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Research Article  |  Open Access  |  23 May 2024

The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

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Conn Health Telemed 2024;3:300004.
10.20517/chatmed.2023.37 |  © The Author(s) 2024.
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Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant.

Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models.

Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain.

Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.


Data harmonization, FAIR CookBook, FAIRplus, HL7, FHIR, OHDSI, AI


The need for data harmonization in healthcare is a multidimensional challenge with profound implications for the future of healthcare[1-3]. The primary objective of harmonizing medical data is to enhance patient outcomes. “When data from various healthcare sources are harmonized, it enables more accurate and comprehensive diagnoses, personalized treatment plans, and overall improved patient care”. This is because consistent and comparable data from different sources provide healthcare professionals with a more holistic view of a patient's health, leading to better-informed medical decisions. Moreover, the role of harmonized and FAIRified data in advancing medical research and innovation cannot be overstated. Researchers require large, diverse datasets to conduct robust studies. “Harmonized data facilitate the amalgamation and analysis of information from diverse sources, thereby accelerating the discovery of new treatments and healthcare technologies”. It also ensures that the findings of such research are more representative and widely applicable. Moreover, harmonized data can facilitate collaborative international research efforts to address global health challenges. The ability to analyze data from global regions offers invaluable insights into the discovery of effective treatment strategies. According to Figure 1, the major challenges in data harmonization include: (i) ethical and privacy concerns; (ii) data sharing and interoperability; (iii) data preparation; (iv) international standards and cooperation; (v) biases during AI modeling, and (vi) global health implications.

The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

Figure 1. Challenges in data harmonization.

The integration of FAIR (Findability, Accessibility, Interoperability, and Reusability)-compliant reference ontologies with Observational Health Data Sciences and Informatics (OHDSI) vocabularies can significantly enhance medical data harmonization, addressing interoperability, consistency, and usability gaps. The FAIR principles ensure that data are well-documented, structured for both human and computational use, and efficiently integrated across different systems. OHDSI’s vocabularies can enhance these principles by providing a standardized framework that facilitates data integration from diverse healthcare systems. Standardization of data representation is another critical advantage. Utilizing FAIR-compliant ontologies and OHDSI vocabularies enables the mapping of diverse healthcare data to a unified set of terms and definitions, reducing ambiguity and enhancing data accuracy. These ontologies can ensure consistent data modeling across various sources, which is crucial for effective data aggregation. They also promote scalable data integration through semantic enrichment, which enhances querying and data analysis capabilities. This integration, in turn, enhances data quality and usability. Ontologies facilitate data validation by enforcing adherence to predefined relationships and constraints, ensuring data integrity, quality, and reusability, thus fostering efficiency in research and reducing redundancies in data collection, which is particularly valuable in healthcare.

The regulatory landscape significantly impacts data harmonization efforts. Regulations, such as the Health Insurance Portability and Accountability Act (HIPAA)[4] in the United States or the General Data Protection Regulation (GDPR)[5] in the European Union, define how patient data can be collected, stored, shared, and used. Achieving compliance with these regulations is complex and poses barriers to harmonizing data across different regions. HIPAA primarily focuses on the privacy and security protections of personal health information in the United States, setting standards for how such data should be handled to ensure data confidentiality and integrity. It outlines specific conditions under which protected health information (PHI) can be used or disclosed, mandating physical, administrative, and technical safeguards. This affects the architecture and security features of data management systems used in harmonization, necessitating features such as data encryption, secure access controls, and audit capabilities to comply with HIPAA’s strict safeguards. GDPR, on the other hand, emphasizes the protection of personal data and the privacy of EU citizens. It introduces comprehensive rights for data subjects, such as the right to access, rectify, and erase their data, which influences how data are collected, stored, and utilized in harmonization processes. GDPR also requires explicit consent for data processing activities, impacting how consent is captured and managed in data integration projects. Additionally, it imposes stringent conditions on the transfer of personal data outside the EU, affecting global data harmonization efforts by requiring compliance with specific legal frameworks before data can be transferred internationally. Both HIPAA and GDPR establish a framework that ensures data are handled responsibly and ethically, helping to establish trust among stakeholders, which is essential for successful data harmonization initiatives. However, their stringent requirements also pose challenges, including compliance costs and operational complexities.

