Assess the Preparedness for Artificial Intelligence (A.I.) Implementation in Private Hospitals in Melaka, Malaysia.
Authors
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Malaysia (Malaysia)
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Malaysia (Malaysia)
Albert Feisal@Muhd Feisal Ismail
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Malaysia (Malaysia)
Fakulti Teknologi dan Kejuruteraan Elektronik dan Elektronik, Universiti Teknikal Malaysia Melaka, Malaysia (Malaysia)
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Malaysia (Malaysia)
Politeknik Tun Syed Nasir Syed Ismail, Pagoh, Johor, Malaysia (Malaysia)
Faculty of Business and Economics, Universitas Atma Jaya Yogyakarta, Indonesia (Indonesia)
Article Information
DOI: 10.47772/IJRISS.2026.100600799
Subject Category: Technology
Volume/Issue: 10/6 | Page No: 11452-11457
Publication Timeline
Submitted: 2026-06-11
Accepted: 2026-06-16
Published: 2026-07-06
Abstract
The incorporation of A.I. in healthcare can improve patient care, optimize operations, and decrease expenses. This research seeks to evaluate the degree of A.I. deployment, ascertain the acceptance level of target elements influencing attitudes and patient involvement and experience, and analyze the obstacles and benefits experienced by healthcare professionals, specifically via the lens of the technology acceptance model. Data were gathered via semi-structured interviews with an administrator, a physician, and a nurse from different private hospitals in Melaka, with participants chosen based on their experience, job responsibilities, and expertise in the private hospital environment. The findings indicate an increasing interest in the implementation of A.I., especially in diagnostic imaging and patient management systems. Nonetheless, obstacles such as elevated implementation expenses impede extensive adoption. Notwithstanding this challenge, the study underscores the considerable potential of A.I. to revolutionize healthcare service in Melaka, contingent upon strategic investments and policy backing.
Keywords
Artificial Intelligence, Healthcare, Adoption, Technology Acceptance Model
Downloads
References
1. Baroni, I., Re Calegari, G., Scandolari, D., & Celino, I. (2022). AI-TAM: a model to investigate user acceptance and collaborative intention inhuman-in-the-loop AI applications. Human Computation, 9(1), 1–21. https://doi.org/10.15346/hc.v9i1.134 [Google Scholar] [Crossref]
2. Boulding, W., Glickman, S. W., Manary, M. P., Schulman, K. A., & Staelin, R. (2011). [Google Scholar] [Crossref]
3. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. The American Journal of Managed Care, 17(1), 41–48. [Google Scholar] [Crossref]
4. Chaitin, G. (2013). Computing Machinery and Intelligence. Alan Turing: His Work and Impact, 551–621. https://doi.org/10.1016/B978-0-12-386980-7.50023-X [Google Scholar] [Crossref]
5. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]
6. Jelovac, D. (2022). Korisničko Prihvaćanje Digitalizacije Hotelskih Restorana: Primjena Modela Proširenog Prihvaćanja Tehnologije Customer Acceptance Of Digitalisation Of Hotel Restaurants: Applying an Extended Technology Acceptance Model. https://api.semanticscholar.org/CorpusID:254172776 [Google Scholar] [Crossref]
7. Kacmar, C. J., Fiorito, S. S., & Carey, J. M. (2009). The Influence of Attitude on the Acceptance and Use of Information Systems. Information Resources Management Journal, 22(2), 22–49. https://doi.org/10.4018/irmj.2009040102 [Google Scholar] [Crossref]
8. Kharde, Y., & Madan, P. (2018). ISSN (Print): 2319-801X www.ijbmi.org || Volume 7 Issue 2 Ver. II || February. In International Journal of Business and Management Invention (IJBMI) ISSN. www.ijbmi.org [Google Scholar] [Crossref]
9. Lak, A., Rashidghalam, P., Myint, P. K., & Bradaran, H. R. (2020). Comprehensive 5P framework for active aging using the ecological approach: An iterative systematic review. BMC Public Health, 20(1). https://doi.org/10.1186/s12889-019-8136-8Maxwell, J. A. (2008). Designing a qualitative study (Vol. 2). The SAGE handbook of applied social research methods. [Google Scholar] [Crossref]
10. Md. Aftab Uddin. (2021). The Essentials of Machine Learning in Finance and Accounting (1st ed.). Routledge. [Google Scholar] [Crossref]
11. Moroz, G. Z., Holovanova, I. A., Bychkova, S. A., & Dzyzinska, O. O. (2023). [Google Scholar] [Crossref]
12. Current Aspects of Engaging Patients to Shared Decision-Making And Partner Participation In The Treatment Process (review). Клінічна Та Профілактична Медицина, 2, 89–98. https://doi.org/10.31612/2616-4868.2(24).2023.13 [Google Scholar] [Crossref]
13. Peters, K., & Halcomb, E. (2015). Interviews in qualitative research. Nurse Researcher, 22(4), 6–7. https://doi.org/10.7748/nr.22.4.6.s2 [Google Scholar] [Crossref]
14. Roulston, K. (2023). Interviews in Qualitative Research. In The Encyclopedia of Applied Linguistics (pp. 1–9). Wiley. https://doi.org/10.1002/9781405198431.wbeal0572.pub3 [Google Scholar] [Crossref]
15. Samira Adus, Jillian Macklin, & Andrew Pinto. (2023). Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care. [Google Scholar] [Crossref]
16. Saw, S. N., & Ng, K. H. (2022). Current challenges of implementing artificial intelligence in medical imaging. Physica Medica, 100, 12–17. https://doi.org/10.1016/j.ejmp.2022.06.003 [Google Scholar] [Crossref]
17. Singh, R. P., Hom, G. L., Abramoff, M. D., Campbell, J. P., & Chiang, M. F. (2020). [Google Scholar] [Crossref]
18. Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient. Translational Vision Science & Technology, 9(2), 45. https://doi.org/10.1167/tvst.9.2.45 [Google Scholar] [Crossref]
19. Turner, D., Ting, H., Wong, M. W., Lim, T.-Y., & Tan, K.-L. (2021). Applying Qualitative Approach in Business Research. Asian Journal of Business Research,11(3). https://doi.org/10.14707/ajbr.210111 [Google Scholar] [Crossref]
20. Ulrich, P., & Frank, V. (2021). Relevance and adoption of AI technologies in German SMEs - Results from survey-based research. Procedia Computer Science, 192, 2152–2159. https://doi.org/10.1016/j.procs.2021.08.228 [Google Scholar] [Crossref]
21. Vladyka, R. (2023, June 23). Using Machine Learning Algorithms To Analyze Genetic Data For Disease Diagnosis. Theoretical And Empirical Scientific Research: Concept And Trends.https://doi.org/10.36074/logos-23.06.2023.36 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
- Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
- Integrating GIS into Traffic Incident Management: A Web-Based System
- RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
- Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape