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.

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.

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.

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.