Personal Health Data Spaces for precision prevention and continuity of care: integrating genomic, imaging, and clinical data in primary care

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Despite substantial advances in disease prevention, current clinical approaches remain largely anchored in short-term risk estimation models that are dominated by age and conventional risk factors. As a result, individuals with substantial inherited susceptibility, particularly younger patients, are frequently underrecognized until disease has already developed. This limitation reflects a broader disconnect between lifetime risk and episodic clinical assessment.Primary care providers, including general practitioners, family physicians, pediatricians and internal medicine providers, serve as the first point of contact for most patients (depending on the specific health system) and act as gatekeepers to specialized care. With the increasing availability of DNA-based tests and expanding opportunities for genetic screening, these clinicians are increasingly confronted with questions about genetic risk and testing, highlighting the need for better alignment between advances in genomics and the evolving realities of clinical practice. With growing knowledge of the genetic basis of both common chronic diseases (such as diabetes, cancer, and cardiovascular disease) and their monogenic subtypes, including Maturity-Onset Diabetes of the Young (MODY), hereditary breast and ovarian cancer (BRCA1/2), Lynch syndrome, familial hypercholesterolemia (FH), and long QT syndrome (LQTS), as well as recognizing possible rare genetic disorders, polygenic risk scores (PRS) and pharmacogenomic reasons for ineffectiveness or side effects of medications, requires a higher level of genetic literacy in primary care, highlighting the need for better alignment between advances in genomics and the evolving realities of clinical practice.Studies consistently show that clinicians often lack confidence, practical knowledge, and adequate infrastructure to integrate genetic services into routine care, highlighting the need for better alignment between advances in genomics and everyday clinical practice. [1][2][3][4] While genomics has been a major driver of personalized medicine, it represents only one component of a broader transformation toward integrating diverse clinical data streams, including imaging, biomarkers, and longitudinal health information, into coherent, actionable insights for patient care.This article brings together three interrelated developments that are often discussed separately.First, continuity of care remains a defining strength of primary care and an essential prerequisite for effective prevention and chronic disease management. Second, advances in genomics, pharmacogenomics, imaging, and other precision health approaches increasingly enable earlier and more individualized risk assessment, particularly in cardiovascular disease prevention.Third, emerging Personal Health Data Spaces (PHDS) offer a potential infrastructure for integrating these diverse data sources across healthcare settings and over time. We propose that the greatest value of PHDS lies not simply in enabling access to health data, but in supporting continuity of care by connecting multimodal information to longitudinal clinical decisionmaking. Throughout this article, genomic and cardiovascular prevention examples are used to illustrate how PHDS may help translate fragmented data into actionable insights within routine primary care.We argue that PHDS should be viewed not primarily as a new source of health data, but as an enabling infrastructure for continuity of care. When implemented within robust governance frameworks, supported by interoperable standards, integrated into multidisciplinary workflows, and designed to promote equity, PHDS may enable precision prevention through the longitudinal integration of genomic, imaging, pharmacogenomic, behavioral, and clinical information. We use cardiovascular and genomic risk assessment as illustrative use cases to examine opportunities, challenges, and implementation requirements.Personalized medicine, traditionally framed as tailoring (pharmacological) treatment to the individual, is increasingly understood as a broader clinical approach that integrates family history, genomic, and pharmacogenetic information into routine care. [5] In primary care practice, this includes recognizing inherited risk patterns, interpreting pharmacogenetic variation in drug response, and incorporating these insights into longitudinal decision-making. Access to an individual's genomic and pharmacogenetic profile provides an additional, actionable layer of personalized data when interpreted within clinical knowledge frameworks and guideline-based decision-making. Beyond optimizing therapy, such data may also enable earlier identification of disease risk and support presymptomatic and preventive strategies, particularly for hereditary conditions such as cancer syndromes and familial cardiovascular disease, as well as common chronic diseases including diabetes. [6] Importantly, this approach is also relevant for populationbased genetic conditions, such as hemoglobinopathies, where early identification in primary care can facilitate targeted screening, (preconceptional) counseling, cascade screening and timely intervention. Together, these developments illustrate how personalized medicine extends beyond isolated tests to a longitudinal, integrated model of care that aligns closely with the principles of continuity and coordination inherent to primary care.Genomic medicine offers an opportunity to address this gap. Both monogenic conditions, such as FH, and polygenic susceptibility captured through PRS demonstrate that a significant proportion of cardiovascular risk is present from birth and accumulates over time. [21,22] However, while genetic information can identify predisposition, it does not consistently translate into clinical action. [6,7] At the same time, advances in imaging, particularly coronary artery calcium (CAC) scoring and coronary CT angiography (CCTA), allow direct assessment of subclinical disease, providing a bridge between latent risk and phenotypic expression. Yet these data streams remain fragmented, often interpreted in isolation rather than as part of a longitudinal, integrated risk model. [8] These challenges extend across chronic conditions. The core issue is not the absence of data, but the lack of integration. Genomic results, imaging findings, laboratory data, and clinical information are often generated in parallel but remain fragmented and difficult to interpret within routine workflows.Personal health data spaces (PHDS) provide a conceptual framework to address this challenge by integrating multimodal data into a continuous, patient-centered environment. For the purposes of this article, a PHDS refers to a patient-centered, interoperable environment that may enable aggregation, governance, sharing, and longitudinal use of multimodal health data originating from multiple sources. Unlike electronic health records, which are typically organization-centric, PHDS are intended to be person-centric and span organizational boundaries. Similarly, PHDS extend beyond patient portals and personal health records by enabling structured integration of genomic, imaging, clinical, and behavioral information, together with governance mechanisms that support data sharing and reuse. PHDS are also distinct from the European Health Data Space (EHDS), which provides a regulatory and infrastructural framework for data exchange at a societal level; PHDS describe the patient-level environment in which such data can be integrated and used for person-and patient-centric care. [16] What Is a Personal Health Data Space?For the purposes of this article, we regard a Personal Health Data Space (PHDS) as a patientcentered, interoperable environment that enables the longitudinal aggregation, governance, sharing, and use of health-related data originating from multiple sources. PHDS should be viewed as a conceptual model rather than a single technology platform. Its primary p

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