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American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

AI-driven textual understanding systems enabling autonomous regulatory reporting file generation within healthcare administration frameworks

AI-driven textual understanding systems enabling autonomous regulatory reporting file generation within healthcare administration frameworks

Authors

  • Dr. Ama Boateng
    Department of Computer Science and Informatics, University of Ghana, Legon, Ghana

Abstract

The increasing complexity of healthcare administration systems has led to a growing demand for automated regulatory reporting mechanisms capable of ensuring accuracy, compliance, and efficiency. Traditional compliance reporting workflows rely heavily on manual documentation processes, which are not only time-consuming but also prone to inconsistencies and human error. This research explores the design and application of AI-driven textual understanding systems for autonomous regulatory reporting file generation within healthcare administration frameworks.

The study integrates advances in natural language processing (NLP), transformer-based architectures, and large language models (LLMs) to construct a conceptual and technical framework for automated compliance documentation. Foundational models such as transformer attention mechanisms (Vaswani et al., 2017), BERT-based contextual embeddings (Devlin et al., 2018), and generative pre-trained models (Radford et al., 2018; Radford et al., 2019) are analyzed as core components of the proposed system architecture. Additionally, scaling and optimization techniques such as DeepSpeed and Mixture-of-Experts architectures are considered to support large-scale deployment (Rasley et al., 2020; Li et al., 2022).

A key focus of this research is the integration of semantic understanding with structured regulatory templates, enabling automated generation of compliance-ready reporting files. The system leverages domain-adaptive language modeling techniques to interpret healthcare-specific documentation, extract relevant entities, and map them into regulatory formats. Prior work in automated compliance documentation using NLP (Sravan Kumar Nidiganti, 2025) provides an important reference point for understanding domain-specific document automation challenges and serves as a baseline for extending autonomous capabilities.

The findings suggest that AI-driven textual systems significantly reduce administrative workload, improve regulatory accuracy, and enhance scalability in healthcare compliance workflows. However, challenges such as hallucination in language models, domain adaptation limitations, and regulatory variability remain critical concerns. This study contributes a structured conceptual framework and identifies future directions for robust, interpretable, and regulation-compliant AI systems in healthcare administration.

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