Publication date: Sep 25, 2024
Healthcare has witnessed an increased digitalization in the post-COVID world. Technologies such as the medical Internet of Things and wearable devices are generating a plethora of data available on the cloud anytime from anywhere. This data can be analyzed using advanced artificial intelligence techniques for diagnosis, prognosis, or even treatment of disease. This advancement comes with a major risk to protecting and securing protected health information (PHI). The prevailing regulations for preserving PHI are neither comprehensive nor easy to implement. The study first identifies twenty activities crucial for privacy and security, then categorizes them into five homogeneous categories namely: [Formula: see text] (Policy and Compliance Management), [Formula: see text] (Employee Training and Awareness), [Formula: see text] (Data Protection and Privacy Control), [Formula: see text] (Monitoring and Response), and [Formula: see text] (Technology and Infrastructure Security) and prioritizes these categories to provide a framework for the implementation of privacy and security in a wise manner. The framework utilized the Delphi Method to identify activities, criteria for categorization, and prioritization. Categorization is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and prioritization is performed using a Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS). The outcomes conclude that [Formula: see text] activities should be given first preference in implementation and followed by [Formula: see text] and [Formula: see text] activities. Finally, [Formula: see text] and [Formula: see text] should be implemented. The prioritized view of identified clustered healthcare activities related to security and privacy, are useful for healthcare policymakers and healthcare informatics professionals.
Semantics
Type | Source | Name |
---|---|---|
disease | MESH | privacy |