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Anesthesia Information Management Systems

Simpao, Allan F. MD, MBI*; Rehman, Mohamed A. MD

doi: 10.1213/ANE.0000000000002545
Technology, Computing, and Simulation

Anesthesia information management systems (AIMS) have evolved from simple, automated intraoperative record keepers in a select few institutions to widely adopted, sophisticated hardware and software solutions that are integrated into a hospital’s electronic health record system and used to manage and document a patient’s entire perioperative experience. AIMS implementations have resulted in numerous billing, research, and clinical benefits, yet there remain challenges and areas of potential improvement to AIMS utilization. This article provides an overview of the history of AIMS, the components and features of AIMS, and the benefits and challenges associated with implementing and using AIMS. As AIMS continue to proliferate and data are increasingly shared across multi-institutional collaborations, visual analytics and advanced analytics techniques such as machine learning may be applied to AIMS data to reap even more benefits.

From the *Department of Anesthesiology and Critical Care, Division of General Anesthesia, Perelman School of Medicine at the University of Pennsylvania and The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania

Department of Anesthesiology, Johns Hopkins All Children’s Hospital and Johns Hopkins University School of Medicine, Baltimore, Maryland.

Published ahead of print October 17, 2017.

Accepted for publication September 8, 2017.

Funding: None.

The authors declare no conflicts of interest.

Reprints will not be available from the authors.

Address correspondence to Allan F. Simpao, MD, MBI, Department of Anesthesiology and Critical Care, Division of General Anesthesia, Perelman School of Medicine at the University of Pennsylvania and The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104. Address e-mail to

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In the 1890s, Drs Codman and Cushing pioneered the use of paper documentation of a patient’s physiologic status during anesthesia, and the paper anesthesia record remained the standard throughout the 1900s.1 As computers became increasingly available in the 1970s and 1980s, there was growing interest among anesthesia providers in the electronic capture, storage, retrieval, and formatting of perioperative data.2 Computers were used to manually record patients’ physiologic data in the 1970s, and the Duke Automatic Monitoring Equipment system, developed in the early 1980s, was one of the first systems to interface directly with clinical monitors to capture patient physiologic data.3 Subsequent systems evolved in sophistication from simple anesthesia record keepers to comprehensive software (and often hardware) solutions with functionality across the entire perioperative experience, and the term anesthesia information management systems (AIMS) was originated.4 AIMS are currently in widespread use, particularly in academic anesthesiology departments, of which 75% had adopted AIMS as of 2014 and 84% adoption expected between 2018 and 2020.5

One of the most significant changes to AIMS over the past 5 years has been the widespread installation of integrated electronic health record (EHR) systems Epic (Epic Systems, Verona, WI), Cerner (Cerner, Kansas City, MO), and others.6 In fact, most practices are no longer independently converting to an AIMS but rather doing so in conjunction with their hospital or health system. This shift toward installing an AIMS component of a hospital EHR improves interoperability with and access to other components of the EHR, such as laboratory results and computerized order entry, yet in some cases can make the AIMS data more challenging to access for secondary uses such as research and quality improvement projects.

AIMS are used currently not only in the preoperative, intraoperative, and postoperative phases of traditional operating room (OR) care, but also in other types of patient care, such as on the labor and delivery floor to document obstetric anesthesia, and throughout the hospital to document acute pain services as well as delivery of anesthesia during bedside procedures and in the intensive care unit.7,8

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An AIMS is most commonly composed of either a stand-alone software product or an anesthesia module within a hospital’s EHR system coupled with hardware components and physiologic device interfaces.7 Software is installed on computer workstations at the point of anesthetic care, such as the OR, postanesthesia care unit, or even on mobile workstations in the intensive care unit.7,8 The software on each workstation—also called a client—provides a clinician with a user interface that allows access to the anesthesia record as well as a central computer server that stores patient data. Stand-alone AIMS software on a workstation typically handles the collection of anesthesia data on the workstation and then files periodically to the central server (also known as a “thick client”). If an AIMS is a component of the hospital’s EHR, then typically the software on the workstation is simply a front-end user interface with most or all of the data filed directly to the central server (also called a “thin client”).

