This use case is detailed in Tackling the Write-to-Read Web of Data with Trustflows, the first project in which we coined the term “Trustflows” while implementing PACSOI’s Solid-based architecture.

PACSOI extends these Trustflows principles into a research and prototyping programme that demonstrates how patient-centric data spaces can be realised in practice. As outlined on the PACSOI project page, the consortium combines user-controlled Solid pods with Linked Data techniques to keep consent enforceable, data local, and analytics privacy-aware across organisations.

Key capabilities highlighted by PACSOI that apply directly to this use case include:

• Patient-controlled consent and governance so individuals decide which clinicians, researchers, or care coaches may access each record stored in their pods.
• Conversion of clinical, lifestyle, and sensor data into Linked Data shapes that can be shared with only the attributes needed for a given purpose, supporting strict data minimisation and GDPR compliance.
• Privacy-preserving analytics and scalable, federated queries that let researchers and policymakers reuse secondary health data without centralising sensitive records.
• A proof-of-concept for obesity care that integrates data from 12 sources for 200 patients to coordinate bariatric surgery decisions and personalised lifestyle interventions.

For the Patient-Centric Solid infrastructures use case, PACSOI’s approach maps directly: hospitals publish structured discharge artefacts to patient pods, patients or their agents grant insurers time-bound access, and regulators reuse anonymised, verifiable audit trails to check compliance in near real time—reducing duplication, improving care coordination, and preserving data sovereignty.

The Trustflows paper further explains how PACSOI applies Command Query Responsibility Segregation (CQRS) to separate write interfaces from multiple read views, and Event Sourcing to store every update—including provenance, actor, and device metadata—as immutable events. Those techniques allow:

• Determining whether a weight measurement is authoritative by checking if the event originated from a calibrated smart scale or a self-reported entry.
• Replaying longitudinal histories to answer cohort-wide questions without regenerating raw data.
• Generating declarative mappings (e.g., SPARQL CONSTRUCT views) for each stakeholder so they always retrieve the semantics and granularity they require while retaining the trust context.

By anchoring these flows in Trustflows, PACSOI demonstrates how healthcare actors can move from CRUD-style pods to a write-to-read pipeline that preserves semantics, provenance, and compliance as first-class outputs.