Another critical challenge lies in the technical barriers that are introduced during data sharing, including data standardization and interoperability. The first major technical obstacle is data interoperability. Medical data are stored in different formats, creating significant challenges for seamless data exchange and integration. Developing and maintaining universal standards that facilitate global data sharing requires extensive coordination among a myriad of stakeholders, alongside continuous technological updates. Another substantial technical challenge is maintaining data quality and integrity. Inconsistencies, errors, and incomplete data can severely impact patient care and the validity of research. Implementing thorough data validation and cleaning processes, though crucial, is resource-intensive and technically demanding. Furthermore, the scalability and infrastructure required to manage the vast amounts of data generated globally pose their own set of challenges. Establishing the necessary infrastructure for large-scale data harmonization requires significant financial investment and sophisticated technological solutions, including the utilization of cloud computing and high-performance computing platforms. On the ethical front, patient privacy and data security are critical. The risk of data breaches and unauthorized access increases as data are shared across borders, complicating compliance with diverse and sometimes conflicting data protection regulations (GDPR, HIPAA). Another ethical concern is ensuring proper consent and autonomy. The varying cultural norms and legal frameworks around consent across different countries complicate the process of informing patients and obtaining their consent regarding data use. Ensuring patients are well-informed and retain control over their data is critical. Additionally, there is a risk of equity and fairness issues. Data harmonization efforts could inadvertently overlook or underutilize data from underrepresented or less developed regions, potentially reinforcing existing healthcare disparities. To this end, cooperation between governments, healthcare organizations, researchers, and technology providers is essential to develop and agree upon common data standards and exchange protocols. Initiatives like the Health Level Seven International (HL7)[6] and the Global Alliance for Genomics and Health (GA4GH)[7] are examples of such efforts. “Access to a broad range of harmonized data allows AI algorithms to learn more efficiently, recognize patterns more accurately, and make more precise predictions”. “Harmonized data can reduce biases in AI models, enabling more accurate global health surveillance to enable health organizations to track the spread of diseases, identify emerging health threats, and coordinate effective prevention strategies”.

Various semi-automated tools have been proposed to harmonize diverse data in biobanks, clinical registries, and cohorts. These include the DataSHaPER[8,9], which was successfully used to align 53 epidemiological databases with a 36% rate of compatibility, the SORTA system[10], yielding a 97% recall rate in correlating 5,120 entries within a single biobank, and the BiobankConnect[11], which demonstrated a 74% precision in integrating data across six biobanks. Semantic matching approaches were also proposed to map cohort data to reference model elements[12], yielding 67 different mapping scenarios to model all possible associations between 8 EU cohorts and a reference data model. Statistical methods like Item Response Theory (IRT) analysis[13] have also been applied to investigate the influence of various factors on certain items, such as psychiatric phenotypes, to achieve uniformity in scale. However, these methods face significant limitations in their applicability across different clinical areas. They rely on a semi-automated approach that necessitates close cooperation between clinical experts and technical professionals to establish specific lexical matching rules. The efficiency of these existing systems is often compromised, either by the intricate nature of the clinical domain being studied or by the absence of computational techniques for automated terminology matching.

To address these challenges, we propose a cutting-edge, holistic data harmonization approach that goes beyond the current state of the art. Our approach is based on the development of a “smart” knowledge-based strategy. This strategy utilizes FAIR-compliant reference ontologies to model the domain knowledge of various diseases. The ontologies are augmented with synonyms from the Natural Language Toolkit (NLTK)[14], and terminologies from the OHDSI Athena vocabulary[15], particularly from SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms)[16], RxNorm[17], LOINC (Logical Observation Identifiers, Names, and Codes)[18], ICD-10/11 (International Classification of Diseases)[19], ATC (Anatomical Therapeutic Chemical)[20], and OMOP (Observational Medical Outcomes Partnership)[21]. Word embeddings are calculated to further enrich these terminologies. Advanced lexical and semantic analyzers are applied to identify overlapping terminologies between the reference ontologies and metadata from diverse clinical centers. The proposed approach is built on principles from the FAIR CookBook[22] (and FAIRplus[23]) to yield interconnected and semantically rich data. The term “holistic” refers to the fact that the proposed approach can be applied to any domain of interest (any disease) as long as a reference ontology for that disease is available.



