Journal of Ai ML DL

A Project Management Model for Deploying AI-Based Healthcare Infrastructure

Kh Maksudul Hasan^1*, Md Sakib Mia²

The study determines essential elements of AI healthcare implementation which enables better project management and operational performance.

Abstract

Background: Artificial Intelligence (AI) technology continues to advance its presence within healthcare systems through its diagnostic improvement and patient monitoring and operational optimization capabilities. A structured project management approach needs to implement governance systems and technological infrastructure and workforce preparation and ethical compliance standards to achieve effective deployment of this system.

Methods: The study used a cross-sectional survey method to gather data from 305 healthcare professionals and administrative staff members. The study employed descriptive statistics with regression analysis and Pearson correlation to determine which variables predict outcomes and how governance interacts with data management and technical infrastructure and workforce training and monitoring systems.

Results: The study found that governance and policy elements together with technical infrastructure elements make up 45% of the total critical components while workforce training represents 15% and monitoring and evaluation and ethical compliance each contribute 10% to the total critical components. The system provides three main advantages which include 25% faster and more accurate diagnosis and 22% reduction in human errors and 20% better patient monitoring. The regression analysis showed that technical infrastructure (β = 0.31, P = 0.001), ethical and privacy compliance (β = 0.29, P = 0.008), and governance (β = 0.28, P = 0.012) function as main determinants for AI project achievement.

Conclusion: The implementation of AI healthcare systems demands a complete strategy which unites governance structures with technological deployment and human resource development and system monitoring and ethical guidelines.

Keywords: Technical Infrastructure, Governance, Workforce Training, Artificial Intelligence, Healthcare Project Management

References

Agarwal, Y., Jain, M., Sinha, S., & Dhir, S. (2019). Delivering high-tech, AI-based health care at Apollo Hospitals. Global Business and Organizational Excellence, 39(2), 20–30. https://doi.org/10.1002/joe.21981

Akhir, A., Rahman, F., Islam, A., Chowdhury, N., Mia, M. S., Hossain, M. I. (2024). "Strategic Role of Business Analytics in Healthcare Systems Performance Optimization", Journal of Primeasia, 5(1),1-8,10347. https://doi.org/10.25163/primeasia.5110347

Bibhu, V., Das, L., Rana, A., Sharma, S., & Salagrama, S. (2023). AI model for blockchain based industrial application in healthcare IoT. In Engineering cyber-physical systems and critical infrastructures (pp. 163–184). https://doi.org/10.1007/978-3-031-31952-5_8

Biswas, A., Hossain, M. I., Jahan, I., Chowdhury, N., Mia, M. S. (2024). "Advanced Analytics in IT Systems Unlocking Insights Enhancing Decision Making and Maximizing Business Value", Applied IT & Engineering, 2(1),1-8,10379. https://doi.org/10.25163/engineering.2110379

Bizzo, B. C., Dasegowda, G., Bridge, C., Miller, B., Hillis, J. M., Kalra, M. K., Durniak, K., Stout, M., Schultz, T., Alkasab, T., & Dreyer, K. J. (2023). Addressing the challenges of implementing artificial intelligence tools in clinical practice: Principles from experience. Journal of the American College of Radiology, 20(3), 352–360. https://doi.org/10.1016/j.jacr.2023.01.002

Cartolovni, A., Tomicic, A., & Mosler, E. L. (2022). Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. International Journal of Medical Informatics, 161, 104738. https://doi.org/10.1016/j.ijmedinf.2022.104738

Davenport, T. H., & Glaser, J. P. (2022). Factors governing the adoption of artificial intelligence in healthcare providers. Discover Health Systems, 1(1), 4. https://doi.org/10.1007/s44250-022-00004-8

Dicuonzo, G., Donofrio, F., Fusco, A., & Shini, M. (2022). Healthcare system: Moving forward with artificial intelligence. Technovation, 120, 102510. https://doi.org/10.1016/j.technovation.2022.102510

Dlugatch, R., Georgieva, A., & Kerasidou, A. (2023). Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care. BMC Medical Ethics, 24(1), 42. https://doi.org/10.1186/s12910-023-00917-w

Dow, E. R., Keenan, T. D., Lad, E. M., Lee, A. Y., Lee, C. S., Loewenstein, A., Eydelman, M. B., Chew, E. Y., Keane, P. A., & Lim, J. I. (2022). From data to deployment. Ophthalmology, 129(5), e43–e59. https://doi.org/10.1016/j.ophtha.2022.01.002

Fisher, S., & Rosella, L. C. (2022). Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health, 22(1), 2146. https://doi.org/10.1186/s12889-022-14422-z

Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Elsevier eBooks (pp. 295–336). https://doi.org/10.1016/b978-0-12-818438-7.00012-5

Goirand, M., Austin, E., & Clay-Williams, R. (2021). Implementing Ethics in Healthcare AI-Based Applications: A scoping review. Science and Engineering Ethics, 27(5), 61. https://doi.org/10.1007/s11948-021-00336-3

Greenspan, H., Estépar, R. S. J., Niessen, W. J., Siegel, E., & Nielsen, M. (2020). Position paper on COVID-19 imaging and AI: From the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare. Medical Image Analysis, 66, 101800. https://doi.org/10.1016/j.media.2020.101800

