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Perioperative Concerns Regarding COVID-19

COVID-19 is an infectious syndrome caused by the SARS-CoV-2 virus. The virus has spread quickly since it was first discovered on December 31, 2019. There have been more than 17 million confirmed cases of COVID-19, around 4.5 million of which are in the United States. Globally, there have been around 670,000 deaths from the virus, with around 151,000 of those deaths occurring in the United States [1]. For anesthesiologists, who are often in close contact with patients during surgery, certain protective measures addressing perioperative concerns regarding COVID-19 can reduce the risk of disease transmission. 

Hospitals have responded proactively by instituting a variety of protective measures, including creating isolation units for patients with symptoms of COVID-19, providing medical staff with protective gear, and instituting regular temperature checks and testing of staff and patients. The American Society of Anesthesiologists recommends that all patients should be screened for symptoms before being admitted. Those that exhibit symptoms should be redirected to additional screening. When possible, those without symptoms should undergo nucleic acid amplification testing (commonly known as PCR testing) prior to surgery [2]. 

Patients with COVID-19 can initially present as asymptomatic or show mild symptoms, only to see their condition rapidly decline. In an early study from Wuhan, China by Wang, et al., some patients presented with minimal respiratory symptoms. One patient, who presented abdominal symptoms, was admitted to surgery and infected 10 healthcare workers [3]. A study by Huang, et al. found that all 49 patients in the sample developed signs of pneumonia, while 29% developed acute respiratory distress syndrome [4]. Common radiographic findings for patients with COVID-19 include ground glass opacities, which present alongside cord-like opacities. As the disease progresses, “reverse halo” or “crazy-paving” patterns often appear [5]. 

Anesthesiologists regularly execute aerosol-generating procedures such as intubation, suctioning and cardiopulmonary resuscitation. In addition to the baseline contact and preventative measures, the World Health Organization recommends that medical professionals engaged in these procedures also take droplet and airborne precautions [6]. Managing aerosol dispersal through liberal use of suction devices and thorough cleaning procedures have also been found to limit perioperative transmission of COVID-19 between patients and medical staff [7]. If patients are suspected of having COVID-19, Al Balas et al. suggest performing operations in a negative pressure environment and to limit the number of medical staff involved in the procedure [8].  

Vascular care should also be reconfigured to avoid contamination and prevent infection. Dexter et al. suggest the creation of a closed lumen IV system. Open lumens, which may increase risk of viral transmission, should be outfitted with needleless devices that can be disinfected. Syringes should be kept outside of the contaminated environment. Ports should be thoroughly cleaned prior to injection and covered with disinfecting caps during and after the procedure [9]. 

Certain subpopulations, many of whom are likely to undergo procedures that require anesthesia, are at higher risk for contracting COVID-19. In particular, patients with cancer and transplant recipients appear to be at higher risk. A study by Liang, et al. analyzed 2,007 cases of COVID-19 from across China. The authors found that patients with cancer were 31% more likely to experience severe events that required admission into the ICU and invasive ventilation [10]. Michaels et al. noted that while there have been no known cases of COVID-19 in transplant patients, previous coronaviruses like SARS and MERS have had fatal consequences for transplant recipients [11]. 

Though there are many factors involved, carefully investigating and protecting against perioperative concerns regarding COVID-19 will benefit both patients and providers.

References 

[1] “COVID-19 Map.” Johns Hopkins Coronavirus Resource Center, Johns Hopkins University, 2020, coronavirus.jhu.edu/map.html. 

[2] “The ASA and APSF Joint Statement on Perioperative Testing for the COVID-19 Virus.” The ASA and APSF Joint Statement on Perioperative Testing for the COVID-19 Virus | American Society of Anesthesiologists (ASA), American Society of Anesthesiologists, 1 June 2020, www.asahq.org/about-asa/newsroom/news-releases/2020/06/asa-and-apsf-joint-statement-on-perioperative-testing-for-the-covid-19-virus. 

[3] Wang, Dawei, et al. “Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China.” JAMA, vol. 323, no. 11, 2020, p. 1061., doi:10.1001/jama.2020.1585. 

[4] Huang, Chaolin, et al. “Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China.” The Lancet, 2020, www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(20)30183-5.pdf. 

