Allan F. Simpao, MD
2015 Annual Meeting Program Chair,
Society for Technology in Anesthesia
Assistant Professor of Anesthesiology and Critical Care
Perelman School of Medicine at the University of Pennsylvania and
The Children’s Hospital of Philadelphia
Jorge A. Galvez, MD
Assistant Professor of Anesthesiology and Critical Care
Hospital of the University of Pennsylvania
Perelman School of Medicine at the University of Pennsylvania and The Children’s Hospital of Philadelphia
Technology has been integrated into almost every facet of anesthesia practice, from patient monitors and anesthesia machines to documentation and drug delivery. In the course of a typical day, anesthesia practitioners care for patients using an array of technologies whose sophistication and utility are continually increasing.
Meanwhile, researchers and innovators are constantly working on leveraging new technologies (and new uses for old technology) for myriad benefits: improved patient care, enhanced financial oversight and responsibility, outcomes research, and many others.
This review will focus on recent technological developments in anesthesia and prognosticate on developments that will likely be coming in the near future. Software-related solutions will be discussed first, followed by descriptions of novel devices and hardware.
Anesthesia Information Management and Clinical Decision Support Systems
Anesthesia information management systems (AIMS) began as simple automated intraoperative record keepers, the core function of which remains the generation of an automated, continuous electronic anesthesia record that captures and documents the patient’s physiologic data (eg, vital signs) and allows for the manual notation of intraoperative events (eg, drug administration).1
Since then, AIMS have evolved into sophisticated hardware and software systems that are currently available as either stand-alone products or part of a hospital’s electronic health record (EHR) system; both types offer features to expand their capabilities beyond intraoperative record keeping to enhance other aspects of the perioperative experience. For example, most AIMS allow their users to view patients’ previous anesthetic records and preoperative assessment forms. AIMS that are part of a hospital’s EHR can retrieve relevant patient information (eg, age, weight, allergies, medication lists) from the EHR and then load that information automatically into the preoperative assessment and intraoperative record.2
AIMS have been shown to improve the quality and safety of patient care, and clinical decision support systems (CDSS) are one of the factors that have contributed to these benefits.3,4 CDSS have become increasingly integrated into AIMS, and can be grouped into 3 categories: process of care (eg, improving adherence to clinical protocols and guidelines, such as perioperative antibiotic administration5), practice management (eg, billing, maximizing operating room efficiency and throughput6), and outcome-based decision support (eg, facilitating care that leads to better patient outcomes7).
CDSS are currently an active area of anesthesia research and development, largely due to their potential to improve patient care and outcomes. Nair et al demonstrated numerous benefits with the use of their Smart Anesthesia Manager (SAM),8 a near–real time AIMS-based CDSS module, including beta-blocker medication administration,9 reduced wastage of inhalation anesthetics,10 and improved management of intraoperative hypertension and hypotension11 (Figure 1). Anesthesiologists at Colorado Children’s Hospital use iCare (iCare Finland), an emergency AIMS-based CDSS module that provides real-time guidance and calculates drug doses automatically during common anesthesia emergencies.12
AlertWatch (AlertWatch Inc.), another recently developed CDSS, is a secondary monitoring system with FDA 510(k) clearance that integrates aggregated data from physiologic monitors, the AIMS, and the EHR (eg, patient history and physical and lab values), and presents quality and safety alerts to clinicians13 (Figures 2 and 3).
Although AIMS with CDSS have been shown to provide numerous benefits, much room remains for future advancement of the technology. As AIMS evolve, their user interfaces can be optimized to facilitate perioperative workflow. In the case of stand-alone AIMS, interoperability with hospital EHR and other AIMS systems should be the goal. The ongoing integration and validation of CDSS within AIMS offers many avenues for enhancing patient safety and outcomes.
