Composite CDE: modeling composite relationships between common data elements for representing complex clinical data

BackgroundKnowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. ObjectiveTo develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. MethodsWe developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. ResultsA beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. ConclusionThe FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

ABSTRACTObjectiveMedication errors are the third leading cause of death. There are several methods to prevent prescription errors, one of which is to use a Computerized Physician Order Entry system (CPOE). In a CPOE system, necessary data needs to be collected so that making decisions about prescribing medications and treatment plans could be made. Although many CPOE systems have been developed worldwide, studies have yet to identify the necessary data and data elements of CPOE systems. This study aims to identify data elements of CPOE and standardize these data with Fast Healthcare Interoperability Resources (FHIR) to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity.MethodsPubMed, Web of Science, Embase, and Scopus databases for studies up to October 2019 were searched. Two reviewers independently assessed original articles to determine eligibility for inclusion in this review. All articles describing data elements of a COPE system were included. Data elements were obtained from the included articles’ text, tables, and figures.Classification of the extracted data elements and mapping them to FHIR was done to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity. The final data elements of CPOE were categorized into five main categories of FHIR (foundation, base, clinical, financial, and specialized) and 146 resources, where possible. One of the researchers did mapping and checked and verified by the second researcher. If a data element could not be mapped to any FHIR resources, this data element was considered an extension to the most relevant resource.ResultsWe retrieved 5162 articles through database searches. After the full-text assessment, 21 articles were included. In total, 270 data elements were identified and mapped to the FHIR standard. These elements have been reported in 26 FHIR resources of 146 ones (18%). In total, 71 data elements were considered an extension.ConclusionsThe results of this study showed that the same data elements were not used in the CPOE systems, and the degree of homogeneity of these systems is limited. The mapping of extracted data with data elements used in the FHIR standard shows the extent to which these systems comply with existing standards. Considering the standards in these systems’ design helps developers design more coherent systems that can share data with other systems.

Evaluating potential data losses from mapping proprietary medical data formats to standards is essential for decision making. The article implements a method to evaluate the preliminary content overlap of proprietary medical formats, including national terminologies and Fast Healthcare Interoperability Resources (FHIR)-international medical standard. Three types of mappings were evaluated in the article: proprietary format matched to FHIR, national terminologies matched to the FHIR mappings, and concepts from national terminologies matched to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). We matched attributes of the formats with FHIR definitions and calculated content overlap. The article reports the results of a manual mapping between a proprietary medical format and the FHIR standard. The following results were obtained: 81% of content overlap for the proprietary format to FHIR mapping, 88% of content overlap for the national terminologies to FHIR mapping, and 98.6% of concepts matching can be reached from national terminologies to SNOMED CT mapping. Twenty tables from the proprietary format and 20 dictionaries were matched with FHIR resources; nine dictionaries were matched with SNOMED CT concepts. Mapping medical formats is a challenge. The obtained overlaps are promising in comparison with the investigated results. The study showed that standardization of data exchange between proprietary formats and FHIR is possible in Russia, and national terminologies can be used in FHIR-based information systems.

ObjectiveTo assess whether HL7 Fast Healthcare Interoperability Resources (FHIR) can underpin a fully standards-based, end-to-end digital research architecture, demonstrate it in a live study, and quantify its benefits for interoperability and development efficiency.Materials and MethodsWe designed a generalizable standards-based architecture to accelerate digital health research relying on FHIR as the sole transactional model throughout a participant research lifecycle starting from API-based study discovery to results. It was instantiated for People Heart Study, a real-world digital health cardiovascular-risk assessment study with its protocol transformed into FHIR resources (eligibility, consent, tasks, and results). Evaluation examined workflow coverage, validator conformance across independent servers, and points requiring custom extensions or app logic.ResultsThe architecture was implemented using cloud managed FHIR stores including an illustrative public research discovery API for first-/third-party apps. A participant-facing iOS app was published on the App Store. Our evaluation reveals that 6 of 10 research app workflows could be executed entirely from FHIR artifacts; 2 were partially standards-driven and 2 remained limited requiring custom development. All FHIR resources passed structural, semantic validation with minimal custom extension usage and terminology integrity issues.DiscussionOur approach addresses persistent challenges in digital health research by enhancing data interoperability, minimizing redundant development, and supporting the full research lifecycle. The architecture aligns with national priorities and complements healthcare standardization efforts.ConclusionBy leveraging FHIR, our architecture enables generalizability, interoperability, and reuse across diverse digital health research contexts, transforming study design into data modeling rather than software development, and fostering a more inclusive and agile digital health ecosystem.