[5] Greenland, John R., et al. “COVID-19 Infection: Implications for Perioperative and Critical Care Physicians.” Anesthesiology, vol. 132, no. 6, June 2020, pp. 1346–1361., doi:10.1097/aln.0000000000003303. 

[6] Clinical Management of COVID-19. World Health Organization, 2020. 

[7] Zheng, Min Hua, et al. “Minimally Invasive Surgery and the Novel Coronavirus Outbreak: Lessons Learned in China and Italy.” Annals of Surgery, vol. 272, no. 1, 2020, doi:10.1097/sla.0000000000003924. 

[8] Al-Balas, Mahmoud, et al. “Surgery during the COVID-19 Pandemic: A Comprehensive Overview and Perioperative Care.” The American Journal of Surgery, vol. 219, no. 6, 2020, pp. 903–906., doi:10.1016/j.amjsurg.2020.04.018. 

[9] Dexter, Franklin, et al. “Perioperative COVID-19 Defense: An Evidence-Based Approach for Optimization of Infection Control and Operating Room Management.” Anesthesia & Analgesia, vol. 131, no. 1, 2020, pp. 37–42., doi:10.1213/ane.0000000000004829. 

[10] Liang, Wenhua, et al. “Cancer Patients in SARS-CoV-2 Infection: a Nationwide Analysis in China.” The Lancet, vol. 21, 14 Feb. 2020, pp. 335–336. 

[11] Michaels, Marian G., et al. “Coronavirus Disease 2019: Implications of Emerging Infections for Transplantation.” American Journal of Transplantation, vol. 20, no. 7, 2020, pp. 1768–1772., doi:10.1111/ajt.15832. 

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The Evolution of Robotic Anesthesia

The onset of the technological revolution has changed the way healthcare is practiced. Robotic surgery became popular as healthcare professionals moved toward more minimally invasive surgical techniques. These techniques, along with the technological innovations associated with them, led to benefits such as reduced wound access trauma, shorter hospital stay, improved visualization, less postoperative wound complications, and less disfigurement (1). Furthermore, innovations such as monitoring technology (EKG, EEG, ultrasound, etc.) as well as the development of faster-acting, less toxic agents have changed anesthesia administration. While many surgeries now are rapidly approaching automation, anesthesiology has until recently had relatively little exposure to the transition towards a robotic surgeon. Currently, there are three main categories that represent automation in anesthesia: 1) pharmacological, 2) mechanical, and 3) decision making robots (2). While these systems are not yet in common use, they are becoming increasingly accepted as tools for error minimization, precision, and optimal patient care.

Pharmacological robots, predominantly closed-loop systems, are manufactured to assist with anesthetic drug titration. Within the closed loop system, the general structure has three main components: central operating system, target control variables, and a drug delivery system (3). Through a feedback flux, the system can continuously adjust and maintain a target without manual input by the healthcare professional (3). A major step in achieving this automation of drug application was the development of the Bispectral Index (BIS), which measures the depth of anesthesia (2). BIS was first applied in a closed loop system to isoflurane, then to propofol. (4,5). Later, the CLADS (closed loop anesthesia delivery system) was designed with the BIS as the control variable, the delivery system as an infusion pump, and the control system calibrated on the clinical pharmacokinetic and pharmacodynamic profile of propofol. These systems were collectively known as single loop systems, because they could only control one component of anesthesia. Most recently, usage of M-Entropy instead of BIS monitoring has allowed for a dual loop system (6). This has led to the development of McSleepy, which is considered the first true pharmacological robot because it can control three components: hypnosis, analgesia, and muscle relaxation (3).

Manual robots for intubation are used to give support to manual gestures during the intubation process. The DaVinci Surgical System (DVS) is perhaps the earliest example of such a machine, which contains several arms, a video camera, and graspers to assist hand movements and grip (7). Likewise, the Kepler Intubation system (KIS) was designed containing a joystick, robotic arm, and video laryngoscope. In the case of the DVS, tracheal and nasal fiber optic intubations were performed; in the case of the KIS, oral intubations were performed (3). The Magellan system was then designed for robotic nerve blocks in the case of regional anesthesia (3).

Decision support systems (DSS) and telemedicine have also rapidly become more popular in recent years to enhance clinical practice. DSS are designed to update the healthcare professional with clinical suggestions and treatment options. Furthermore, they can be designed with the purpose of detecting adverse events. Specifically, in anesthesia, they have been shown to be effective in detecting/managing intraoperative hyper/hypotension and critical events during sedation (8,9). While these systems are not yet common in anesthesia, they have been shown to be effective in reducing clinical mistakes in other practices. Telemedicine, on the other hand, is the delivery of healthcare using information and communication across a long distance. This has been applied to pre, intra, and post operative anesthesia procedures, with positive results (2).

Altogether, the evolution of robotic anesthesia holds promise for increased accuracy, safety, and efficiency in anesthesia application/management, as well as improved quality of care.

References

1. Ashrafian H, Clancy O, Grover V, Darzi A. The evolution of robotic surgery: surgical and anaesthetic aspects. British Journal of Anaesthesia. 2017 Dec;119:i72–84.  

2. Hemmerling T, Giacalone M. An Introduction to Robots in Anaesthesia. 2016;16(2):96–100.  

3. Hemmerling TM, Terrasini N. Robotic anesthesia: not the realm of science fiction any more. Current Opinion in Anaesthesiology. 2012 Oct;1.  

4. Absalom AR, Kenny GNC. Closed-loop control of propofol anaesthesia using bispectral index TM : performance assessment in patients receiving computer-controlled propofol and manually controlled remifentanil infusions for minor surgery. British Journal of Anaesthesia. 2003 Jun;90(6):737–41.  

5. Kharisov E, Beck CL, Bloom M. Control of Patient Response to Anesthesia using ℒ 1 Adaptive Methods. IFAC Proceedings Volumes. 2012;45(18):391–6.  

6. Liu N, Le Guen M, Benabbes-Lambert F, Chazot T, Trillat B, Sessler DI, et al. Feasibility of Closed-loop Titration of Propofol and Remifentanil Guided by the Spectral M-Entropy Monitor: Anesthesiology. 2012 Feb;116(2):286–95.  

7. Tighe PJ, Badiyan SJ, Luria I, Lampotang S, Parekattil S. Robot-Assisted Airway Support: A Simulated Case. Anesthesia & Analgesia. 2010 Oct;111(4):929–31.  

8. Nair BG, Horibe M, Newman S-F, Wu W-Y, Peterson GN, Schwid HA. Anesthesia Information Management System-Based Near Real-Time Decision Support to Manage Intraoperative Hypotension and Hypertension: Anesthesia & Analgesia. 2014 Jan;118(1):206–14.  

9. Zaouter C, Wehbe M, Cyr S, Morse J, Taddei R, Mathieu PA, et al. Use of a decision support system improves the management of hemodynamic and respiratory events in orthopedic patients under propofol sedation and spinal analgesia: a randomized trial. Journal of Clinical Monitoring and Computing. 2014 Feb;28(1):41–7.  

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Effects of Anesthesia Exposure on the Developing Brain

General anesthesia is typically used during surgeries to put patients in an unconscious state, meaning that it interrupts their cognitive networks1. This interruption can be harmful when the brain is in early stages of development and new neural networks are still forming1. For instance, much of the data on the effects of anesthesia on young animals revealed that anesthesia can cause neurocognitive impairments2. This has thus prompted researchers to study these effects in young human children in order to understand whether or not it is truly safe to administer anesthesia during surgeries for children2.

Backeljauw et al. (2015) conducted a prominent correlational study by assessing language development and intelligence in children who had been exposed to anesthesia before the age of four1. They compared these assessments with those of children who had not been exposed to any anesthesia1. Using the results of various tests, the researchers found that children who had been exposed to anesthesia at a young age had lower scores on both IQ and learning tests1. Backeljauw et al. (2015) took this a step further by looking at brain structure using MRI scans, which revealed “lower grey matter density in the occipital cortex and cerebellum” of children exposed to anesthesia, a pattern commonly associated with decreased learning 1. Despite these concerning findings, much of this work is correlational; therefore, a deeper understanding of how early exposure to anesthesia disrupts brain development is necessary.

To gain more insight into this issue, Sun et al. (2016) did a sibling-matched cohort study, which allowed the researchers to control various external factors2. In this study, one sibling had early exposure to anesthesia, whereas the other sibling had not, and the researchers then investigated various aspects of cognition and behavior of the siblings, including memory, visuospatial function and language2. Interestingly, there were no significant differences between the two groups of children. One reason why this may be is because the study only looked at anesthesia exposure before 36 months2. It might be worthwhile to study different times of exposure in future studies to fully understand the risks of early exposure.

With an FDA warning in 2017 regarding exposure to anesthesia for young children, there was increased research on the topic3. Davidson and Sun (2018) conducted a large-scale review to assess this research, finding mostly mixed results3. A majority of the research did not show a strong association between anesthesia exposure in young children and subsequent neurocognitive impairment, and in cases where there was some association, it was fairly weak in that the risk of impairment was considered to be very minimal3. Given these results, Davidson and Sun (2018) suggested that increased research is needed to further understand the cases in which there is some risk of developing cognitive impairments by exploring different extrinsic and intrinsic factors that make these cases different3.

With the FDA warning as well as mixed results of current research, it is still difficult for medical professionals to be sure of the extent to which administering anesthesia to young children is dangerous. More in-depth investigations are needed to fully understand the risks of exposing young children to anesthesia during surgery.

References:

(1) Backeljauw B, Holland SK, Altaye M, Loepke AW. Cognition and Brain Structure Following Early Childhood Surgery with Anesthesia. Pediatrics. 2015;136(1):e1‐e12. doi:10.1542/peds.2014-3526

(2) Sun LS, Li G, Miller TL, et al. Association Between a Single General Anesthesia Exposure Before Age 36 Months and Neurocognitive Outcomes in Later Childhood. JAMA. 2016;315(21):2312‐2320. doi:10.1001/jama.2016.6967

(3) Andrew J. Davidson, Lena S. Sun; Clinical Evidence for Any Effect of Anesthesia on the Developing Brain. Anesthesiology 2018;128(4):840-853. doi: https://doi.org/10.1097/ALN.0000000000001972

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The Differences between Private and Government Insurance Plans

Insurance in the United States can be divided into two funding categories: private and government. Government insurance is paid for or subsidized by the government, while private insurance is either employer-sponsored or paid for by the individual. In the United States, both types of insurance function similarly, with insurance paying a certain amount for a service and sometimes requiring a copay, deductible, or monthly/annual premium from individuals. The benefits of insurance policies have become increasingly important in recent years as healthcare costs have increased. In 2018, healthcare spending increased by 4.6% to a total of $3.6 trillion, according to a study by Hartman et al [1]. 

Government insurance in the United States is offered to certain vulnerable groups. Medicare and CHIP are government insurance programs that offer coverage to individuals over 65 years of age, under 18 years old, and, in certain states, those significantly below the poverty line. Medicaid, however, is not an insurance product but a program that helps cover medical bills for low-income Americans and those with disabilities. In 2018, the U.S. government paid out a total of $731 billion for these government insurance programs, which accounted for 15% of federal spending [2].  

Despite its high price tag, government insurance tends to be more affordable and offers lower administrative costs than private insurance. In a study by Ku and Broaddus, the researchers simulated moving from private coverage to government insurance and found that, on average, costs dropped by 26% per person [3]. While government insurance tends to be the lower-cost option, it also tends to be less flexible. Government insurance has fewer, if any, options for plans, and little-to-no coverage for procedures it deems unnecessary. 

Overall, private health insurance tends to cost significantly more than government insurance. The tradeoff, however, is that plans tend to be more flexible and individuals can usually select from a variety of plans or customize their own according to their needs. Private insurance, which accounted for 34% of spending on healthcare in 2017, almost always requires a monthly or annual premium [4]. These premiums tend to be significantly higher than government plans but are sometimes covered or split with employers. For those who are covered under government insurance, private plans can also provide supplemental coverage for items or procedures not covered under the government plan [5]. 

In the United States, public healthcare marketplaces can blur the line between public and private insurance. Under the Affordable Care Act, which was put into law in 2010, every state was required to launch a public healthcare marketplace. Those who qualify for the Affordable Care Act can choose plans from an array of options on the marketplace [6]. While the federal government mandated that plans on these marketplaces follow certain regulations regarding coverage and cost, the plans themselves are offered by private companies. 

Recently, the role of private companies and the government in providing insurance has come to the forefront with the rise of “Medicare for All.” This proposal would eliminate private insurance, expanding Medicare to cover everyone in the United States [7]. Another option is the so-called “public option,” which would expand government insurance to cover everyone, while allowing individuals to keep private coverage. According to an article by Herzlinger and Boxer in the Harvard Business Review, this option has proven successful in Germany and the Netherlands, two countries with strong universal healthcare programs [8].  

While private insurance tends to be more flexible and can be split with employers, government insurance is usually more affordable and has lower administrative costs. Indeed, it is the difference between these two models that is at the core of recent proposals to expand government insurance programs. 

References 

[1] Hartman, Micah, et al. “National Health Care Spending In 2018: Growth Driven By Accelerations In Medicare And Private Insurance Spending.” Health Affairs, vol. 39, no. 1, 2020, pp. 8–17., doi:10.1377/hlthaff.2019.01451. 

[2] Cubanski, Juliette, et al. “The Facts on Medicare Spending and Financing.” Medicare, Kaiser Family Foundation, 20 Aug. 2019, www.kff.org/medicare/issue-brief/the-facts-on-medicare-spending-and-financing/. 

[3] Ku, Leighton, and Matthew Broaddus. “Public And Private Health Insurance: Stacking Up The Costs.” Health Affairs, vol. 27, no. 1, 2008, doi:10.1377/hlthaff.27.4.w318. 

[4] “Health Care Almanac.” CHCF, May 2019, www.chcf.org/wp-content/uploads/2019/05/HealthCareCostsAlmanac2019.pdf. 

[5] Shafrin, Jason. “What Should Be Covered by Government vs. Private Insurance?” Healthcare Economist, 20 Aug. 2018, www.healthcare-economist.com/2018/08/20/what-should-be-covered-by-government-vs-private-insurance/. 

[6] Carrns, Ann. “It’s Enrollment Time for Obamacare.” The New York Times, The New York Times, 22 Nov. 2019, www.nytimes.com/2019/11/22/business/obamacare-enrollment.html. 

[7] Qiu, Linda. “Examining Conflicting Claims About ‘Medicare for All’.” The New York Times, The New York Times, 9 Nov. 2019, www.nytimes.com/2019/11/09/us/politics/medicare-for-all-fact-check.html. 

[8] Herzlinger, Regina, and Richard Boxer. “The Case for the Public Option Over Medicare for All.” Harvard Business Review, Harvard Business School Publishing, 10 Oct. 2019, hbr.org/2019/10/the-case-for-the-public-option-over-medicare-for-all.  

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The Smart Infusion Pump

One of the anesthesia provider’s main roles is to administer anesthetic and analgesic drugs before, during and after a procedure.1 This delivery can occur via a variety of routes, including—but not limited to—orally; through intravenous, epidural or intramuscular injection; or through inhalation.2,3 For continuous medication infusion, an anesthesiology professional may use large-volume, patient-controlled analgesia (PCA), elastomeric, syringe, enteral and insulin pumps.4 Some newer infusion pumps, known as “smart pumps,” alert the provider when there is a risk of adverse drug interaction or when the pump is set to deliver doses outside of safety limits.4 In order to provide the best care to their patients, anesthesia providers should be familiar with the technology behind smart infusion pumps and recent data on their efficacy. 

In general, infusion pumps offer advantages over manual fluid administration, as they can provide medicines in very small volumes and at precisely programmed or automated rates.4 Smart infusion pumps in particular include technologies that allow the provider to choose the desired medication from an approved list and input the patient’s information, after which the smart pump calculates the infusion rate.5 These pumps have drug libraries, which contain the most commonly used intravenous medications, and dose error reduction systems (DERSs), which alert the clinician if the calculated infusion rate exceeds normally acceptable dosing limits.5 Hard dose limits prevent the clinician from starting the programmed infusion, while soft dose limits provide a warning that the dose may be out of range but allow the clinician to start the infusion after the warning is acknowledged.5 Drug libraries can be tailored and may vary among certain contexts, such as the intensive care unit or operating room.6 Because of their perceived benefits, the use of smart infusion pumps has become widespread in recent years.7 A survey study by the American Society of Health-System Pharmacists (ASHP) found that the percentage of United States hospitals using smart infusion pumps grew from 44 to 72.9 percent from 2007 to 2013.7 Evidently, smart infusion pumps allow for efficient and precise delivery of anesthetic drugs. 

