Transparency in public affairs interactions between companies and governments is critical to democratic legitimacy, yet existing lobby registers suffer from fragmented reporting, weak record integrity, limited traceability, and compliance gaps. This paper reports a Design Science Research (DSR) study that develops and evaluates a permissioned blockchain architecture for mandatory Public Affairs transparency. Two stakeholder surveys provided empirical grounding: Survey 1 (N = 61 domain professionals) elicited functional, non-functional, and GDPR compliance requirements, while Survey 2 (N = 14 practitioner evaluators) assessed a proof-of-concept implementation on Hyperledger Fabric following a live demonstration. Findings reveal widespread concerns over non-repudiation and auditability in current systems — 87% rated existing record integrity as weak—alongside strong endorsement for blockchain’s immutability, versioned audit trails, and hybrid on-/off-chain design to ensure GDPR-aligned traceability. Post-demonstration evaluation achieved a mean score of 4.6/5 for traceability and integrity, and 86% of evaluators recommended real-world piloting. The study makes three contributions: (i) an empirically derived requirements model and information-lifecycle framework; (ii) a hybrid permissioned-blockchain blueprint implemented on Hyperledger Fabric; and (iii) a replicable stakeholder-centric DSR methodology for sociotechnical artefact design in regulated governance contexts.
João C. Ferreira· Frontiers in Blockchain· 0 citations
Background and Objectives: Cardiology departments generate large volumes of unstructured free-text reports that impose substantial manual review burdens on clinicians; at Hospital de Santa Maria—Portugal’s largest public hospital—manual review of 12,651 reports took approximately seven minutes per report, representing over 1475 h of avoidable administrative work. This study presents Text2FHIRwallet, a health professional digital wallet that automates extraction and structuring of clinical entities from unstructured Portuguese cardiology reports using fine-tuned Named Entity Recognition (NER) models and maps the results to Fast Healthcare Interoperability Resources (FHIR) R4 patient summaries. Materials and Methods: Following the Design Science Research Methodology (DSRM) and CRISP-DM, we fine-tuned four transformer-based models—BERTimbau Base, BERTimbau Large, Albertina PT-PT, and MediAlbertina—on 305 manually annotated cardiology reports (77,309 tokens; κ = 0.85 inter-annotator agreement) covering eight clinical entity types, drawn from a corpus of 12,651 anonymised documents. Entities were mapped to FHIR R4 resources and delivered through a secure, role-based mobile wallet (React Native). Evaluation comprised token-level NER benchmarking with bootstrapped confidence intervals and McNemar’s testing, FHIR mapping accuracy assessment on 100 manually reviewed reports, processing-efficiency measurement, and a usability pilot with 10 cardiologists (SUS, NPS). Results: MediAlbertina achieved the highest NER performance (macro F1 = 0.985, 95% CI: 0.979–0.990), significantly outperforming all baseline models (p < 0.01, McNemar’s test) and comparing favourably with—though not directly comparable to, given differing languages and datasets—published benchmarks such as GPT-4 (F1 = 0.962 in ophthalmology NER) and fine-tuned BERT models for lung cancer NER (F1 ≈ 0.85–0.90). FHIR mapping accuracy was 98% on 100 independently reviewed reports. Report processing time was reduced from approximately seven minutes to 15–30 s (93–96% reduction), with peak batch-inference throughput of up to 1000 reports/h under parallelised GPU load (observed end-to-end throughput in pilot deployment was approximately 250 reports/h). The pilot usability evaluation yielded a SUS score of 87 (excellent) and an NPS of 80. Conclusions: Text2FHIRwallet demonstrates that domain-specific fine-tuning of a Portuguese-language pretrained language model achieves near-ceiling clinical NER accuracy, enabling scalable, interoperable, and privacy-compliant patient summary generation from unstructured cardiology text, offering an end-to-end pathway for integrating AI-driven NLP into clinical workflows and FHIR-based health information ecosystems, with implications for administrative efficiency, care coordination, and clinical research in non-English-language settings.
João C. Ferreira, Isabel Rosa, Ricardo Correia· Applied Sciences· 0 citations
