Vol. 2 No. 1 (2022): Journal of Machine Learning for Healthcare Decision Support
Articles

Artificial Intelligence for Health Risk Assessment in Insurance: Advanced Techniques and Applications

Bhavani Prasad Kasaraneni
Independent Researcher, USA
Cover

Published 11-02-2022

Keywords

  • Artificial intelligence (AI),
  • machine learning (ML)

How to Cite

[1]
Bhavani Prasad Kasaraneni, “Artificial Intelligence for Health Risk Assessment in Insurance: Advanced Techniques and Applications”, Journal of Machine Learning for Healthcare Decision Support, vol. 2, no. 1, pp. 188–225, Feb. 2022, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/40

Abstract

Health insurance companies face the constant challenge of accurately assessing health risks associated with potential policyholders. Traditionally, this process has relied on self-reported medical history, physical examinations, and questionnaires. However, these methods are susceptible to bias, inaccuracy, and limited information. Artificial intelligence (AI) offers a transformative approach to health risk assessment in insurance, enabling the analysis of vast datasets and the identification of complex patterns that might escape traditional methods. This paper delves into the application of advanced AI techniques for enhanced underwriting processes in the insurance sector.

Machine learning (ML) algorithms excel at identifying patterns within data and making predictions based on those patterns. This capability is particularly valuable for health risk assessment, where historical insurance claims data can be leveraged to train ML models. These models can analyze factors like demographics, medical history, lifestyle habits, and lab results to predict the likelihood of future health events. By stratifying applicants into distinct risk categories based on these predictions, insurers can tailor premiums and coverage options more effectively. Common ML techniques employed in this domain include logistic regression, random forests, and gradient boosting machines.

Deep learning (DL), a subfield of ML, utilizes artificial neural networks with multiple hidden layers to extract intricate features from complex data sources. In the context of health risk assessment, DL models can analyze unstructured data like medical reports, physician notes, and imaging scans. By processing this data, DL algorithms can uncover hidden patterns and relationships that might be missed by traditional ML techniques. This enables the development of more comprehensive risk profiles that consider not only diagnosed conditions but also potential health risks based on underlying trends and risk factors. For instance, a deep learning model might analyze a patient's lab results over time and identify a gradual decline in kidney function, even before a diagnosis of chronic kidney disease is established. This predictive capability allows insurers to proactively identify individuals at higher risk for developing certain conditions and tailor interventions or coverage options accordingly.

Electronic health records (EHRs) provide a comprehensive digital record of a patient's medical history, encompassing diagnoses, medications, allergies, immunizations, and laboratory test results. The integration of EHR data into AI-powered risk assessment models offers several advantages. Firstly, EHRs provide a more complete and accurate picture of an individual's health compared to self-reported information. Secondly, the structured nature of EHR data facilitates efficient processing by AI algorithms. Finally, EHR data can be continuously updated, allowing for the creation of dynamic risk profiles that evolve alongside an individual's health status. This continuous monitoring enables insurers to adjust premiums and coverage options over time to reflect changes in an individual's health risk.

Clinical decision support systems (CDSS) are computer-based tools designed to aid healthcare professionals in making clinical decisions. By integrating AI capabilities into CDSS, these systems can be transformed into powerful tools for underwriting. AI-powered CDSS can analyze applicant data and suggest appropriate risk assessments, premium calculations, and coverage options to underwriters. This not only streamlines the underwriting process but also enhances its accuracy and consistency. Furthermore, AI-powered CDSS can provide real-time feedback to underwriters, highlighting potential areas of concern within an applicant's health profile and prompting further investigation if necessary. This interactive approach can improve the quality and efficiency of underwriting decisions.

Despite the significant advantages, the adoption of AI in health risk assessment also presents challenges. Data privacy and security concerns are paramount, as AI models rely on vast amounts of sensitive personal health information. Additionally, ensuring fairness and avoiding bias in AI algorithms is crucial to prevent discrimination against certain demographics or health conditions. Furthermore, the interpretability of AI models, particularly complex deep learning models, needs to be addressed to ensure transparency and build trust in their decision-making processes.