The hardware components of AIMS include a computer workstation and mounting equipment and/or mobile wheeled tower, and in some cases can include vendor-specific hardware such as special keyboards, bar code scanners, or syringe pumps.7 AIMS hardware should comply with hospital infection control guidelines (and thus be easy to disinfect) and be ergonomic, durable, water resistant, and usable in multiple environments.7 Compatible physiologic device interfaces are crucial to enable patient physiologic monitors, anesthesia machines, ventilators, and other monitors to communicate and record automatically via the AIMS hardware and software. The device data flow from the devices to the client and then the server—or first to the server and then the client workstations—depending on the AIMS setup. In some implementations, the physiologic device data flow to the AIMS hardware and software through a separate medical device integration interface (also known as “middleware”) that typically offers additional functionality such as higher fidelity data sampling.9

Table 1.

Table 1.

A successful AIMS implementation requires not only the appropriate hardware and software but also a minimum investment of time and “peopleware”—that is, individuals with sufficient expertise within a department to drive the decision-making and implementation processes. Table 1 lists the necessary minimum requirements for a basic AIMS implementation as well as advanced requirements for departments that desire to become a state-of-the-art AIMS champion within an institution. Financial requirements for AIMS implementations will obviously vary depending on the sophistication level of the implementation, the hardware and software required for the chosen level of sophistication, and most importantly, the implementation team’s time, because clinicians are taken from the clinical production line to guide the AIMS implementation efforts through meetings, site visits, and other time-consuming efforts.

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The core function of AIMS remains the generation of an electronic anesthesia record that captures data in automated fashion from devices, such as patient monitors and the anesthesia machine, and allows the user to manually document a variety of data: administered medications, fluids, and blood products; clinical events such as anesthesia induction, patient positioning, and lines placed; required documentation for billing or regulatory guidelines (eg, anesthesia start and stop times, procedure notes); and other relevant data that are not captured in automated fashion, such as train-of-four counts.7,10 AN AIMS displays the anesthesia record in the user interface that the clinician uses to access, review, and edit the record as it is generated.

In addition to facilitating intraoperative documentation, most AIMS contain preoperative patient assessment documentation tools that range from a simple data entry form with free-text fields to comprehensive questionnaires with systems-based assessments, richly populated drop-down menus, the capability to store and display patient photos, and graphical tools for annotating images of dentition and other patient characteristics. An AIMS that either has access to or is integrated within the hospital’s EHR system can load relevant patient data such as allergies, medications, and procedure information into the preoperative assessment and the intraoperative record.7,11 Other typical features in preoperative AIMS modules include the ability to peruse a patient’s previous anesthesia records, record a review of systems and physical examination, and document an anesthetic assessment and plan.7

Many AIMS facilitate postoperative documentation using a computer workstation at the patient’s recovery destination, such as the postanesthesia care unit or intensive care unit. The AIMS record that has been generated to that point can be used during the handoff of patient care to the receiving clinician, and postoperative vital signs can be recorded.7 The AIMS can also display prompts to the user to enter quality improvement data on relevant perioperative events.12 Documentation checks can notify the user of deficiencies, and AIMS with real-time alerts or automated e-mail or text messaging notification can improve the quality of documentation.13

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Table 2 lists the areas where AIMS have been shown to provide positive benefits to patients, anesthesia departments, and hospital systems in the peer-reviewed literature.14 Anesthesia practices that install AIMS modules as part of a hospital EHR mandate acquire improved access to relevant patient data within the EHR, such as laboratory results, medications, and clinicians’ orders, as well as the ability to more easily incorporate those data into the perioperative record.

Table 2.

Table 2.

