STEMM Institute Press
Science, Technology, Engineering, Management and Medicine
Leveraging AI for Strategic Management in Healthcare: Enhancing Operational and Financial Performance
DOI: https://doi.org/10.62517/jike.202404301
Author(s)
Hongyan Guo1, Yinhang Wu2, Ruixiang Zhao3, Xuecan Yang1,4, Laurent Peyrodie5, Jean-Marie Nianga6, Zefeng Wang3,4,5,*
Affiliation(s)
1ASIR, Institute - Association of intelligent systems and robotics, Paris, France 2Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang, China 3College of Engineering, Huzhou University, Huzhou, Zhejiang, China 4IEIP, Institute of Education and Innovation in Paris, Paris, France 5ICL, Junia, Université Catholique de Lille, LITL, F-59000 Lille, France 6Sino-Congolese Foundation for Development, Brazzaville, Republic of the Congo *Corresponding Author.
Abstract
This paper explores the transformative potential of artificial intelligence (AI) in revolutionizing hospital management practices, particularly in enhancing operational and financial performance. The incorporation of artificial intelligence (AI) technologies, including machine learning, predictive analytics, and natural language processing, hospitals are addressing inherent inefficiencies in traditional management approaches, thereby improving patient care quality and operational efficiency. Evidence increasingly supports AI's significant impact on optimizing hospital operations, from resource allocation and patient flow management to scheduling and financial processes. AI's role in optimizing staffing, resource utilization, streamlining billing, and claims processing, along with its application in decision support systems for strategic planning and performance monitoring, highlights its effectiveness in tackling long-standing inefficiencies. The strategic integration of AI provides healthcare executives with the necessary instruments to optimize decision-making processes, reduce costs, and enhance overall performance. To maximize AI's benefits, hospital leaders are advised to prioritize comprehensive training, robust data governance, and continuous system evaluation. The paper also suggests avenues for future research, including the development of advanced AI models for complex medical scenarios and the integration of AI with technologies like blockchain and IoT to bolster data security and real-time decision-making. As AI technology advances, its transformative role in healthcare administration will expand, paving the way for a future where hospitals can deliver care with unparalleled quality and efficiency.
Keywords
Artificial Intelligence (AI); Hospital Management; Operational Efficiency; Financial Performance; Machine Learning; Strategic Planning; Data Governance
References
[1]Mi, D., Li, Y., Zhang, K., Huang, C.-Y., Shan, W., & Zhang, J. (2023). Exploring intelligent hospital management mode based on artificial intelligence. Frontiers in Public Health, 11, 1182329. https://doi.org/10.3389/FPUBH.2023.1182329. [2]Božić, V. (2023). Integrated Risk Management and Artificial Intelligence in Hospital. Journal of Artificial Intelligence, 14(2), 1329224. https://doi.org/10.61969/JAI.1329224. [3]Cahyo, L. M., & Astuti, S. D. (2023). Early Detection of Health Problems through Artificial Intelligence (AI) Technology in Hospital Information Management: A Literature Review Study. Journal of Management and Health Sciences, 4(3), 37–42. https://doi.org/10.32996/JMHS.2023.4.3. [4]Bhagat, S. V., & Kanyal, D. (2024). Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review. Cureus Journal of Medical Science, 16(2), e54518. https://doi.org/10.7759/CUREUS.54518. [5]Dammavalam, S. R., Chandana, N., Rao, T. R., Lahari, A., & Aparna, B. (2022). AI based chatbot for hospital management system. Journal of Computing and Healthcare Applications, 7(2), 123-127. https://doi.org/10.1109/ICAN56228.2022.10007105 [6]Gautam, A. K., Vasu, T. V., & Mamatha, G. N. (2023). Optimal allocation of resources and hospital capacity planning for critical diseases using AI and data mining. IEEE Journal of Artificial Intelligence (AI) in Medicine and Modern Healthcare Systems. Advance online publication. pp. 1-6,https://doi.org/10.1109/ICTBIG59752.2023.10455968 [7]Kumar, N. A., & Suresh, S. (2019). A Proposal of Smart Hospital Management using Hybrid Cloud, IoT, ML, and AI. In IEEE International Conference on Computing and Communication Engineering (pp. 45898, 9002098). https://doi.org/10.1109/ICCES45898.2019.9002098 [8]Varnosfaderani, S. M., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4), 337. https://doi.org/10.3390/bioengineering11040337 [9]Niu, M., Liu, W., Gao, X., Zhao, Z., & Wang, J. (2023). IoT, Heterogeneous Data Processing, and AI Automation Synergy for Improved Efficiency and Maintenance: Revolutionizing Hospital Operations. In IEEE International Conference on Signal Processing, Communications and Computing (pp. 59353, 10400261). https://doi.org/10.1109/ICSPCC59353.2023.10400261 [10]Rojas-López, T., Álvarez-Martín, D., Pujol-deCastro, A., Moreno-Domínguez, Ó., Barquiel, B., García-Pérez-de-Sevilla, E., ... González-Pérez-de-Villar, N. (2024). 821-P: Comparison of In-Hospital Glucose Management of People with Type 2 Diabetes Made by Artificial Intelligence Chatbot vs. Physicians—A Cross-Sectional Study. Diabetes, 71(Supplement 1), 321-329. https://doi.org/10.2337/db24-821-p. [11]Dubey, K., Bhowmik, M., Pawar, A., Patil, M. K., Deshpande, P. A., & Khartad, S. S. (2023). Enhancing operational efficiency in healthcare with AI-powered management. In IEEE International Conference on AI in Healthcare and Innovations.pp.1-7, https://doi.org/10.1109/ICAIIHI57871.2023.10488953. [12]Taliento, M. (2023). AI in healthcare management and accounting: Novelties and trends from a literature review and illustrative cases. Journal of Healthcare Management, 14(2), 4252023. https://doi.org/10.5171/2023.4252023. [13]Yadav, S., Mane, P., Swarnkar, V., Rawandale, S. K., Patil, A. S., & Katke, K. S. (2023). The role of AI in healthcare policy development and management. In IEEE International Conference on AI in Healthcare and Innovations. pp. 1-6, https://doi.org/10.1109/ICAIIHI57871.2023.10489810. [14]Nasir, S., Khan, R. A., & Bai, S. (2023). Ethical framework for harnessing the power of AI in healthcare and beyond. IEEE vol. 12, pp.31014-31035, https://doi.org/10.1109/ACCESS.2024.3369912. [15]Dhingra, M. (2023). Bioethical considerations of artificial intelligence in healthcare management. International Journal of Science and Research, 14(2), sr23709144913. https://doi.org/10.21275/sr23709144913. [16]Ranjbar, A., Mork, E., Ravn, J., Brøgger, H., Myrseth, P., Østrem, H. P., & Hallock, H. (2024). Managing risk and quality of AI in healthcare: Are hospitals ready for implementation? Risk Management and Healthcare Policy, 14(2), S452337. https://doi.org/10.2147/RMHP.S452337. [17]Abdul, S., Adeghe, E. P., Adegoke, B. O., Adegoke, A. A., & Udedeh, E. H. (2024). AI-enhanced healthcare management during natural disasters: Conceptual insights. Environmental Science and Technology Journal, 14(5), 1155. https://doi.org/10.51594/estj.v5i5.1155. [18]Bouderhem, R. (2024). Shaping the future of AI in healthcare through ethics and governance. Palgrave Communications,11, 416, 02894-w. https://doi.org/10.1057/s41599-024-02894-w. [19]Bheema, S. (2023). AI