Registries and ‘Big Data’
The proliferation of AIMS across anesthesia departments and practices has enabled the establishment and growth of large national data sets (“Big Data”14) describing anesthesia patients, procedures, and outcomes.15 Even though reporting outcomes data is a sensitive topic, participation in a national registry allows anesthesia practices to benchmark their performance against other providers and practices, and enables researchers to explore data from rare anesthesia-related events.16 The Anesthesia Quality Institute’s National Anesthesia Clinical Outcomes Registry and the Multicenter Perioperative Outcomes Group are two examples of large, anesthesiology-specific databases that have been used recently for quality improvement and data-driven outcomes studies.17,18
Over time, national anesthesia registries will continue to recruit contributors and increase in size, thereby offering a greater pool of data for research and data mining. In the United States, federal initiatives such as the Meaningful Use program will continue to foster aggregation of data at a national level through health information exchanges and national repositories designed to track population health. There will continue to be ongoing development of passive transfer of outcomes data from the AIMS and EHR rather than the manual, self-reported method that requires the active participation of the clinician.16 Efforts by the federal government should also increase the interoperability and data-sharing capabilities across various EHRs, further fueling the contribution of data to these anesthesia “Big Data” initiatives. As reporting and data pooling improve, so should the ability for queries of the registries’ entries and logs to obtain earlier identification of adverse drug or equipment event patterns, as others have reported with Web search data.19
Barcode Labeling and Readers
Software-based technological solutions such as AIMS and CDSS provide one layer of protection against patient harm, while anesthesia Big Data registries can be used to report and warn others of adverse events. Anesthesia researchers and innovators are also constantly endeavoring to design and create physical devices and systems that can reduce the likelihood of patient harm and supply another layer of protection against patient harm.
For instance, medication error in the operating room is a leading cause of adverse events in patients undergoing anesthesia due to misidentification of medication vials and syringes.20 There are various modalities that have been implemented to prevent this error, including a barcode reader linked to a computer, customized drug trays, prefilled syringes, color-coded drug labels, and integrated AIMS warnings.21 Of these modalities, the barcode reader has been the focus of multiple recent research efforts as well as commercially developed medical devices that are subject to FDA approval.
The Codonics Safe Label System is an FDA-approved medication syringe barcode labeling and reader system, which one group found improved compliance with labeling requirements from 63.8% to 98.6% using conventional labeling22 (Figure 4). Jelacic et al experienced greater than 75% compliance with their medication syringe labels after combining the Codonics system with their SAM software.23 The BD Intelliport Medication Management System is a new FDA-approved medical device that not only incorporates automated barcode scanning and labeling of medication syringes, but also automatically identifies, measures, and documents IV bolus injections in the AIMS and EHR.24
Barcode technology has also been applied as a safety check for blood transfusions. A study reported 100% accuracy in matching patients with their blood component transfusions using barcode scanners,25 while another study found that barcoding significantly increased the identification of near-miss events.26
Smart Pumps and Computer-Controlled Drug Infusion Delivery
Optimizing medication dosage and delivery can also lead to improved patient safety. IV infusion is one of the common routes by which medications are delivered to patients in the operating room, and safe and effective clinical use of these infusions depends on understanding critical parameters, such as fluid flows and dead volume that influence performance and safety.27 Smart infusion pumps incorporate drug libraries and dose error reduction systems that intercept errors, such as the wrong rate, wrong dose, and pump setting errors, and have been shown to reduce (albeit not eliminate) programming errors.28
Although smart pumps are helpful in decreasing the risk for drug errors, substantial delays in achieving steady state may still occur when anesthesia providers set very low flow infusion rates; such delays can be potentially hazardous, especially for pediatric patients.29 Parker et al recently reported novel algorithm-based computer control to reduce the temporal lags in carrier and drug flows, thereby improving the match between the user’s intent and the reality of pump-driven infusion of medications.30
Integrated Information Displays
Novel approaches in patient monitors and monitoring systems are also aimed at reducing errors and enhancing patient safety. Traditional device displays include those on the patient monitor, infusion pumps, EHR, and ventilator control panel. This array of information sources can affect nurses’ situational awareness of the patient in the ICU.31 In response, integrated information displays have been developed to display in close proximity the information that is used for comparable tasks; this has been shown to increase nurses’ situational awareness and decrease the time needed to complete tasks.32
The potential for integrated informational displays to decrease errors and improve reaction time to a patient’s clinical needs has been shown mainly in the ICU,33 but such displays could be employed throughout the perioperative period to enhance anesthesia providers’ situational awareness.
