MODEL BIG DATA UNTUK OPTIMALISASI KOMUNIKASI PASIEN DALAM PROSES INFORMED CONSENT
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This study examines the application of Big Data models to optimize patient communication in the informed consent process within healthcare settings. Effective informed consent is a cornerstone of patient centered care, yet its implementation is frequently hampered by communication gaps, information overload, and insufficient personalization. This research proposes a Big Data-based framework that integrates structured and unstructured health data including electronic medical records (EMR), patient interaction logs, and demographic information to generate personalized communication strategies for informed consent. Using a mixed-methods approach combining Systematic Literature Review (SLR) and prototype system design, the study identifies key data variables influencing patient comprehension and consent quality. The proposed model leverages machine learning algorithms (Random Forest, NLP) to predict patient health literacy levels and tailor information delivery accordingly. Results from synthetic dataset simulations indicate that the Big Data framework can significantly improve patient understanding (84.6%), reduce consent-related errors by 41.7%, and enhance overall healthcare communication efficiency. The prototype system built on a Lambda Architecture with real-time personalization modules demonstrates high predictive accuracy (87.3%) and F1-score (86.8%). This study contributes to the intersection of health informatics and patient rights, offering a scalable, data-driven solution applicable across various hospital settings.
Keywords: Big Data, informed consent, patient communication, electronic medical records, machine learningTeks Lengkap:
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Abu‐Jeyyab, M., Alrosan, S., & Alkhawaldeh, I. M. (2023). Harnessing Large Language Models in Medical Research and Scientific Writing: A Closer Look to The Future. High Yield Medical Reviews, 1(2). https://doi.org/10.59707/hymrfbya5348
Adeghe, E. P., Okolo, C. A., & Ojeyinka, O. T. (2024). The role of big data in healthcare: A review of implications for patient outcomes and treatment personalization [Review of The role of big data in healthcare: A review of implications for patient outcomes and treatment personalization]. World Journal of Biology Pharmacy and Health Sciences, 17(3), 198. https://doi.org/10.30574/wjbphs.2024.17.3.0133
Adegoke, B. O., Odugbose, T., & Adeyemi, C. (2024). Harnessing big data for tailored health communication: A systematic review of impact and techniques [Review of Harnessing big data for tailored health communication: A systematic review of impact and techniques]. International Journal of Biology and Pharmacy Research Updates, 3(2), 1. https://doi.org/10.53430/ijbpru.2024.3.2.0024
Adeniyi, A. O., Arowoogun, J. O., Okolo, C. A., Chidi, R., & Babawarun, O. (2024). Ethical considerations in healthcare IT: A review of data privacy and patient consent issues [Review of Ethical considerations in healthcare IT: A review of data privacy and patient consent issues]. World Journal of Advanced Research and Reviews, 21(2), 1660. GSC Online Press. https://doi.org/10.30574/wjarr.2024.21.2.0593
Akter, M. S., Sultana, N., Khan, M. A., & Mohiuddin, M. M. (2023). BUSINESS INTELLIGENCE-DRIVEN HEALTHCARE: INTEGRATING BIG DATA AND MACHINE LEARNING FOR STRATEGIC COST REDUCTION AND QUALITY CARE DELIVERY. American Journal of Interdisciplinary Studies, 4(2), 1. https://doi.org/10.63125/crv1xp27
