Next-Generation Patient-Based Real-Time Quality Control Models

Area of Science:

• Clinical Chemistry
• Medical Informatics
• Artificial Intelligence in Healthcare

Background:

• Patient-based real-time quality control (PBRTQC) monitors laboratory assay performance using patient data.
• Advancements in computer science and computing power have driven PBRTQC algorithm development.
• Artificial Intelligence (AI) has seen rapid adoption in PBRTQC, promising significant advantages.

Purpose of the Study:

• To critically compare various PBRTQC algorithms, including AI-based methods.
• To contrast AI-driven PBRTQC with conventional quality control approaches.
• To evaluate the feasibility and benefits of implementing AI in PBRTQC.

Main Methods:

• Description and contrast of PBRTQC algorithms: moving averages, regression-adjusted real-time QC, neural networks, and anomaly detection.
• Comparative analysis of AI-based versus conventional PBRTQC techniques.
• Assessment of AI tool characteristics relevant to laboratory implementation.

Main Results:

• AI tools are increasingly available, user-friendly, and computationally efficient for PBRTQC.
• Traditional disadvantages of AI, such as complexity and high resource needs, are diminishing.
• AI offers attractive solutions for enhancing PBRTQC.

Conclusions:

• AI-powered PBRTQC methods offer significant advantages over conventional techniques.
• The increasing accessibility and efficiency of AI tools facilitate their adoption in clinical laboratories.
• AI is poised to revolutionize PBRTQC by overcoming limitations of traditional methods.