Articles 2026-09-30 Peer Reviewed

Examining Patient Acceptance of Emerging Healthcare Technologies: The Mediating Role of Trust and Perceived Value

Shubham Naresh Bhoir

Authors

  • Shubham Naresh Bhoir
    Assistant Manager Volo Health Services, RH No.9, Villa Delphine, Opp Infant Jesus School, Mahada Colony Ambarnath East 421501,

Keywords

Health literacy technology trust perceived value

Abstract

The fast digital revolution in healthcare brought new technologies such as artificial intelligence (AI), telemedicine, mobile health applications, wearables, and remote monitoring devices that can potentially enhance the quality of healthcare delivery through higher efficiency and personalization of services. However, their success will be contingent upon patient acceptance, which will be influenced by psychological and technological factors. This research takes a look at how eHealth literacy, trust, and perceived value affect attitudes towards healthcare technologies that are based on artificial intelligence. It sees the use of trust and perceived value as mediators in this relationship. The number of participants in the quantitative cross-sectional study was 300 and statistical analyses conducted included ANOVA, regression and correlation. The most significant determinant of a positive attitude toward the use of artificial intelligence (AI) healthcare technologies is eHealth literacy, with trust and perceived value acting as important intermediaries. It demonstrates that trust, perceived value, and usability are the three defining characteristics of AI technology acceptance. These findings have important implications for the philosophy and practice of technology acceptance research as well as for patients' attitudes toward digital healthcare aids.

Downloads

Download data is not yet available.

