AI-Powered Regulatory Deficiency Management in Healthcare

Abstract

Addressing the gaps in regulation goes to the root of compliance and safety in the healthcare and biomedical industries. The regulatory response to deficiencies will include the natural consequences of a lack of consistency, delays in processing, and risk of compliance response. This paper introduces an AI-driven regulatory workflow to bring modernization into regulatory practices at any healthcare facility. This research processed 7,600 health care regulatory cases on clinical documentation, quality assurance, and pharmacovigilance submissions using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex>-fold cross-validation on four machine learning algorithms: Random Forest, XGBoost, SVM, and Logistic Regression. Out of these, Random Forest achieved a maximum accuracy of 97.7 %, reduced response time by a factor of approximately 51 %, and decreased manual errors by up to 57 %. The framework is scalable and flexible across multiple compliance scenarios to ensure faster, audit-ready, and easily understood responses. These results hold importance for furthering biomedical compliance practices by cognitive automation, shortening regulatory cycles, and ensuring data integrity in AI-enabled health technology management.

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