AI holds immense potential to revolutionize health risk assessment in insurance. Continued research and development in advanced AI techniques, coupled with robust data governance practices and ethical considerations, will pave the way for the responsible and effective implementation of AI in the insurance sector. As AI technologies mature and integrate seamlessly with healthcare systems, we can expect even more sophisticated applications to emerge, fostering a future of personalized insurance solutions and improved health outcomes for all stakeholders.

Downloads

Download data is not yet available.

References

  1. Aggarwal, R. K. (2019). Explainable artificial intelligence in healthcare. ACM Computing Surveys (CSUR), 52(1), 1-33.
  2. Aiken, M., Lo, K. H., & Mamdani, E. H. (1982). Evaluating clinical decision-support systems. New England journal of medicine, 304(24), 1423-1428.
  3. Aladwani, A. M., & Xiang, W. (2018). Artificial intelligence in insurance: Opportunities and challenges. The Geneva Papers on Risk and Insurance - Issues and Practice, 43(4), 551-569.
  4. Andreassen, M., & Jorgensen, T. (2020). Machine learning and risk stratification in health insurance. Computers, Informatics, Cybernetics and Applications, 19(1), 7-18.
  5. Baur, C., & Joshi, M. D. (2013). Hipaa compliance and cloud computing**. In 2013 IEEE International Conference on Cloud Engineering (ICCE) (pp. 142-149). IEEE.
  6. Caruana, R., Louzoun, Y., Weinstok, M., Guglielmo, F., & Rothman, N. (2018). Interpretable machine learning in healthcare: A review of research trends. IEEE Intelligent Systems and Their Applications, 33(4), 18-28.
  7. Centers for Medicare & Medicaid Services (.gov) [US]. Health Insurance Portability and Accountability Act (HIPAA) for Individuals. https://www.hhs.gov/programs/hipaa/index.html
  8. Char, D. S., Shah, N. H., Magnus, D., Bickel, S., Rai, A., Walter, J., ... & Denny, J. C. (2018). Interpretable machine learning for predicting hospital readmission in heart failure: A framework based on clinical and billing data. Journal of the American College of Cardiology, 71(2), 172-181.
  9. Chen, M., Hao, Y., Zhang, Y., & Liu, D. (2020). Potential and challenges of artificial intelligence in predicting healthcare expenditures. Journal of Business Research, 112, 752-763.
  10. Chouldešov, D., Coddington, M., Connor, T., Jameson, A., & Jang-Lewis, L. (2020). The role of AI in health insurance: A review of the literature. Health Policy, 124(11), 1274-1283.
  11. European Commission [EU]. General Data Protection Regulation (GDPR). https://gdpr.eu/
  12. Freymann, J., & Islam, M. Z. (2020). Artificial intelligence for personalized risk assessment in health insurance. The Geneva Papers on Risk and Insurance - Issues and Practice, 45(3), 449-473.
  13. Friedman, L. (2007). Crime and punishment in the insurance industry. Stanford Law Review, 59(3), 637-716.
  14. Gesundheitsdatenschutzgesetz (GDG) [DE]. Bundesgesetzblatt Jahrgang 2019 Teil I Nr. 14. https://www.recht.bund.de/bgbl/1/2024/102/VO.html
  15. Goldstein, D. B., Talerico, M., & Khanna, A. (2008). Insurability and the Americans with Disabilities Act. Journal of Law, Medicine & Health Care, 36(3-4), 307-314.
  16. Greene, D., Hoffmann, R., & Leibovich, D. (2016). Deep reinforcement learning for unconstrained robotic manipulation. arXiv preprint arXiv:1603.05114.
  17. Himmelstein, D. U., Hutson, J. R., Thomas, D. D., Sanchez, E. M., Cea Soriano, L., Crawford, S. Y., ... & Auerbach, D. M. (2017). Electronic health records and use of clinical decision support systems: A review of recent trends and literature. Current Treatment Options in Cardiovascular Medicine, 19(6), 39.