Advances in next-generation sequencing technologies have expanded the clinical availability of genomic testing, creating new opportunities for precision and personalized medicine [1,2]. Genomic findings now support disease diagnosis, prognosis, treatment selection, and pharmacogenomics-informed prescribing [3-5]. However, despite ongoing European initiatives, practical guidance for integrating clinically relevant genomic findings into electronic health records (EHRs) remains limited [6,7].

EHRs are structured, continuously updated records of an individual’s health information throughout life [8,9]. They support patient management, improve care coordination, strengthen clinical decision-making, and facilitate public health and biomedical research [10,11]. They also enable the integration of clinical decision support systems (CDSSs) and other intelligent tools that support the workforce, contributing to safer, more efficient, and more personalized health care [2,11]. Their effectiveness depends on interoperability, which allows health information to be exchanged and interpreted consistently across different health care systems [8,12]. This provides the foundation for integrating emerging data types, including genomic information [13-15].

Within Europe, several initiatives are supporting this transition, including the Extended EHR@EU Data Space (Xt-EHR) Joint Action and the European Health Data Space for Secondary Use @CY (CY-EHDS-2nd) project [16,17]. Together, they contribute to the implementation of the European Health Data Space (EHDS) [18]. Xt-EHR focuses on the primary use of health data for clinical care and prevention, whereas CY-EHDS-2nd supports the secondary use of health data for research and innovation. Both initiatives promote secure, interoperable, and citizen-centered exchange of health information across Member States.

Despite these advances, genomic findings are still commonly returned to EHRs as static reports that are difficult to exchange, update, and reuse within modern health care systems [6,7,19,20]. This limits their clinical value and hinders the broader implementation of genomics into routine patient care. To fully realize the potential of genomic medicine, EHR systems must evolve to support the structured representation, exchange, and reuse of clinically relevant genomic information [1,2,21].

To examine these implementation needs, the National eHealth Authority of Cyprus (NeHA), the University of Cyprus (UCY), and the Cyprus Institute of Neurology and Genetics (CING) organized the Gen2EHR Implementation Guidelines (IG) Workshop under the framework of the Xt-EHR and CY-EHDS-2nd EU-funded projects. The workshop brought together experts from clinical, technical, and health policy backgrounds to discuss the technical requirements, coding systems, and interoperability standards needed for the meaningful integration of genomics into EHRs.

Rather than reviewing all aspects of genomic medicine, in this viewpoint, we draw on selected published literature, relevant technical standards, operational experience with genomic testing and reporting at CING, and multidisciplinary discussions from the Gen2EHR Workshop. We discuss the technical, organizational, clinical, ethical, legal, and governance challenges that currently limit the routine integration of genomics into EHRs. Building on these considerations, we propose a practical roadmap to support implementation across the EHDS.

EHRs provide a longitudinal view of patient information and support multidisciplinary care and clinical decision-making [2,22]. Their value can be further extended when clinically relevant genomic findings are represented as structured, phenotype-linked, interoperable, and versioned data [20]. Because the interpretation of genomic variants may change as scientific knowledge evolves, structured integration also supports variant reinterpretation and allows updated findings to be considered alongside diagnoses, medications, laboratory results, and other clinical information [23,24].

Once available in this form, genomic findings may support clinical decision-making, personalized treatment strategies, and longitudinal patient management [2]. They may also contribute to a learning health system in which knowledge generated through clinical practice and research informs future patient care. However, these benefits depend on high-quality data, clinical validation, and integration into routine workflows [20,24].

A well-established example is pharmacogenomics [25]. When validated pharmacogenomic findings are represented in a structured format within EHRs, they become available for use by applications such as CDSSs. CDSSs can then alert clinicians to gene-drug interactions and genetic variants associated with altered drug response or toxicity. This reduces reliance on manual review of static genomic reports and makes clinically relevant information available within routine workflows [26,27]. However, CDSSs should complement, rather than replace, professional judgment and access to laboratory or clinical genetics expertise.

The value of genomic information also extends beyond direct patient care. Under appropriate legal, ethical, and governance frameworks, harmonized genomic and phenotypic data may support secondary uses such as genome-wide association studies, phenome-wide association studies, and the interpretation of rare genetic variants by comparing patients with similar genomic and clinical profiles [20]. These applications can improve our understanding of disease mechanisms, facilitate variant interpretation, and support future advances in clinical care and research [2,10,28].

