Design and Analysis of an ML-Based Model for Protected Health Information Governance

Abstract

Purpose & Objective:

With the increasing digitization of healthcare data, ensuring the privacy, security, and compliance of Protected Health Information has become a critical challenge. This paper presents a Machine Learning (ML)-based governance algorithms designed to enhance the protection of data by identifying vulnerabilities, detecting anomalies, and ensuring regulatory compliance. The objective is to develop an intelligent, adaptable, and automated system that strengthens healthcare data governance while minimizing risks associated with unauthorized access and data breaches.

Methodology:

The proposed algorithms integrate ML-driven algorithms for real-time monitoring, anomaly detection, and predictive risk assessment. Multiple algorithms, including supervised and unsupervised learning models, have been implemented to classify potential threats and unauthorized access patterns. The model has been trained on diverse datasets to enhance accuracy and adaptability. A rule-based governance layer has been incorporated to ensure compliance with healthcare data protection regulations. The implementation phase involves testing the model effectiveness in real-world healthcare environments, evaluating its accuracy, efficiency, and scalability.

Outcomes:

The experimental results demonstrate that the proposed ML-based governance algorithms significantly improve the security and privacy of PHI. The system successfully detects anomalies with high accuracy, reduces false positives, and ensures data integrity through automated policy enforcement. The findings indicate that ML-driven governance can effectively mitigate risks, enhance compliance, and optimize healthcare data management by proactively addressing security concerns.

Limitations & Future Scope:

Despite its promising results, the proposed algorithm has certain limitations, including dependency on high-quality training data, potential biases in ML models, and computational overhead in large-scale deployments. Future research will focus on refining algorithmic efficiency, integrating federated learning for enhanced privacy, and expanding the model to accommodate evolving data governance regulations. Incorporating explainable AI techniques will improve transparency and trust in automated decision-making processes within healthcare data governance.

Index terms Machine Learning (ML), Protected Health Information (PHI), Data Governance, Privacy Protection