Authors :
Dr. M. A. Kumar; Pradeep Naidu Penta; Grushnesh Rajagiri; VTSNV Surya Teja; Ramireddy Srihitha Reddy
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/5bn8r7ky
Scribd :
https://tinyurl.com/3yy763pp
DOI :
https://doi.org/10.38124/ijisrt/26apr1663
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Abstract modern healthcare environments produce large amounts of various data, including structured EHR
fields, narratives in clinical documentation, transcripts of telemedicine sessions, diagnostic imaging reports, and wearables
and remote monitoring devices. A large fraction of this data is unstructured or semi-structured or inconsistently encoded,
further contributing to documentation burden, impeding interoperability, and limiting the provision of timely and datadriven decision support.
This paper presents a self-directed, AI-based agent that is capable of autonomously organizing, harmonizing and
integrating patient information that has multiple and heterogeneous sources. The suggested agent is a combination of
transformer-based clinical natural language processing (NLP) and a reinforcement learning (RL)-controlled control layer
that allows policy-directed choices regarding entity identification, semantic normalization, interrecord conflict, and general
redundancy reduction.
The system is conceptually inspired by agentic AI architectures designed to operate in smart-city governance and
clinical workflow orchestration; it consists of a few coordinated layers: (i) a data ingestion and harmonization layer, which
connects with disparate clinical data sources; (ii) an NLP-oriented text processing module; (ii) a structured data
normalization and The agent is tested by means of the digital-twin simulations and shadow-mode deployments built into the
real clinical processes. Empirical findings show that it can identify entities with very high accuracy and resolve them, cut
data curation and correction costs by over 80 percent, and produce quantifiable benefits on downstream tasks, like clinical
decision support and predictive analytics. It is important to note that the system exhibits the ability to autonomously evolve
in response to changing data schema and documentation patterns and to execute within a clearly stipulated set of safety,
equity and regulatory limits.
This work bridges an important methodological gap between powerful clinical NLP methods and autonomous
management of healthcare data by making the structuring of data a dynamic, self-organizing decision-making problem
instead of a static, pipeline-based process. The suggested agent prepares the groundwork of scalable, high-fidelity
representations of patient data that can be used to enable personalized medicine, continuous learning health systems, and
powerful clinical and operational analytics.
Keywords :
Self-Governing AI, Intelligent Patient Data Structuring, Agentic AI, Clinical Natural Language Processing, Transformer Architectures, Reinforcement Learning, Healthcare Interoperability, Governance and Safety.
References :
- M. Payal, T. Ananth Kumar, K. Suresh Kumar, “Integrating Natural Language Processing (NLP) and Machine Learning Techniques for Healthcare Industries,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 10, no. 5, 2022.
- T. C. Reis, “Artificial Intelligence and Natural Language Processing for Improved Telemedicine: Before, During and After Remote Consultation,” Atencion Primaria, vol. 57, 103228, 2025.
- S. Nerella et al., “Transformers in Healthcare: A Survey,” University of Florida, preprint, 2023.
- D. B. Rathod et al., “AI‑Driven Decision‑Making Architectures for Future‑Ready Smart Urban Infrastructures,” 2025 IEEE 5th International Conference on ICT in Business, Industry and Government (ICTBIG), 2025.
- A. Warrier, A. K. S. et al., “Autonomous Agentic AI for Clinical Workflow Orchestration: Self‑Managing Healthcare Operations,” Proceedings of the 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS 2025), IEEE, 2025.
- Additional foundational works on agentic AI and clinical NLP are cited within the text as per the original provided references.
Abstract modern healthcare environments produce large amounts of various data, including structured EHR
fields, narratives in clinical documentation, transcripts of telemedicine sessions, diagnostic imaging reports, and wearables
and remote monitoring devices. A large fraction of this data is unstructured or semi-structured or inconsistently encoded,
further contributing to documentation burden, impeding interoperability, and limiting the provision of timely and datadriven decision support.
This paper presents a self-directed, AI-based agent that is capable of autonomously organizing, harmonizing and
integrating patient information that has multiple and heterogeneous sources. The suggested agent is a combination of
transformer-based clinical natural language processing (NLP) and a reinforcement learning (RL)-controlled control layer
that allows policy-directed choices regarding entity identification, semantic normalization, interrecord conflict, and general
redundancy reduction.
The system is conceptually inspired by agentic AI architectures designed to operate in smart-city governance and
clinical workflow orchestration; it consists of a few coordinated layers: (i) a data ingestion and harmonization layer, which
connects with disparate clinical data sources; (ii) an NLP-oriented text processing module; (ii) a structured data
normalization and The agent is tested by means of the digital-twin simulations and shadow-mode deployments built into the
real clinical processes. Empirical findings show that it can identify entities with very high accuracy and resolve them, cut
data curation and correction costs by over 80 percent, and produce quantifiable benefits on downstream tasks, like clinical
decision support and predictive analytics. It is important to note that the system exhibits the ability to autonomously evolve
in response to changing data schema and documentation patterns and to execute within a clearly stipulated set of safety,
equity and regulatory limits.
This work bridges an important methodological gap between powerful clinical NLP methods and autonomous
management of healthcare data by making the structuring of data a dynamic, self-organizing decision-making problem
instead of a static, pipeline-based process. The suggested agent prepares the groundwork of scalable, high-fidelity
representations of patient data that can be used to enable personalized medicine, continuous learning health systems, and
powerful clinical and operational analytics.
Keywords :
Self-Governing AI, Intelligent Patient Data Structuring, Agentic AI, Clinical Natural Language Processing, Transformer Architectures, Reinforcement Learning, Healthcare Interoperability, Governance and Safety.