Several obstacles have been stated as reasons to avoid AIMS: reluctance to abandon paper records, unacceptable costs of installation and maintenance, distraction of the anesthesia providers, medicolegal concerns, and resistance to changes in clinical workflow patterns.15–17 Numerous studies have established that electronic records are more accurate and reliable than paper records.18,19 While AIMS implementations indeed bear significant costs, the return on investment depends on an institution’s financial, billing, and management practices, and AIMS can result in a positive net return on investment in 4 areas: (1) more efficient staff scheduling resulting in decreased staffing costs; (2) decreased anesthesia medication costs; (3) improved charge and billing capture; and (4) increased hospital reimbursement from improved hospital coding.14,20 AIMS had minimal impact on vigilance in a study that found no difference in the accuracy of practitioners’ recall of patient variables when using computerized or manual entry record keeping systems.21 As for medicolegal concerns, the majority of 24 anesthesia departments reported in a survey that AIMS was valuable for risk management, and there were no cases in which AIMS hindered the defense process.22

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Clinical decision support is a recent advancement in AIMS development, and it can be grouped into either administrative (such as maximizing OR efficiency and throughput) or impacting care processes (such as improving adherence to clinical protocols and guidelines).23 Recent literature reviews suggest that AIMS-based clinical decision support systems can significantly improve some aspects of clinical performance and patient care, particularly if the decision support is smoothly integrated into clinical workflow and comprises evidence-based recommendations rather than assessments.24,25

AIMS-based clinical decision support consists of a wide variety of alerts, reminders, and notifications to modify the behavior of anesthesia practitioners that can affect many perioperative processes25,26: correcting OR location errors,27 ameliorating intraoperative hypothermia,28 restoring alarms after cardiopulmonary bypass,29 improving β-blocker medication compliance, and guiding postoperative nausea and vomiting and antibiotic prophylaxis.30,31 Decision support can also take the form of post hoc reports to impact myriad aspects of clinical care and documentation.23

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The implementation of AIMS has facilitated clinical research and collaborative projects across many institutions and anesthesia practices.32 Numerous studies have been published using data from multicenter AIMS data registries such as the Multicenter Perioperative Outcomes Group and the Anesthesia Quality Institute’s National Anesthesia Clinical Outcomes Registry.33,34 However, more data are not necessarily higher quality data, and the quality and reliability of the data captured and stored in AIMS are dependent on the users.35 Clear, consistent definitions of perioperative events and outcomes facilitate valid, reliable documentation that will enhance billing, reporting, and increasingly, clinical decision support functions. These definitions routinely vary across and sometimes even within institutions. For example, there are several different scenarios that constitute the “induction of anesthesia” (eg, administration of a hypnotic, provision of anxiolytics, or start of preoxygenation). Research, quality improvement, and administrative initiatives based on AIMS data must account for these artifacts to avoid faulty conclusions (“garbage in, garbage out”).36,37

Some anesthesia practices that had stand-alone AIMS in place for many years are now migrating or have recently migrated to EHR-based AIMS modules. This has placed those groups in a position wherein data from the older legacy system must be stored in a separate “perioperative data warehouse” if it is to be used for secondary purposes, because the EHR software typically is not designed to store and make available the legacy data.38 Data warehouses can also be used to make AIMS data more easily accessible for secondary purposes that might otherwise be difficult to obtain directly from the EHR.39

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In the future, AIMS should continually improve in terms of user interface, portability, and either interoperability with or functionality within hospital EHRs. As hospitals with EHRs continue to push for the installation of integrated AIMS modules, those modules should continue to expand in both use and functionality, and automated data capture will be feasible from an ever-widening array of devices, such as barcode medication labeling systems and “smart” medication infusion pumps.40,41 Analytical techniques such as machine learning and visual analytics will facilitate the mining of large AIMS databases and perioperative data warehouses for meaningful insights to drive quality and performance improvements.42 Machine learning algorithms will also be applied to AIMS data to automate perioperative event capture and derive clinical meaning from or assign clinical relevance to physiologic data.43,44 Real-time analysis of patient data across disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is another promising application of technology to AIMS data.45 AIMS-based clinical decision support will continue to be developed in more sophisticated and meaningful fashion to enhance various facets of patient care.

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Name: Allan F. Simpao, MD, MBI.

Contribution: This author helped in conception or design of the work; acquisition, analysis, or interpretation of data for the work; drafting the work or revising it critically for important intellectual content; final approval of the version to be published; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved; reviewing the literature; creating the original draft; and editing and revising the manuscript.

Name: Mohamed A. Rehman, MD.