Alkalbani, A. M., Alrawahi, A. S., Salah, A., Haghighi, V., Zhang, Y., Alkindi, S., & Sheng, Q. Z. (2024). A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions [Review of A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions]. Research Square (Research Square). Research Square (United States). https://doi.org/10.21203/rs.3.rs-5128451/v1
Allen, J., Earp, B. D., Koplin, J., & Wilkinson, D. (2023). Consent GPT: Is It Ethical to Delegate Procedural Consent to Conversational AI? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4520613
Allen, J. W., Earp, B. D., Koplin, J., & Wilkinson, D. (2023). Consent-GPT: is it ethical to delegate procedural consent to conversational AI? Journal of Medical Ethics, 50(2), 77. https://doi.org/10.1136/jme-2023-109347
Allen, J. W., Hannikainen, I. R., Savulescu, J., Wilkinson, D., & Earp, B. D. (2025). Is Consent-GPT valid? Public attitudes to generative AI use in surgical consent. AI & Society. https://doi.org/10.1007/s00146-025-02644-9
Allen, J. W., Levy, N., & Wilkinson, D. (2025). Empowering Patient Autonomy: The Role of Large Language Models (LLMs) in Scaffolding Informed Consent in Medical Practice. Bioethics, 40(2), 183. https://doi.org/10.1111/bioe.70030
Breuer, T., Frihat, S., Fuhr, N., Lewandowski, D., Schaer, P., & Schenkel, R. (2025). Large Language Models for Information Retrieval: Challenges and Chances. Datenbank-Spektrum. https://doi.org/10.1007/s13222-025-00503-x
Busch, F., Hoffmann, L., Rueger, C., Dijk, E. H. C. van, Kader, R., Ortiz‐Prado, E., Makowski, M. R., Saba, L., Hadamitzky, M., Kather, J. N., Truhn, D., Cuocolo, R., Adams, L. C., & Bressem, K. K. (2024). Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges. medRxiv. https://doi.org/10.1101/2024.03.04.24303733
Busch, F., Hoffmann, L., Rueger, C., Dijk, E. H. C. van, Kader, R., Ortiz‐Prado, E., Makowski, M. R., Saba, L., Hadamitzky, M., Kather, J. N., Truhn, D., Cuocolo, R., Adams, L. C., & Bressem, K. K. (2025). Current applications and challenges in large language models for patient care: a systematic review. Communications Medicine, 5(1), 26. https://doi.org/10.1038/s43856-024-00717-2
Comeau, D. S., Bitterman, D. S., & Gallifant, J. (2025). Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability. Npj Digital Medicine, 8(1), 42. https://doi.org/10.1038/s41746-025-01443-2
Ding, Q., Ding, D., Wang, Y., Guan, C., & Ding, B. (2023). Unraveling the landscape of large language models: a systematic review and future perspectives [Review of Unraveling the landscape of large language models: a systematic review and future perspectives]. Journal of Electronic Business & Digital Economics, 3(1), 3. https://doi.org/10.1108/jebde-08-2023-0015
Dömbekci, H. A., & Meral, K. K. (2025). Ethics and Informed Consent in Health Communication. DergiPark (Istanbul University). https://dergipark.org.tr/en/pub/toguesy/issue/93030/1682637
Fadila, A. N., Hasibuan, A. A., Hasibuan, H. M., & Khairiyyahni, S. (2025). OPTIMALISASI PRAKTIK INFORMED CONSENT: MEMBANGUN KOMUNIKASI ETIS ANTARA TENAGA KESEHATAN DAN PASIEN. PENDALAS Jurnal Penelitian Tindakan Kelas Dan Pengabdian Masyarakat, 4(3), 172. https://doi.org/10.47006/pendalas.v4i3.502
Gottlieb, S., & Silvis, L. (2023). How to Safely Integrate Large Language Models Into Health Care. JAMA Health Forum, 4(9). https://doi.org/10.1001/jamahealthforum.2023.3909
Gupta, G., Singh, A., Manikandan, S. V., & Ehtesham, A. (2024). Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.06712
Hajijama, S., Juneja, D., & Nasa, P. (2024). Large Language Model in Critical Care Medicine: Opportunities and Challenges. Indian Journal of Critical Care Medicine, 28(6), 523. https://doi.org/10.5005/jp-journals-10071-24743
He, K., Mao, R., Lin, Q., Ruan, Y., Xiang, L., Feng, M., & Cambria, E. (2024). A Survey of Large Language Models for Healthcare: From Data, Technology, and Applications to Accountability and Ethics. https://doi.org/10.2139/ssrn.4809363