The proposed framework is executed in the form of a web service through a secure, GDPR-compliant, federated cloud computing environment[24]. The workflow of the framework is depicted in Figure 2. It has been designed to support different types of unstructured data and consists of five stages, including: (i) metadata extraction and relational mapping stage; (ii) data modeling stage; (iii) “smart” knowledge-based mapping stage; and (iv) lexical and semantic analysis stage. Stage (i) is automated and uses as input a . JSON file which represents the input tabular dataset and automatically extracts the feature names (terminologies), the range values (minimum and maximum values per feature), and the data types (integer, float, string, etc.). Stage (ii) is manual and involves the definition of a reference ontology (i.e., a hierarchical data model) to represent the domain knowledge of a disease of interest using Protégé to map the features into entities (classes, subclasses) and to define object properties (relationships among them). This stage requires close collaboration between the clinical experts in the field and the technical experts to map each entity into codes from international data models like SNOMED-CT, RxNorm, LOINC, etc., to promote data interoperability following the FAIR CookBook principles. Stage (iii) is also automated and involves the definition of a large corpus as described in A1. Stage (iv) is automated and involves the hybrid application of lexical and semantic analysis, where the Levenshtein distance and the Jaro distance are used to identify lexically relevant features and the extracted information from the word embeddings is used to identify conceptually relevant features. The output of the workflow is a data harmonization report which summarizes the main findings of the harmonization process. At the heart of our framework lies the robust definition and development of HL7 compliant data models. Our approach adheres to and is inspired by the principles outlined in the FAIR CookBook[22] for making data FAIR, and our methodology extends these principles into practical application. We focus on ensuring that the resulting harmonized data are not only standardized but also semantically enriched and globally accessible (the first three stages in Figure 2 are related to data FAIRification).

The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

Figure 2. The stages of the proposed holistic approach for data harmonization.


The proposed workflow can support a variety of unstructured tabular data which are stored in the form of csv, xlsx, SQL, and txt formats. Data sharing/processing agreements are distributed to the data providers to fulfill all necessary GDPR and HIPAA requirements for data sharing.


Metadata extraction and relational mapping stage

Useful metadata are extracted from the input raw data, including feature names, range values, and data types. This information is stored in the form of a JSON (JavaScript Object Notation) structure. Relational modeling is then applied to transform the JSON structure into an XML (Extensible Markup Language) format for the application of the data modeling stage.

Data modeling stage

A reference ontology is constructed for the domain of interest to encapsulate the disease’s knowledge using the Protégé open source ontology editor[25]. This process involves a detailed mapping and modeling of disease-specific terminologies and concepts from the metadata extraction stage, ensuring that the ontologies serve as robust, accurate representations of medical knowledge. The reference ontology serves as a gold-standard model for enriching the medical corpus in Section "Smart" knowledge-based mapping stage and for the lexical and semantic analysis in Section Lexical and semantic analysis stage. According to the FAIR CookBook[22] criteria, we select proper identifier schemes, data standards, and data vocabularies. To this end, the terminologies are mapped to codes from international data models and vocabularies, including the SNOMED-CT[16], RxNorm[17], LOINC[18], ICD-10/11[19], ATC[20], and OMOP[21] (OMOP Extension, OMOP Genomic and OMOP Invest Drug vocabularies).

