Cognitive AI for Anticipating Member Needs In Healthcare Insurance Using Behavioral and Claims Data
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P113Keywords:
Cognitive AI, Predictive Analytics, Behavioral Data, Claims Data, Healthcare Insurance, Member Needs, Machine Learning, Personalized Healthcare, Data Fusion, Preventive CareAbstract
Cognitive Artificial Intelligence (AI) uses highly advanced machine learning, natural language processing (NLP), and predictive analytics to foresee the needs of healthcare members by combining large and varied datasets. When AI systems analyze the combination of human behavior, demographic information, and historical claims data, they can detect hidden health risks, predict the need for future care, and suggest the most efficient interventions. The method consists of integrating different types of data sources into one predictive model which keeps updating itself with member interactions and healthcare results. Such mechanisms allow insurers to spot the populations who are at risk earlier, adjust the care coordination programs to the needs of the patients, and make it possible for the patients to receive the most suitable recommendations which promote preventive care. Research reveals that cognitive AI is capable of precision in care as well as increases patient satisfaction and simultaneously, it optimizes healthcare costs by lessening the occurrence of unnecessary procedures and hospitalizations. In the end, the use of cognitive intelligence is the major factor that changes the healthcare insurance system from being a reactive one to a proactive healthcare model which is based on member engagement, predictive insights, and sustainable health outcomes.
References
1. Silverman, Barry G., et al. "Artificial intelligence and human behavior modeling and simulation for mental health conditions." Artificial intelligence in behavioral and mental health care. Academic Press, 2016. 163-183.
2. Kumar, Naman, Jayant Dev Srivastava, and Harshit Bisht. "Artificial intelligence in insurance sector." Journal of the Gujarat Research society 21.7 (2019): 79-91.
3. Chang, Anthony C. Intelligence-based medicine: artificial intelligence and human cognition in clinical medicine and healthcare. Academic Press, 2020.
4. Ahmed, Zeeshan, et al. "Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine." Database 2020 (2020): baaa010.
5. Hassani, Hossein, Stephan Unger, and Christina Beneki. "Big data and actuarial science." Big Data and Cognitive Computing 4.4 (2020): 40.
6. Panesar, Arjun. Machine learning and AI for healthcare. Vol. 10. Coventry, UK: Apress, 2019.
7. Ho, Anita. "Are we ready for artificial intelligence health monitoring in elder care?." BMC geriatrics 20.1 (2020): 358.
8. Singhal, Shubham, and Stephanie Carlton. "The era of exponential improvement in healthcare." McKinsey & Company (2019): 1-16.
9. Bardhan, Indranil, Hsinchun Chen, and Elena Karahanna. "Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management." MIS Quarterly 44.1 (2020).
10. Molfenter, Todd D., Abhik Bhattacharya, and David H. Gustafson. "The roles of past behavior and health beliefs in predicting medication adherence to a statin regimen." Patient preference and adherence (2012): 643-651.
11. Ravi, Vadlamani, and Sk Kamaruddin. "Big data analytics enabled smart financial services: opportunities and challenges." International conference on big data analytics. Cham: Springer International Publishing, 2017.
12. Davenport, Thomas, and Ravi Kalakota. "The potential for artificial intelligence in healthcare." Future healthcare journal 6.2 (2019): 94-98.
13. Guntupalli, Bhavitha. "Clean Code in the Real World: Principles I Actually Use." International Journal of Emerging Trends in Computer Science and Information Technology 1.1 (2020): 66-74.
14. 14 Aggarwal, Nakul, et al. "Advancing artificial intelligence in health settings outside the hospital and clinic." NAM perspectives 2020 (2020): 10-31478.
15. Roski, Joachim, et al. "How artificial intelligence is changing health and healthcare." Artificial intelligence in health care: The hope, the hype, the promise, the peril. Washington DC: National Academy of Medicine (2019): 58.
16. Martinez-Martin, Nicole, et al. "Data mining for health: staking out the ethical territory of digital phenotyping." NPJ digital medicine 1.1 (2018): 68.
