Strategic Adoption of Artificial Intelligence for Human Resource Management Practices Transforming Healthcare Sector
DOI:
https://doi.org/10.58818/ijems.v3i3.133Keywords:
Artificial Intelligence, HRM Practices, Healthcare Sector, Human CapitalAbstract
The incorporation of Artificial Intelligence (AI) technology into several industries has significantly impacted the usual workflows and processes in recent years, including the healthcare industry. Human Resource Management (HRM) is essential in healthcare businesses as it is responsible for the recruitment, training, and the retention of skilled staff members who are capable of providing high-quality patient care. This paper investigates different methods in which AI is used in HRM in the healthcare industry on the basis of existing research in the area. It analyzes how AI affects recruitment, talent management, workforce optimization, and employee well-being. This paper also discusses the challenges and future prospects of AI-driven approaches in HRM practices. It explores how these approaches are changing the way healthcare organizations operate and improving patient outcomes. The results provide some valuable contributions to the field of artificial intelligence in the healthcare sector. Initially, the chapter gives a factual foundation for the current presumptions on the implementation and difficulties of artificial intelligence in the healthcare domain. Further, it shows how artificial intelligence provides numerous opportunities to expedite Human Resource operations by offering automated applicant screening, customized learning systems, optimizing the workforce and enhancing employee engagement. Although AI has the capacity to revolutionize HRM practices in the healthcare industry, it also presents some challenges and obstacles. In order to ensure that AI-driven solutions promote fairness, transparency, and equity, it is crucial to address issues such as algorithmic bias, privacy of data and the impact on the human workforce in a deliberate manner. In addition, healthcare firms need to invest funds for implementing rigorous cyber security measures in order to ensure the privacy of patient and employee data from cyber-attacks and potential breaches.
Downloads
References
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 100333. DOI: https://doi.org/10.1016/j.jik.2023.100333
Bankins, S. (2021). The ethical use of artificial intelligence in human resource management: a decision-making framework. Ethics and Information Technology, 23(4), 841-854. DOI: https://doi.org/10.1007/s10676-021-09619-6
Black, J. S., & van Esch, P. (2020). AI-Enabled Recruiting: What Is It and How Should a Manager Use It? Business Horizons, 63(2), 215–226. DOI: https://doi.org/10.1016/j.bushor.2019.12.001
Bostrom, N. (2016). The control problem. Excerpts from superintelligence: Paths, dangers, strategies. Science Fiction and Philosophy: From Time Travel to Superintelligence, 308-330. DOI: https://doi.org/10.1002/9781118922590.ch23
Cavanagh, J., Pariona‐Cabrera, P., & Halvorsen, B. (2023). In what ways are HR analytics and artificial intelligence transforming the healthcare sector?. Asia Pacific Journal of Human Resources, 61(4), 785-793. DOI: https://doi.org/10.1111/1744-7941.12392
Clark, D. (2020). Artificial Intelligence for Learning: How to Use AI to Support Employee Development. Kogan Page Publishers.
Cristofaro, C. L., Ventura, M., Reina, R., & Gentile, T. (2022). Measuring healthcare performance in digitalization era: An empirical analysis. In C. L. Cristofaro et al. (Eds.), Do Machines Dream of Electric Workers? (pp. 137−147). Springer. DOI: https://doi.org/10.1007/978-3-030-83321-3_10
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fallucchi, F., Coladangelo, M., Giuliano, R., & De Luca, E. W. (2020). Predicting Employee Attrition Using Machine Learning Techniques. Computers, 9(4), 86. DOI: https://doi.org/10.3390/computers9040086
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1–2), 567–592. DOI: https://doi.org/10.1007/s10479-018-2818-y
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590-1610). DOI: https://doi.org/10.1108/IJPPM-08-2020-0427
Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R., & Martin, N. R. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 1-16. DOI: https://doi.org/10.1016/j.chb.2022.107179
