# A Design Architecture for Decentralized and Provenance-Assisted eHealth Systems for Enhanced Personalized Medicine

## TL;DR

This paper introduces a decentralized eHealth system with provenance tracking to improve data security and support personalized medicine.

## Contribution

A novel decentralized architecture with provenance-assisted data management for electronic health records.

## Key findings

- The system successfully processed 190 instances of medical record entities.
- It generated 573 detailed provenance entries for medical data context tracking.
- The feasibility of a decentralized EHR/PHR system with formal provenance tracking was validated.

## Abstract

Background/Objectives: Electronic medical record systems play a crucial role in the operation of modern healthcare institutions, enabling the foundational data necessary for advancements in personalized medicine. Despite their importance, the software supporting these systems frequently experiences data availability and integrity issues, particularly concerning patients’ personal information. This study aims to present a decentralized architecture that integrates both clinical and personal patient data, with a provenance mechanism to enable data tracing and auditing, ultimately supporting more precise and personalized healthcare decisions. Methods: A system implementation based on the solution was developed, and a feasibility study was conducted with synthetic medical records data. Results: The system was able to correctly receive data of 190 instances of the entities designed, which included different types of medical records, and generate 573 provenance entries that captured in detail the context of the associated medical information. Conclusions: For the first cycle of the research, the system developed served to validate the main features of the solution, and through that, it was possible to infer the feasibility of a decentralized EHR and PHR health system with formal provenance data tracking. Such a system lays a robust foundation for secure and reliable data management, which is essential for the effective implementation and future development of personalized medicine initiatives.