in healthcare: Enhancing diagnosis, treatment, and healthcare systems for a smarter future in India. Asian Biotechnology and Biosafety Review, 14(10). https://doi.org/10.33140/abbsr.06.10.02 [20]Nizam, V., & Aslekar, A. (2021). Challenges of applying AI in healthcare in India. Journal of Pharmaceutical Research International, 14(2), 31969. https://doi.org/10.9734/jpri/2021/v33i36b31969. [21]Alrashdi, I., Hossin, M. A., & Kamruzzaman, M. (2023). AI-assisted risk management systems in healthcare industries of smart cities. In IEEE Global Communications Workshops. pp.2113-2117 https://doi.org/10.1109/GCWkshps58843.2023.10464736. [22]Chaturvedi, V. M., Khadilkar, S. M., Karwande, V. S., Rokade, A. H., & Nagargoje, Y. (2023). Patient engagement and satisfaction in AI-enhanced healthcare management. In IEEE International Conference on AI in Healthcare and Innovations. pp. 1-7, https://doi.org/10.1109/ICAIIHI57871.2023.10489712. [23]Dhawan, S., & Kumar, K. (2024). Ethical implications of AI in healthcare. International Journal of Science Research and Engineering Management, 14(2), ijsrem29006. https://doi.org/10.55041/ijsrem29006. [24]Reddy, S. (2024). Generative AI in healthcare: An implementation science informed translational path on application, integration, and governance. Implementation Science, 19, 27, 01357-9. https://doi.org/10.1186/s13012-024-01357-9. [25]Poornima, D., Premalatha, J. S., Abirami, G., Triveni, K., & Bobby, M. P. (2023). Real-time AI and machine learning applications in healthcare management. In IEEE International Conference on Emerging Research in Computing Science. pp. 1-6, https://doi.org/10.1109/ICERCS57948.2023.10434180. [26]Sehrawat, S. K. (2023). Intelligent healthcare management: Advancing healthcare with integrated AI and ML solutions. International Journal of Research in Medical Sciences and Technology, 14(1), v16i01.016. https://doi.org/10.37648/ijrmst.v16i01.01. [27]Shekhar, A. et al. (2023). Breaking barriers: How neural network algorithm in AI revolutionize healthcare management to overcome key challenges. International Journal on Recent and Innovation Trends in Computing and Communication, 14(9), 9929. https://doi.org/10.17762/ijritcc.v11i9.992. [28]Rathore, Y., Sinha, U., Haladkar, J. P., Mate, N. R., Bhosale, S. A., & Chobe, S. (2023). Optimizing patient flow and resource allocation in hospitals using AI. In IEEE International Conference on Artificial Intelligence in Healthcare and Innovations. pp. 1-6. https://doi.org/10.1109/ICAIIHI57871.2023.10489698. [29]Wang, S., Wang, H., & Li, Y. (2023). Resource allocation problem based on robust optimization in cloud diagnosis background. In IEEE Chinese Control Conference. pp.1820-1827.https://doi.org/10.23919/CCC58697.2023.10239774. [30]Momeni, M. A., Mostofi, A., Jain, V., & Soni, G. (2022). COVID-19 epidemic outbreak: Operating rooms scheduling, specialty teams timetabling, and emergency patients' assignment using the robust optimization approach. Annals of Operations Research, 313(1), 43-67. https://doi.org/10.1007/s10479-022-04667-7. [31]Padthe, K. K., Kumar, V., Eckert, C., Mark, N., Zahid, A., Ahmad, M., & Teredesai, A. (2021). Emergency department optimization and load prediction in hospitals. arXiv preprint. https://arxiv.org/abs/2102.03672. [32]Peshane, V., Baig, M. M., Sonekar, S., & Sawwashere, S. S. (2023). Revolutionizing healthcare through IoT and edge computing: An evaluation of optimization techniques. In IEEE International Conference on Computing, Communication, and Energy Engineering. pp.1-6.https://doi.org/10.1109/ICCCEE55951.2023.10424655. [33]Wang, Y., Liu, N., Pan, Z., & You, X. (2023). AI-based