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

Intraoperative blood loss (IBL) monitoring is an important factor in decision-making of anesthesia, and real-time and reliable IBL is vital in the safety of the patient during the perioperative period. But even now after decades of technological development in medicine, this basic clinical issue has not been addressed. The current strategies of monitoring do not usually achieve all the needs of accuracy, timeliness, and procedural flexibility in regular clinical care. This article re-examines this critical issue of intraoperative blood loss monitoring from the perspective of actual clinical surgery and anesthesiology. We analyzed the structural reasons for the long-term unreliability of IBL clinical measurements and proposed some core principles for future surveillance strategies. Traditional methods for assessing intraoperative blood loss include visual estimation, gravimetric methods, volumetric methods, and laboratory or spectrophotometric methods. These methods all have inherent limitations. We believe these shortcomings are not isolated technical problems, but rather stem from a design philosophy that did not address the needs of clinical decision-making from the outset. To better explain the clinical challenges faced by IBL monitoring, we propose the "Irreconcilable Triangle of IBL Monitoring" model. This model consists of three conflicting requirements: accuracy, timeliness, and workflow compatibility. Optimizing one dimension of technology inevitably compromises other dimensions. Therefore, we redefine accuracy as "functional accuracy". That is, the accuracy of intraoperative bleeding monitoring should reach the level necessary to support intraoperative clinical decision-making, without pursuing absolute accuracy. Simultaneously, it should be made as time-relevant and clinically feasible as possible to meet clinical needs. This paper envisions future strategies for intraoperative blood loss monitoring. Future clinical practice requires automated equipment capable of continuously quantifying free blood, absorbed blood, and blood clots, shifting from inferential estimation to direct automated measurement. Furthermore, this equipment must be seamlessly integrated into the anesthesia workflow. The paper discusses the availability of artificial intelligence (AI) in surgical blood loss monitoring. We believe that while AI has limitations in interpretability, it can serve as a complication prediction and backend computational tool for the entire monitoring system. In the future, we hope to develop IBL monitoring into an integrated system centered on clinical usability and decision relevance through new technology development and system integration.