. T., Stearns, F., Kanov, L., Sylvester, K. G., Widen, E., McElhinney, D., & Ling, X. B. (2020). Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International Journal of Medical Informatics, 137. https://doi.org/10.1016/j.ijmedinf.2020.104105
Zhou, Y., Li, Y., & Li, Z. (2021). Interdisciplinary collaboration between nursing and engineering in health care: A scoping review. International Journal of Nursing Studies, 117. https://doi.org/10.1016/j.ijnurstu.2021.103900
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Wiweka Wiweka
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.