Overall, structured, clinically relevant genomic findings have the potential to improve both clinical care and biomedical research. Realizing these benefits, however, requires addressing the implementation challenges discussed in the following section.

Integrating genomic findings into EHRs requires addressing several technical, organizational, clinical, and governance issues. These include data representation, storage, interoperability, workforce readiness, privacy, and the evolving nature of genomic knowledge.

EHRs contain structured, semistructured, and unstructured data. Structured data use standardized, encoded, and machine-readable formats that support interoperability and CDSSs [7,10,29,30]. In contrast, genomic test results are still commonly stored and exchanged as free-text/unstructured reports or PDF files [31,32]. These formats are intended for human interpretation but limit data reuse, interoperability, and integration into clinical workflows [2,7,10]. Semistructured formats, such as JSON and XML, provide an intermediate approach but do not fully address these limitations [10,20,33].

Genomic analysis also produces large and technically complex datasets that most EHRs are not designed to store or process. Therefore, specialized genomic repositories should store raw and intermediate data, while EHRs should contain only clinically relevant findings needed for patient care [20,24,34].

Another challenge is that genomic knowledge evolves over time. Variant interpretation may change as new evidence becomes available. EHR-integrated genomic findings therefore require provenance, date-stamped interpretations, version control, and clear processes for review and update. Responsibilities for reinterpretation, clinical validation, communication of updated findings, and, where appropriate, patient recontact should be clearly defined [20,24,34].

Successful implementation also depends on clinical workflow and workforce readiness. Clinicians need appropriate education to understand the significance and limitations of genomic findings and to communicate uncertain results to patients. Although CDSSs can aid routine care, their effectiveness depends on appropriate clinical training, professional judgment, and collaboration with laboratory and clinical genetics specialists. Their usability, reliability, and integration into routine care should also be carefully evaluated [31,32,35,36].

Genomic information is highly sensitive and may have implications for biological relatives as well as individual patients. Its integration into EHRs raises important privacy, security, ethical, legal, and governance considerations [37,38]. Appropriate governance should address data processing, informed consent where required, role-based access, auditability, data retention, primary and secondary use of genomic information, and responsibilities for returning clinically relevant findings. Furthermore, many variants remain classified as variants of uncertain significance (VUSs), increasing the risk of misinterpretation, overdiagnosis, or unnecessary anxiety [39-41]. Transparent communication and appropriate professional support are therefore essential.

In summary, addressing these challenges requires more than technical system development. It requires standardized representation of clinically relevant findings, appropriate data governance, workforce preparedness, and reliable interoperability across laboratories, genomic repositories, EHR systems, health care organizations, and jurisdictions. The following section discusses the key dimensions of interoperability needed to support the effective integration of genomics into health care systems.

To effectively address these challenges and facilitate the integration and exchange of genomic data across EHR systems, appropriate regulations and interoperability standards must be established. The Institute of Electrical and Electronics Engineers defines interoperability as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged” [33,42,43]. As portrayed in Figure 1, there are 5 dimensions of interoperability, each addressing the need for standards to facilitate seamless communication between EHR systems, exchange, understand, interpret, and protect patient health information [42].

When working with genomic data, it is important that EHR systems can communicate effectively with Laboratory Information Systems. As shown in Figure 2, the process usually begins when a clinician submits a ServiceRequest form within the EHR system, specifying the genomic test to be performed (eg, whole exome sequencing). A specimen (eg, blood sample, saliva, or tissue biopsy) is then collected and forwarded to the laboratory. The lab conducts a genomic analysis to detect relevant variants, which are captured as structured Observations (eg, variant, genotype, implication data, etc). These Observations are then aggregated into a DiagnosticReport that summarizes the findings. Often, this report is supplemented by files such as PDFs or raw genomic data (eg, variant call filess), linked via a DocumentReference resource. In cases where the genetic findings suggest a specific clinical action (eg, changing a medication or referring the patient for further testing), a Task resource is created to formalize and transmit that recommendation, enabling downstream care teams to act. This entire workflow, from the initial test request to the clinical action, is supported by interoperability standards (see Figure 1), which ensure that data are shared and understood in the same way across different systems [32,44,45].

