Bioinformatics for Healthcare Quality Management: Clinical Outcomes, Implementation Barriers, and Care Improvement

BACKGROUND AND OBJECTIVES: The bioinformatics-enabled workflows as quality-management tools have become central to modern healthcare, enabling genomic sequencing, transcriptomics, proteomics, and multiomics integration to support clinical decision-making. Despite expanding adoption, synthesized evidence on measurable clinical value and organizational, workforce, interoperability, governance, and equity barriers remains limited. To synthesize evidence on the contribution of bioinformatics-enabled workflows to healthcare quality improvement, care-process performance, clinical decision-making, patient outcomes, and implementation challenges from 2015 to 2025. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, Embase, and IEEE Xplore. We included studies from real-world healthcare or public health settings where bioinformatics supported diagnostic quality, timeliness, treatment appropriateness, patient safety, and workflow efficiency, or operational decision-making, reporting quantitative outcomes including diagnostic yield, time-to-diagnosis, treatment modification, or economic impact. RESULTS: Twenty studies met the selection criteria, spanning precision oncology, pharmacogenomics, infectious disease surveillance, clinical genomics and rare disease diagnosis, and multiomics biomarker translation. Across these domains, bioinformatics-enabled workflows improved diagnostic yield (25%-40% in rare disease genomics) and diagnostic accuracy, and shortened time-to-diagnosis, from 4-6 weeks to as little as 3 days in acute pediatric care and from weeks to under 24 hours for pathogen genomic surveillance. Workflows also supported treatment modification, with matched or altered therapy in 33%-37% of oncology cases, and improved pathogen detection and antimicrobial-resistance monitoring. Economic evidence, although still limited, was favorable: first-line exome sequencing was cost-saving, with average reductions of approximately US$9000 per patient in rare disease diagnosis. CONCLUSIONS: Although precision medicine relies heavily on bioinformatics, improvements to infrastructure, consistent governance, and capacity building for clinical staff are essential to ensure its scalable and equitable implementation.