Open Access

Understanding Consumer Adoption of Ai-Enabled Health Insurance Services: An Empirical Investigation

https://doi.org/10.55640/irjmshc-v03i10-04

PDF

Shubham Naresh Bhoir ¹ ,

⁴ Assistant Manager, Volo Health Services, RH No.9, Villa Delphine, Opp Infant Jesus School, Mahada Colony Ambarnath East 421501, India

Abstract

The incorporation of AI is causing a sea change in the health insurance industry. From boosting client interactions to reducing fraud and expediting underwriting, AI is revolutionising the health insurance market. Today, the healthcare insurance sector is in a transformation process, where Artificial Intelligence (AI) is playing a role from optimising underwriting processes to processing claims, preventing fraud, and tailoring insurance offerings to customers. Factors including trust, usefulness, simplicity of use, privacy protection, and perceived dangers still impact customers' readiness to utilize AI-powered health insurance plans, despite these technological breakthroughs. This article delves further into the possible uses of AI in healthcare, whereas the following hypothesis describes the elements to check when determining if clients are ready to use AI: Here are only a few examples: TAM, DOI, TRA, UTAUT, and the Theory of Reasoned Action. It also compares and contrasts the most recent empirical findings from the literature with functional, personal, and risk factors that impact behavioural intention and trust. Despite the privacy and security issues, the most important review factors are trust and functional aspects such as expected performance, effort, and usefulness. The results shed light on how insurers, lawmakers, and software developers can create AI-enabled health insurance systems that are upfront, safe, and focused on the customer.

Keywords

Artificial Intelligence, Health Insurance, Consumer Adoption, Trust

References

A. K. N. and R. B.C., “Adoption of Insurtech-Enabled Health Insurance: An Empirical Study,” Int. J. Manag. Issues Res., vol. 14, no. 2, pp. 1–12, Mar. 2026, doi: 10.69711/sharda.ijmir.v14i2.2501.

N. K. S. Patel, “Enterprise AI Operations in Healthcare IT Infrastructure,” J. Inf. Syst. Eng. Manag., vol. 11, no. 3, pp. 2508–2522, September, 2026, doi: 10.5281/zenodo.22773821.

A. Warrier and A. K S, “Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations,” in 2025 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), IEEE, Dec. 2025, pp. 1450–1457. doi: 10.1109/ICICNIS66685.2025.11315712.

C. S. Pareek, “Testing For The Unexpected: Ensuring Insurance System Stability During Covid-19,”,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 1, pp. 1680–1684, Nov. 2022, doi: 10.51219/JAIMLD/Chandra-shekhar-pareek/375.

S. Mukherjee, “Machine Learning-Enabled Classification of Diabetes Using Clinical via Lifestyle Health Data,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), Houston, TX, USA: IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/ICAIC67076.2026.11395709.

V. Rengaraj, Y. Lnu, Y. Patel, and S. Vinod, “Decoding-Enhanced Bidirectional Encoder Representations with Attention-Based Pooling Mechanism for Insurance Fraud Detections,” in 2026 3rd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), IEEE, Apr. 2026, pp. 1–9. doi: 10.1109/RMKMATE69073.2026.11518574.

C. S. Pareek, “Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks,” INTERANTIONAL J. Sci. Res. Eng. Manag., vol. 08, no. 12, pp. 1–6, Jul. 2024, doi: 10.55041/IJSREM9554.

S. R. Chanthati, “Integrating Large Language Models (LLMs) in Cognitive Medical Robotics,” in Proceedings of the International Conference on AI and Robotics (AIR 2025), May 2025. doi: 10.5281/zenodo.20316245.

M. Mittal, “Ai In Healthcare: The Next Frontier In Medical Diagnosis,” Int. J. Inf. Technol. Manag. Inf. Syst., vol. 16, no. 2, pp. 1448–1455, Mar. 2025, doi: 10.34218/IJITMIS_16_02_091.

S. Gupta, W. Ghardallou, D. K. Pandey, and G. P. Sahu, “Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework,” Res. Int. Bus. Financ., vol. 63, p. 101757, Dec. 2022, doi: 10.1016/j.ribaf.2022.101757.

S. A. D. Poovaiah and P. K. Pareek, “AI-Optimized Multimodal Neuroimaging Pipelines for Early Alzheimer’s Disease Detection: A System-Level Perspective,” in Alzheimer’s Disease - Diagnostics, Pathogenesis, and Treatment [Working Title], IntechOpen, 2026. doi: 10.5772/intechopen.1015774.

S. Mahmud, K. K. Mohammed, V. R. Jain, and S. A. Shah, “Artificial Intelligence-Driven Predictive Models for Identifying Risk Factors of Chronic Diseases,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA: IEEE, Mar. 2026, pp. 01–06. doi: 10.1109/ISDFS69419.2026.11459003.

R. S. Snehamruth, “Data-Driven Optimization of Pharmaceutical Manufacturing Processes using Quality by Design ( QbD ) Frameworks,” Int. J. Curr. Eng. Technol., vol. 14, no. 6, pp. 557–566, 2024, doi: 10.14741/ijcet/v.14.6.19.

R. V. S. S. B. R, R. R. Al-Fatlawy, S. M. Sundaram, E. A. Rathnakumari, and M. Sudha, “Quantum variational based support vector machine for early detection of sepsis,” in 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkuru, India: IEEE, 2024, pp. 1–5, December. doi: 10.1109/ICMNWC63764.2024.10872228.

