Successful Integration of EN/ISO 13606-Standardized Extracts From a Patient Mobile App Into an Electronic Health Record: Description of a Methodology

Abstract

Background: There is an increasing need to integrate patient-generated health data (PGHD) into health information systems (HISs). The use of health information standards based on the dual model allows the achievement of semantic interoperability among systems. Although there is evidence in the use of the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) framework for standardized communication between mobile apps and electronic health records (EHRs), the use of European Norm/International Organization for Standardization (EN/ISO) 13606 has not been explored yet, despite some advantages over FHIR in terms of modeling and formalization of clinical knowledge, as well as flexibility in the creation of new concepts.

Objective: This study aims to design and implement a methodology based on the dual-model paradigm to communicate clinical information between a patient mobile app (Xemio Research) and an institutional ontology-based clinical repository (OntoCR) without loss of meaning.

Methods: This paper is framed within Artificial intelligence Supporting CAncer Patients across Europe (ASCAPE), a project that aims to use artificial intelligence (AI)/machine learning (ML) mechanisms to support cancer patients' health status and quality of life (QoL). First, the variables "side effect" and "daily steps" were defined and represented with EN/ISO 13606 archetypes. Next, ontologies that model archetyped concepts and map them to the standard were created and uploaded to OntoCR, where they were ready to receive instantiated patient data. Xemio Research used a conversion module in the ASCAPE Local Edge to transform data entered into the app to create EN/ISO 13606 extracts, which were sent to an Application Programming Interface (API) in OntoCR that maps each element in the normalized XML files to its corresponding location in the ontology. This way, instantiated data of patients are stored in the clinical repository.

Results: Between December 22, 2020, and April 4, 2022, 1100 extracts of 47 patients were successfully communicated (234/1100, 21.3%, extracts of side effects and 866/1100, 78.7%, extracts of daily activity). Furthermore, the creation of EN/ISO 13606-standardized archetypes allows the reuse of clinical information regarding daily activity and side effects, while with the creation of ontologies, we extended the knowledge representation of our clinical repository.

Conclusions: Health information interoperability is one of the requirements for continuity of health care. The dual model allows the separation of knowledge and information in HISs. EN/ISO 13606 was chosen for this project because of the operational mechanisms it offers for data exchange, as well as its flexibility for modeling knowledge and creating new concepts. To the best of our knowledge, this is the first experience reported in the literature of effective communication of EN/ISO 13606 EHR extracts between a patient mobile app and an institutional clinical repository using a scalable standard-agnostic methodology that can be applied to other projects, data sources, and institutions.

Keywords: artificial intelligence; electronic health records; health information interoperability; health information standards; machine learning; mobile app.

Figure 1

Information systems within the ASCAPE project. Patients register side effects in Xemio Research, which also tracks patients’ daily steps. These data are standardized using a conversion module within the HCB environment (see the Methodology section, step 3), and it is both stored in the OntoCR and sent to ASCAPE Local Edge, which generates and updates ASCAPE’s AI predictive models, which are shared and evaluated in its accuracy in the federated node. AI: artificial intelligence; ASCAPE: Artificial intelligence Supporting CAncer Patients across Europe; HCB: Hospital Clínic de Barcelona.

Figure 2

Mindmap of the “side effect” archetype (in Spanish), edited with LinkEHR. The “side effect” entry has 4 elements: date, finding, value, and severity.

Figure 3

Ontologies of “side effect” modeled locally (upper left) and with EN/ISO 13606 (right) and modeling of the concept “severe” using the international edition of SNOMED CT (lower left), all of them in Spanish and edited with Protégé. EN/ISO: European Norm/International Organization for Standardization; SNOMED CT: Systematized Nomenclature of Medicine – Clinical Terms.

Figure 4

OntoCR GUI for physicians. The ontology modeling the clinical variables is visualized as a web-based structured form. The side effects menu item is selected in the hierarchical menu on the left side of the image. The right side of the image shows the properties regarding patient information, ASCAPE recruitment date, and side effects. ASCAPE: Artificial intelligence Supporting CAncer Patients across Europe; GUI: graphical user interface.

Figure 5

Example of a deidentified EHR extract of side effects. EHR: electronic health record.

Figure 6

Overview of the process of knowledge modeling and extract communication and integration into OntoCR. Blue arrows indicate knowledge-related processes, while red arrows indicate data-related processes. API: Application Programming Interface; EHR: electronic health record; ISO: International Organization for Standardization; SNOMED CT: Systematized Nomenclature of Medicine – Clinical Terms.