ISSN : 2583-2646

Integrating Artificial Intelligence and Machine Learning into Healthcare ERP Systems: A Framework for Oracle Cloud and Beyond

ESP Journal of Engineering & Technology Advancements
© 2023 by ESP JETA
Volume 3  Issue 2
Year of Publication : 2023
Authors : Vinay Singh, DhirajKumar Pathak, Prashant Gupta
:10.56472/25832646/JETA-V3I6P114

Citation:

Vinay Singh, DhirajKumar Pathak, Prashant Gupta, 2023. "Integrating Artificial Intelligence and Machine Learning into Healthcare ERP Systems: A Framework for Oracle Cloud and Beyond ESP Journal of Engineering & Technology Advancements" 3(2): 171-178.

Abstract:

Through improving diagnosis, therapy personalizing, and operational efficiency, artificial intelligence (AI) and machine learning (ML) are transforming healthcare. Faster and more accurate diagnosis, predictive analytics, and enhanced automation spanning clinical and administrative operations are made possible by these technologies. Although its advantages are clear-cut, integration of artificial intelligence/machine learning with ERP (Enterprise Resource Planning) systems—especially on platforms like Oracle Cloud—remains limited and understudied in healthcare environments. This work highlights present use cases, explores the transforming possibilities and major hurdles of AI-ERP integration in healthcare. We provide a paradigm for seamless AI-ERP integration to improve decision-making, resource allocation, and patient outcomes by means of a mix of literature study, case analysis, and model design.

References:

[1] De Fauw, J., Ledsam, J. R., Romera-Paredes, B., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342–1350. https://doi.org/10.1038/s41591-018-0107-6

[2] Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

[3] Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

[4] Jha, S. I., & Topol, E. J. (2016). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 316(22), 2353–2354. https://doi.org/10.1001/jama.2016.17100

[5] Nature Medicine. (2023, March 14). Accelerating drug discovery with artificial intelligence. Nature Medicine. https://www.nature.com/articles/s41591-023-02415-6

[6] Cleveland Clinic. (2018, September 27). Cleveland Clinic and Oracle shape the future of healthcare. PR Newswire. https://www.prnewswire.com/news-releases/cleveland-clinic-and-oracle-shape-the-future-of-healthcare-300718130.html

[7] Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1–9. https://doi.org/10.1186/s12911-020-01332-6

[8] Kellermann, A. L., & Jones, S. S. (2013). What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Affairs, 32(1), 63–68. https://doi.org/10.1377/hlthaff.2012.0693

[9] Mesko, B., Hetényi, G., & Győrffy, Z. (2018). Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Services Research, 18(1), 545. https://doi.org/10.1186/s12913-018-3359-4

[10] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

[11] Reddy, S., Fox, J., & Purohit, M. P. (2020). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 113(1), 22–28. https://doi.org/10.1177/0141076819877552

[12] Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296. https://doi.org/10.48550/arXiv.1708.08296

[13] Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

[14] Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. https://doi.org/10.1038/s41591-019-0441-1

[15] Oracle. (2018, September 19). Healthcare organizations select Oracle Cloud Applications to drive business transformation. Oracle.https://www.oracle.com/corporate/pressrelease/healthcare-orgs-select-oracle-cloud-apps-091918.html

[16] Oracle. (2021, February 23). Northwell Health uses Oracle Cloud to help manage COVID-19 healthcare crisis. Oracle. https://www.oracle.com/news/announcement/northwell-health-uses-oracle-cloud-to-help-manage-covid19-healthcare-crisis-022321/

[17] Rao, M. (2020, February 21). Apollo Hospitals utilized decade-long patient data to develop AI for heart disease risk. Healthcare IT News. https://www.healthcareitnews.com/news/asia/apollo-hospitals-utilised-decade-long-patient-data-develop-ai-heart-disease-risk

Keywords:

Artificial Intelligence, Machine Learning, Healthcare ERP, Oracle Cloud, Predictive Analytics, Digital Health, Decision Support Systems, Interoperability, Data Integration, Data Privacy, Interoperability, AI Ethics.