M. N. Paralath, “Towards Interpretable and Attentive Deep Visual Perception Through Iterative Refinement in Convolutional Neural Networks,” in 2025 International Conference on Advances in Next-Gen Computer Science (ICANCS), IEEE, Nov. 2025, pp. 1–6. doi: 10.1109/ICANCS65819.2025.11376668.

S. K. Anumula, “Brain-Inspired Computing: Revolutionizing Medical Devices Through Neuromorphic Engineering, A Review Of Different Options,” Int. J. Comput. Sci. Eng. Res. Dev., vol. 15, no. 2, pp. 88–105, 2025, doi: 10.63519/IJCSERD_15_02_007.

R. Lingam, “Agentic AI and Federated Learning for Privacy-Preserving Dental Imaging,” in 2026 Innovations in Machine, Engineering, and Digital Conference (IMED), Kota Kinabalu, Malaysia: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IMED68921.2026.11484887.

R. Palwe and A. Kumar, “Redefining usability in the age of generative AI: Towards a new evaluation paradigm,” Int. J. Comput. Artif. Intell., vol. 6, no. 2, pp. 155–163, Jul. 2025, doi: 10.33545/27076571.2025.v6.i2b.193.

S. Dharmavaram and P. Bhanushali, “AI-Driven Blood Glucose Forecasting in Real-World Diabetes Care: Evaluating Wearable-Based Predictive Models,” Int. J. Emerg. Res. Eng. Technol., vol. 7, no. 2, pp. 176–182, 2026, doi: 10.63282/3050-922X.IJERET-V7I2P122.

S. Kakkar, “Prompt-Robotics: Leveraging Large Language Model (Llm) Agents To Enhance Robotic Process Automation (Rpa) Supervision And Efficiency,” Int. J. Financ. DATA Sci., vol. 3, no. 2, pp. 7–29, Sep. 2025, doi: 10.34218/IJFDS_03_02_002.

S. R. Somula, “Intelligent Fraud Prevention: Implementing Aml Solutions For Mcca Reimbursement Claims Protection,” Int. J. Comput. Eng. Technol., vol. 16, no. 1, pp. 3609–3622, Feb. 2025, doi: 10.34218/IJCET_16_01_249.

Shiva Kumar Madishetty, “Cyber Resilience and DevOps in Operating Rooms: Securing Connected Embedded Medical Devices in High Risk Clinical Environments,” J. Electr. Syst., vol. 19, no. 3, pp. 244–253, Jan. 2024, doi: 10.52783/jes.9597.

P. Patel, “Revolutionizing Healthcare: Multicast Medicine Engineering for Real-Time Patient Monitoring at Scale,” in 2026 2nd International Conference on Emerging and Intelligent Technologies and Systems (EITS), Valencia, Spain: IEEE, Jun. 2026, pp. 270–276. doi: 10.1109/EITS70066.2026.11609590.

H. Taherdoost, “A review of technology acceptance and adoption models and theories,” Procedia Manuf., vol. 22, pp. 960–967, 2018, doi: 10.1016/j.promfg.2018.03.137.

A. A. Alkhelb and S. Alshagrawi, “Role of artificial intelligence in healthcare insurance: systematic literature review,” Explor. Digit. Heal. Technol., vol. 3, p. 101145, Apr. 2025, doi: 10.37349/edht.2025.101145.

S. Bhattacharya, G. Castignani, L. Masello, and B. Sheehan, “AI revolution in insurance: bridging research and reality,” Front. Artif. Intell., vol. 8, Apr. 2025, doi: 10.3389/frai.2025.1568266.

Z. Hamid, F. Khalique, S. Mahmood, A. Daud, A. Bukhari, and B. Alshemaimri, “Healthcare insurance fraud detection using data mining,” BMC Med. Inform. Decis. Mak., vol. 24, no. 1, pp. 1–24, 2024, doi: 10.1186/s12911-024-02512-4.

N. Ramanjaneyulu, G. H. Kumar, and M. Umrez, “Behavioural Dynamics of Health Insurance Adoption: A Consumer Perspective,” Indian J. Manag. Lang., vol. 5, no. 2, pp. 29–33, 2025.

X. Wei, X. Peng, and V. Prybutok, “Consumer behavioral intention of adopting emerging healthcare technology,” IEEE Trans. Eng. Manag., vol. 71, pp. 888–898, 2022.

A. Zarifis, P. Kawalek, and A. Azadegan, “Evaluating If Trust and Personal Information Privacy Concerns Are Barriers to Using Health Insurance That Explicitly Utilizes AI,” J. Internet Commer., vol. 20, no. 1, pp. 66–83, Jan. 2021, doi: 10.1080/15332861.2020.1832817.

A. Goel, N. Soti, U. Taneja, and A. Kumar, “Decoding consumer perspective for tech-based health insurance products,” Innov. Manag. Rev., vol. 22, no. 4, pp. 251–265, Dec. 2025, doi: 10.1108/INMR-10-2023-0191.

J. Su, Y. Wang, H. Liu, Z. Zhang, Z. Wang, and Z. Li, “Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model,” Sci. Rep., vol. 15, no. 1, p. 18215, May 2025, doi: 10.1038/s41598-025-01897-0.

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>

International Research Journal of Medical Sciences and Health Care

Understanding Consumer Adoption of Ai-Enabled Health Insurance Services: An Empirical Investigation

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles