title: Using Visual Analytics to Determine the Utilization of Preoperative Anesthesia Assessments.
abstract: BACKGROUND
  Preoperative assessments are a required and essential element of anesthetic care, yet little is known about the utilization of these documents by clinicians who are not part of the anesthesia care team. As part of perioperative workflow restructuring, we implemented a data visualization technique of electronic medical record audit log data to understand the utilization of preoperative anesthesia assessments by non-anesthesia personnel.
  
  
  METHODS
  An audit log cache containing 140 days of data was queried for all accesses of preoperative anesthesia assessment documents for any patient who had a preoperative anesthesia assessment that was accessed during that period. User roles were aggregated into categories. Descriptive statistics and data visualization were generated using R (R Software Foundation, Vienna, Austria). Comparisons were performed with the Wilcoxon signed rank test with continuity correction.
  
  
  RESULTS
  During the study period, 73 802 (0.015%) of the 485 062 902 audit log accesses were preoperative anesthesia assessments representing 412 departments, 302 user roles, and 3 916 distinct users who accessed preoperative anesthesia assessments from 14 235 surgical cases. Each assessment was accessed 2.9 times on average. Assessments performed in the preoperative anesthesia assessment clinic were accessed more frequently than those created on the day of surgery in the preoperative holding room (3.58 ± 5.18 v. 1.98 ± 1.76 average views; p<0.0001). We observed accesses of these documents by pathology and general surgery researchers, as well as orthopedics attending physicians accessing documents that were two years old.
  
  
  CONCLUSIONS
  This approach revealed patterns of utilization that had not been previously identified, including usage by surgical residents, surgical faculty, and pathology researchers both before and after the surgical event for which the documents are generated. Knowledge of these dependencies directly informed perioperative workflow restructuring efforts. This visual analytic approach could be broadly utilized to understand documentation dependencies in a variety of clinical contexts.
authors.affiliation: Departments of Anesthesiology and Biomedical Informatics, Vanderbilt University , Nashville, TN, United States.
authors.forename: J P
authors.initials: JP
authors.lastname: Wanderer
authors.affiliation: Department of Anesthesiology, Vanderbilt University , Nashville, TN, United States.
authors.forename: C L
authors.initials: CL
authors.lastname: Gruss
authors.affiliation: Departments of Anesthesiology, Biomedical Informatics and Surgery, Vanderbilt University , Nashville, TN, United States.
authors.forename: J M
authors.initials: JM
authors.lastname: Ehrenfeld
mesh_terms: D000465:Algorithms; D000758:Anesthesia / Q000706:statistics & numerical data*; D004282:Documentation; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D008485:Medical Audit; D057234:Preoperative Period
keywords: Visual; analytics; anesthesia