Research Article
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Year 2025, Volume: 9 Issue: 1, 133 - 146, 30.06.2025
https://doi.org/10.26650/acin.1631851

Abstract

References

  • Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial Intelligence in Medicine, 57(1), 9-19. doi: https://doi.org/10.1016/j.artmed.2012.12.003 google scholar
  • Biswas, A., and Talukdar, W. (2024). Intelligent clinical documentation: Harnessing generative ai for patient-centric clinical note gener-ation. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2405.18346 google scholar
  • Buch, V. H., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: current trends and future possibilities. British Journal of General Practice, 68(668), 143-144. google scholar
  • Davenport, T., & Kalakota, R. (2019). The potential of artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. doi: https://doi.org/10.7861/futurehosp.6-2-94 google scholar
  • Ezechukwu, C. V. (2024). Artificial intelligence and healthcare: the opportunities, risks and barriers of ai adoption in healthcare in Sub-Saharan Africa. (Master thesis). Available at: https://sapientia.ualg.pt/entities/publication/6786b359-5d37-464f-8565-6b108440a 419/full. google scholar
  • Hayyolalam, V., Aloqaily, M., Özkasap, Ö., & Guizani, M. (2021). Edge intelligence for empowering IoT-based healthcare systems. IEEE Wireless Communications, 28(3), 6-14. doi: 10.1109/MWC.001.2000345 google scholar
  • Hirani, R., Noruzi, K., Khuram, H., Hussaini, A. S., Aifuwa, E. I., Ely, K. E., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., & Etienne, M. (2024). Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel, Switzerland), 14(5), 557. doi: https://doi.org/10.3390/life14050557 google scholar
  • Ingole, B. S., Ramineni, V., Krishnappa, M. S., & Jayaram, V. (2024). AI-driven innovation in medicaid: enhancing access, cost efficiency, and population health management. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2410.21284 google scholar
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., and Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. google scholar
  • Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., Singh, I. M., Laird, J. R., Fatemi, M., Alizad, A., Saba, L., Agarwal, V., Sharma, A., Teji, J. S., Al-Maini, M., ... Suri, J. S. (2022). Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare, 10(12), 2493. https://doi.org/10.3390/healthcare10122493 google scholar
  • Matheny, M., Israni, S. T., Ahmed, M., & Whicher, D. (2019). Artificial intelligence in health care: The hope, the hype, the promise, the peril. Washington, DC: National Academy of Medicine, 10. google scholar
  • McGillion, M. H., Parlow, J., Borges, F. K., Marcucci, M., Jacka, M., Adili, A., . & Devereaux, P. J. (2021). Post discharge after surgery virtual care with remote automated monitoring technology (PVC-RAM): protocol for a randomized controlled trial. Canadian Medical Association Open Access Journal, 9(1), E142-E148. doi: https://doi.org/10.9778/cmajo.20200176 google scholar
  • National Academy of Medicine. (2022). Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. google scholar
  • 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. doi:10.1126/science.aax2342 google scholar
  • Paul, J., & Barari, M. (2022). Meta-analysis and traditional systematic literature reviews—What, why, when, where, and how? Psychology and Marketing, 39(6), 1099-1115. https://doi.org/10.1002/mar.21657 google scholar
  • Prabhod, K. J. (2024). The role of artificial intelligence in reducing healthcare costs and improving operational efficiency. Quarterly Journal of Emerging Technologies and Innovations, 9(2), 47-59. google scholar
  • Rajkomar, A., Dean, J. & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. doi:10.1056/NEIMra1814259 google scholar
  • Rossi, J. G., Rojas-Perilla, N., Krois, J., & Schwendicke, F. (2022). Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Network Open, 5(3), e220269. https://doi.org/10.1001/jamanetworkopen.2022.0269 google scholar
  • Sahni, N., Stein, G., Zemmel, R., and Cutler, D. (2024). The potential impact of artificial intelligence on healthcare spending. National Bureau of Economic Research, doi: 10.3386/w30857 google scholar
  • Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Mining and Knowledge Discovery, 13(2), e1485. https://doi.org/10.1002/ widm.1485 google scholar
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039 google scholar
  • Subramanian, S., Pamplin, J. C., Hravnak, M., Hielsberg, C., Riker, R., Rincon, F., . & Herasevich, V. (2020). Tele-critical care: an update from the society of critical care medicine tele-ICU committee. Critical Care Medicine, 48(4), 553-561. doi : https://doi.org/10.1097/ CCM.0000000000004190 google scholar
  • Tiwari, R. K., and Etienne, M. (2024). Artificial intelligence and healthcare: a journey through history, present innovations, and future possibilities. Life, 14(5), 557-562. google scholar
  • Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK. google scholar
  • Wang, Z., Wei, L., & Xue, L. (2024). Overcoming medical overuse with ai assistance: An experimental investigation. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2405.10539 google scholar
  • World Economic Forum. (2024). How AI Can Transform Healthcare and Treatment. https://www.ache.org/blog/2022/how-ai-can-transform-healthcare-management google scholar
  • Zhang, Y., Jiang, B., Zhang, L., Greuter, M. J., de Bock, G. H., Zhang, H., & Xie, X. (2022). Lung nodule detectability of artificial intelligence-assisted CT image reading in lung cancer screening. Current Medical Imaging Reviews, 18(3), 327-334. https://doi.org/10.2174/ 1573405617666210806125953 google scholar

Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations

Year 2025, Volume: 9 Issue: 1, 133 - 146, 30.06.2025
https://doi.org/10.26650/acin.1631851

Abstract

This study employs a traditional literature review approach to examine the role of artificial intelligence (AI) in cost-reduction strategies in healthcare management. As healthcare systems face increasing financial pressures, AI has been widely explored for its potential to enhance operational efficiency and optimize resource utilization. By synthesizing recent academic literature and theoretical discussions, this review aims to provide a comprehensive evaluation of AI-driven cost reduction strategies and their broader implications for healthcare management. This study explores AI applications in automating administrative workflows, enhancing predictive analytics, and optimizing clinical decision-making. AI’s contributions to supply chain management and early disease detection are also highlighted as significant factors in achieving operational cost savings. However, despite its transformative potential, AI adoption in healthcare presents notable challenges, including high initial investment costs, data security risks, ethical dilemmas, and regulatory constraints. These challenges necessitate a structured and strategic approach to ensure sustainable and effective AI implementation. Through an extensive review of the existing literature, this study critically analyzes both the opportunities and limitations associated with AI-driven healthcare cost reduction. The findings underscore the need for robust regulatory frameworks, ethical AI deployment, and continuous workforce adaptation to facilitate the successful integration of AI technologies into healthcare systems. By offering key strategic insights, this study contributes to the academic discourse on AI’s cost-effectiveness in healthcare and provides a foundation for future research and policy development.

