Rethinking intraoperative blood loss monitoring: a decision-oriented framework for clinically integrated assessment

Area of Science:

• Anesthesiology and Surgical Monitoring
• Medical Technology Innovation
• Clinical Decision Support Systems

Background:

• Intraoperative blood loss (IBL) monitoring is critical for patient safety and anesthesia decision-making.
• Current IBL monitoring methods lack accuracy, timeliness, and workflow compatibility.
• Decades of technological advancement have not resolved fundamental challenges in IBL measurement.

Purpose of the Study:

• To re-examine intraoperative blood loss monitoring challenges from clinical surgery and anesthesiology perspectives.
• To analyze structural reasons for the unreliability of current IBL clinical measurements.
• To propose core principles and future strategies for reliable IBL surveillance.

Main Methods:

• Analysis of traditional IBL assessment methods (visual, gravimetric, volumetric, spectrophotometric) and their limitations.
• Proposal of the "Irreconcilable Triangle of IBL Monitoring" model (accuracy, timeliness, workflow compatibility).
• Redefinition of accuracy as "functional accuracy" to support clinical decision-making.

Main Results:

• Traditional IBL monitoring methods possess inherent limitations.
• The "Irreconcilable Triangle" highlights the trade-offs between accuracy, timeliness, and workflow compatibility.
• Functional accuracy is proposed as a more clinically relevant metric for IBL monitoring.

Conclusions:

• Future IBL monitoring requires automated systems for direct quantification of blood loss (free, absorbed, clots).
• Seamless integration into anesthesia workflows is essential for clinical feasibility.
• Artificial intelligence (AI) can enhance monitoring as a prediction and computational tool, prioritizing clinical usability and decision relevance.