An AI-driven Low-Code Framework for Healthcare System Digital Transformation
Abstract
The healthcare sector is under increasing pressure to optimize operational efficiency, reduce administrative burdens, and enhance patient-centric services; all while maintaining strict regulatory compliance. This study investigates the integration of Robotic Process Automation (RPA) with low-code development platforms as a transformative approach to healthcare automation. The study proposes a comprehensive framework that leverages low-code RPA to automate complex, rule-based processes, including electronic medical record (EMR) updates, insurance claims processing, and compliance reporting, without requiring extensive software development expertise.
Using system architecture modeling, workflow analysis, and proof-of-concept implementation, this research evaluates the technical efficacy, scalability, and security of low-code RPA solutions in healthcare environments. Key contributions include a domain-specific methodology for identifying automation candidates, a modular orchestration model for RPA deployment, and performance metrics demonstrating improvements in task execution time, accuracy, and resource utilization. The study also addresses critical challenges, including interoperability with legacy healthcare information systems, data privacy (e.g., HIPAA compliance), and governance of autonomous processes.
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Alowais, S., Alghamdi, S., Alsuhebany, N., Alqahtani, T., Alshaya, A., Almohareb, S., . . . Albekairy, A. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, https://doi.org/10.1186/s12909-023-04698-z.
Anderson, J., & Singh, J. (2021). A Case Study of Using Telehealth in a Rural Healthcare Facility to Expand Services and Protect the Health and Safety of Patients and Staff. MDPI - healthcare, https://doi.org/10.3390/healthcare9060736.
Andrew, J., Isravel, D. P., Sagayam, K. M., Bhushan, B., Sei, Y., & Eunice, J. (2023). Blockchain for healthcare systems: Architecture, security challenges, trends and future directions. Journal of Network and Computer Applications, 215, 103633.
Arashpour, M. (2023). AI explainability framework for environmental management research. Journal of environmental management, 342, 118149. doi:doi.org/10.1016/j.jenvman.2023.118149
Avokati Popullit. (2024). Albania's Digital Transformation Journey; Challenges and steps towards a digital-inclusive approach. Retrieved January 12, 2026, from https://www.avokatipopullit.gov.al/media/manager/website/reports/04%20Raporti%20Anglisht%20Digjitalizimi%20i%20sh%C3%ABrbimeve%20Pdf.pdf
Bahtiri, S., & Selmani, E. (2023). Digital Public Healthcare in Kosovo: An overview of the situation and challenges.
Bai, Y., Ding, Z., & Taylor, A. (2025). From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context. arXiv.
Balakrishnan, K., Velusamy, D., Hinkle, H., Li, Z., Ramasamy, K., Khan, H., . . . Shah, P. (2025). Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation. arXiv.
Bellgard, M., Snelling, T., & McGree, J. (2019). RD-RAP: beyond rare disease patient registries, devising a comprehensive data and analytic framework. Orphanet J. Rare Dis., 14, 176. doi:https://doi.org/10.1186/s13023-019-1139-9
Bharath, R., Peter, E., Patrick, W., Vijay, P., Karl, L., & Zhenqin, W. (2019). Deep Learning for the Life Sciences. O'Reilly Media.
Carlo, C., Beatrice, A., Riccardo, B., Andreas, H., Jason, H. M., Marinka, Z., & John, H. H. (2022). A manifesto on explainability for artificial intelligence in medicine. Artificial intelligence in medicine. 133, 102423. doi:doi.org/10.1016/j.artmed.2022.102423
Chalasani, S., Syed, J., Ramesh, M., Patil, V., & Kumar, T. (2023). Artificial intelligence in the field of pharmacy practice: A literature review. Exploratory Research in Clinical and Social Pharmacy.
Chen, G., Liu, C., Ooi, G., Tan, M., Xie, Z., Yin, J., . . . Ooi, B. (2025). Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives. arXiv.
ChengZhang , Y., YingRu, H., Hongyan, C., Nuo, C., Zhixing, L., Dongxu, M., . . . Zhanpeng, J. (2025). From Passive to Proactive: A Multi-Agent System with Dynamic Task Orchestration for Intelligent Medical Pre-Consultation. arXiv.
Dasho, E., Natarajan, A., & Kuneshka, L. (2023). Primary Healthcare Information System in Albania: Ensuring System’s Interoperability.
Deloitte Consulting LLP. (2025). LSHC Industry Market Research, Quantum Computing in Life Sciences and Health Care. Retrieved January 2026, 6, from https://www.precedenceresearch.com/quantum-computing-in-healthcare-market
Elendu, C., Amaechi, C., Elendu, T., Jingwa, K., Okoye, O., Okah, M., . . . Alimi, H. (2023). Ethical implications of AI and robotics in healthcare A review. Medicine.
Emeka Okeke, H., & Demola Akinbolajo, O. (2023). Integrating AI and automation into low-code development: Opportunities and challenges. IJSRA.
European Commission . (2023). Recovery and resilience scoreboard: Thematic analysis – Health. European Union. https://ec.europa.eu/economy_finance/recovery-and-resilience-scoreboard/assets/thematic_analysis/5_Health.pdf.
Fagbenle, E., Khameneh, J., & Solomon, O. (2024). A Low Code Framework for Improving Healthcare Access Equity in Medically Underserved Areas. International Journal of Science, Architecture, Technology, and Environment, DOI: 10.63680/ijsate052456.010.
Fonsêca, A. L., Barbalho, I. M., Fernandes, F., Júnior, E. A., Nagem, D. A., Cardoso, P. H., . . . Valentim, R. A. (2024). Blockchain in Health Information Systems: A Systematic Review. International Journal of Environmental Research and Public Health, https://doi.org/10.3390/ijerph21111512.
Garodia, A. (2024). Transforming Healthcare and Pharmaceutical Operations through AI-Based Automation Solutions. DOI: 10.13140/RG.2.2.19062.41283.
Georgiou, M. (2023). Redefining Healthcare through Automation: Challenges and Opportunities.
Gjika, I., Qurku, R., & Pano, N. (2023). Digital Technologies in Health Services: Old and New Challenges. Economic Alternatives, DOI: https://doi.org/10.37075/EA.2023.2.12.
Hallberg, A. (2021). Using Low-Code Platforms to Collect Patient-Generated Health.
Healy, M. J. (1973). What Computers Can and Cannot Do. Proceedings of the Royal Society of London. Series B, Biological Sciences, 184(1077), 375–378. doi:https://doi.org/10.1098/rspb.1973.0056
Hofmann, G. (2023, October). eHealth in Albania; An Evaluation of the State of Healthcare Digitalization in Urban Areas.
Jenko, S., Papadopoulou, E., Kumar, V., Overman, S., Krepelkova, K., Wilson, J., . . . Exarchos, T. (2025). Artificial Intelligence in Healthcare: How to Develop and Implement Safe, Ethical and Trustworthy AI Systems. MDPI.
Jevtic, M. (2025). POLICY REPORT: HEALTH POLICY CONSIDERATIONS IN THE WESTERN BALKANS.
Joy, M. (2025). Agentic Workflows in Healthcare: Advancing Clinical Efficiency through AI Integration. www.ijsrcseit.com, doi : https://doi.org/10.32628/CSEIT25112396.
Kadiu, F., Kosova, A., & Kosova, R. (2025). HEALTHCARE SYSTEM AND MEDICAL DEVICES MARKET IN ALBANIA: STRUCTURAL DYNAMICS, DIGITAL TRANSFORMATION AI INTEGRATION. International Research Journal of Modernization in Engineering Technology and Science, DOI: https://www.doi.org/10.56726/IRJMETS83138.
Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), 1-12. doi:doi.org/10.1016/j.patter.2023.100804
Kemi Atobatele, O., Oluwanifemi Ajayi, O., Qudus Hungbo, A., & Adeyemi, C. (2023). Transforming Digital Health Information Systems with Microsoft Dynamics, SharePoint, and Low-Code Automation Platforms. Gyanshauryam, International Scientific Refereed Research Journal.
Kokosi, T., & Harron, K. (2022). Synthetic data in medical research. BMJ medicine, 1. doi:https://doi.org/10.1136/bmjmed-2022-000167
Kummaragunta, J. P. (2024). THE ROLE OF ARTIFICIAL INTELLIGENCE IN REDUCING HEALTHCARE COSTS AND IMPROVING OPERATIONAL EFFICIENCY.
Laxman Mhatre, A. (2024). AI Automation in Healthcare. IJIRMPS.
Lengerich, B. J., Sebastian, B., Harsha, N., Mark, E. N., Yin, A., Manolis, K., & Rich, C. (2023). LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. doi:https://doi.org/10.48550/arXiv.2308.01157
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I., . . . Yin, Y. (2025). A foundational architecture for AI agents in healthcare. Cell Reports Medicine Review.
Maloku, B. (2025). Kosovo in the Digital Agenda of the Western Balkans.
Matthew, J. P., Joanne, E. M., Patrick, M. B., Isabelle, B., Tammy, C. H., Cynthia, D. M., . . . et, a. (2021). The PRISMA 2020 statement: an updated guideline for reporting . Research Methods and Reporting, 372, n71. doi:http://dx.doi.org/10.1136/bmj.n71
McDermott, M., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., & Ghassemi, M. (2021). Reproducibility in machine learning for health research: Still a way to go. Sci. Transl. Med. 1, 13, eabb1655. doi:doi.org/10.1126/scitranslmed.abb1655
Mhatre, A. (2023). Generative AI for Health Care Contact Center. Journal of Artificial Intelligence & Cloud Computing.
Mogili, V. (2025). Healthcare and Finance Transformation through Enterprise Content, Low-Code, and Automation: A Multinational Technology Corporation's Approach. Sarcouncil Journal of Engineering and Computer Sciences.
Nandhakumar, F. Q., Gorrepati, R. N., & Kareem, S. A. (2024). Blockchain Applications in Electronic Health Records (EHRs) . International Journal of Global Innovations and Solutions (IJGIS) , https://ijgis.pubpub.org/pub/9ittndsh.
Ness, C., & Hansen, M. (2019). An exploratory study of the potential of low-code development in the healthcare sector in Norway.
Nidumolu, A. (2020). webXplore. MIT. Retrieved from https://github.com/arnavn101/WebXplore/blob/master/
Nikhil. (2025). Low-Code/No-Code Development Platforms: Empowering the Next Generation of Software Creators. International Journal of Research in Engineering and Science (IJRES).
Nwankwo, E. I., Emeihe, E. V., Ajegbile, M. D., Olaboye, J. A., & Maha, C. C. (2024). Integrating Telemedicine and AI to Improve Healthcare Access in Rural Settings. International Journal of Life Science Research Archieve, https://doi.org/10.53771/ijlsra.2024.7.1.0061.
Nweke, I., Ogadah, C., Koshechkin, K., & Oluwasegun, P. (2025). Multi-Agent AI Systems in Healthcare: A Systematic Review Enhancing Clinical Decision-Making. Asian Journal of Medical Principles and Clinical Practice.
Oluremi, D. (2025). Blockchain-Integrated AI Framework for Secure Healthcare Data Sharing.
Open Data Institute. (2023). Opportunities to encourage the secondary use of health data amongst Western Balkan countries.
Page, M., & al., e. (2021). The PRISMA 2020 statement: an updated guideline for reporting. BMJ, 372, n71. doi:http://dx.doi.org/10.1136/bmj.n71
Prince, S. J. (2023). Understanding Deep Learning. MIT Press.
Rabbani, S., El-Tanani, M., Sharma, S., Rabbani, S., El-Tanani, Y., Kumar, R., & Saini, M. (2025). Generative Artificial Intelligence in Healthcare: Applications, Implementation Challenges, and Future Directions. MDPI, https://doi.org/10.3390/biomedinformatics5030037.
Rocco, G., Affonso, D., Caruso, R., Duka, B., Giardina, B., Notarnicola, I., . . . Stievano, A. (2021). Nursing in Albania: A Catalytic Force in Transforming Health Professionals and Health Care. Journal of Nursing Scholarship, 53(4), 479-489. doi:https://doi.org/10.1111/jnu.12655
Rony, M., Parvin, M., & Ferdousi, S. (2023). Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nursing Open Access Wiley, DOI: 10.1002/nop2.2070.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1, 206–215. doi:doi.org/10.1038/s42256-019-0048-x
Söylemez, I. (2024). Accelerating Low Code Automation Development with Generative Artificial Intelligence.
The Royal Society. (2024 ). Science in the age of AI: How artificial intelligence is changing the nature and method of scientific research. Retrieved January 2026, 13, from royalsociety.org/science-in-the-age-of-ai
Thomas, J., & Edward, E. (2023). Framework-Driven Development for AI-Enabled Healthcare Technology. REVISTA DE INTELIGENCIA ARTIFICIAL EN MEDICINA.
Tushe, M. (2025). The Use of Artificial Intelligence in Patient Care and the Role of Nurses and Opportunities in Albania. doi: 10.20944/preprints202503.1978.v1.
Ukeje, G. (2023, June). Evaluation and Design of Low Code/No Code App Development Platforms to be Used by Medical Professionals.
Viswanadhapalli, V. (2025). The Future of Intelligent Automation: How Low-Code/No-Code Platforms are Transforming AI Decisioning. International Journal Of Engineering And Computer Science, DOI: 10.18535/ijecs/v14i01.4946.
World Health Organization. (2016). WHO European Framework for Action on Integrated Health Services Delivery. Retrieved January 12, 2026, from https://iris.who.int/server/api/core/bitstreams/db8882ce-d972-459e-9238-4fe1da611c95/content
World Health Organization. (2024). Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. Retrieved March 2024, 5, from https://www.who.int/publications/i/item/9789240084759
Yang, C., Wu, Z., Jiang, P., Lin, Z., Gao, J., Danek, B., & Sun, J. (2023). PyHealth: A Deep Learning Toolkit for Healthcare Predictive Modeling., (p. Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2023).
Zahavy, Tom; Google DeepMind. (2026). LLMs cannot jump. Google DeepMind. Retrieved August 1, 2026, from https://www.tomzahavy.com/projects/llms-cant-jump
Zhang, D., Wald, R., Adeli, E., Cryst, E., Ho, D. E., Meinhardt, C., . . . Fei-Fei, L. (2026). The World Model and Spatial Intelligence Era: Governing AI Beyond Language . Stanford University, Human-Centered Artificial Intelligence. Issue Brief HAI Policy & Society.
Zheng, H., Shi, Z., & Yi, P. (2025). MedCoAct: Confidence-Aware Multi-Agent Collaboration for Complete Clinical Decision. arXiv.
Zhong, X., Brian, G., Shusen, L., Bhavya, K., Anna, H., & Yong-Jin, H. (2022). Explainable machine learning in materials science. NPJ Comput Mater, 8, 204. doi:doi.org/10.1038/s41524-022-00884-7
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