Izquierdo, I. G., & Albi, A. B. (2024). Communication in healthcare contexts: Multilingual technological resources to improve the communicative effectiveness of the Informed Consent. Cadernos de Tradução, 44, 1. https://doi.org/10.5007/2175-7968.2024.e95247
Khalid, Amna, Khalid, A., & Khalid, U. (2024). The Role of Language Models in Modern Healthcare: A Comprehensive Review [Review of The Role of Language Models in Modern Healthcare: A Comprehensive Review]. arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2409.16860
Knott, M., Krebs, M., & Kerscher, A. (2026). Large language models in healthcare quality management: a European perspective on process automation and compliance. Frontiers in Digital Health, 8. https://doi.org/10.3389/fdgth.2026.1761641
Kusumastuti, W., Yunila, E., & Arso, S. P. (2025). Legal Review of Informed Consent: Role and Implementation Challenges in Improving Hospital Health Services in Indonesia. Springer Link (Chiba Institute of Technology). https://doi.org/10.1051/bioconf/202519300027/pdf
Lee, H. S., Song, S., Park, C., Seo, J., Kim, W. H., Kim, J.-I., Kim, S., Han, K., & Lee, Y. H. (2025). The ethics of simplification: balancing patient autonomy, comprehension, and accuracy in AI-generated radiology reports. BMC Medical Ethics, 26(1), 136. https://doi.org/10.1186/s12910-025-01285-3
Levita, B., Eminovic, S., Lüdemann, W. M., Schnapauff, D., Schmidt, R., Haack, A.-M., Dell’Orco, A., Nawabi, J., & Penzkofer, T. (2025). Large language models for patient education prior to interventional radiology procedures: a comparative study. CVIR Endovascular, 8(1). https://doi.org/10.1186/s42155-025-00609-z
Li, L., Zhou, J., Gao, Z., Hua, W., Fan, L., Yu, H., Hagen, L., Zhang, Y., Assimes, T. L., Hemphill, L., & Ma, S. (2024). A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs) [Review of A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)]. arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2405.03066
McLean, A. L., Wu, Y., McLean, A. C. L., & Hristidis, V. (2024). Large language models as decision aids in neuro-oncology: a review of shared decision-making applications [Review of Large language models as decision aids in neuro-oncology: a review of shared decision-making applications]. Journal of Cancer Research and Clinical Oncology, 150(3). Springer Science+Business Media. https://doi.org/10.1007/s00432-024-05673-x
Moodley, K. (2023). Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities. South African Medical Journal, 114(1), 22. https://doi.org/10.7196/samj.2024.v114i1.1631
Omutoko, L., Chingarande, G. R., Botes, M., Moyana, F., Singh, S., Jaoko, W., Sevene, E., Mtande, T., Edwin, A. K., Matandika, L., Burgess, T., & Moodley, K. (2024). Understanding and processing informed consent during data-intensive health research in sub-Saharan Africa: challenges and opportunities from a multilingual perspective. Research Ethics, 21(3), 503. https://doi.org/10.1177/17470161241274809
Ong, J. C. L., Chang, Y., Wasswa, W., Butte, A. J., Shah, N. H., Chew, L., Liu, N., Doshi‐Velez, F., Lü, W., Savulescu, J., & Ting, D. S. W. (2024). Ethical and regulatory challenges of large language models in medicine. The Lancet Digital Health, 6(6). https://doi.org/10.1016/s2589-7500(24)00061-x
Park, Y., Pillai, A., Deng, J., Guo, E., Gupta, M., Paget, M., & Naugler, C. (2023). Assessing the research landscape and clinical utility of large language models: A scoping review [Review of Assessing the research landscape and clinical utility of large language models: A scoping review]. Research Square (Research Square). Research Square (United States). https://doi.org/10.21203/rs.3.rs-3472000/v1