resource allocation in E2E network slicing with both public and non-public slices. Applied Sciences, 13(22), 12505. https://doi.org/10.3390/app132212505. [34]Xu, Z., Xie, Y., Dong, F., Fu, S., & Hao, J. (2023). Joint optimization of task offloading and resource allocation for edge video analytics. In IEEE International Conference on Computer Supported Cooperative Work in Design.pp.636-641 https://doi.org/10.1109/CSCWD57460.2023.10152681. [35]Islam, M. M. A. F. (2024). Dynamic resource allocation for AI/ML applications in edge computing: Framework architecture and optimization methods. Journal of Artificial Intelligence and Geographical Sciences, 3(1), 65. https://doi.org/10.60087/jaigs.vol03.issue01.p65. [36]Cai, D., Hu, K., Fu, Y., & Zou, S. (2023). Optimal task assignment and path planning for multiple patrol robots in makeshift hospitals. In IEEE Chinese Control Conference. pp.1814-1819.https://doi.org/10.23919/CCC58697.2023.10241105. [37]Dai, G., Li, R., & Ma, S. (2022). Research on the equity of health resource allocation in TCM hospitals in China based on the Gini coefficient and agglomeration degree: 2009-2018. International Journal for Equity in Health, 21(1), 149. https://doi.org/10.1186/s12939-022-01749-7. [38]Al Otaibi, A. M., Tham, J., & Ahmad, A. (2023). Factors affecting the health care insurance inclusion and Saudi hospital management operation efficiency. International Journal of Management Science and Technology, 10(5), 2498. https://doi.org/10.15379/ijmst.v10i5.2498. [39]Al Harbi, S., Aljohani, B., Elmasry, L., Baldovino, F. L., Raviz, K. B., Altowairqi, L., & Alshlowi, S. (2024). Streamlining patient flow and enhancing operational efficiency through case management implementation. BMJ Open Quality, 13(1), e002484. https://doi.org/10.1136/bmjoq-2023-002484. [40]Liu, L., Wu, A., Yu, H., Wang, N., & Li, H. (2014). Comprehensive evaluation of operations management of a hospital by TOPSIS and GRA. International Journal of Health Management, 2(8), 351–354. https://doi.org/10.3109/23256176.2014.988977. [41]Lin, X., Duan, G., Huang, J., Zhou, Q., Huang, H., Xiao, J., Xu, Z., Shen, H., & Zhuo, H. (2024). Construction of a smart hospital innovation platform using the Internet + technology. Journal of Medical Internet Research. https://pubmed.ncbi.nlm.nih.gov/38639608 [42]Da Silva, M. M. L., Pantoja, L. C., Silva, V. K. M., de Melo, A. M. M., de Oliveira, J. M. L., & Elleres, P. A. d. P. (2023). CONTROLPHARM - medication management system in the urgent and emergency hospital environment. International Journal of Advanced Research, 17795. p14-19. https://doi.org/10.21474/ijar01/17795. [43]Nurhaliza, P. A., Aditya, M. P., Pratiwi, A. I., Andestri, M. A., Angelita, E., & Ainy, A. (2024). Systematic review of lean management: Hospital transformation for increasing operational efficiency and patient care. Kuwait Journal of Health Management, 10(1), 2763. https://doi.org/10.24903/kujkm.v10i1.2763. [44]Ryan, J., Doster, B., Daily, S., & Lewis, C. (2014). A balanced perspective to perioperative process management aligned to hospital strategy. International Journal of Healthcare Information Systems and Informatics, 10(1), 1036. https://doi.org/10.4018/ijhisi.2014100101. [45]Alhaider, A. A., Lau, N., Davenport, P. B., & Morris, M. K. (2020). Quantitative evidence supporting distributed situation awareness model of patient flow management. Journal of Patient Safety and Risk Management.9(1), https://doi.org/10.1177/2327857920091000 [46]Mungai, M. K., & Peter, K. (2023). E-supply chain management practices and operational performance of hospitals of Kiambu