Using Visual Analytics to Determine the Utilization of Preoperative Anesthesia Assessments

Summary

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 pre-operative 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.

Keywords

Visual - analytics - anesthesia

• References

• 1 Ferschl MB, Tung A, Sweitzer B, Huo D, Glick DB. Preoperative clinic visits reduce operating room cancellations and delays. Anesthesiology 2005; 103 (04) 855-859.
• 2 Cantlay KL, Baker S, Parry A, Danjoux G. The impact of a consultant anaesthetist led pre-operative assessment clinic on patients undergoing major vascular surgery. Anaesthesia 2006; 61 (03) 234-239.
• 3 Hepner DL, Bader AM, Hurwitz S, Gustafson M, Tsen LC. Patient satisfaction with preoperative assessment in a preoperative assessment testing clinic. Anesth Analg 2004; 98 (04) 1099-1105.
• 4 Sweitzer BJ. Preoperative screening, evaluation, and optimization of the patient’s medical status before outpatient surgery. Current opinion in anaesthesiology 2008; 21 (06) 711-718.
• 5 Walsh T. Ahima. Security audits of electronic health information (updated). Journal of AHIMA / American Health Information Management Association 2011; 82 (03) 46-50.
• 6 Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc 2011; 18 (02) 112-117.
• 7 McLean TR, Burton L, Haller CC, McLean PB. Electronic medical record metadata: uses and liability. J Am Coll Surg 2008; 206 (03) 405-411.
• 8 Vigoda MM, Lubarsky DA. Failure to recognize loss of incoming data in an anesthesia record-keeping system may have increased medical liability. Anesth Analg 2006; 102 (06) 1798-1802.
• 9 Simpao AF, Ahumada LM, Galvez JA, Rehman MA. A review of analytics and clinical informatics in health care. J Med Syst 2014; 38 (04) 45.
• 10 Shahar Y, Cheng C. Intelligent visualization and exploration of time-oriented clinical data. Topics in health information management 1999; 20 (02) 15-31.
• 11 Klimov D, Shahar Y. A framework for intelligent visualization of multiple time-oriented medical records. AMIA Annu Symp Proc 2005; 2005: 405-409.
• 12 Simpao AF, Ahumada LM, Desai BR, Bonafide CP, Galvez JA, Rehman MA, Jawad AF, Palma KL, Shelov ED. Optimization of drug-drug interaction alert rules in a pediatric hospital’s electronic health record system using a visual analytics dashboard. J Am Med Inform Assoc 2015; 22 (02) 361-369.
• 13 Joshi R, Szolovits P. Prognostic physiology: modeling patient severity in Intensive Care Units using radial domain folding. AMIA Annu Symp Proc 2012; 2012: 1276-1283.
• 14 Boxwala AA, Kim J, Grillo JM, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. J Am Med Inform Assoc 2011; 18 (04) 498-505.
• 15 Kim J, Grillo JM, Boxwala AA, Jiang X, Mandelbaum RB, Patel BA, Mikels D, Vinterbo SA, Ohno-Machado L. Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs. AMIA Annu Symp Proc 2011; 2011: 723-731.
• 16 Stol IS, Ehrenfeld JM, Epstein RH. Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States. Anesthesia and analgesia 2014; 118 (03) 644-650.
• 17 Malin B, Nyemba S, Paulett J. Learning relational policies from electronic health record access logs. J Biomed Inform 2011; 44 (02) 333-342.
• 18 Gray JE, Feldman H, Reti S, Markson L, Lu X, Davis RB, Safran CA. Using Digital Crumbs from an Electronic Health Record to identify, study and improve health care teams. AMIA Annu Symp Proc 2011; 2011: 491-500.