References

  • Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial Intelligence in Medicine, 57(1), 9-19. doi: https://doi.org/10.1016/j.artmed.2012.12.003 google scholar
  • Biswas, A., and Talukdar, W. (2024). Intelligent clinical documentation: Harnessing generative ai for patient-centric clinical note gener-ation. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2405.18346 google scholar
  • Buch, V. H., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: current trends and future possibilities. British Journal of General Practice, 68(668), 143-144. google scholar
  • Davenport, T., & Kalakota, R. (2019). The potential of artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. doi: https://doi.org/10.7861/futurehosp.6-2-94 google scholar
  • Ezechukwu, C. V. (2024). Artificial intelligence and healthcare: the opportunities, risks and barriers of ai adoption in healthcare in Sub-Saharan Africa. (Master thesis). Available at: https://sapientia.ualg.pt/entities/publication/6786b359-5d37-464f-8565-6b108440a 419/full. google scholar
  • Hayyolalam, V., Aloqaily, M., Özkasap, Ö., & Guizani, M. (2021). Edge intelligence for empowering IoT-based healthcare systems. IEEE Wireless Communications, 28(3), 6-14. doi: 10.1109/MWC.001.2000345 google scholar
  • Hirani, R., Noruzi, K., Khuram, H., Hussaini, A. S., Aifuwa, E. I., Ely, K. E., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., & Etienne, M. (2024). Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel, Switzerland), 14(5), 557. doi: https://doi.org/10.3390/life14050557 google scholar
  • Ingole, B. S., Ramineni, V., Krishnappa, M. S., & Jayaram, V. (2024). AI-driven innovation in medicaid: enhancing access, cost efficiency, and population health management. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2410.21284 google scholar
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., and Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. google scholar
  • Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., Singh, I. M., Laird, J. R., Fatemi, M., Alizad, A., Saba, L., Agarwal, V., Sharma, A., Teji, J. S., Al-Maini, M., ... Suri, J. S. (2022). Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare, 10(12), 2493. https://doi.org/10.3390/healthcare10122493 google scholar
  • Matheny, M., Israni, S. T., Ahmed, M., & Whicher, D. (2019). Artificial intelligence in health care: The hope, the hype, the promise, the peril. Washington, DC: National Academy of Medicine, 10. google scholar
  • McGillion, M. H., Parlow, J., Borges, F. K., Marcucci, M., Jacka, M., Adili, A., . & Devereaux, P. J. (2021). Post discharge after surgery virtual care with remote automated monitoring technology (PVC-RAM): protocol for a randomized controlled trial. Canadian Medical Association Open Access Journal, 9(1), E142-E148. doi: https://doi.org/10.9778/cmajo.20200176 google scholar
  • National Academy of Medicine. (2022). Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. google scholar
  • 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. doi:10.1126/science.aax2342 google scholar
  • Paul, J., & Barari, M. (2022). Meta-analysis and traditional systematic literature reviews—What, why, when, where, and how? Psychology and Marketing, 39(6), 1099-1115. https://doi.org/10.1002/mar.21657 google scholar
  • Prabhod, K. J. (2024). The role of artificial intelligence in reducing healthcare costs and improving operational efficiency. Quarterly Journal of Emerging Technologies and Innovations, 9(2), 47-59. google scholar
  • Rajkomar, A., Dean, J. & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. doi:10.1056/NEIMra1814259 google scholar
  • Rossi, J. G., Rojas-Perilla, N., Krois, J., & Schwendicke, F. (2022). Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Network Open, 5(3), e220269. https://doi.org/10.1001/jamanetworkopen.2022.0269 google scholar
  • Sahni, N., Stein, G., Zemmel, R., and Cutler, D. (2024). The potential impact of artificial intelligence on healthcare spending. National Bureau of Economic Research, doi: 10.3386/w30857 google scholar
  • Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Mining and Knowledge Discovery, 13(2), e1485. https://doi.org/10.1002/ widm.1485 google scholar
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039 google scholar
  • Subramanian, S., Pamplin, J. C., Hravnak, M., Hielsberg, C., Riker, R., Rincon, F., . & Herasevich, V. (2020). Tele-critical care: an update from the society of critical care medicine tele-ICU committee. Critical Care Medicine, 48(4), 553-561. doi : https://doi.org/10.1097/ CCM.0000000000004190 google scholar
  • Tiwari, R. K., and Etienne, M. (2024). Artificial intelligence and healthcare: a journey through history, present innovations, and future possibilities. Life, 14(5), 557-562. google scholar
  • Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK. google scholar
  • Wang, Z., Wei, L., & Xue, L. (2024). Overcoming medical overuse with ai assistance: An experimental investigation. arXiv preprint. doi: https://doi.org/10.48550/arXiv.2405.10539 google scholar
  • World Economic Forum. (2024). How AI Can Transform Healthcare and Treatment. https://www.ache.org/blog/2022/how-ai-can-transform-healthcare-management google scholar
  • Zhang, Y., Jiang, B., Zhang, L., Greuter, M. J., de Bock, G. H., Zhang, H., & Xie, X. (2022). Lung nodule detectability of artificial intelligence-assisted CT image reading in lung cancer screening. Current Medical Imaging Reviews, 18(3), 327-334. https://doi.org/10.2174/ 1573405617666210806125953 google scholar
There are 27 citations in total.

Details

Primary Language English
Subjects Computing Applications in Health, Health Services and Systems (Other)
Journal Section Research Article
Authors

Canan Bulut 0000-0001-5092-5261

Publication Date June 30, 2025
Submission Date February 2, 2025
Acceptance Date May 7, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Bulut, C. (2025). Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations. Acta Infologica, 9(1), 133-146. https://doi.org/10.26650/acin.1631851
AMA Bulut C. Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations. ACIN. June 2025;9(1):133-146. doi:10.26650/acin.1631851
Chicago Bulut, Canan. “Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations”. Acta Infologica 9, no. 1 (June 2025): 133-46. https://doi.org/10.26650/acin.1631851.
EndNote Bulut C (June 1, 2025) Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations. Acta Infologica 9 1 133–146.
IEEE C. Bulut, “Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations”, ACIN, vol. 9, no. 1, pp. 133–146, 2025, doi: 10.26650/acin.1631851.
ISNAD Bulut, Canan. “Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations”. Acta Infologica 9/1 (June2025), 133-146. https://doi.org/10.26650/acin.1631851.
JAMA Bulut C. Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations. ACIN. 2025;9:133–146.
MLA Bulut, Canan. “Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations”. Acta Infologica, vol. 9, no. 1, 2025, pp. 133-46, doi:10.26650/acin.1631851.
Vancouver Bulut C. Artificial Intelligence and Cost Reduction Strategies for Healthcare Management: Opportunities and Limitations. ACIN. 2025;9(1):133-46.