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Clinical Decision Support in Anesthesia Information Management Systems (AIMS)

Clinical decision support using Anesthesia Information Management Systems

Intraoperative recording of vital signs has been an integral component and standard of care for anesthesiologists and anesthesia care providers. Until recently, anesthesia information management systems (AIMS) were primarily used to record intraoperative data, including basic physiological vital signs (blood pressure, heart rate, oxygen saturation, end-tidal carbon dioxide), and more complicated ones (e.g. fraction of inspired and expired oxygen and anesthetic gases, central venous pressure readings, beat-to-beat arterial pressure readings, pulse pressure variation, etc).

As with other electronic health record keeping systems, AIMS has moved beyond serving its simple record-keeping functionality and is being used in many instances to serve as a clinical decision support (CDS) tool. Greenes (Greenes RA, 2014) defined CDS as “the use of information and communication technologies to bring relevant knowledge to bear on the health care and well-being of a patient.” (Epstein et al., 2015). lists the following as CDS tools, including: computerized alerts and reminders to both providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support; and contextually relevant reference information ( In addition to these, CDS using AIMS in the perioperative setting has been specifically used to improve reimbursement, reduce costs, monitor physiologic data in near real time, and improve regulatory compliance. In general, CDS has been purported to increase quality of care and outcomes, improve efficiency, improve patient safety (by avoiding adverse events), and enhance the patient-provider relationship and satisfaction ( In anesthesiology, CDS has been used extensively in the form of real-time alerts and computer alerts (either pager or computer) in conjunction with AIMS in operating rooms across the country.

Clinical Decision Support using AIMS (preoperative, intraoperative, postoperative, and education/training):

CDS during the preoperative period

Studies examining the utility of AIMS in affecting CDS during the preoperative period were some of the first research of its kind in the specialty of anesthesiology. Specifically, CDS has been studied to improve the administration and timing of preoperative antibiotics and antibiotic prophylaxis administration. One study from Mt. Sinai (one of the first anesthesiology departments in the country to implement an AIMS), demonstrated a statistically significant difference with the implementation of a visual reminder on the appropriate timing of preoperative antibiotic administration (Wax et al., 2007). They showed an increased compliance rate from 82.4% (prior to reminder intervention) to 89.1% (after intervention), which was statistically significant (Wax et al., 2007). Another group from the University of Washington, used an AIMS-based decision support module called the Smart Anesthesia Manager (SAM), developed at their institution to study preoperative prophylactic antibiotic administration (Nair et al., 2010). In their study, they implemented several feedback mechanisms to improve compliance, including sending email feedback after the missed antibiotic administration, sending monthly summary reports, and creating real-time electronic alerts with CDS (Nair et al., 2010). Although all three interventions showed improvement in compliance, the highest compliance came from using real-time computer alerts (>99% compliance rate with this method) (Nair et al., 2010).

There have also been several studies examining the effect of reminders for antiemetic administration to reduce postoperative nausea and vomiting (Kooij et al., 2008) (Kooij et al., 2012). One study from the Netherlands described a two-fold increase of PONV guideline adherence with the CDS intervention on their AIMS display (Kooij et al., 2008). However, once the automated reminder was removed, they found the guideline adherence rate returned to baseline, without any long-term demonstrable effect of the PONV CDS reminder from the intervention period (Kooij et al., 2008). Another frequently cited study from the same group in the Netherlands studied the effects of using automated AIMS reminders for high-risk patients (Kooij et al., 2012). One of the goals was to avoid overexposure of antiemetic medications to low-risk groups as all medications carry unwanted side effects. CDS reduced PONV from 47% to 30% in the high-risk population, a statistically significant finding (Kooij et al., 2012). Interestingly, a similar study using near real-time alerts to reduce PONV incidence did not find a reduction in the intervention group (Kappen et al., 2014). The authors speculated that one reason may have been the lack of actionable recommendations for the anesthesiologists (Kappen et al., 2014). Thus, in this set of studies, it seems that the utility of CDS on improving patient outcomes should be validated prior to clinical implementation.

