Sensors for Continuous Monitoring of Surgeon's Cognitive Workload in the Cardiac Operating Room

Abstract

Monitoring healthcare providers' cognitive workload during surgical procedures can provide insight into the dynamic changes of mental states that may affect patient clinical outcomes. The role of cognitive factors influencing both technical and non-technical skill are increasingly being recognized, especially as the opportunities to unobtrusively collect accurate and sensitive data are improving. Applying sensors to capture these data in a complex real-world setting such as the cardiac surgery operating room, however, is accompanied by myriad social, physical, and procedural constraints. The goal of this study was to investigate the feasibility of overcoming logistical barriers in order to effectively collect multi-modal psychophysiological inputs via heart rate (HR) and near-infrared spectroscopy (NIRS) acquisition in the real-world setting of the operating room. The surgeon was outfitted with HR and NIRS sensors during aortic valve surgery, and validation analysis was performed to detect the influence of intra-operative events on cardiovascular and prefrontal cortex changes. Signals collected were significantly correlated and noted intra-operative events and subjective self-reports coincided with observable correlations among cardiovascular and cerebral activity across surgical phases. The primary novelty and contribution of this work is in demonstrating the feasibility of collecting continuous sensor data from a surgical team member in a real-world setting.

Keywords: cardiac surgery; cognitive workload; heart rate; near-infrared spectroscopy.

Figure 1

NIRS sensor placement on attending surgeon.

Figure 2

NIRS acquisition device placement in relation to the attending surgeon and cardiopulmonary bypass pump. A. highlights the preamplifier and B. highlights the INVOS™ monitor receiving data from the sensors.

Figure 3

Relationship between mean HR and mean rSO₂. A significant positive correlation was found between HR and rSO₂ data. Each data point represents the same 60-second interval of HR data and of rSO₂ data. The 177 datapoints shown in this figure encompass all phases included in Table 1.

Figure 4

Mean HR and mean rSO₂ curves during the Aortic Clamp and Cardioplegia sub-phase.