Using physiological monitoring data for performance feedback: an initiative using thermoregulation metrics

Feedback of performance data can improve professional practice and outcomes. Vital signs are not routinely used for quality improvement because of their limited access. Intraoperative hypothermia has been associated with deleterious effects, including surgical site infections and bleeding. We speculated that providing feedback could help keep temperature monitoring and management a priority in the anesthesiologist's mind, thereby improving perioperative temperature management. We hypothesized that feedback on thermoregulation metrics, without changes in policy, could reduce temperature-monitoring delays at the start of scoliosis correction surgery.

Although our tertiary pediatric centre does not have an anesthesia information management system, vital signs for all surgical cases are recorded in real time. Temperature data from children undergoing spine surgery are extracted from a vital signs databank and analyzed using MATLAB. Spine team anesthesiologists are provided with both team and individualized feedback regarding two variables: the percentage of time that patients are hypothermic and the time delay from the start of the case to the first temperature monitoring (our primary outcome). These data are shared every six months as run charts for the entire group and as anonymized (coded) box-and-whisker plots for each anesthesiologist.

This feedback of temperature-delay data reduced the median [interquartile range] delay from 39.0 [18.7-61.5] min to 14.4 [10.8-22.9] min (median reduction, 21.8 min; 95% confidence interval, 14.9 to 28.2; P < 0.001). It did not, however, further reduce the percentage of time patients remained hypothermic beyond the improvements already achieved with prewarming.

Feedback of intraoperative thermoregulation management improved both group and individual performances as measured by significant, sustained reductions in temperature-monitoring delays. Thus, intraoperative vital signs data may improve the quality of, and reduce the variability in, anesthetic practice.