599 Background: The White House Cancer Moonshot initiative aims to reduce cancer death rates by half before 2047 and emphasizes the importance of access to clinical data to improve patient outcomes through enhanced data standardization and interoperability. Effective data capture remains a critical challenge with manual abstraction consuming significant clinician administrative time. The US Oncology Network (The Network) partnered with Ontada to leverage artificial intelligence (AI) and Fast Healthcare Interoperability Resources (FHIR) standards to advance clinical data submission for the Center for Medicare and Medicaid Innovation (CMMI) Enhancing Oncology Model (EOM). Methods: A pilot study was conducted with The Network (Compass Oncology) to implement Ontada's AI-driven technology to submit 58 clinical data elements for 232 patients required for EOM Performance Period 1. Structured clinical data elements were sourced from iKnowMed Generation 2 EHR using FHIR mCODE (Minimal Common Oncology Data Elements) HL7 industry standards. Natural Language Processing (NLP) and AI models were developed to enhance data capture from unstructured clinical documentation for diagnosis date, primary tumor characteristics, regional lymph nodes, distant metastases, and histology. A time and motion study was conducted via manual chart abstraction to measure administrative time reduction and data quality improvements. Data was submitted to CMMI via FHIR. Results: Across five critical data elements representing 909 possible data points analyzed for this study, 53.5% were sourced from structured data vs. 40.6% from unstructured clinical documentation using AI. The time and motion study confirmed a 38% reduction in provider administrative time. The AI-driven submission successfully achieved 98% data completeness for EOM patients, exceeding the 90% threshold required by CMMI. Conclusions: Results from the pilot study underscore the transformative potential of AI and data interoperability in oncology care. By leveraging NLP technology and FHIR-compliant data standards, substantial improvements in data capture were achieved while significantly reducing administrative burden. This supports the Cancer Moonshot vision for standardized, interoperable oncology data and provides a scalable model for enhancing practice efficiency. The success of this pilot establishes a foundation for expanded implementation across additional practices to meet future EOM data submission requirements. Source of clinical data elements. Data Element Structured Unstructured-AI Manual Chart Abstraction Not Found Total Diagnosis Date 106 108 16 2 232 Primary Tumor (T) 99 47 3 0 149 Regional Nodes (N) 98 42 8 0 148 Distant Mets (M) 96 43 9 0 148 Histology 87 129 13 3 232 Total 486 (53.5%) 369 (40.6%) 49 (5.4%) 5 (0.5%) 909 (100%)

The digitization of healthcare data has heightened concerns about security, privacy, and interoperability. Traditional centralized systems are vulnerable to cyberattacks and data breaches, risking the exposure of sensitive patient information and decreasing trust in digital healthcare services. In addition, healthcare stakeholders use various standards and formats, creating challenges for data sharing and seamless communication. To address these points, this article identifies all the healthcare stakeholders and translates each useful element of a patient’s electronic health record (EHR) into Fast Healthcare Interoperability Resources (FHIR), to propose a complete role-based access control model that specifies which FHIR resources an actor is allowed to access. To validate this role model, three new use cases are defined, in which the various stakeholders interact and access the FHIR resources. Moreover, specific smart contracts are detailed to implement the role model in an automated way and provide a robust access control mechanism within healthcare organizations. The feasibility of the proposed access control mechanism is demonstrated through proof-of-concept and test performance measurements. Finally, the solution is validated as a realistic solution adapted to the scale of a country based on health statistics. • Healthcare participants: This work researches the different healthcare participants found in this domain. This contribution clearly provides a basis for designing precise solutions regarding the healthcare actors. • Role-Based Access Control for Healthcare Data: This study proposes a comprehensive role-based access control model that defines which Fast Healthcare Interoperability Resources (FHIR) each healthcare stakeholder can access, ensuring secure and structured data management. • Smart Contract Implementation for Automated Security: The model is reinforced by smart contracts that automate access control, enhancing security, efficiency, and trust in digital healthcare environments. • Scalable and Validated Solution: The proposed access control mechanism is validated through proof-of-concept implementation, performance evaluations, and an analysis demonstrating its feasibility at a national scale based on healthcare statistics.