References

1
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.
2
A. I. Stoumpos, F. Kitsios, and M. A. Talias, “Digital Transformation in Healthcare: Technology Acceptance and Its Applications,” Int. J. Environ. Res. Public Health, vol. 20, no. 4, p. 3407, Feb. 2023, doi: 10.3390/ijerph20043407.
3
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.
4
G. Tetik, S. Türkeli, S. Pinar, and M. Tarim, “Health information systems with technology acceptance model approach: A systematic review,” Int. J. Med. Inform., vol. 190, p. 105556, Oct. 2024, doi: 10.1016/j.ijmedinf.2024.105556.
5
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.
6
N. Alhammad, M. Alajlani, A. Abd-alrazaq, G. Epiphaniou, and T. Arvanitis, “Patients’ Perspectives on the Data Confidentiality, Privacy, and Security of mHealth Apps: Systematic Review,” J. Med. Internet Res., vol. 26, p. e50715, May 2024, doi: 10.2196/50715.
7
M. Mittal and S. Balamurugan, “Ethical Challenges In Ai-Based Healthcare: Balancing Innovation With Patient Privacy And Fairness,” Int. J. Artif. Intell. Med., vol. 3, no. 2, pp. 1–20, Jul. 2025, doi: 10.34218/IJAIMED_03_02_001.
8
R. Snehamrutha, “Development and In-Vitro Evaluation of a Sustained-Release Transdermal Patch for Improved Patient Adherence,” ESP Int. J. Adv. Sci. Technol., vol. 2, no. 4, pp. 34–43, 2024, doi: 10.56472/25839233/IJAST-V2I4P105.
9
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.
10
S. Sawesi, M. Rashrash, K. Phalakornkule, J. S. Carpenter, and J. F. Jones, “The Impact of Information Technology on Patient Engagement and Health Behavior Change: A Systematic Review of the Literature,” JMIR Med. Informatics, vol. 4, no. 1, p. e1, Jan. 2016, doi: 10.2196/medinform.4514.
11
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.
12
A. Warrier and K. Abhilash, “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), Bengaluru, India: IEEE, 2025, pp. 1450–1457, December. doi: 10.1109/ICICNIS66685.2025.11315712.
13
S. Murumkar and S. Sen, “A Critical Review on Ethical and Privacy Challenges of AI-Powered Heal thcare Systems,” Int. J. Artif. Intell. Data Sci. Mach. Learn., vol. 6, no. 1, pp. 254–257, March, 2025, doi: 10.63282/3050-9262.IJAIDSML-V6I1P129.
14
T. D. N. Thanh D. Nguyen and O. H. N. Oanh H. Nguyen, “The Role of Perceived Value on Adoption and Usage of Artificial Intelligence in Business,” Glob. Bus. Financ. Rev., vol. 30, no. 9, pp. 183–197, Sep. 2025, doi: 10.17549/gbfr.2025.30.9.183.
15
B. Sachdeva, J. Panda, and S. Taran, “Recent advancements in emerging technology for healthcare management systems,” in Artificial Intelligence, IOP Publishing, 2024, pp. 2-1-2–26. doi: 10.1088/978-0-7503-5964-1ch2.
16
B. Rahimi, H. Nadri, H. Lotfnezhad Afshar, and T. Timpka, “A Systematic Review of the Technology Acceptance Model in Health Informatics,” Appl. Clin. Inform., vol. 09, no. 03, pp. 604–634, Jul. 2018, doi: 10.1055/s-0038-1668091.
17
M. Wang et al., “Health workers’ adoption of digital health technology in low- and middle-income countries: a systematic review and meta-analysis.,” Bull. World Health Organ., vol. 103, no. 2, pp. 126-135F, Feb. 2025, doi: 10.2471/BLT.24.292157.
18
A. Shachak, C. Kuziemsky, and C. Petersen, “Beyond TAM and UTAUT: Future directions for HIT implementation research,” J. Biomed. Inform., vol. 100, p. 103315, Dec. 2019, doi: 10.1016/j.jbi.2019.103315.
19
A. Haleem, M. Javaid, R. P. Singh, and R. Suman, “Telemedicine for healthcare: Capabilities, features, barriers, and applications.,” Sensors Int., vol. 2, p. 100117, 2021, doi: 10.1016/j.sintl.2021.100117.
20
I. J. Borges do Nascimento et al., “Barriers and facilitators to utilizing digital health technologies by healthcare professionals,” npj Digit. Med., vol. 6, no. 1, p. 161, Sep. 2023, doi: 10.1038/s41746-023-00899-4.
21
A. K. Conduah, S. Ofoe, and D. Siaw-Marfo, “Data privacy in healthcare: Global challenges and solutions,” Digit. Heal., vol. 11, May 2025, doi: 10.1177/20552076251343959.
22
A. Yusuf, L. Olaniyan, H. S. Ayobami, A. K. H. Oluwakemi, and L. A. Igbin, “The Role of Technology in Enhancing Healthcare Administration and Service Delivery,” CogNexus, vol. 1, no. 02, pp. 37–50, Apr. 2025, doi: 10.63084/cognexus.v1i02.77.
23
P. Wissawaswaengsuk, P. Kumar, B. Frank, and Y. F. Badir, “The role of trust as the facilitator and contingency factor in the adoption of digital healthcare services: A telemedicine context,” Comput. Human Behav., vol. 172, p. 108722, Nov. 2025, doi: 10.1016/j.chb.2025.108722.
24
J. M. Myszewski and M. Sinha, “A model for determining the value of patient satisfaction in healthcare,” Bus. Process Manag. J., vol. 26, no. 3, pp. 798–815, Sep. 2019, doi: 10.1108/BPMJ-03-2019-0123.
25
A. Warrier, “Real-Time Healthcare Event Processing: Stream Analytics for Clinical Decision Support,” Int. J. Emerg. Res. Eng. Technol., vol. 1, no. 4, Decmber, pp. 47–54, 2020, doi: 10.63282/3050-922X.IJERET-V1I4P106.
26
I. Koo, U. Zaman, H. Ha, and S. Nawaz, “Assessing the interplay of trust dynamics, personalization, ethical AI practices, and tourist behavior in the adoption of AI-driven smart tourism technologies,” J. Open Innov. Technol. Mark. Complex., vol. 11, no. 1, p. 100455, Mar. 2025, doi: 10.1016/j.joitmc.2024.100455.
27
D. Hammoudi Halat, R. Shami, A. Daud, W. Sami, A. Soltani, and A. Malki, “Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross‐Sectional Study,” Clin. Exp. Dent. Res., vol. 10, no. 4, Aug. 2024, doi: 10.1002/cre2.925.
28
Z. Chen et al., “The mediating effects of technology trust and perceived value in the relationship between eHealth literacy and attitude toward the usage of artificial intelligence in nursing: a cross-sectional study,” BMC Nurs., vol. 24, no. 1, p. 989, Jul. 2025, doi: 10.1186/s12912-025-03577-w.
29
R. W. Cooksey, “Descriptive Statistics for Summarising Data,” in Illustrating Statistical Procedures: Finding Meaning in Quantitative Data, Singapore: Springer Singapore, 2020, pp. 61–139. doi: 10.1007/978-981-15-2537-7_5.
30
L. Zhang et al., “Deep Negative Correlation Classification,” Mach. Learn., Dec. 2022, doi: 10.1007/s10994-024-06604-0.
31
R. Hirsch, “Regression Analysis,” in Synthesis Lectures on Mathematics and Statistics, 2024, pp. 83–92. doi: 10.1007/978-3-031-41914-0_9.
32
M. I. Ahmed, B. Spooner, J. Isherwood, M. Lane, E. Orrock, and A. Dennison, “A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare,” Cureus, Oct. 2023, doi: 10.7759/cureus.46454.