Improving diagnosis in health care: laboratory medicine.

Abstract

Accurate and timely diagnosis remains one of the most complex and challenging processes in medicine. Diagnostic errors pose a significant burden on patients and healthcare systems, with laboratory-related errors playing a substantial role, especially in the pre- and post-analytical phases of the testing process. However, recent innovations have mitigated some key challenges by optimizing workflows and reducing human errors. Notable advancements include automated systems for specimen check-in, preparation, aliquoting and storage for downstream analysis. Technologies such as automated interference detection, alongside sensors monitoring specimen volume and integrity, have enhanced standardization and reliability. Automated sample storage and retrieval systems have improved traceability and retrospective analyses while preserving specimen integrity. In the analytical phase, automation has facilitated real-time anomaly detection, enabling reflex or repeat testing to ensure result accuracy. The multiple integration of different analytical platforms, coupled with automated quality control features, has reduced inter-system variability, minimized manual errors and enhanced efficiency. Advancements in molecular and genetic diagnostics have enabled more precise and personalized treatments, reducing ineffective therapies and side effects. The ongoing deployment of lab-on-a-chip technology, integration of artificial intelligence, and reinforced patient safety culture highlight the vital role of continuous innovation in laboratory medicine to enhance patient safety. However, several challenges remain, including diagnostic errors from test result misinterpretation, poor sample quality, regulatory and compliance constraints, limited data sharing among laboratories, high cost of advanced diagnostic tools and shortage of trained laboratory professionals and pathologists. Addressing these barriers is essential for further safeguarding patient safety.