The lack of reproducible computational workflows makes it difficult to integrate genomic variant analysis into clinical information systems, despite the fact that it offers tremendous potential for precision medicine. A methodological framework for integrating genomic variant data into hospital information systems (HIS) is presented in this study. Representative genes, such as APOE, LDLR, and MEFV, were identified by extracting variant call format (VCF) data from the 1000 Genomes Project. To calculate similarity scores, mismatch counts, and gap frequencies, genotypes were encoded as symbolic sequences and subjected to dynamic programming-based global sequence alignment (Needleman–Wunsch algorithm). To find genetically homogeneous subgroups, these alignment-derived features were aggregated into quantitative matrices and then put through unsupervised clustering using k-means. The feature sets and cluster labels that were produced were exported in an HIS-compatible structured format, which allowed for their subsequent incorporation into decision-support modules. In addition to showing that it is feasible to combine data mining techniques with genomic alignment algorithms for clinical informatics applications, this work focuses on the creation of a reproducible and extensible data processing pipeline. To validate the method in actual healthcare settings, future research will include outcome data and carefully selected risk alleles.