Noninvasive Patient Monitoring Systems
Numerous recent advances in noninvasive monitoring of patients’ vital signs and lab values have been reported in the literature. One new device uses a cloth sensor that is applied to a patient’s neck and employs acoustic signal processing to monitor the patient’s respiratory rate.34 The novel acoustic sensor method has been shown to be more precise than thoracic impedance and better tolerated than capnometry with a face mask in obese patients recovering from anesthesia.35 The acoustic sensor technology has also demonstrated rapid detection of changes in respiratory rate under conditions of general anesthesia with a laryngeal mask airway.36 Thus, noninvasive acoustic monitoring is a promising adjunctive modality for measuring and verifying respiratory rates in patients.
Noninvasive monitoring technology has also been developed to trend blood hemoglobin levels continuously via CO oximetry (SpHb, Masimo). A recent study in volunteers demonstrated high precision of SpHb during hemodilution and fluid bolus, yet suboptimal accuracy found during comparisons between SpHb and arterial blood sampling suggested that the device is likely not sufficient to be the sole factor for making transfusion decisions.37 The portable, noninvasive, instant SpHb was also studied as a preoperative assessment tool for low hemoglobin levels, and was found to have high sensitivity for detecting anemia in males (93%) and lower sensitivity in females (75%).38 Ongoing advancement of this technology should be aimed at improving precision in order to achieve the significant benefit of accurate, timely hemoglobin measurements without the need for blood draws.
Research continues on functional noninvasive hemodynamic monitoring, with the goal of determining whether patients will be volume-responsive without the use of an invasive catheter or an esophageal Doppler probe.39 The ccNexfin (Edwards Lifesciences) is a noninvasive hemodynamic monitoring system about which there are numerous reports in the peer-reviewed literature, such as in goal-directed fluid therapy during moderate- to high-risk surgery40 and for hemodynamic management during electroconvulsive therapy in a patient with an unrepaired abdominal aortic aneurysm41 (Figure 6). The device also demonstrated promising results in a study of hemodynamic stability to improve perioperative outcomes during spinal anesthesia for cesarean delivery.42
Smartphones and Mobile Devices
The ubiquity of the smartphone has driven the steady development of monitoring devices that interface with smartphones. A device that integrates a pulse oximeter with a smartphone has been shown to provide a portable, at-home screening tool for intermittent hypoxia in children with sleep-disordered breathing.43 A low-cost version of this device has been proposed as a solution for monitoring children with diseases such as pneumonia in low- and middle-income countries.44 A novel method for noninvasive measurement of blood glucose concentration using a smartphone has also been described in the literature.45 Smartphone electrocardiogram single-lead devices accurately detect baseline intervals as well as atrial rate and rhythm, enabling screening in diverse populations.46 Some of these applications of smartphone monitoring technology are considered medical devices and thus are subject to FDA approval.
A smartphone can be useful to the anesthesia provider even without attached patient monitoring devices. A study showed that pediatric patients who were distracted with a smartphone app had anxiety scores that were lower than similar patients who were randomized to receive midazolam instead.47 Smartphone apps designed for pediatric anesthesia48 are available for download, but a recent review of pain-related apps found multiple areas for improvement.49 Perioperative crisis event management and education apps are also available for free download.50,51
This review is not an exhaustive description of the ongoing convergence of technology and anesthesiology, and many important topics not discussed here (eg, robotic anesthesia, teleanesthesia) have been the subjects of lengthy review articles.52,53 In the future, these technological advances will be increasingly complementary and interoperable, so that anesthesia providers will use EHR-AIMS to document an anesthetic in a patient whose preoperative vital signs and lab values were assessed with his smartphone and a wearable noninvasive monitoring bracelet. A preoperative airway exam will have been performed using smartphone-based telemedicine. Smart pumps, computer-controlled infusions, and barcode technology have made medication errors almost-never events; on the rare occasions when such errors occur, sophisticated monitoring systems alert the clinician while also providing patient- and medication-specific decision support.
The rapid pace of development of consumer technology has great potential to merge commercial products into anesthesia practice and education. Will wearable health monitors be used as gauges of cardiovascular tolerance? Will virtual reality headsets enhance anesthesia education and simulation exercises, and might they be used routinely for preoperative anxiolysis in patients? Discovering the answers to these questions will be an exciting endeavor indeed.
