โก Quick Summary
This article introduces a trust-aware architecture for personalized digital health, leveraging Blueprint Personas and ontology-based reasoning to enhance patient care. The system aims to provide context-sensitive support through intelligent interactions, promoting user engagement and trust.
๐ Key Details
- ๐ System Components: User modeling, symbolic reasoning, adaptive trust mechanisms
- ๐งฉ User Profiles: Detailed patient profiles capturing clinical, behavioral, and emotional traits
- โ๏ธ Technology: Ontology-based reasoning layer integrating real-time data
- ๐ Trust Modeling: Reference Ontology of Trust (ROT) for flexible communication strategies
- ๐ Application: Simulated scenario for chronic obstructive pulmonary disease management
๐ Key Takeaways
- ๐ก Personalization is achieved through detailed patient profiles and real-time data integration.
- ๐ค Intelligent agents provide context-sensitive support to patients and healthcare professionals.
- ๐ Trust mechanisms adapt based on user feedback and evolving trust levels.
- ๐ Proactive interventions include medication reminders and air quality alerts.
- ๐ ๏ธ Modular architecture allows for scalability and future integration with clinical platforms.
- ๐ Empirical validation and real-world deployment are planned for future research.
- ๐ Ethical AI development aims to enhance autonomy and accessibility in digital health.
๐ Background
The integration of technology in healthcare has opened new avenues for personalized patient care. However, ensuring trust in digital health systems remains a challenge. This study addresses this gap by proposing a trust-aware architecture that not only personalizes healthcare interventions but also fosters a trusting relationship between patients and healthcare providers.
๐๏ธ Study
The authors developed a novel architecture that combines user modeling with symbolic reasoning and adaptive trust mechanisms. By utilizing Blueprint Personas, the system captures comprehensive patient profiles, which guide an intelligent agent in delivering tailored healthcare support. The architecture’s effectiveness was demonstrated through a simulated scenario involving a patient with chronic obstructive pulmonary disease.
๐ Results
The proposed system successfully showcased its ability to deliver proactive and personalized healthcare interventions. For instance, it provided timely medication reminders and air quality alerts, demonstrating the potential for improved patient outcomes through enhanced engagement and support.
๐ Impact and Implications
This architecture represents a significant step towards creating explainable and ethically aligned AI systems in healthcare. By integrating trust modeling and personalized support, it aims to enhance patient autonomy and accessibility, ultimately improving the quality of care in digital health environments. The implications of this work could lead to more effective patient-provider interactions and better health outcomes.
๐ฎ Conclusion
The development of a trust-aware architecture for personalized digital health is a promising advancement in the field. By focusing on user modeling and adaptive trust mechanisms, this system has the potential to transform how healthcare is delivered, making it more personalized and trustworthy. Continued research and real-world application will be crucial in validating its effectiveness and scalability.
๐ฌ Your comments
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A Trust-Aware Architecture for Personalized Digital Health: Integrating Blueprint Personas and Ontology-Based Reasoning.
Abstract
This paper presents a trust-aware architecture for personalized digital health that combines user modeling, symbolic reasoning, and adaptive trust mechanisms. The proposed system uses Blueprint Personas to capture detailed patient profiles, including clinical, behavioral, and emotional traits. These profiles guide an intelligent agent that interacts with patients and healthcare professionals to provide context-sensitive support. Personalization is achieved through an ontology-based reasoning layer that interprets user needs and integrates real-time data from electronic health records, wearable devices, and environmental sources. To promote transparency and foster long-term user engagement, the system includes a formal trust modeling component based on a Reference Ontology of Trust (ROT), allowing the system to flexibly tailor communication strategies in response to user feedback and evolving trust levels. A simulated scenario involving a patient with chronic obstructive pulmonary disease demonstrates how the system delivers proactive and personalized healthcare interventions, such as medication reminders and air quality alerts. While the architecture is modular and designed for scalability, it has not yet been deployed in real-world clinical settings. Empirical validation and integration with clinical platforms remain part of future work. Nevertheless, this ongoing work contributes to the development of explainable and ethically aligned AI systems that enhance autonomy, accessibility, and trust in digital health environments through explainable reasoning.
Author: [‘Vozna A’, ‘Monaldini A’, ‘Costantini S’]
Journal: J Med Syst
Citation: Vozna A, et al. A Trust-Aware Architecture for Personalized Digital Health: Integrating Blueprint Personas and Ontology-Based Reasoning. A Trust-Aware Architecture for Personalized Digital Health: Integrating Blueprint Personas and Ontology-Based Reasoning. 2025; 49:125. doi: 10.1007/s10916-025-02255-3