Jahan, I., Chowdhury, N., Mia, M. S., Hossain, M. I., Biswas, A. (2024). "Transforming Business Operations Through IT-Enabled Predictive and Prescriptive Analytics: Challenges, Opportunities, and Best Practices", Applied IT & Engineering, 2(1),1-7,10381. https://doi.org/10.25163/engineering.2110381

Kondylakis, H., Kalokyri, V., Sfakianakis, S., Marias, K., Tsiknakis, M., Jimenez-Pastor, A., Camacho-Ramos, E., Blanquer, I., Segrelles, J. D., López-Huguet, S., Barelle, C., Kogut-Czarkowska, M., Tsakou, G., Siopis, N., Sakellariou, Z., Bizopoulos, P., Drossou, V., Lalas, A., Votis, K., . . . Lekadir, K. (2023). Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects. European Radiology Experimental, 7(1), 20. https://doi.org/10.1186/s41747-023-00336-x

Kong, L. (2021). A study on the AI-based online triage model for hospitals in sustainable smart city. Future Generation Computer Systems, 125, 59–70. https://doi.org/10.1016/j.future.2021.06.023

Leone, D., Schiavone, F., Appio, F. P., & Chiao, B. (2020). How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. Journal of Business Research, 129, 849–859. https://doi.org/10.1016/j.jbusres.2020.11.008

Mujawar, M., Gohel, H., Bhardwaj, S., Srinivasan, S., Hickman, N., & Kaushik, A. (2020). Nano-enabled biosensing systems for intelligent healthcare: towards COVID-19 management. Materials Today Chemistry, 17, 100306. https://doi.org/10.1016/j.mtchem.2020.100306

Nagulpelli, S., Chavan, A., Kandalkar, A., & Kulkarni, S. (2022). AI-Based Health Management System. In Lecture notes in electrical engineering (pp. 379–389). https://doi.org/10.1007/978-981-19-6581-4_29

Nashid, &., Papia, S. K., Islam, A., Akhir, A., Rahman, F., Biswas, A. (2024). "The Role of Deep Learning and AI in Revolutionizing Business Analytics: Frameworks, Applications, and Managerial Implications", Applied IT & Engineering, 2(1),1-8,10365. https://doi.org/10.25163/engineering.2110365

Nashid, &., Papia, S. K., Islam, A., Akhir, A., Rahman, F., Biswas, A. (2024). "The Role of Deep Learning and AI in Revolutionizing Business Analytics: Frameworks, Applications, and Managerial Implications", Applied IT & Engineering, 2(1),1-8,10365. https://doi.org/10.25163/engineering.2110365

Papia, S. K., Rahman, F., Nashid, S., Akhir, A., Biswas, A., Islam, A. (2024). "The Role of AI and IT in Transforming Stock Price Analysis and Decision-Making Frameworks", Journal of Primeasia, 5(1),1-7,10376. https://doi.org/10.25163/primeasia.5110376

Retico, A., Avanzo, M., Boccali, T., Bonacorsi, D., Botta, F., Cuttone, G., Martelli, B., Salomoni, D., Spiga, D., Trianni, A., Stasi, M., Iori, M., & Talamonti, C. (2021). Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Physica Medica, 91, 140–150. https://doi.org/10.1016/j.ejmp.2021.10.005

Shah, S. I. H., Naeem, M., Paragliola, G., Coronato, A., & Pechenizkiy, M. (2023). An AI-empowered infrastructure for risk prevention during medical examination. Expert Systems With Applications, 225, 120048. https://doi.org/10.1016/j.eswa.2023.120048

Solanki, P., Grundy, J., & Hussain, W. (2022). Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers. AI And Ethics, 3(1), 223–240. https://doi.org/10.1007/s43681-022-00195-z

Sweeney, D., Nair, S., & Cormican, K. (2023). Scaling AI-based industry 4.0 projects in the medical device industry: An exploratory analysis. Procedia Computer Science, 219, 759–766. https://doi.org/10.1016/j.procs.2023.01.349

Ta, A. W. A., Goh, H. L., Ang, C., Koh, L. Y., Poon, K., & Miller, S. M. (2022). Two Singapore public healthcare AI applications for national screening programs and other examples. Health Care Science, 1(2), 41–57. https://doi.org/10.1002/hcs2.10

Tutun, S., Johnson, M. E., Ahmed, A., Albizri, A., Irgil, S., Yesilkaya, I., Ucar, E. N., Sengun, T., & Harfouche, A. (2022). An AI-based decision support system for predicting mental health disorders. Information Systems Frontiers, 25(3), 1261–1276. https://doi.org/10.1007/s10796-022-10282-5

Wang, G., Badal, A., Jia, X., Maltz, J. S., Mueller, K., Myers, K. J., Niu, C., Vannier, M., Yan, P., Yu, Z., & Zeng, R. (2022). Development of metaverse for intelligent healthcare. Nature Machine Intelligence, 4(11), 922–929. https://doi.org/10.1038/s42256-022-00549-6

Widner, K., Virmani, S., Krause, J., Nayar, J., Tiwari, R., Pedersen, E. R., Jeji, D., Hammel, N., Matias, Y., Corrado, G. S., Liu, Y., Peng, L., & Webster, D. R. (2023). Lessons learned from translating AI from development to deployment in healthcare. Nature Medicine, 29(6), 1304–1306. https://doi.org/10.1038/s41591-023-02293-9