Integrated Risk Management and Artificial Intelligence in Hospital
The topic revolves around the integration of Artificial Intelligence (AI) in Hospital Integrated Risk Management (IRM). AI offers significant advantages in enhancing risk identification, assessment, and mitigation across various areas of hospital operations. It can contribute to patient safety by enabling early detection of critical conditions, improving clinical risk management, and enhancing decisionmaking processes. AI also plays a vital role in information security and privacy, operational risk management, regulatory compliance, and human resources in hospitals. However, the use of AI in Hospital IRM comes with certain disadvantages and risks that need to be mitigated. These include data quality and bias, interpretability and transparency challenges, privacy and security concerns, reduced human oversight, ethical considerations, and implementation challenges. Mitigating these risks requires robust data governance, addressing bias in AI algorithms, ensuring transparency and accountability, implementing strong cybersecurity measures, and upholding ethical guidelines. To achieve successful implementation, hospitals should prioritize employee competencies, such as domain knowledge, data literacy, AI and data science skills, critical thinking, collaboration, adaptability, and ethical awareness. By developing these competencies and adhering to best practices, hospitals can optimize the use of AI in IRM, improve patient outcomes, enhance operational efficiency, and mitigate risks effectively.
Anahtar Kelimeler:
Artificall Intelligence, Hospital, Integrated Risk Management, Patient
Integrated Risk Management and Artificial Intelligence in Hospital
The topic revolves around the integration of Artificial Intelligence (AI) in Hospital Integrated Risk Management (IRM). AI offers significant advantages in enhancing risk identification, assessment, and mitigation across various areas of hospital operations. It can contribute to patient safety by enabling early detection of critical conditions, improving clinical risk management, and enhancing decisionmaking processes. AI also plays a vital role in information security and privacy, operational risk management, regulatory compliance, and human resources in hospitals. However, the use of AI in Hospital IRM comes with certain disadvantages and risks that need to be mitigated. These include data quality and bias, interpretability and transparency challenges, privacy and security concerns, reduced human oversight, ethical considerations, and implementation challenges. Mitigating these risks requires robust data governance, addressing bias in AI algorithms, ensuring transparency and accountability, implementing strong cybersecurity measures, and upholding ethical guidelines. To achieve successful implementation, hospitals should prioritize employee competencies, such as domain knowledge, data literacy, AI and data science skills, critical thinking, collaboration, adaptability, and ethical awareness. By developing these competencies and adhering to best practices, hospitals can optimize the use of AI in IRM, improve patient outcomes, enhance operational efficiency, and mitigate risks effectively.
Keywords:
Artificall Intelligence, Hospital, Integrated Risk Management, Patient,
___
- Mun, J., Housel, T., Jones, R., Carlton, B., & Skots, V. (2020). Acquiring artificial intelligence systems: Development challenges, implementation risks, and cost/benefits opportunities. Naval Engineers Journal, 132(2), 79-94.
- Rodrigues, A. R. D., Ferreira, F. A., Teixeira, F. J., & Zopounidis, C. (2022). Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognitiondriven framework. Research in International Business and Finance, 60, 101616.
- Oboni, F., Angelino, C., & Moreno, J. (2007, May). Using artificial intelligence in an integrated risk management programme for a large alpine landslide. In Landslides and Climate Change: Challenges and Solutions: Proceedings of the International Conference on Landslides and Climate Change, Ventnor, Isle of Wight, UK, 21-24 May 2007 (p. 143). CRC Press.
- Sun, Y., Bi, K., & Yin, S. (2020). Measuring and integrating risk management into green innovation practices for green manufacturing under the global value chain. Sustainability, 12(2), 545.
- Johns, M. L. (1990). The CIO and IRM (information resources management) alliance: maneuvering for the competitive edge in hospital information management. Topics in Health Record Management, 11(1), 1-7.
- Miniati, R., Frosini, F., & Dori, F. (2016). Integrated risk and quality management in hospital systems. Clinical Engineering: From Devices to Systems, 117.
- Somayajula, U. (2021). Artificial Intelligence: An approach to Integrated Risk Management. Available at SSRN 3980768.
- Ferdosi, M., Rezayatmand, R., & Molavi Taleghani, Y. (2020). Risk management in executive levels of healthcare organizations: insights from a scoping review (2018). Risk management and healthcare policy, 215-243.
- European Parliamentary Research Service (2022). Artificial intelligence in healthcare:Applications, risks, and ethical and societal impacts. https://www.europarl.europa.eu/RegData/etudes/STUD/2022/729512/EPRS_STU(2022)729512_EN.pdf; (11. 7. 2023)
- Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Science Reports, 6(3), e1138.
- Wilkins, D. E. (2014). Practical planning: extending the classical AI planning paradigm. Elsevier.
- Bhbosale, S., Pujari, V., & Multani, Z. (2020). Advantages and disadvantages of artificial intellegence. Aayushi International Interdisciplinary Research Journal, 77, 227-230.
- Sidorenko, E. L., Khisamova, Z. I., & Monastyrsky, U. E. (2021). The main ethical risks of using artificial intelligence in business. Current achievements, challenges and digital chances of knowledge based economy, 423-429.
- Sunarti, S., Rahman, F. F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: opportunities and risk for future. Gaceta Sanitaria, 35, S67-S70.
- Argaw, S. T., Troncoso-Pastoriza, J. R., Lacey, D., Florin, M. V., Calcavecchia, F., Anderson, D., ... &Flahault, A. (2020). Cybersecurity of Hospitals: discussing the challenges and working towards mitigating the risks. BMC medical informatics and decision making, 20, 1-10.
- McClean, K., Cross, M., & Reed, S. (2021). Risks to healthcare organizations and staff who manage obese (Bariatric) patients and use of obesity data to mitigate risks: a Literature Review. Journal of Multidisciplinary Healthcare, 577-588.
- Intellipat (2023). Artificial Intelligence in Healthcare - AI Applications and Uses. Intellipat Blogs. https://intellipaat.com/blog/artificial-intelligence-in-healthcare/?US (10.7. 2023.)
- Massachusetts General Hospital (2023). Massachusetts General Hospital Web Site. https://www.massgeneral.org/ (11. 6. 2023.)
- The Johns Hopkins Hospital (2023). The Johns Hopkins Hospital Web Site. https://www.hopkinsmedicine.org/the_johns_hopkins_hospital/ (11.6.2023)
- Moorfields Private Eye Hospital (2023). Moorfields Private Eye Hospital Web Site. https://www.moorfields-private.co.uk/ (12. 6. 2023)
- Chang Gung Memorial Hospital (2023) Chang Gung Memorial Hospital International Medical Center Web Site. https://www.changgung.hospital/en/ (12.6. 2023)
- SNUH (2023) Seoul National University Hospital Web Site http://www.snuh.org/global/en/main.do (14.6-2023)
- UCSF Health (2023) University of California, San Francisco Medical Center Web Site. https://www.ucsfhealth.org/ (14.6.2023)
- Yayın Aralığı: Yıllık
- Başlangıç: 2017
- Yayıncı: İzmir Akademi Derneği