The digitalization of healthcare has led to a rapid increase in data volume alongside growing demands for privacy and regulatory compliance. In this context, many healthcare organizations face difficulties integrating fragmented information systems while maintaining full control over sensitive data. This paper proposes a conceptual framework for a modular analytics platform designed to support predictive decision-making in medical institutions without direct access to identifiable patient information. The study focuses on the digital transfor mation of healthcare management processes using de-identified institutional data. The methodology combines systems analysis with architectural modeling, resulting in a set of structured diagrams that describe the platform’s deployment logic, component interaction, and business model. The proposed architecture supports both cloud-based and on-premises deployment options, allowing institutions to choose between flexibility and full data sovereignty. The platform includes modules for integration and visualization, along with secure API-based data exchange mechanisms. architectural and BPMN diagrams are presented to illustrate the platform structure and subscription-based financial model. The results demonstrate the feasibility of implementing the proposed architecture in healthcare environments constrained by legal, technical, and organizational factors. The concept provides a foundation for future prototyping and pilot deployment in healthcare systems aiming to achieve secure, scalable analytics.