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@article{198953,
        author = {Abhibhoo Anand and Prasad Ajay Mahankal and Debjyoti Mukhopadhyay and Madhur Patil and Shivani Pandey},
        title = {Digital Workflow Automation System for Dental and Clinical SOPs with Real-Time Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11468-11473},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198953},
        abstract = {Dental and clinical practices face challenges due to fragmented manual workflows including registration, diagnosis, treatment planning, billing, and prescription handling. Such workflows increase administrative burden, delay treatments, and reduce patient satisfaction [3], [4]. This paper presents a digital workflow automation system that standardizes dental and clinical Standard Operating Procedures (SOPs), integrates secure patient data handling, role-based access, automated prescription delivery, and real-time analytics via Microsoft Power BI [2]. Additionally, the system leverages Large Language Model (LLM)-assisted preliminary diagnosis to support clinical decision-making [1], [3]. Pilot evaluation indicates improvements in clinical efficiency, data accuracy, and patient service delivery [2], [4]. The system’s architecture for clinical process automation is conceptually related to the principles of openEHR Task Planning (TP), which focuses on developing executable pathways for standardized clinical workflows and integrating clinical data with task execution [4]. The integration of Artificial Intelligence (AI) and Machine Learning (ML) is essential for streamlining these complex and resource-intensive hospital and clinical management tasks [3].},
        keywords = {—Healthcare Information Systems, Workflow Automation, Clinical SOPs, Electronic Health Records, LLM, Power BI, Medical Data Security, openEHR, Predictive Analytics},
        month = {April},
        }