Contribution: This author helped in conception or design of the work; acquisition, analysis, or interpretation of data for the work; drafting the work or revising it critically for important intellectual content; final approval of the version to be published; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved; reviewing the literature; and editing and revising the manuscript.

This manuscript was handled by: Maxime Cannesson, MD, PhD.

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1. Bruce SS, Bruce JNHarvey Cushing, neurosurgical pioneer. Curr Surg. 2005;62:138–140.
2. Gravenstein JSThe uses of the anesthesia record. J Clin Monit. 1989;5:256–265.
3. Block FE Jr, Burton LW, Rafal MDThe computer-based anesthetic monitors: the Duke Automatic Monitoring Equipment (DAME) system and the microDAME. J Clin Monit. 1985;1:30–51.
4. Stonemetz JAnesthesia information management systems marketplace and current vendors. Anesthesiol Clin. 2011;29:367–375.
5. Stol IS, Ehrenfeld JM, Epstein RHTechnology diffusion of anesthesia information management systems into academic anesthesia departments in the United States. Anesth Analg. 2014;118:644–650.
6. Monica K“Most significant Epic, Cerner health IT achievements of 2017.” Available at: Accessed September 5, 2017.
7. Shah NJ, Tremper KK, Kheterpal SAnatomy of an anesthesia information management system. Anesthesiol Clin. 2011;29:355–365.
8. Simpao AF, Galvez JA, England WRA technical evaluation of wireless connectivity from patient monitors to an anesthesia information management system during intensive care unit surgery. Anesth Analg. 2016;122:425–429.
9. Zaleski J“Medical device integration: a world of possibilities.” Available at: Accessed September 5, 2017.
10. Kadry B, Feaster WW, Macario A, Ehrenfeld JMAnesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med. 2012;79:154–165.
11. Ehrenfeld JM, Rehman MAAnesthesia information management systems: a review of functionality and installation considerations. J Clin Monit Comput. 2011;25:71–79.
12. Peterfreund RA, Driscoll WD, Walsh JLEvaluation of a mandatory quality assurance data capture in anesthesia: a secure electronic system to capture quality assurance information linked to an automated anesthesia record. Anesth Analg. 2011;112:1218–1225.
13. Sandberg WS, Sandberg EH, Seim ARReal-time checking of electronic anesthesia records for documentation errors and automatically text messaging clinicians improves quality of documentation. Anesth Analg. 2008;106:192–201.
14. Simpao AF, Ehrenfeld JMEhrenfeld JM, Cannesson MSpecial case: perioperative information management systems. Monitoring Technologies in Acute Care Environments. 2014:New York, NY: Springer Verlag391–397.
15. Gardner RM, Prakash OChallenges and opportunities for computerizing the anesthesia record. J Clin Anesth. 1994;6:333–341.
16. Quinzio L, Junger A, Gottwald BUser acceptance of an anaesthesia information management system. Eur J Anaesthesiol. 2003;20:967–972.
17. Bloomfield EL, Feinglass NGThe anesthesia information management system for electronic documentation: what are we waiting for? J Anesth. 2008;22:404–411.
18. Thrush DNAre automated anesthesia records better? J Clin Anesth. 1992;4:386–389.
19. Reich DL, Wood RK Jr, Mattar RArterial blood pressure and heart rate discrepancies between handwritten and computerized anesthesia records. Anesth Analg. 2000;91:612–616.
20. O’Sullivan CT, Dexter F, Lubarsky DA, Vigoda MMEvidence-based management assessment of return on investment from anesthesia information management systems. AANA J. 2007;75:43–48.
21. Davis TC, Green JA, Colquhoun A, Hage BL, Biddle CAnesthesia recordkeeping: accuracy of recall with computerized and manual entry recordkeeping. J Clin Monit Comput. 2012;26:163–169.
22. Feldman JMDo anesthesia information systems increase malpractice exposure? Results of a survey. Anesth Analg. 2004;99:840–843.