CDS during the intraoperative period

Some of the more significant research with CDS has been conducted during the intraoperative period, with many of them focusing on billing and documentation issues, managing blood pressure variations, and changing the rates of inhaled anesthetic gases. The first category, improving documentation (and subsequently billing issues) has had a number of studies in this area. One of the best and most recent ones was a prospective randomized controlled trial published in 2013 by a well-known AIMS group from the University of Michigan (Freundlich et al., 2013) studying the effect of AIMS on anesthesia start times. (Anesthesia start time is defined as the intraoperative time point at which an anesthesia care provider begins continuous care, often coinciding with either the exact time or the few minutes immediately preceding the patient entering the operating room.) Two groups of anesthesia providers were randomly assigned to either a control or an alert group and followed over three years, the longest follow-up in any AIMS study so far. The second group received a pager reminder if anesthesia start time was not documented within thirty minutes of occurrence. The study broke the time periods into four periods each consisting of 45-day intervals including a baseline period (showing a compliance rate of 33%), the intervention period, short-term follow up and long-term follow-up three years later. It showed that even after three years, there was a statistically significant difference between the two groups, with an 85% compliance rate in the authors’ definition of anesthesia start time (Freundlich et al., 2013). Another study which used computer alerts to improve compliance with previously unbillable anesthesia records published in 2006 demonstrated a corresponding increase of $400,000 in revenue per year after the customized alerts were implemented (Spring et al., 2007). Compliance with billing related to intraoperative procedures, such as arterial line placement, was also demonstrated by the Michigan group with a corresponding increase in revenue to the department. Clearly, in the area of improving billing compliance, the use of computer and pager alerts in departments using AIMS has been beneficial.

Another area of CDS using AIMS during the intraoperative period has been studying the effect of alert reminders on blood pressure variations, especially with hypertension or hypotension (both associated with increased postoperative morbidity and mortality). A study published in 2014 from the University of Washington used SAM to study the effects of intraoperative hypotension and hypertension in association with near real-time alerts (Nair et al., 2014). They alerted anesthesia providers through a pop-up reminder on the computer screen if the patient was experiencing hypotension (systolic blood pressure <80 mmHg) with simultaneous high concentrations of inhaled anesthetic agents and hypertension (systolic blood pressure > 160 mmHg) with simultaneous phenylephrine infusion. The effect of these messages were than analyzed retrospectively to evaluate their effect on the patient’s blood pressure management. The authors analyzed over 15,000 patients both during pre-intervention period and post-intervention period (with the intervention being the SAM pop-up messages) (Nair et al., 2014). Not surprisingly, they found a statistically significant difference in the patient group with hypotensive episodes, with a significant reduction in the median duration of the hypotension. However, there was not a statistically significant reduction found in the hypertensive patient group with the implementation of SAM. The authors surmised that multiple reasons existed as to why providers did not behave the way they had hypothesized, including being too busy with direct patient care at the time of the message, the message being based on one single BP measurement as opposed to a trend, anesthesia personnel changes, and deliberate maintenance of hypertension or hypotension (as required in certain surgical cases) (Nair et al., 2014).

Clinical Decision Support during the postoperative and PACU period

Very few studies have examined the role of CDS during the patient’s recovery from surgery. Most of the work in this area has centered on the ability to improve PACU delays, a critical issue during the postoperative period, as PACU delays requires the patients to stay in the operating room for longer periods of time after the end of surgery than necessary. The most recent, comprehensive, and statistically rigorous study in this area was published in 2013 by the well-known anesthesiology bioinformatics group at Vanderbilt University. Based on three years of PACU data, the authors constructed a model decision support system that simulated alerts to the PACU to alert them when recovery beds were needed in advance (Ehrenfeld et al., 2013). Although the authors hypothesized that a CDS would reduce PACU delay significantly, the study instead found the opposite. They found a <50% utility of automated message alerts when the PACU was alerted 15-90 minutes prior to PACU admission. The maximum potential utility of <50% was less than the 70% minimum threshold for the utility of the message alerts. (The authors explain this was derived from a previously published metaanalysis which determined “below 70% utility, the combined performance of the human and the automation system was worse than had no automation been used at all.” (Ehrenfeld et al., 2013) Overall, this study, and others during the postoperative period, do not necessarily support the use of CDS.

Clinical Decision Support in Education/Training

There is little investigation in this area of CDS utilizing AIMS, and the best known and most often cited paper in this area was published in 2013 by a group from Massachusetts General Hospital (with the first author now currently at Vanderbilt University) (Wanderer et al., 2013). The authors designed a web-based system called the Residents Helping in Navigating OR Scheduling (Rhinos) that allowed residents to submit case and scheduling requests. Their goal was that by allowing residents to have some shared responsibility and participation with attendings in creating their schedule, they would facilitate improved resident knowledge, engagement, and satisfaction. They also devised a second separate system that was an ACGME case-log visualization tool which allowed the residents to compare their case and procedure experiences to resident peer groups and necessary case minimums (as required by the ACGME for graduation). There was a 6-week pilot period with a total of 165 assignment requests and an 8-week full implementation period used on eight different surgical services with a total of 754 assignment requests. Between both groups, they found an overall match success rate of 68.4% with the majority of them being first-choice matches (Wanderer et al., 2013).


Clinical decision support has been extensively studied in other area of medicine and public health. Although within the specialty CDS using AIMS has been explored and studied in the last decade, it has not been widely discussed in the field of bioinformatics. Hopefully others outside the profession will see that CDS is widely used in a variety of both clinical and non-clinical ways in anesthesiology and anesthesia. These include improving billing and compliance documentation, improving adherence to clinical guidelines and protocols, near-real time physiological monitoring and near-real time point-of-care automated alerts, among others. Furthermore, CDS lends itself to improvement with quality assurance and improvement reporting. Finally, the use of AIMS and improved clinical care with CDS has led to the development of large, multicenter national anesthesia registries with the ultimate goal of producing high-quality outcomes research in the future.


Thys, DM. (2001). The role of information systems in anesthesia. . In Anesthesia Patient Safety Foundation Newsletter, p. 16.

Ehrenfeld, J. M. (2009). Anesthesia Information Management Systems: A Guide to Their Successful Installation and Use. In Anesthesiology news, (McMahon Publishing), pp. 1-8.

Ehrenfeld JM, Dexter F, Rothman BS, Minton BS, Johnson D, Sandberg WS, and Epstein RH. Lack of utility of a decision support system to mitigate delays in admission from the operating room to the postanesthesia care unit. Anesthesia and analgesia. 2013;117(6):1444-52.

Epstein, R. H., Dexter, F., and Patel, N. (2015). Influencing Anesthesia Provider Behavior Using Anesthesia Information Management System Data for Near Real-Time Alerts and Post Hoc Reports. Anesthesia and analgesia 121, 678-692.

Freundlich, R. E., Barnet, C. S., Mathis, M. R., Shanks, A. M., Tremper, K. K., and Kheterpal, S. (2013). A randomized trial of automated electronic alerts demonstrating improved reimbursable anesthesia time documentation. Journal of clinical anesthesia 25, 110-114.

Greenes RA, e. (2014). Clinical Decision Support: The Road to Broad Adoption., Vol Second edition, Second Edition edn (Amsterdam, The Netherlands: Elsevier). (Accessed October 24, 2015.).

Kappen TH, Moons KG, van Wolfswinkel L, Kalkman CJ, Vergouwe Y, and van Klei WA. Impact of risk assessments on prophylactic antiemetic prescription and the incidence of postoperative nausea and vomiting: a cluster-randomized trial. Anesthesiology. 2014;120(2):343-54.

Kooij, F. O., Klok, T., Hollmann, M. W., and Kal, J. E. (2008). Decision support increases guideline adherence for prescribing postoperative nausea and vomiting prophylaxis. Anesthesia and analgesia 106, 893-898, table of contents.

Kooij, F. O., Vos, N., Siebenga, P., Klok, T., Hollmann, M. W., and Kal, J. E. (2012). Automated reminders decrease postoperative nausea and vomiting incidence in a general surgical population. British journal of anaesthesia 108, 961-965.

Nair, B. G., Horibe, M., Newman, S. F., Wu, W. Y., Peterson, G. N., and Schwid, H. A. (2014). Anesthesia information management system-based near real-time decision support to manage intraoperative hypotension and hypertension. Anesthesia and analgesia 118, 206-214.

Nair, B. G., Newman, S. F., Peterson, G. N., Wu, W. Y., and Schwid, H. A. (2010). Feedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesthesia and analgesia 111, 1293-1300.

Spring, S. F., Sandberg, W. S., Anupama, S., Walsh, J. L., Driscoll, W. D., and Raines, D. E. (2007). Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology 106, 157-163.

Stol, I. S., Ehrenfeld, J. M., and Epstein, R. H. (2014). Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States. Anesthesia and analgesia 118, 644-650.

Wanderer, J. P., Charnin, J., Driscoll, W. D., Bailin, M. T., and Baker, K. (2013). Decision support using anesthesia information management system records and accreditation council for graduate medical education case logs for resident operating room assignments. Anesthesia and analgesia 117, 494-499.

Wax, D. B., Beilin, Y., Levin, M., Chadha, N., Krol, M., and Reich, D. L. (2007). The effect of an interactive visual reminder in an anesthesia information management system on timeliness of prophylactic antibiotic administration. Anesthesia and analgesia 104, 1462-1466, table of contents.

Submitted by Aclu (Amy L) [Category:BMI512-FALL-15]