Challenges in Combining Pharmacogenomics with Electronic Health Records in the Field of Biomedical Data Science and Informatics

Abstract

The discipline of biological data science and informatics has considerable obstacles in integrating pharmacogenomics with electronic health records (EHRs). Pharmacogenomics is the study of how genetic variations affect drug responses. To do this, it combines information from molecular science, pharmacology, and clinical medicine. Addressing genetic variability among populations, developing data interchange standards, merging disparate information, mining scientific literature, and comprehending how structural variations and quality articulation impact drug absorption are some of the major hurdles. Furthermore, given that genetic information is unique to each individual, securing sensitive patient data is essential. The intricacy of integrating genetic data into clinical practice is highlighted by the fact that, although some healthcare systems have successfully integrated pharmacogenomic decision support systems, or clinical decision support (CDS), into EHRs, these initiatives typically rely on institutional infrastructure and custom-fabricated rules. In order to improve tailored medicine, effective CDS devices incorporate latent and active cautions that direct physicians in drug prescription based on a patient's genetic profile. Still, there are a lot of issues with EHR analytics, such as the need for standardized, repeatable procedures to impede research reproducibility and the difficulty of phenotyping and data pre-processing. These problems include the need for interdisciplinary cooperation, advanced computation sharing, and methodical procedures in order to fully address pharmacogenomics in healthcare.