Operationalizing Sensemaking in Learning Health Systems: A Sociotechnical Case Study in Anesthesiology

Learning health systems require sociotechnical frameworks that align human cognition, technology, and organizational practice to support continuous improvement in care delivery. Anesthesiology presents a compelling context for examining how clinicians make sense of monitoring data under uncertainty, time, and resource constraints. This study applies and evaluates an adapted sensemaking framework, grounded in Weick's organizational sensemaking theory and Klein et al.'s data-frame theory of sensemaking, to characterize how an expert anesthesiology clinician engages with neuromuscular monitoring data intraoperatively. A think-aloud protocol and semi-structured interview were conducted with a board-certified anesthesiologist in a high-fidelity operating room simulation. Transcript data were analyzed using hybrid deductive and inductive thematic analysis. Deductive analysis yielded five themes operationalized from the adapted framework: Familiarization, Cue Extraction, Plausibility, Trust Calibration, and Collaborative Sensemaking, each populated with empirically grounded subthemes. Inductive analysis identified an emergent theme, Consequence Awareness, capturing the clinician's reasoning about downstream patient safety implications when monitoring is absent, proposed as an additional construct for subsequent theoretical refinement. Barriers to neuromuscular monitoring spanned individual, workflow, and institutional levels, encompassing knowledge gaps, distrust of monitoring technology, device unavailability, and inadequate integration with electronic health records. Corresponding opportunities were identified in clinical decision support and technology interoperability. The study provides initial evidence for the construct validity, feasibility, and translational utility of the adapted framework in a high-acuity clinical setting. It further demonstrates the viability of a researcher-practitioner partnership model. The findings offer a theoretically grounded foundation for developing low-burden, practice-oriented tools to support clinicians and implementation teams in mapping, reflecting on, and redesigning intraoperative work systems.