31, October 2026

Decision Making Using AI and Business Performance in Healthcare Organizations: A Gap Analysis of the Use of Business Analytics

VOLUME-12; ISSUE-10; OCT-2026

Author(s): 1. Majjiga Nandini, 2. Dr.C.Padamavathi, 3. D. Saravanan

Authors Affiliations:

13rd year bba(AI & DS), ICFAI Business School(IBS),Hyderabad.

2Dean, Undergraduate programme, ICFAI Business School(IBS),Hyderabad.

3Faculty of Operations & IT, ICFAI Business School(IBS),Hyderabad

The ICFAI Foundation for Higher Education(IFHE),IBS Hyderabad

(Deemed to be university u/s 3 of the UCG ACT 1956)

Hyderabad.

DOIs:10.2015/IJIRMF/202610026     |     Paper ID: IJIRMF202610026


Abstract
Keywords
Cite this Article/Paper as
References
Artificial Intelligence (AI) and data-driven technologies are becoming increasingly important in healthcare management, especially in areas such as decision-making, operational management, information processing, and organizational performance. The five selected studies reviewed in this research gap examine different dimensions of AI adoption and data-driven decision-making in healthcare organizations. They consider studies on AI-supported hospital management, the performance outcomes of AI adoption, data exploitation for healthcare decision-making, AI-driven operational efficiency, and Business Analytics capabilities in electronic medical record systems. The reviewed literature demonstrates that AI can help healthcare organizations to improve information accessibility, analytical capabilities, operational processes, and managerial activities. Evidence also shows that AI adoption can be associated with improvements in hospital performance and operational efficiency. Furthermore, Business Analytics capabilities, such as the ability to aggregate, analyse, and interpret data, can contribute to more effective decision-making. Nevertheless, the studies differ in their research settings, variables, levels of analysis, and outcomes, and therefore do not provide a complete explanation of how these factors interact. The literature review shows that important relationships remain insufficiently integrated. Existing studies examine AI adoption and organizational performance, AI and operational efficiency, data and decision-making, or Business Analytics capabilities and decision-making, but these elements are generally investigated separately. In particular, the studies lack empirical evidence to explain whether Business Analytics capabilities function as an intermediate mechanism through which AI adoption influences decision-making effectiveness, and whether improved decision-making subsequently contributes to organizational performance. Therefore, the identified research gap offers a chance to develop an integrated framework connecting AI Adoption, Business Analytics Capabilities, Decision-Making Effectiveness, and Healthcare Organizational Performance. Addressing the gap would provide a better understanding of not only whether AI creates organizational value in a healthcare context, but also the mechanisms through which this value could be achieved.
Artificial Intelligence (AI), AI Adoption, Business Analytics Capabilities, Decision-Making Effectiveness, Healthcare Management, Organizational Performance, Data-Driven Decision-Making, Operational Efficiency, Healthcare Organizations.

Majjiga Nandini, Dr.C.Padamavathi,  D. Saravanan (2026); Decision Making Using AI and Business Performance in Healthcare Organizations: A Gap Analysis of the Use of Business Analytics, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-12, Issue-10, Available on –   https://www.ijirmf.com/

  1. Alves, M., Seringa, J., Silvestre, T., & Magalhães, T. (2024). Use of artificial intelligence tools in supporting decision-making in hospital management. BMC Health Services Research, 24 , 1282, https://doi.org/10.1186/s12913-024-11602-y
  2. Pham, P., Zhang, H., Gao, W., & Zhu, X. (2024). Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. hospitals. Journal of Business Research, 172 , 114402. https://doi.org/10.1016/j.jbusres.2023.114402
  3. Basile, L. J., Carbonara, N., Panniello, U., & Pellegrino, R. (2025). The exploitation of data to support decision-making in healthcare: A systematic literature review and future research directions. Management Review Quarterly, 76 (2), 1073–1105. https://doi.org/10.1007/s11301-024-00482-5
  4. Kumar, R., Singh, A., Ahmed Kassar, A. S., Humaida, M. I., Joshi, S., & Sharma, M. (2025). Impact of an artificial intelligence-driven operational management system on operational efficiency in health care organization in Saudi Arabia: A mediating role of staff attitude. Frontiers in Public Health, 13 , 1558644. https://doi.org/10.3389/fpubh.2025.1558644
  5. Liew, E. J. Y., Kwok, A. O. J., Koh, S. G. M., Ruslan, S. R., Hasan, M. S., & Poh, Y. H. (2025). Examining doctors’ business analytics capabilities in using the electronic medical record system for decision-making effectiveness in intensive care units: Impact of the COVID-19 pandemic. PLOS ONE, 20 (7), e0317954. https://doi.org/10.1371/journal.pone.0317954
  6. Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024). Artificial intelligence and decision-making in healthcare: A thematic analysis of a systematic review of reviews. Health Services Research and Managerial Epidemiology, 11. https://doi.org/10.1177/23333928241234863
  7. Al Teneiji, A. S., Abu Salim, T. Y., & Riaz, Z. (2024). Factors impacting the adoption of big data in healthcare: A systematic literature review. International Journal of Medical Informatics, 187 , 105460. https://doi.org/10.1016/j.ijmedinf.2024.105460
  8. Elragal, R., Elragal, A., & Habibipour, A. (2023). Healthcare analytics-A literature review and proposed research agenda. Frontiers in Big Data, 6 , 1277976. https://doi.org/10.3389/fdata.2023.1277976
  9. A comprehensive and systematic review of multi-criteria decision-making methods and applications in healthcare.(2023). Healthcare Analytics, 4 , 100232. https://doi.org/10.1016/j.health.2023.100232
  10. A systematic review of literature on sustaining decision-making in healthcare organizations amid imperfect information in the big data era. (2023). Sustainability, 15 (21), 15476. https://doi.org/10.3390/su152115476
  11. Zhang, W., Zhu, Y., et al. (2024). Leverage machine learning to identify key measures in hospital operations management: A retrospective study to explore feasibility and performance of four common algorithms. BMC Medical Informatics and Decision Making, 24 , 286. https://doi.org/10.1186/s12911-024-02689-8
  12. Lopes, J., Faria, M., & Santos, M. F. (2024). Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review. PLOS ONE, 19 (5), e0302697. https://doi.org/10.1371/journal.pone.0302697

 

Views: 23

Download Full Paper

Download PDF No. of Downloads:0 | No. of Views: 20