Unambiguous data exchange among healthcare systems is essential for error-free reporting and improved patient care. Mapping of different standards plays a crucial role in making different systems communicate with each other and have an efficient healthcare systems. This work focuses on exploring the possibilities of semantic interoperability between two widely used clinical modelling standards, OpenEHR and FHIR (Fast Healthcare Interoperability Resources). A manually curated map is being developed where the same semantically meaning OpenEHR Archetypes are mapped to the relevant FHIR Resources.

Precision oncology mandates developing standardized common data models (CDMs) to facilitate analyses and enable clinical decision making. Expert-opinion-based precision oncology initiatives are epitomized in Molecular Tumor Boards (MTBs), which process large volumes of clinical-genomic data to match genotypes with molecularly guided therapies. We used the Johns Hopkins University MTB as a use case and developed a precision oncology core data model (Precision-DM) to capture key clinical-genomic data elements. We leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements model (mCODE). Our model was defined as a set of profiles with multiple data elements, focusing on next-generation sequencing and variant annotations. Most elements were mapped to terminologies or code sets and the Fast Healthcare Interoperability Resources (FHIR). We subsequently compared our Precision-DM with existing CDMs, including the National Cancer Institute's Genomic Data Commons (NCI GDC), mCODE, OSIRIS, the clinical Genome Data Model (cGDM), and the genomic CDM (gCDM). Precision-DM contained 16 profiles and 355 data elements. 39% of the elements derived values from selected terminologies or code sets, and 61% were mapped to FHIR. Although we used most elements contained in mCODE, we significantly expanded the profiles to include genomic annotations, resulting in a partial overlap of 50.7% between our core model and mCODE. Limited overlap was noted between Precision-DM and OSIRIS (33.2%), NCI GDC (21.4%), cGDM (9.3%), and gCDM (7.9%). Precision-DM covered most of the mCODE elements (87.7%), with less coverage for OSIRIS (35.8%), NCI GDC (11%), cGDM (26%) and gCDM (33.3%). Precision-DM supports clinical-genomic data standardization to support the MTB use case and may allow for harmonized data pulls across health care systems, academic institutions, and community medical centers.

Over the past decade, Fast Healthcare Interoperability Resources (FHIR) have become increasingly relevant in health care data standardization. However, the complex structure of FHIR makes cohort analytics with many-to-many relations extremely time-consuming, and, impossible in many cases. To support exploratory cohort building and data visualization in oncology, especially for nontechnical users, we developed the DermaDashboard, an interactive dashboard built on top of a relational FHIR-compliant PostgreSQL database. Relevant oncology data was preaggregated with a materialized view, and the subsequent visualization layer was implemented using an open-source visualization tool, enabling clinicians to filter and analyze data without requiring familiarity with FHIR or SQL. The database encompassed data from 3949 patients with melanoma and included 82,783 health records. Core FHIR resources were Patient, DiagnosticReport, and QuestionnaireResponse, with 54 mapped attributes spanning demographics, stagings, mutations, and treatments. The resulting dashboard allowed filtering across 29 variables to construct subcohorts and generate aggregation analyses. This implementation shows how open interoperability data standards, such as FHIR, can be used in the development of modular, user-friendly clinical dashboards for cohort analysis, and the architecture demonstrates a feasible path toward democratizing access to structured health care data.

