Alain April

Public sector organizations are under increasing pressure to modernize service delivery while preserving transparency, interoperability, accountability, and citizen trust. Blockchain technology offers relevant capabilities for these objectives, particularly through shared ledgers, cryptographic verification, and programmable rules. However, its adoption in public sector contexts remains constrained by the lack of architectural guidance tailored to inter-organizational services. This study proposes BRA-PS, a Blockchain Reference Architecture for Public Sector Citizen-Centric Applications, developed from a real-world digitalization project in Quebec, Canada. The architecture organizes components into six layers (presentation, business, communication, smart contract, blockchain, and data) with cross-cutting concerns addressing governance, access control, security, and monitoring. A key design principle is the public–private workflow separation, which enables inter-organizational collaboration while preserving each organization’s operational autonomy and data confidentiality. We validated the architecture through a case study involving a vehicle registration process between two public agencies, supported by a proof-of-concept implementation using Hyperledger Fabric. An Architecture Trade-off Analysis Method (ATAM) evaluation, conducted with a panel of five domain experts, identified six architectural risks, including InterPlanetary File System (IPFS) confidentiality exposure and smart contract inflexibility, six non-risks, six sensitivity points, and six trade-offs across three key quality attributes: autonomy, collaboration, and functional suitability. The results show that BRA-PS can support implementation decisions, clarify stakeholder responsibilities, and expose relevant architectural trade-offs. The recommendations derived from the evaluation provide practical guidance for the adoption of blockchain in citizen-centric public sector services.

Traceability mechanisms ensure transparency and accountability in distributed systems, where autonomous entities interact concurrently with shared resources without complete knowledge of the global state. Most existing approaches are descriptive: they record interactions after execution without constraining them according to the predefined usage rules. This limitation is important when shared resources must be accessed in compliance with global constraints under concurrent executions. We introduce directed traceability as a protocol-level mechanism that governs interaction admission according to usage constraints and produces a compliant execution trace by construction. Unlike descriptive mechanisms or locally-validated approaches such as smart contracts, which enforce only local constraints, directed traceability achieves compliance through execution-time validation against global state. We formalize the problem through a multi-agent model and establish three theoretical properties: (i) directed mechanisms guarantee zero violation rate; (ii) descriptive and locally-validated mechanisms exhibit structural violations under concurrency and global constraints; and (iii) directed traceability induces a bounded latency overhead of O(k · (n+m)), where k is the number of constraints, n the number of agents, and m the number of resources. Numerical evaluation through a distributed container-based implementation yields violation rates of 28%–35% (descriptive), 5%–10% (locally validated), and 0% (directed); a rise to 10% violations for local validation under global constraints; and a latency factor of approximately 100× for the directed mechanism under the tested container-based deployment. Compliance follows from protocol design rather than post-execution audit.

Public sector organizations face growing pressure to modernize service delivery through digitalization while ensuring transparency, interoperability, and citizen trust. Although blockchain technology offers promising capabilities for addressing these challenges, the absence of clear architectural guidelines for public sector contexts limits effective adoption. This study proposes BRA-PS, a Blockchain Reference Architecture for Public Sector Citizen-Centric Applications, developed from a realworld digitalization project in Quebec, Canada. The architecture organizes components into six layers (presentation, business, communication, smart contract, blockchain, and data) with cross-cutting concerns addressing governance, access control, security, and monitoring. A key design principle is the publicprivate workflow separation, which enables inter-organizational collaboration while preserving each organization’s operational autonomy and data confidentiality. We validated the architecture through a case study involving a vehicle registration process between two public agencies, supported by a proof-of-concept implementation using Hyperledger Fabric. An Architecture Tradeoff Analysis Method (ATAM) evaluation, conducted with a panel of five domain experts, identified six architectural risks, including IPFS confidentiality exposure and smart contract inflexibility, six non-risks, six sensitivity points, and six trade-offs across three key quality attributes: autonomy, collaboration, and functional suitability. The results confirm that BRA-PS effectively guides implementation decisions and stakeholder alignment. Practical recommendations derived from the evaluation provide actionable guidance for blockchain adoption in public sector services.

The patient problem list is a key component of an electronic health record (EHR) and must be accurate and accessible for all professionals involved in patient care. Unfortunately, such a list is mostly found in an unstructured text format, is not easily sharable across digital health systems, and lacks semantic interoperability. Natural language processing (NLP) techniques are widely used for clinical concept extraction, particularly for English text. However, in the Canadian context, the clinical notes in a patient problem list can also be found in French. This research presents a framework based on Fast Healthcare Interoperability Resources (FHIR) consisting of an NLP clinical pipeline and a rule-based approach to converting the textual patient problem list, including notes regarding allergies, into an FHIR model. The proposed approach considers concept modifiers to map to the International Patient Summary (IPS) FHIR model element. The main contributions of this research include the early detection of FHIR resources from unstructured data written in the French language and the design of a rule-based algorithm to identify and map extracted data to the appropriate FHIR resource attributes using an annotator. A primary evaluation of the resource tag which uses the rule-based method demonstrates the feasibility of the proposed model to facilitate semantic interoperability. The assessment was conducted using the French FRASIMED corpora.

The patient problem list is a key component of an electronic health record (EHR), and must be accurate and accessible for all professionals involved in patient care. Unfortu-nately, such a list is mostly found as an unstructured text format, is not easily sharable across digital health systems, and lacks semantic interoperability. Natural Language Processing (NLP) techniques are widely used for clinical concept ex-traction, particularly for English text. However, in the Canadian context, clinical notes in a patient problem list can also be found in French. This research presents a framework based on Fast Healthcare Interoperability Resource (FHIR) consisting of an NLP clinical pipeline and a rule-based approach to convert the textual patient problem list, including notes regarding allergies, to an FHIR model. The proposed approach considers concept modifiers to map to the International Patient Summary (IPS) FHIR model element. The main contributions of this research include the early detection of FHIR resources from unstructured data written in the French language, and the design of a rule-based algo-rithm to identify and map extracted data to the appropriate FHIR resource attributes using an annotator. A primary evaluation of the Resource tag which uses the rule-based method demonstrates the feasibility of the proposed model to facilitate semantic interoperability. The assessment was conducted using the French FRASIMED Corpora.

Background: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard. Objective: The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies. Methods: A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard. Results: A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language–based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability. Conclusions: This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

Medical imaging technology is producing a growing number of medical images types as well as patient-related information. The benefits of using modern medical imaging systems in healthcare are undeniable. Picture archiving and communication system (PACS) have revolutionized medical imaging practice. PACS have widely impacted the accessibility of medical images, reduced imaging costs, eliminated the physical storage of films, improved time management of radiologists, and allowed automated decision-making and diagnosis. Many health organizations and manufacturers have invested on developing commercial PACS. However, commercial PACS are not affordable for all hospitals while open-source PACS are increasingly becoming a viable option. Our research project is looking for an open-source PACS for the Donka University hospital of Guinea. Open-source PACS are currently available and are offering varying functionalities, documentation, and technical support from their developer communities. Selecting an open-source PACS is not an easy task and not only depends on the hospital requirements but also requires assessing each open-source PACS to find the best match. In this paper, the most popular open-source PACS are evaluated using a simple comparison approach based on four criteria. The result of this assessment shows that Orthanc, DCM4CHE, DCMTK, and Dicoogle are the most mature open-source PACS according to our criteria and the needs of Donka.

BACKGROUND The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard. OBJECTIVE The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies. METHODS A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard. RESULTS A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language–based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability. CONCLUSIONS This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such Open Data can shed light on the causes of disease and effects of treatment including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual’s characteristics, known as personalized or precision medicine. Precision medicine treatments will take personalization to the next level and be effective for individual patients based on their genomic, environmental, and lifestyle factors. High-throughput sequencing technologies and open databases have made precision medicine possible but up to now only large research centres could handle the large scale technology needed for its processing. Aside from issues such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, recent advances by Berkeley using open source Big Data technologies and Cloud Computing Services has allowed precision medicine studies to be conducted by small and agile research labs and researchers around the world.