A Multi-Level Framework for AI Adoption in Health Care: Integrating Behavioral, Organizational and Ethical Perspectives
Author(s): Syed Kavish Ali
Affiliation: Research Scholar, Department of Business Administration, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Page No: 54-71-
Volume issue & Publishing Year: Volume 3, Issue 4, 2026/04/27
Journal: International Journal of Modern Engineering and Management | IJMEM
ISSN NO: 3048-8230
DOI: https://doi.org/10.5281/zenodo.19808853
Abstract:
Purpose: Research on the adoption of artificial intelligence in health care has proceeded in four streams that seldom communicate: acceptance theorists study clinicians, organisational theorists study institutions, fairness theorists study algorithmic discrimination, and sustainability researchers study distributional consequences. This review integrates these four streams to build a conceptual framework for empirical work.
Design / Methodology: A narrative literature review was conducted using Scopus, Web of Science, PubMed, and Google Scholar (January 2015–March 2026). Around 430 records were filtered to 130 papers through title-abstract-fulltext screening, then thematically reviewed along four theoretical axes: UTAUT2, the Technology-Organisation-Environment (TOE) framework, the AI Bias Taxonomy, and the Triple Bottom Line (TBL).
Findings: The review reveals five gaps: fragmentation across individual and organisational levels; weak integration of bias into adoption theory; narrow outcome framings dominated by financial measures; scarce India-specific evidence; and under-use of mediation and moderation methods. The paper develops a multi-level conceptual model with 17 testable propositions linking acceptance, organisational context, bias, trust, and sustainability outcomes.
Originality / Value: The framework proposed here is the first to recognise algorithmic bias as a first-class adoption construct—a direct barrier to intention, a constraint on trust, and a moderator of the equity benefits of adoption. It offers a systematic empirical research agenda for scholars and a common language for leaders and policymakers assessing institutional AI decisions.
Keywords:
Healthcare AI; UTAUT2; TOE Framework; Algorithmic Bias; Triple Bottom Line; Conceptual Framework.
Reference:
1. Adithyan, R., et al. (2024). Knowledge and attitude towards artificial intelligence among healthcare professionals in a tertiary care hospital in India. Journal of Clinical and Diagnostic Research, 18(5), LC01–LC05.
2. Ammenwerth, E. (2019). Technology acceptance models in health informatics: TAM and UTAUT. Studies in Health Technology and Informatics, 263, 64–71.
3. Analytics Insight. (2026, February 18). India AI Impact Summit 2026: Centre sets AI healthcare guardrails with SAHI, BODH rollout. Analytics Insight.
4. Baker, J. (2012). The technology–organisation–environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information systems theory: Explaining and predicting our digital society, Vol. 1 (pp. 231–245). Springer.
5. BCG (Boston Consulting Group). (2026). How digital and AI will reshape health care in 2026. BCG Publications.
6. Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023). Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 7(6), 719–732.
7. Chettri, S. K., et al. (2025). Bridging the gap in the adoption of trustworthy AI in Indian healthcare: Challenges and opportunities. AI (MDPI), 6(1), Article 10.
8. Chief Healthcare Executive. (2026, January 15). AI in health care: 26 leaders offer predictions for 2026. Chief Healthcare Executive Magazine.
9. Cobelli, N., & Blasioli, E. (2023). To be or not to be digital? A bibliometric analysis of adoption of eHealth services. The TQM Journal, 35(9), 299–331.
10. Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024). Bias in medical AI: Implications for clinical decision-making. PLOS Digital Health, 3(11), e0000651.
11. Dai, Q., Li, M., Yang, M., Shi, S., Wang, Z., Liao, J., Li, Z., E, W., Tao, L., & Tang, Y.-D. (2025). Attitudes, perceptions, and factors influencing the adoption of AI in health care among medical staff: Nationwide cross-sectional survey study. Journal of Medical Internet Research, 27, e75343.
12. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
13. Doximity. (2026). 2026 state of AI in medicine report. Doximity Inc.
14. Dwivedi, Y. K., et al. (2025). Generative AI organisational adoption: A mixed-methods study extending the TOE framework. Information Systems Frontiers, 27(2), 345–368.
15. Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone Publishing.
16. Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., Kuo, P.-C., Lungren, M. P., Palmer, L. J., Price, B. J., Purkayastha, S., Pyrros, A. T., Oakden-Rayner, L., Okechukwu, C., Seyyed-Kalantari, L., … Zhang, H. (2022). AI recognition of patient race in medical imaging: A modelling study. The Lancet Digital Health, 4(6), e406–e414.
17. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108.
18. Hasanzadeh, F., et al. (2025). Bias in health AI: Sources, mitigation strategies, and ethical considerations. AI and Ethics, 5(1), 89–112.
19. Hassan, M. K., et al. (2026). AI adoption in healthcare organizations: Spheres of development and the virtue of visible value. Technological Forecasting and Social Change. Advance online publication.
20. Kumar, A., et al. (2024). Representational bias in global health AI datasets: Implications for Indian healthcare. BMJ Global Health, 9(3), e014231.
21. Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine, 6(1), 111.
22. NVIDIA. (2026). State of AI in healthcare and life sciences 2026. NVIDIA Corporation.
23. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
24. PIB (Press Information Bureau). (2026, February 17). Union Minister launches SAHI and BODH initiatives at India AI Impact Summit 2026. Government of India.
25. Pinto, A. S., Abreu, A., Pérez Cota, M., et al. (2025). A meta-analysis of TOE factors driving organizational adoption of artificial intelligence across industries. Discover Artificial Intelligence, 5, Article 36.
26. Pumplun, L., Fecho, M., Wahl, N., Peters, F., & Buxmann, P. (2021). Adoption of machine learning systems for medical diagnostics in clinics: Qualitative interview study. Journal of Medical Internet Research, 23(10), e29301.
27. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866–872.
28. Salah, O. H., & Ayyash, M. M. (2024). A systematic review of TOE framework in AI adoption research. Information Technology & People, 37(4), 1456–1489.
29. Schiavone, F., Mancini, D., Leone, D., & Lavorato, D. (2022). Digital business models and ridesharing for value co-creation in healthcare. Technological Forecasting and Social Change, 176, 121462.
30. Siddique, S., et al. (2024). Accuracy disparities of AI diagnostic models across minority populations. Journal of Health Equity, 8(2), 156–171.
31. Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., & Valley, T. S. (2020). Racial bias in pulse oximetry measurement. New England Journal of Medicine, 383(25), 2477–2478.
32. Su, Y., et al. (2025). Factors influencing adoption of AI health assistants: An extended UTAUT model. Scientific Reports, 15(1), Article 2345.
33. Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers, 23(4), 987–1005.
34. Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, Article 102269.
35. Thieme, A., et al. (2025). Surgeon adoption of AI surgical technology: A mixed methods study. International Journal of Medical Informatics, 189, Article 105467.
36. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. Lexington Books.
37. Ueda, D., et al. (2024). Environmental sustainability dimensions of AI in healthcare. Sustainability, 16(5), Article 2048.
38. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
39. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
40. Wolters Kluwer. (2026). 2026 healthcare AI trends: Insights from experts. Wolters Kluwer Health.
41. Xie, Y., et al. (2025). A bibliometric analysis of artificial intelligence in healthcare: Thirty years of research. Artificial Intelligence in Medicine, 148, Article 102757.