“Smart” knowledge-based mapping stage

To further enhance the interconnectivity of the reference ontology, we introduce a linguistic layer by incorporating synonyms from NLTK[14]. This integration broadens the semantic scope of the ontologies, allowing for more comprehensive data interpretation. We define a “smart” knowledge-based repository in the form of a large corpus that is built on top of the OHDSI Athena vocabulary[15] including terminologies from globally recognized healthcare data models. This integration ensures that our reference ontologies are not only detailed but also aligned with global healthcare data practices. The corpus offers the basis for the lexical and semantic analysis stage in Section Lexical and semantic analysis stage. A key innovative aspect of our methodology is the application of word embeddings using the Word2Vec language modeling method[26]. These embeddings are computed to augment the terminologies, adding semantic and contextual layers based on the CBOW (Continuous Bag of Words) architecture[27]. This augmentation yields even more dynamic and interpretive vocabularies, which are essential for effective data harmonization. For example, the terminologies “blood tests” and “hematological tests” are not lexically relevant but are conceptually the same. Therefore, if we calculate the word embeddings for the terminology “blood test”, we can capture all the conceptually relevant terminologies and thus significantly improve the precision of the data harmonization process.

Lexical and semantic analysis stage

Moving beyond the conventional lexical analysis, we propose a hybrid semantic analysis process, utilizing a combination of the Levenshtein and the Jaro distances on top of extracted object properties and entity relations from the reference ontology[28]. This analytical phase is critical in identifying and mapping lexical and conceptual overlaps between our reference ontologies and the terminologies from various clinical centers. Through the examination of the object properties, entity relationships are extracted and word embeddings are calculated for each entity and included in the lexical analysis to further reduce information loss.


The output of the proposed approach is a data harmonization report which summarizes the matched terminologies between the reference ontology and the input set of terminologies along with the level of lexical or conceptual overlap and the standardized value ranges according to the predefined value ranges in the reference ontology.


The proposed approach was applied in three different domains, including the: (i) autoimmune diseases, using unstructured data (clinical, laboratory tests, medical conditions, demographic, therapies) from 21 EU cohorts with 7,551 patients who have been diagnosed with primary Sjogren’s Syndrome (pSS)[29]; (ii) cardiovascular diseases (CVD), using unstructured data (clinical, genomics, laboratory tests, medical conditions, demographic) from 7 EU cohorts with 25,000 patients who were diagnosed with cardiovascular diseases[30]; and (iii) mental disorders (MD; particularly depression and anxiety), using unstructured data (clinical, laboratory tests, medical conditions, demographic) from three EU cohorts with 3,500 patients[30].

Our main findings are summarized in Table 1. Metadata were extracted from each unstructured raw dataset and were stored in dictionaries. Three reference ontologies were constructed upon clinical guidance to reflect the minimum requirements that describe the domain knowledge for each case (i.e., pSS, CVD, MD). We refer to those ontologies as ROPSS, ROCVD, and ROMD, respectively. The ontologies are open (the ROCVD and ROMD can be found under the following GitHub link:; the ROPSS is located in the following GitHub link: and are expressed into RDF (Resource Description Framework)/OWL (Web Ontology Language) formats to facilitate interoperability between different systems and applications.

Table 1

Data harmonization results

DomainReference ontologyNumber of patientsNumber of entities (terminologies)Final set of terminologiesPrecision(%)
ADROPSS7,55135 (150)4845/48 (93.75%)
CVDROCVD25,00010 (792)7162/71 (87.32%)
MDROMD3,8009 (34)1412/14 (85.71%)

Synonyms and word embeddings were calculated for the terminologies per reference ontology, yielding three large corpora, namely CPSS, CCVD, and COMD. The lexical and semantic analyzers were applied to identify overlapping terminologies between CPSS, CCVD, COMD, and the extracted metadata per cohort. Our analysis revealed a set of: (i) 45 common terminologies with 93.75% precision across the 21 EU cohorts in pSS; (ii) 62 common terminologies with 87.32% precision across the 7 EU cohorts in CVD; and (iii) 12 common terminologies with 85.71% precision across the 3 EU cohorts in MD.