23. Wanderer JP, Sandberg WS, Ehrenfeld JMReal-time alerts and reminders using information systems. Anesthesiol Clin. 2011;29:389–396.
24. Epstein RH, Dexter F, Patel NInfluencing anesthesia provider behavior using anesthesia information management system data for near real-time alerts and post hoc reports. Anesth Analg. 2015;121:678–692.
25. Nair BG, Gabel E, Hofer I, Schwid HA, Cannesson MIntraoperative clinical decision support for anesthesia: a narrative review of available systems. Anesth Analg. 2017;124:603–617.
26. Simpao AF, Tan JM, Lingappan AM, Gálvez JA, Morgan SE, Krall MAA systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. J Clin Monit Comput. 2017;31:885–894.
27. Epstein RH, Dexter F, Piotrowski EAutomated correction of room location errors in anesthesia information management systems. Anesth Analg. 2008;107:965–971.
28. Chau A, Ehrenfeld JMUsing real-time clinical decision support to improve performance on perioperative quality and process measures. Anesthesiol Clin. 2011;29:57–69.
29. Eden A, Pizov R, Toderis L, Kantor G, Perel AThe impact of an electronic reminder on the use of alarms after separation from cardiopulmonary bypass. Anesth Analg. 2009;108:1203–1208.
30. Kooij FO, Vos N, Siebenga P, Klok T, Hollmann MW, Kal JEAutomated reminders decrease postoperative nausea and vomiting incidence in a general surgical population. Br J Anaesth. 2012;108:961–965.
31. Nair BG, Newman SF, Peterson GN, Wu WY, Schwid HAFeedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg. 2010;111:1293–1300.
32. Dutton RPLarge databases in anaesthesiology. Curr Opin Anaesthesiol. 2015;28:697–702.
33. Lee LO, Bateman BT, Kheterpal SMulticenter Perioperative Outcomes Group Investigators. Risk of epidural hematoma after neuraxial techniques in thrombocytopenic parturients: a report from the multicenter perioperative outcomes group. Anesthesiology. 2017;126:1053–1063.
34. Dexter F, Epstein RH, Dutton RPDiversity and similarity of anesthesia procedures in the United States during and among regular work hours, evenings, and weekends. Anesth Analg. 2016;123:1567–1573.
35. Einav S, O’Connor MDoes only size matter or is there still a place for single-center studies in the era of big data? Anesth Analg. 2016;123:1623–1628.
36. Kool NP, van Waes JA, Bijker JBArtifacts in research data obtained from an anesthesia information and management system. Can J Anesth. 2012;59:833–841.
37. Simpao AF, Ahumada LM, Gálvez JA, Rehman MAA review of analytics and clinical informatics in health care. J Med Syst. 2014;38:45.
38. Levin MA“Building a perioperative data warehouse.” Available at: Accessed September 5, 2017.
39. Hofer IS, Gabel E, Pfeffer M, Mahbouba M, Mahajan AA systematic approach to creation of a perioperative data warehouse. Anesth Analg. 2016;122:1880–1884.
40. Jelacic S, Bowdle A, Nair BG, Kusulos D, Bower L, Togashi KA system for anesthesia drug administration using barcode technology: the Codonics Safe Label System and Smart Anesthesia Manager. Anesth Analg. 2015;121:410–421.
41. Kim UR, Peterfreund RA, Lovich MADrug infusion systems: technologies, performance, and pitfalls. Anesth Analg. 2017;124:1493–1505.
42. Simpao AF, Ahumada LM, Rehman MABig data and visual analytics in anaesthesia and health care. Br J Anaesth. 2015;115:350–356.
43. Jalali A, Rehman M, Lingappan A, Nataraj CAutomatic detection of endotracheal intubation during the anesthesia procedure. J Dyn Syst Meas Control. 2016;138:1110131–1110138.
44. Gálvez JA, Jalali A, Ahumada L, Simpao AF, Rehman MANeural network classifier for automatic detection of invasive versus noninvasive airway management technique based on respiratory monitoring parameters in a pediatric anesthesia. J Med Syst. 2017;41:153.
45. Klumpner TT, Bauer ME, Kheterpal SCurrent applications of big data in obstetric anesthesiology. Curr Opin Anaesthesiol. 2017;30:300–305.
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