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

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.

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.

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.

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.