Ranttila, P., Sahebi, G., Kontio, E., & Salmi, J. (2024). Medical AI in the EU: Regulatory Considerations and Future Outlook. In Artificial intelligence. IntechOpen. https://doi.org/10.5772/intechopen.1007443
Rudra, P., Balke, W., Kacprowski, T., Ursin, F., & Salloch, S. (2025). Large language models for surgical informed consent: an ethical perspective on simulated empathy. Journal of Medical Ethics, 52(2), 85. https://doi.org/10.1136/jme-2024-110652
Saenz, A., McCoy, T. P., Mantha, A. B., Martin, R. F., Rizzoni, D., Adair, D., Heaney, D., Sisodia, R., Park, L., Forsberg, R., Tuffy, G., Murphy, S. N., Dreyer, K. J., Jones, M. W., Cosier, H. J., Logan, M., Bundela, Y., Centi, A., Ting, D. S. ‐K., … Mishuris, R. G. (2024). Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions. Npj Digital Medicine, 7(1), 348. https://doi.org/10.1038/s41746-024-01300-8
Santos, L., & Bublitz, F. M. (2024). Data quality and Big Data in the health industry: a scoping review protocol [Review of Data quality and Big Data in the health industry: a scoping review protocol]. medRxiv (Cold Spring Harbor Laboratory). Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.10.18.24315741
Schulz, A., & Bohnet-Joschko, S. (2023). Expanding the horizon of patient informed consent: beyond the written word . Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-3258323/v1
Shanmugam, D., Agrawal, M., Movva, R., Chen, I. Y., Ghassemi, M., & Pierson, E. (2024). Generative AI in Medicine. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.10337
Sharaf, S., & Anoop, V. S. (2023). An Analysis on Large Language Models in Healthcare: A Case Study of BioBERT. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2310.07282
Shi, Q., Luzuriaga, K., Allison, J. J., Oztekin, A., Faro, J. M., Lee, J., Hafer, N., McManus, M., & Zai, A. (2025). Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study. JMIR Medical Informatics, 13. https://doi.org/10.2196/68139
Sugamiasa, I. W., Kadang, Y., Rahman, A., & Tumewu, Y. (2023). HUBUNGAN TINGKAT PENGETAHUAN DENGAN TINGKAT KEPUASAN PEMBERIAN INFORMED CONSENT PADA PASIEN PRE OPERASI DI UPT. RUMAH SAKIT UMUM DAERAH BANGGAI LAUT. Jurnal Kesehatan Tambusai, 4(4), 6964. https://doi.org/10.31004/jkt.v4i4.20879
Susanto, D. P., Pratama, B. S., & Hariyanto, T. (2017). Analisis Faktor -Faktor yang Mempengaruhi Pemahaman Pasien terhadap Informed Consent di Rumah Sakit. Jurnal Manajemen Kesehatan Indonesia, 5(2), 73. https://doi.org/10.14710/jmki.5.2.2017.73-81
Susilo, L. E., Suryono, A., & Makbul, A. (2025). Informed Consent dalam Perspektif Perlindungan Hukum Pasien dalam Transaksi Terapeutik. Jurnal Ilmu Hukum Humaniora Dan Politik, 6(1), 588. https://doi.org/10.38035/jihhp.v6i1.6373
Uçar, A., & İlkılıç, İ. (2019). Büyük Verinin Sağlık Hizmetlerinde Kullanımında Epistemolojik ve Etik Sorunlar. Sağlık Bilimlerinde İleri Araştırmalar Dergisi / Journal of Advanced Research in Health Sciences, 2(2). https://doi.org/10.26650/jarhs2019-616389
Wang, C., Li, M., He, J., Wang, Z., Darzi, E., Chen, Z., Ye, J., Li, T., Su, Y., Ke, J., Qu, K., Li, S., Yu, Y., Lió, P., Wang, T., Wang, Y. G., & Shen, Y. (2024). A Survey for Large Language Models in Biomedicine. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2409.00133
Wen, B., Norel, R., Liu, J., Stappenbeck, T. S., Zulkernine, F., & Chen, H. (2024a). Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.13659
Wen, B., Norel, R., Liu, J., Stappenbeck, T. S., Zulkernine, F., & Chen, H. (2024b). Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health. 104. https://doi.org/10.1109/icdh62654.2024.00027
DOI: https://doi.org/10.33559/eoj.v8i9.3873
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