The authors thank their colleagues and fellow members of the Society for Technology in Anesthesia, many of whom have published works that are cited as references in this manuscript, and Mohamed A. Rehman, MD, for his mentorship and guidance during the writing of this article.
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- Nair BG. Development and use of the Smart Anesthesia Manager (SAM) – An AIMS based real-time decision support module. The Society for Technology in Anesthesia 2015 Annual Meeting.http://www.stahq.org/index.php/download_file/view/541/75/. Accessed February 24, 2015.
- Nair BG, Peterson GN, Newman SF, et al. Improving documentation of a beta-blocker quality measure through an anesthesia information management system and real-time notification of documentation errors.Jt Comm J Qual Patient Saf. 2012;38(6):283-288.
- Nair BG, Peterson GN, Neradilek MB, et al. Reducing wastage of inhalation anesthetics using real-time decision support to notify of excessive fresh gas flow.Anesthesiology. 2013;118(4):874-884.
- Nair BG, Horibe M, Newman SF, et al. Anesthesia information management system-based near real-time decision support to manage intraoperative hypotension and hypertension.Anesth Analg. 2014;118(1):206-214.
- Guffey P. Driving reporting and quality improvement. The Society for Technology in Anesthesia 2015 Annual Meeting.http://www.stahq.org/index.php/download_file/view/545/75/. Accessed February 24, 2015.
- http://www.alertwatch.com. Accessed March 24, 2015.
- Laney D. 3D data management: controlling data volume, velocity, and variety.http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed February 24, 2015.
- Dutton RP. Using big data for big research: MPOG, NACOR and other anesthesia registries.http://www.anesthesiallc.com/index.php/current-issue/66-winter-2014/684-using-big-datafor-big-research-mpog-nacor-and-other-anesthesia-registries. Accessed February 24, 2015.
- Dutton RP. Quality management and registries.Anesthesiol Clin. 2014;32(2):577–586.
- Shapiro FE, Jani SR, Liu X, et al. Initial results from the National Anesthesia Clinical Outcomes Registry and overview of office-based anesthesia.Anesthesiol Clin. 2014;32(2):431-444.
- Kheterpal S, Healy D, Aziz MF, et al. Multicenter Perioperative Outcomes Group (MPOG) Perioperative Clinical Research Committee. Incidence, predictors, and outcome of difficult mask ventilation combined with difficult laryngoscopy: a report from the Multicenter Perioperative Outcomes Group.Anesthesiology. 2013;119(6):1360-1369.
- White RW, Tatonetti NP, Shah NH, et al. Web-scale pharmacovigilance: listening to signals from the crowd.J Am Med Inform Assoc. 2013;20(3):404-408.
- Orser BA, Hyland S, U D, Sheppard I, Wilson CR. Review article: improving drug safety for patients undergoing anesthesia and surgery.Can J Anaesth. 2013;60(2):127-135.
- Merry AF, Webster CS, Hannam J, et al. Multimodal system designed to reduce errors in recording and administration of drugs in anaesthesia: prospective randomised clinical evaluation.BMJ. 2011;343:d5543.
- Ang SB, Hing WC, Tung SY, et al. Experience with the use of the Codonics Safe Label System(™) to improve labelling compliance of anaesthesia drugs.Anaesth Intensive Care. 2014;42(4):500-506.
- Jelacic S, Bowdle A, Nair BG, et al. A system for anesthesia drug administration using barcode technology: the Codonics Safe Label System and Smart Anesthesia Manager™.Anesth Analg. 2014. [Epub ahead of print]
- BD Intelliport Medication Management System.http://www.bd.com/intelliport/products/. Accessed February 24, 2015.
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- Nuttall GA, Abenstein JP, Stubbs JR, et al. Computerized bar code-based blood identification systems and near-miss transfusion episodes and transfusion errors.Mayo Clin Proc. 2013;88(4):354-359.
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- Ohashi K, Dalleur O, Dykes PC, et al. Benefits and risks of using smart pumps to reduce medication error rates: a systematic review.Drug Saf. 2014;37(12):1011-1020.
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