Hazarika, I. (2020). Artificial intelligence: opportunities and implications for the health workforce. International health, 12(4), 241-245. DOI: https://doi.org/10.1093/inthealth/ihaa007
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586. DOI: https://doi.org/10.1016/j.bushor.2018.03.007
Johnson, A., Dey, S., Nguyen, H., Groth, M., Joyce, S., Tan, L., Glozier, N., & Harvey, S. B. (2020). A review and agenda for examining how technology-driven changes at work will impact workplace mental health and employee well-being. Australian Journal of Management, 45(3), 402–424. DOI: https://doi.org/10.1177/0312896220922292
Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2021). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 40–52. DOI: https://doi.org/10.1108/JTF-02-2020-0013
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25. DOI: https://doi.org/10.1016/j.bushor.2018.08.004
Kaul, S., & Kumar, Y. (2020). Artificial intelligence-based learning techniques for diabetes prediction: challenges and systematic review. SN Computer Science, 1(6), 322. DOI: https://doi.org/10.1007/s42979-020-00337-2
Khosla, R., & Chu, M. T. (2013). Embodying care in Matilda: An affective communication robot. Transactions on Management Information Systems (TMIS), 4(4), 1–33). DOI: https://doi.org/10.1145/2544104
Khosla, R., Chu, M. T., Khaksar, S. M. S., Nguyen, K., & Nishida, T. (2019). Engagement and experience of older people with socially assistive robots in home care. Assistive Technology, 2, 1–15. DOI: https://doi.org/10.1080/10400435.2019.1588805
Langer, M., Baum, K., König, C. J., Hähne, V., Oster, D., & Speith, T. (2021). Spare me the details: How the type of information about automated interviews influences applicant reactions. International Journal of Selection and Assessment, 29(2), 154–69). DOI: https://doi.org/10.1111/ijsa.12325
Li, P., Bastone, A., Mohamad, T. A., & Schiavone, F. (2023). How does artificial intelligence impact human resources performance. Evidence from a healthcare institution in the United Arab Emirates. Journal of Innovation & Knowledge, 8(2), 100340. DOI: https://doi.org/10.1016/j.jik.2023.100340
Liang, H. F., Wu, K. M., Weng, C. H., & Hsieh, H. W. (2019). Nurses’ views on the potential use of robots in the pediatric unit. Journal of Pediatric Nursing, 47, 58–64. DOI: https://doi.org/10.1016/j.pedn.2019.04.027
Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334–354. DOI: https://doi.org/10.1108/IJM-03-2021-0173
Meskó, B., Hetényi, G., & Győrffy, Z. (2018). Will artificial intelligence solve the human resource crisis in healthcare?. BMC health services research, 18, 1-4. DOI: https://doi.org/10.1186/s12913-018-3359-4
Michaelides, M. P. (2018). The Challenges of AI and Blockchain on HR Recruiting Practices. The Cyprus Review, 30(2), 169-180.
Murugesan, U., Subramanian, P., Srivastava, S., & Dwivedi, A. (2023). A study of artificial intelligence impacts on human resource digitalization in industry 4.0. Decision Analytics Journal, 100249. DOI: https://doi.org/10.1016/j.dajour.2023.100249
Nawaz, N., & Gomes, A. M. (2019). Artificial Intelligence Chatbots Are New Recruiters. International Journal of Advanced Computer Science and Applications, 10(9), 1-5. DOI: https://doi.org/10.14569/IJACSA.2019.0100901
Nazareno, L., & Schiff, D. S. (2021). The impact of automation and artificial intelligence on worker well-being. Technology in Society, 67, 101679. DOI: https://doi.org/10.1016/j.techsoc.2021.101679
Oosthuizen, R. M. (2019). Smart technology, artificial intelligence, robotics and algorithms (STARA): Employees’ perceptions and wellbeing in future workplaces. In I. Potgieter, N. Ferreira, & M. Coetzee (Eds.), Theory, research and dynamics of career wellbeing. Springer. DOI: https://doi.org/10.1007/978-3-030-28180-9_2
Park, C. S. Y., & Park, J. Y. (2019). Optimal safe staffing standard for right workforce planning. Journal of Learning and Teaching in Digital Age, 4(2), 42-44.
Pillai, A. S. (2023). AI-enabled Hospital Management Systems for Modern Healthcare: An Analysis of System Components and Interdependencies. Journal of Advanced Analytics in Healthcare Management, 7(1), 212-228.
Reddy, K., Kumar, P., & Rangaiah, S. (2019). Artificial Intelligence (AI) in learning and development: A conceptual paper. Journal of Management Development, 38(1), 34–49.
Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA Environment. Sustainability, 15(6), 5019. DOI: https://doi.org/10.3390/su15065019
Sahlin, J., & Angelis, J. (2019). Performance management systems: Reviewing the rise of dynamics and digitalization. Cogent Business & Management, 6(1), 1642293. DOI: https://doi.org/10.1080/23311975.2019.1642293
Samson, K., & Bhanugopan, R. (2022). Strategic human capital analytics and organizational performance: The mediating effects of managerial decision-making. Journal of Business Research, 144, 637–649. DOI: https://doi.org/10.1016/j.jbusres.2022.01.044
Sardi, A., Sorano, E., Garengo, P., & Ferraris, A. (2020). The role of HRM in the innovation of performance measurement and management systems: A multiple case study in SMEs. Employee Relations: The International Journal, 43(2), 589–606. DOI: https://doi.org/10.1108/ER-03-2020-0101
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23. DOI: https://doi.org/10.1186/s12911-021-01488-9
Sekhri, A., & Cheema, D. J. (2019). The new era of HRM: AI reinventing HRM functions. International Journal of Scientific Research and Review, 7(3), 3073-3077.
Shahzad, M. F., Xu, S., Naveed, W., Nusrat, S., & Zahid, I. (2023). Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model. Heliyon, 9(11). DOI: https://doi.org/10.1016/j.heliyon.2023.e21818
Shaikh, F., Afshan, G., Anwar, R. S., Abbas, Z., & Chana, K. A. (2023). Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being. Asia Pacific Journal of Human Resources, 61(4), 794-820. DOI: https://doi.org/10.1111/1744-7941.12385
Sharma, P., & Khan, W. A. (2022). Revolutionizing Human Resources Management with Big Data: From Talent Acquisition to Workforce Optimization. International Journal of Business Intelligence and Big Data Analytics, 5(1), 35-45.
Taylor, C., Carrigan, J., Noura, H., Ungur, S., Van Halder, J., & Dandona, G. S. (2019). Australia’s automation opportunity: Reigniting productivity and inclusive income growth. McKinsey and Company.
Trocin, C., Hovland, I. V., Mikalef, P., & Dremel, C. (2021). How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies. Technological Forecasting and Social Change, 173, 121081. DOI: https://doi.org/10.1016/j.techfore.2021.121081
Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: implications for recruitment. Strategic HR Review, 17(5), 255-258. DOI: https://doi.org/10.1108/SHR-07-2018-0051
van Esch, P., & Black, J. S. (2019). Factors That Influence New Generation Candidates to Engage with and Complete Digital. AI-Enabled Recruiting. Business Horizons, 62(6), 729–739. DOI: https://doi.org/10.1016/j.bushor.2019.07.004
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The international journal of human resource management, 33(6), 1237-1266. DOI: https://doi.org/10.1080/09585192.2020.1871398
Walsh, T., Levy, N., Bell, G., Elliott, A., Maclaurin, J., Mareels, I., & Wood, F. (2019). The effective and ethical development of artificial intelligence: an opportunity to improve our wellbeing. Melbourne, Australia: Australian Council of Learned Academies.
Xu, G., Xue, M., & Zhao, J. (2023). The association between artificial intelligence awareness and employee depression: the mediating role of emotional exhaustion and the moderating role of perceived organizational support. International Journal of Environmental Research and Public Health, 20(6), 5147. DOI: https://doi.org/10.3390/ijerph20065147
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2024 The International Journal of Education Management and Sociology

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright and License Statement
Authors who publish their manuscripts in this Journal agree to the following conditions:
The copyright for any article in The International Journal of Education Management and Sociology (IJEMS) is fully held by the author under a Creative Commons CC BY 4.0 license:
- The author acknowledges The International Journal of Education Management and Sociology (IJEMS) has the right to publish for the first time with a Creative Commons Attribution 4.0 International License / CC BY 4.0.
- Authors can enter writings separately, arrange non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in The International Journal of Education Management and Sociology (IJEMS)
- The International Journal of Education Management and Sociology (IJEMS) published under the terms of a Creative Commons Attribution 4.0 International License / CC BY 4.0. This license permits anyone to copy and redistribute this material in any form or format, compose, modify, and make derivative works of this material for any purpose, including commercial purposes, so long as they include credit to the Author of the original work.