County in Kenya. European Journal of Logistics, Purchasing and Supply Chain Management, 11(3), 5063. https://doi.org/10.37745/ejlpscm.2013/vol11n35063. [47]Yang, Y., Bin, Y., Ma, Y., Zhao, J., Xin, Z., Cheng, C., & Zhai, Z. (2024). Information management of full-cycle inpatient bed reservation for cancer patients under the normalised prevention and control of the COVID-19 pandemic. BMC Health Services Research, 11206-6. Res 24, 806. https://doi.org/10.1186/s12913-024-11206-6 [48]Datzmann, T., Dörfer, L., Freude, G., Hannemann, M., Tharmaratnam, G., Stangl, P., Swoboda, W., Schafmeister, S., Gebhard, F., Kaisers, U. X., & Huber-Lang, M. (2024). Impact of COVID-19 pandemic-induced surgical restrictions on operational performance: A case study at the University Hospital of Ulm. European Journal of Trauma and Emergency Surgery, 2558-z. https://doi.org/10.1007/s00068-024-02558-z [49]Salem, N., AlBrakat, A., Ibraheem, S., Al-Hawary, S., & Muflih, S. (2024). Green supply chain practices and operational performance of Jordanian private hospitals. Uncertain Supply Chain Management 11(2), 523-532 DOI:10.5267/j.uscm.2023.2.012. [50]Zhang, J. (2022). An RPA+AI-based financial process optimization of small- and medium-sized enterprises preparing for IPO. Business and Management Journal, 34, 2865. https://doi.org/10.54691/bcpbm.v34i.2865. [51]Bozic, K., Ward, L., Vail, T., & Maze, M. (2014). Bundled payments in total joint arthroplasty: Targeting opportunities for quality improvement and cost reduction. Clinical Orthopaedics and Related Research, 472(1), p 188-193. https://doi.org/10.1007/s11999-013-3034-3. [52]Amirabadi, M. (2022). Perspectives on implementing AI in resource management. Journal of Resource Management and Development Economics, 2(3), 11-17. https://doi.org/10.61838/kman.jrmde.2.3. [53]Bogojevic Arsic, V. (2021). Challenges of financial risk management: AI applications. Management Journal, 26(3), pp. 27-34. https://doi.org/10.7595/MANAGEMENT.FON.2021.0015. [54]Chen, B., Wu, Z., & Zhao, R. (2023). From fiction to fact: The growing role of generative AI in business and finance. Journal of Corporate Finance, 21(4), 471-496. https://doi.org/10.1080/14765284.2023.2245279. [55]Praveen, U., Farnaz, G., & Hatim, G. (2020). Inventory management and cost reduction of supply chain processes using AI-based time-series forecasting and ANN modeling. Procedia Manufacturing, 38, pp.256-263. https://doi.org/10.1016/j.promfg.2020.01.034. [56]Da Cruz, A. R., & J. A. (2022). Optimisation of processes by recommending cost reduction strategies in Central Sterile Supply Department (CSSD) in a tertiary care hospital. Journal of Health Management, 24(2), 88062. https://doi.org/10.1177/09720634221088062. [57]Braithwaite, J. (1995). Organizational change, patient-focused care: An Australian perspective. Journal of Health Services Research and Policy, 8(3), 303. https://doi.org/10.1177/095148489500800303. [58]Leggat, S., & Yap, K. (2020). How are hospitals using artificial intelligence in strategic decision-making?—a scoping review. Journal of Hospital Management and Health Policy, 4(1), 92. https://doi.org/10.21037/jhmhp-20-92. [59]Ghaffar, A., Arshad, A., Siddqiue, M. U., & Nasir, A. (2024). AI Strategy in Healthcare CHRM: Analyzing the Influence Organization Effective Performance Evidence from the Private Hospitals of Lahore Pakistan. Journal of Hospital Research and Review, 4(1), 339. https://doi.org/10.61919/jhrr.v4i1.339. [60]Aravazhi, A., Helgheim, B., & Aadahl, P. (2023). Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients. Journal of Artificial Intelligence in Medicine, 5(2), 7057. https://doi.org/10.1155/2023/8867057. [61]Ali, A., & Rafi, N. (2024). Enhancing Human Resource Management Through Advanced Decision-Making Strategies: Harnessing The Power Of Artificial Intelligence For Strategic, Data-Driven, And Judicious Choices. Journal of Human Resource Management, 21(8), pp. 881-889. https://doi.org/10.59670/ml.v21is8.9488. [62]Rimawi, D., Liotta, A., Todescato, M., & Russo, B. (2023). CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems. arXiv preprint, 2311, 4562. https://doi.org/10.48550/arXiv.2311.0456. [63]Nurkholis, N., Mardiati, E., Fachriyah, N., & Prayudi, M. (2024). Power Dynamics in Accounting-Related Strategic Decision-Making within Hospital Management: A Narrative Literature Review. Journal of Accounting and Information Management, 7(1), 8827. https://doi.org/10.53682/jaim.vi.8827. [64]Url, P., Paal, S., Rosenzopf, T., Furian, N., Vorraber, W., Vössner, S., Toedtling, M., Zefferer, U., & Schaefer, U. (2021). Using Simulation Models as Early Strategic Decision Support in Health Care - Designing a Medical 3D Printing Center at Point of Care in Hospitals. Winter Simulation Conference Proceedings, pp. 1-12, 5479. https://doi.org/10.1109/WSC52266.2021.9715479. [65]Vummadi, J., & Hajarath, K. (2024). Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility. International Journal of Supply Chain Management, 9(2), pp.77-87. https://doi.org/10.47604/ijscm.2547. [66]Umamaheswari, S., Valarmathi, A., Dhinakaran, D. P., Vijai, C., Sathyakala, S., & Raja, M. (2024). A Novel Approach of Data-Driven Strategic Decision-Making in Management: AI-Enabled Analysis of Market Trends, Competitive Intelligence, and Internal Performance Data. International Conference on Science, Technology, Engineering, and Mathematics, pp.1-5, https://doi.org/10.1109/ICONSTEM60960.2024.10568724. [67]Adesina, A. A., Iyelolu, T. V., & Paul, P. O. (2024). Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights. World Journal of Advanced Research and Reviews, 22(3), 1927-1934. https://doi.org/10.30574/wjarr.2024.22.3.1961. [68]Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M., & Krishna, R. (2022). Explanations Can Reduce Overreliance on AI Systems During Decision-Making. Journal of Artificial Intelligence Research, 7(1), pp. 1-38. https://doi.org/10.1145/3579605. [69]Kaggwa, S., Eleogu, T., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2023). AI in Decision Making: Transforming Business Strategies. International Journal of Research in Science and Innovation, 10(1), pp.423-444. https://doi.org/10.51244/ijrsi.2023.1012032. [70]Kohn, L. T., Corrigan, J. M., & Donaldson, M. S. (2000). To Err is Human: Building a Safer Health System. National Academy Pres Institute of Medicine (US) Committee on Quality of Health Care in America.doi:10.17226/9728. [71]Fenton, J. J., Xing, G., Elmore, J. G., Bang, H., Chen, S. L., Lindfors, K. K., & Baldwin, L. M. (2013). Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees. Annals of internal medicine, 158(8), 580–587. https://doi.org/10.7326/0003-4819-158-8-201304160-00002. [72]Bourla, A. B., Swearingen, R., Karo, G., DeKoven, M., Ambe, A., & Walker, C. (2021). Retrospective Analysis of Health System Utilization Following Introduction of a Comprehensive Electronic Health Record System. Journal of the American Medical Informatics Association, 28(5), pp.1009-1021. https://doi.org/10.1093/jamia/ocab009 [73]Lyell, D., & Coiera, E. (2016). Automation Bias and Verification Complexity: A Systematic Review. Journal of the American Medical Informatics Association, 24(2), 423-431. https://doi.org/10.1093/jamia/ocw105.
Copyright @ 2020-2035 STEMM Institute Press All Rights Reserved