Visualization of aggregate perioperative data improves anesthesia case planning: A randomized, cross-over trial

Abstract

Study objective: A challenge in reducing unwanted care variation is effectively managing the wide variety of performed surgical procedures. While an organization may perform thousands of types of cases, privacy and logistical constraints prevent review of previous cases to learn about prior practices. To bridge this gap, we developed a system for extracting key data from anesthesia records. Our objective was to determine whether usage of the system would improve case planning performance for anesthesia residents.

Design: Randomized, cross-over trial.

Setting: Vanderbilt University Medical Center.

Measurements: We developed a web-based, data visualization tool for reviewing de-identified anesthesia records. First year anesthesia residents were recruited and performed simulated case planning tasks (e.g., selecting an anesthetic type) across six case scenarios using a randomized, cross-over design after a baseline assessment. An algorithm scored case planning performance based on care components selected by residents occurring frequently among prior anesthetics, which was scored on a 0-4 point scale. Linear mixed effects regression quantified the tool effect on the average performance score, adjusting for potential confounders.

Main results: We analyzed 516 survey questionnaires from 19 residents. The mean performance score was 2.55 ± SD 0.32. Utilization of the tool was associated with an average score improvement of 0.120 points (95% CI 0.060 to 0.179; p < 0.001). Additionally, a 0.055 point improvement due to the "learning effect" was observed from each assessment to the next (95% CI 0.034 to 0.077; p < 0.001). Assessment score was also significantly associated with specific case scenarios (p < 0.001).

Conclusions: This study demonstrated the feasibility of developing of a clinical data visualization system that aggregated key anesthetic information and found that the usage of tools modestly improved residents' performance in simulated case planning.

Keywords: Anesthetic planning; Anesthetic training; Data visualization.

Fig. 1.

Example of the anesthesia case planning tool used to identify practice patterns for laparoscopic appendectomy. The summary page shows the number of anesthetic records included in the aggregated view, the distribution of the primary anesthesia types, surgical positions, airways utilized, surgical time and estimated blood loss. Additionally, the vascular access and patient monitoring approach is displayed. The frequency threshold is used to suppress low frequency occurrences in surgical positions, which is helpful in reducing noise from erroneous documentation.

Fig. 2.

This tab demonstrates drug administration by phase of anesthesia care, including the percentage of anesthetics that included each drug and the phase of care that is administered in.

Fig. 3.

This view shows drug administration by relative time points, with the zero time point marking the entry of the patient into the OR room.

Fig. 4.

Distribution of estimated blood loss for the selected procedure.