To enable this workflow in practice, the exchange of genomic data between EHRs and Laboratory Information Systems must be built on a strong technical foundation. This begins with the implementation of basic interoperability standards that allow systems to communicate consistently (see Figure 1A). Traditionally, this has been achieved through standards like HL7 v2 (Health Level 7, version 2), which uses predefined message formats. While still in use, HL7 v2 was not designed with modern genomic data in mind [7,21,32].

Genomic data are inherently complex and voluminous, often requiring the transfer of structured information that includes genetic variants, interpretations, and related files [20]. To support these requirements, HL7 developed FHIR (Fast Health Interoperability Resources). HL7 FHIR uses modern web technologies and Representational State Transfer APIs, offering a more flexible approach to data exchange [32]. Through Representational State Transfer APIs, different software systems can communicate with each other in real time using HTTP methods. These interactions allow systems to send requests and receive responses, making it possible to retrieve, submit, update, or delete data securely and efficiently [6,44,45].

After setting up the basic technical framework for interoperability (see Figure 1A), the next step involves syntactic and semantic interoperability (see Figure 1B,C). These 2 levels ensure that data are not only structured in a standardized format but also consistently understood across systems [31,32]. In genomics, this is crucial due to the precision required in interpreting variant information. For this reason, the HL7 Clinical Genomics Work Group [46] developed the FHIR Genomics Reporting IG [47]. The HL7 FHIR Genomics Reporting IG defines a set of standardized profiles for key FHIR resources such as ServiceRequest, Specimen, Observations, DiagnosticReport, Task, and DocumentReference (see Figure 2) [45].

Each of these resources uses formats like JSON for data representation and relies on widely adopted coding systems. Examples include Logical Observation Identifiers Names and Codes (LOINC) [48] for laboratory test types, ClinVar [49] for variant classification, the Human Genome Variation Society (HGVS) [50] nomenclature for genetic variant notation, and Systematized Nomenclature of Medicine Clinical Terms [51] or HUGO Gene Nomenclature Committee (HGNC) [52] for gene and condition terminology. The combination of structured formats and standardized vocabularies enables consistent interpretation across systems and enhances machine readability of genomic information. In EHR systems that incorporate CDSSs, this structured information may be used to generate timely alerts, treatment recommendations, or risk assessments. Nevertheless, these tools complement clinical expertise and should be used alongside appropriate genomics training, professional judgment, and collaboration with clinical genetics specialists and laboratories [45].

Of course, none of these would be possible without ensuring the protection of patient data (see Figure 1D,E) [6]. In Europe, this is required by the General Data Protection Regulation (GDPR), which sets strict rules for how personal health information is handled [53]. HL7 FHIR helps support GDPR compliance through Substitutable Medical Applications, Reusable Technologies, a framework that adds security and access control features. Substitutable Medical Applications, Reusable Technologies on FHIR uses the OAuth 2.0 framework and ensures that approved apps can access patients’ data only with the appropriate permissions [45]. This helps keep patient information safe and private. It also allows patients to access their own health data, participate in their own care, and make informed health decisions [7,20,21]. The flexible architecture of FHIR makes it ideal for laboratory communications, allowing systems to easily exchange and protect data. It provides a modern alternative to HL7 v2, supporting the integration of complex data types like genomics into EHRs effectively [45].

The HL7 FHIR Genomics Reporting IG was first published in November 2019 [54]. As a relatively recent standard, most published experience comes from pilot implementations and demonstration projects rather than everyday clinical practice [19,55,56]. However, earlier initiatives, including the Electronic Medical Records and Genomics (eMERGE) Network and the Clinical Sequencing Evidence-Generating Research (CSER) program, had already explored the integration of genomic test results into EHRs [20,29,57]. These early efforts revealed a range of implementation challenges, some of which align with the broader issues described earlier in the manuscript:

The eMERGE and CSER initiatives demonstrated both the feasibility and the complexity of incorporating genomic information into clinical systems. Their experience indicates that technical exchange standards must be accompanied by consistent content definitions, clinically appropriate workflows, governance arrangements, and professional support. Building on these lessons, the NeHA, UCY, and CING organized a workshop within the framework of the Xt-EHR and CY-EHDS-2nd projects to investigate the necessary requirements for integrating genomics into EHR systems.

Based on what has been mentioned in the previous sections, we now present the practical perspective of this viewpoint for integrating genomic findings into EHRs. We briefly introduce the European initiatives supporting this work, describe the current genomic service delivery workflow at CING, summarize the main outcomes of the Gen2EHR IG Workshop, and present a practical roadmap for implementation.