The huge amounts of biomedical data are stored in various formats and accessed through numerous interfaces. It is a crucial task for Data integration and exchange in cancer research. Data elements play an important role in data integration. The NCI supports a broad initiative to standardize the common data elements (CDEs) used in cancer research data capture and reporting. The Taiwan Cancer Registry (TCR), established in 1979, is organized and funded by the Health Department of the central government. The TCR's primary goal is to survey the incidence of cancer in Taiwan. The aim of the Taiwan Cancer Common Data Element Project (TCCDEP) is to facilitate convergence towards a common metadata standard in Taiwan cancer registry data. The project is implemented using a set of open source software and tools developed by the NCI, such as the caCORE SDK and caGrid. The experience of building, learning and using the open toolkit, Cancer Data Standards Repository (caDSR), developed by the National Cancer Institute's Center for Bioinformatics, NCICB, in the USA is reported. The caDSR is a metadata repository including CDEs used by NCI-sponsored organizations. The object of this work is to develop a database of metadata for medical data elements, referred to as the TCCDEP and to establish a common classification of data elements used in cancer registry. In this manuscript, we will be developing the common data elements using vocabulary standards, ontology and semantic modeling methodology. The CDEs included demographic data, clinical history, pathology data, and clinical outcome data including treatment, recurrence and vital status. These CDEs will be further enhanced to data sets across the participating cancer institutes to facilitate and supplement translational research. The Taiwan Cancer Registry (TCR) model and standard will be use as the basis for an electronic data standard repository to metadata or data descriptors. The TCCDEP developed 40 data elements to annotate the cancer registry data collected. In this project, we describe the process required to develop the model, the caDSR CDEs, and the results of the modeling effort. We address difficulties we encountered and modifications for solution. The caBIG (TM) grid project, gird model of Taiwan Cancer Registry (girdTCR), using the caCORE tools to define data elements for cancer registry has been shown to caBIG(TM)UML model project. Currently, the Taiwan cancer registry CDEs are released and available in CDE browser for reusing. Furthermore, we will extend our CDEs to daily clinical practice and trials, along with how the methods were used to fully implemented in hospitals and cancer research centers in Taiwan.

Government policies in the United States and the European Union promote standardization and value creation in the use of FAIR (findability, accessibility, interoperability, and reusability) data, which can enhance trust in digital health systems and is crucial for their success. Trust is built through elements such as FAIR data access, interoperability, and improved communication, which are essential for fostering innovation in digital health technologies. This Viewpoint aims to report on exploratory research demonstrating the feasibility of testing a patient-centric data flow model facilitating semantic interoperability on precision medical information. In this global trend, the interoperable interface called Sync for Science-J (S4S-J) for linking electronic medical records (EMRs) and personal health records was launched as part of the Basic Policy for Economic and Fiscal Management and Reform in Japan. S4S-J controls data distribution consisting of EMR and patient-generated health data and converts this information into QR codes that can be scanned by mobile apps. This system facilitates data sharing based on personal information beliefs and unlocks siloed Internet of Things systems with a privacy preference manager. In line with Japanese information handling practices, the development of a mobile cloud network will lower barriers to entry and enable accelerated data sharing. To ensure cross-compatibility and compliance with future international data standardization, S4S-J conforms to the Health Level 7 Fast Health Care Interoperability Resources standard and uses the international standardized logical observation identifiers names and codes (LOINC) to redefine medical terms used in different terminology standards in different medical fields. It is developed as an applied standard in medical information intended for industry, health care services, and research through secondary use of data. A multicenter collaborative study was initiated to investigate the effectiveness of this system; this was a registered, multicenter, randomized controlled clinical trial, the EMBRACE study of the mobile health app M♡Link for hyperglycemic disorders in pregnancy, which implements an EMR–personal health record interoperable interface via S4S-J. Nevertheless, the aforementioned new challenges, the pivotal Health Level 7 Fast Health Care Interoperability Resources system, and LOINC data mapping were successfully implemented. Moreover, the preliminary input of EMR-integrated patient-generated health data was successfully shared between authorized medical facilities and health care providers in accordance with the patients’ preferences. The patient-centric data flow of the S4S-J in Japan is expected to guarantee the right to data portability, which promotes the maximum benefit of use by patients themselves, which in turn contributes to the promotion of open science.

Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies that offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to quality, message size (payload), IoT-orientation and standardisation. Here we describe the first attempt to extend SAREF for specific e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO), which plays the role of a reference model. The methodology was enhanced by following a standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development and tests of an IoT Early Warning System prototype that uses ECG data and collision identification to detect accidents with truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed.

