Applied Clinical Informatics, Table of Contents Appl Clin Inform 2015; 06(04): 629-637DOI: 10.4338/ACI-2015-02-CR-0022 Research Article Schattauer GmbH Using Visual Analytics to Determine the Utilization of Preoperative Anesthesia Assessments J.P. Wanderer 1 Departments of Anesthesiology and Biomedical Informatics, Vanderbilt University, Nashville, TN, United States , C.L. Gruss 2 Department of Anesthesiology, Vanderbilt University, Nashville, TN, United States , J.M. Ehrenfeld 3 Departments of Anesthesiology, Biomedical Informatics and Surgery, Vanderbilt University, Nashville, TN, United States › Author Affiliations Recommend Article Abstract Full Text PDF Download Keywords KeywordsVisual - analytics - anesthesia References 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.