The proposed framework’s compliance with the FAIR CookBook[22] (and FAIRplus[23]) criteria is comprehensive. To facilitate data access and retrieval, it shares and stores data in a secure, GDPR/HIPAA-compliant cloud computing environment, which requires data sharing/processing agreements for access. In modeling the domain, it utilizes metadata extraction, relational mapping, and data modeling stages, employing identifiers from widely recognized HL7 data models such as SNOMED-CT, LOINC, OMOP, Rx-Norm, and ICD-10/11 for constructing reference ontologies. The OHDSI Athena vocabulary also incorporates HL7-related terminologies. The framework addresses identifier mapping to make the data models interoperable. It applies data standards by reusing, developing, applying, and validating HL7 standards. For the selection of data vocabularies, the framework emphasizes the selection, annotation, and management based on the OHDSI Athena. For data interoperability, it focuses on identifier mapping, vocabulary alignment, and data model mapping based on HL7 FHIR-based data models. Data hosting is executed in a secure, GDPR compliant, federated database management environment within the cloud, accessible via proper data processing agreements.

According to Table 2, although the DataSHaPER framework[8,9] offers a data model for mapping biobank data, it faces limitations in diverse clinical domains and relies heavily on expert cooperation. Although the SORTA system[10] is notable for its high recall rate in a single biobank, it is limited to specific biobank mapping scenarios. The BiobankConnect software[11] provides a customized solution to overcome biobank data complexities that are limited to a single biobank. Semantic matching[13] is useful but requires close cooperation between clinical and technical experts. IRT analysis[13] requires context-specific mapping to remove influences of covariates for data standardization. The proposed framework builds on FAIR CookBook (and FAIRplus) principles by utilizing the OHDSI Athena vocabulary to create a “smart” knowledge-based repository, yielding overlapping terminologies with more than 85% precision across three different clinical domains. However, this framework faces challenges in implementation complexity and needs more real-world applications to enhance its generalizability. In future work, we plan to include functionalities to reduce bias and enhance cross-lingual capabilities to ensure that medical data are more universally usable, contributing to equitable healthcare delivery across different linguistic and regional boundaries[31]. As the healthcare sector progresses, straightforward strategies like the proposed one are essential in addressing today’s challenges.

Table 2

Comparison of the proposed framework with similar ones

StudyKey pointsAdvantagesDisadvantages
[8,9]36% compatibility rate in aligning 53 epidemiological databasesA comprehensive data model for mapping the domain knowledge of complex biobank dataSignificant limitations in different clinical domains, require close cooperation between clinical and technical experts, detailed mapping of terminologies to ontologies, absence of computational techniques for fully automated matching
[10]97% recall rate in correlating 5,120 entries within a single biobankProven effectiveness in specific mapping scenarios across biobanks
[11]74% precision in integrating data across six biobanksA customized solution that is fine-tuned to the needs and complexities of biobank data
[12]Mapped cohort data to reference model elements for eight EU cohortsData modeling is based on terminologies from HL7 data models
[13]Models the influence of various factors on psychiatric phenotypesParticularly effective in scenarios where detailed, context-specific mapping and analysis are required
CurrentTerminologies with more than 85% precision across three different clinical domainsBuilds on FAIR CookBook and FAIRplus principles, introduces linguistic layers based on word embeddingsIncreased complexity of implementation, more real-world cases to increase the generalizability of the framework



The research work has received funding from the European Commission under GA 101135932 (FAITH Project).

Authors’ contributions

Concept and design of the study: Pezoulas VC, Fotiadis DI

Data analysis and interpretation: Pezoulas VC, Fotiadis DI

Final review and recommendations for improvement: Fotiadis DI

Availability of data and materials

Not applicable.

Financial support and sponsorship

The research work has received funding from the European Commission under GA 101135932 (FAITH Project).

Conflicts of interest

Both authors declared that there are no conflicts of interest.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.


© The Author(s) 2024.


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Pezoulas VC, Fotiadis DI. The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study. Conn Health Telemed 2024;3:300004.

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Pezoulas VC, Fotiadis DI. The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study. Connected Health And Telemedicine. 2024; 3(2): 300004.

Chicago/Turabian Style

Vasileios C. Pezoulas, Dimitrios I. Fotiadis. 2024. "The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study" Connected Health And Telemedicine. 3, no.2: 300004.

ACS Style

Pezoulas, VC.; Fotiadis DI. The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study. Conn. Health. Telemed. 2024, 3, 300004.

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