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Gene & Protein in Disease Next gen (omics)

electronic health records (EHRs) and their phenotypic 2.3. Integration of digital tools into health-care
data, which can leverage modern Information Technology systems
in computer modeling, mining, and integrated analysis In addition to being a vital tool for managing the vast
of genomic data for the development of customized, amounts of data produced by national health-care systems,
preventive, and predictive medicine, and ultimately enable modern computational tools are also being universally
health-care practitioners to provide, among other things, used in the health-care industry to improve the efficiency
the right medicine at the right dosage to each patient. and precision of medical services. However, there is wide
Other examples include combining genetic data with variability in terms of specific clinical functions across
neuroimaging, wearable device data, and other Internet EMRs, personal health records, artificial intelligence (AI),
of Things modalities, despite “noisy” clinical data. These wearables, EHRs, e-prescriptions, and telemedicine, with
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integrated studies are anticipated to significantly advance the last three being the dominant ones. 5
biomedical science, health care, and society’s advancement
as more diversified data are gathered. The integration of EHRs are being adopted by virtually all health-care
transcriptomic, proteomic, and metabolomic data, which systems, whereas e-prescriptions have been widely adopted
could provide a comprehensive understanding of a patient’s by the National Health Systems in Northern and Southern
condition, represents a further step in fusing different types Europe, such as Scandinavian countries, Italy, Spain,
of health data, aiding clinical decision-making. Greece, the UK, and Iceland, with Central and Eastern
Europe countries such as Germany, France, Austria,
2.2. Emerging pathways for the integration of digital Poland, Bulgaria, and Estonia lagging behind. Other areas
health solutions in genetic medicine of the world have also started adopting e-prescriptions,
Digital health interventions possess the potential to particularly regions in North America and Central-East
revolutionize genetics by offering personalized care Asia. The adoption of e-prescriptions generally improves
to patients. Among the most critical areas for eHealth the efficacy of health-care systems, resource utilization, and
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measures in resolving issues in genetics are disease patient safety. In terms of AI, the tool has been extensively
prevention through early diagnosis and personalized integrated over the years in the health-care industry, but it
medicine. These interventions aid in offering personalized is still in its early stages when it comes to national health
screening strategies to people at high risk of developing systems. As an example, Italy is experimenting with the
a genetic disorder. Through targeted prevention, these development of a national AI health-care system. 7
measures can be initiated before symptom onset, thus 3. Applications and key areas of impact
enhancing patient outcomes and curbing health-care
costs. Moreover, digital health interventions can customize 3.1. Enhancing (digital) genetic counseling with AI
individual treatments: for instance, pharmacogenomic integration
testing can pinpoint patients prone to adverse drug Telemedicine and remote monitoring technologies have
reactions or those requiring different dosages based on revolutionized genetic counseling, especially for patients
their genetic profile. without local access to these services. Digital technologies,

As previously discussed, digital health constitutes a potentially enhanced by AI, facilitate communication
major impetus behind the revolution of genetic counseling between geneticists and primary care physicians, improving
services, offering eHealth platforms to enhance patients’ coordinated care and patient outcomes. AI is also pivotal
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access to genetic counseling services, even in underserved in genomic data analysis (Figure 1), utilizing techniques
areas. Having access to genetic data facilitates a better such as unsupervised machine learning to discern patterns
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understanding of genetic risk factors among patients, within extensive genetic datasets. These analyses can
enabling physicians to make informed decisions about reveal new discoveries and provide deeper insights into
treatments, and familiarizing patients with their health genetic diseases and drug development targets.
risks so that they can make informed decisions regarding Due to the cost-effectiveness of sequencing entire
their lifestyle change, genetic condition, and treatment genomes, next-generation sequencing technologies
options. have become more widely used, translating to increased
Overall, digital health interventions hold the potential availability and demand for genomic data analysis tools.
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to transform genetics by offering personalized, accessible, Software for variant calling, annotation, and interpretation
and patient-oriented care. However, it is imperative to is designed according to the FAIR principles, ensuring
ensure that data sharing occurs in a secure and ethical that data are findable, accessible, interoperable, and
manner, safeguarding patients’ privacy and autonomy. reusable. AI-powered chatbots are also transforming

Volume 3 Issue 3 (2024) 3 doi: 10.36922/gpd.4128