Lack of homogeneous definitions for imaging data and consensus on their relevance in the setting of subarachnoid hemorrhage and unruptured intracranial aneurysms lead to a difficulty of data pooling and lack of robust data. The aim of the National Institute of Health/National Institute of Neurological Disorders and Stroke, Unruptured Intracranial Aneurysm (UIA) and Subarachnoid Hemorrhage (SAH) Common Data Elements (CDE) Project was to standardize data elements to ultimately facilitate data pooling and establish a more robust data quality in future neurovascular research on UIA and SAH. For the subcommittee 'Radiological imaging of SAH,' international cerebrovascular specialists with imaging expertise in the setting of SAH were selected by the steering committee. CDEs were developed after reviewing the literature on neuroradiology and already existing CDEs for other neurological diseases. For prioritization, the CDEs were classified into 'Core,' 'Supplemental-Highly Recommended,' 'Supplemental' and 'Exploratory.' The subcommittee compiled 136 CDEs, 100 out of which were derived from previously established CDEs on ischemic stroke and 36 were newly created. The CDEs were assigned to four main categories (several CDEs were assigned to more than one category): 'Parenchymal imaging' with 42 CDEs, 'Angiography' with 49 CDEs, 'Perfusion imaging' with 20 CDEs, and 'Transcranial doppler' with 55 CDEs. The CDEs were classified into core, supplemental highly recommended, supplemental and exploratory elements. The core CDEs were imaging modality, imaging modality type, imaging modality vessel, angiography type, vessel angiography arterial anatomic site and imaging vessel angiography arterial result. The CDEs were established based on the current literature and consensus across cerebrovascular specialists. The use of these CDEs will facilitate standardization and aggregation of imaging data in the setting of SAH. However, the CDEs may require reevaluation and periodic adjustment based on current research and improved imaging quality and novel modalities.

Neonatal encephalopathy (NE)-including hypoxic-ischemic encephalopathy (HIE)-is associated with significant morbidity and mortality worldwide. While data registries provide hypothesis-generating data, aid in quality improvement, and track management/outcomes over time, we recently demonstrated that only 4 of 1,281 (0.3%) variables were collected by all 22 international NE/HIE registries: birth weight, gestational age, and 1- and 5-minute Apgar scores. In response, our team set out to develop a set of common inpatient data elements to enable future harmonization of NE/HIE registry data. Using a modified Delphi method, a panel of 14 international NE experts voted to separate the 1,281 variables from our previous study into "core elements," "supplemental elements," or those that "do not need to be collected." Based on the anonymous survey results, we created draft lists of core and supplemental common data elements (CDEs). These lists were further revised through group consensus meetings and external feedback from global registry leaders and attendants at 2 international conferences. The final data forms include 164 core elements and 225 supplemental elements stratified into 11 domains: demographics; pregnancy, labor, and delivery; delivery room; transport; acid-base; therapeutic hypothermia; neuromonitoring and seizures; neuroimaging; laboratory values (other than acid-base); hospital course; and discharge. The CDEs are the first of several important steps to further standardize research and reporting of NE/HIE investigations to perform more efficient and powerful clinical research in the field moving forward. Future studies need to establish a clinical and research definition of HIE and develop CDEs for collecting long-term, family-centered follow-up data postdischarge.

ObjectiveThe objective of this study is to develop an approach to evaluate the quality of terminological annotations on the value set (ie, enumerated value domain) components of the common data elements (CDEs) in the context of clinical research using both unified medical language system (UMLS) semantic types and groups.Materials and methodsThe CDEs of the National Cancer Institute (NCI) Cancer Data Standards Repository, the NCI Thesaurus (NCIt) concepts and the UMLS semantic network were integrated using a semantic web-based framework for a SPARQL-enabled evaluation. First, the set of CDE-permissible values with corresponding meanings in external controlled terminologies were isolated. The corresponding value meanings were then evaluated against their NCI- or UMLS-generated semantic network mapping to determine whether all of the meanings fell within the same semantic group.ResultsOf the enumerated CDEs in the Cancer Data Standards Repository, 3093 (26.2%) had elements drawn from more than one UMLS semantic group. A random sample (n=100) of this set of elements indicated that 17% of them were likely to have been misclassified.DiscussionThe use of existing semantic web tools can support a high-throughput mechanism for evaluating the quality of large CDE collections. This study demonstrates that the involvement of multiple semantic groups in an enumerated value domain of a CDE is an effective anchor to trigger an auditing point for quality evaluation activities.ConclusionThis approach produces a useful quality assurance mechanism for a clinical study CDE repository.