Recent studies on smart infusion pumps and their use show the advantages and disadvantages of integrating them into anesthesia care. For example, Keohane et al.’s paper emphasizes the potential for smart infusion pumps to avert high-risk dosing errors and provide data for continuous quality improvement efforts.8 A survey study by Schroeder et al. found a relatively high level of acceptance of a smart intravenous infusion pump by anesthesia providers, which was related to frequency of pump use.9 Another study by Eskew et al. found that in a three-month period of 135 smart pumps, providers received 693 alert messages, 22.8 percent of which led the providers to make a programming change.10 In Wilson and Sullivan’s study, a smart infusion pump prevented errors involving heparin, an anticoagulant drug, and the technology was easy to implement.11 Furthermore, Maddox et al. found that use of a smart infusion pump for PCA prevented respiratory depression.12 Nonetheless, many researchers state that smart infusion pumps have failed to live up to their potential.13 According to the United States Food and Drug Administration (FDA), infusion pumps in general are accompanied by problems related to software defects, user interface issues and mechanical or electrical failures.4 Evidence on the smart infusion pump’s role in harm reduction remains limited, perhaps due to poor design, programming errors, lack of end-user acceptance and unpredictable nature.13 Common sources of error when using the smart infusion pump include manually bypassing drug libraries and DERSs or overriding dose error alerts.5 Indeed, several studies have found that soft limits are relatively ineffective because they are easily overridden.6,14 Also, clinicians may inadvertently program an incorrect dose, resulting in potentially catastrophic consequences.6 Though smart infusion pumps may help anesthesia providers with medication administration and dosing, they are unpredictable and may be used improperly. 

Anesthesia providers commonly use infusion pumps to provide a patient with medication or other fluids. Smart infusion pumps include technologies such as drug libraries and DERSs, which allow clinicians to efficiently select medications and make changes when dosing may be incorrect. Although smart infusion pumps are highly useful in allowing precise medication delivery and preventing mistakes, they are subject to technical issues and misuse by clinicians. Research suggests that future smart infusion pumps would benefit from iterative user-centered design, network and real-time monitoring of alerts, upgraded and standardized drug libraries, decreased number of unnecessary warnings and minimized workaround opportunities.13,14 

1. American Society of Anesthesiologists. Role of Physician Anesthesiologist. When Seconds Count… Physician Anesthesiologists Save Lives 2020; https://www.asahq.org/whensecondscount/anesthesia-101/role-of-physician-anesthesiologist/

2. American Society of Anesthesiologists. Types of Anesthesia. When Seconds Count… Physician Anesthesiologists Save Lives 2020; https://www.asahq.org/whensecondscount/anesthesia-101/types-of-anesthesia/

3. Garimella V, Cellini C. Postoperative pain control. Clinics in Colon and Rectal Surgery. 2013;26(3):191–196. 

4. United States Food and Drug Administration. Infusion Pumps. General Hospital Devices and Supplies August 22, 2018; https://www.fda.gov/medical-devices/general-hospital-devices-and-supplies/infusion-pumps

5. Giuliano KK. IV Smart Pumps: The Impact of a Simplified User Interface on Clinical Use. Biomedical Instrumentation & Technology. 2015;Suppl:13–21. 

6. Cummings K, McGowan R. “Smart” infusion pumps are selectively intelligent. Nursing2020. 2011;41(3):58–59. 

7. Fox BI, Pedersen CA, Gumpper KF. ASHP national survey on informatics: assessment of the adoption and use of pharmacy informatics in U.S. hospitals-2013. American Journal of Health-System Pharmacy. 2015;72(8):636–655. 

8. Keohane CA, Hayes J, Saniuk C, Rothschild JM, Bates DW. Intravenous medication safety and smart infusion systems: Lessons learned and future opportunities. Journal of Infusion Nursing. 2005;28(5):321–328. 

9. Schroeder ME, Carayon P, Li Q. Anesthesia Providers’ Perceptions of Smart IV Infusion Pumps. Anesthesiology. 2005;103:A887. 

10. Eskew JA, Jacobi J, Buss WF, Warhurst HM, Debord CL. Using Innovative Technologies to Set New Safety Standards for the Infusion of Intravenous Medications. Hospital Pharmacy. 2002;37(11):1179–1189. 

11. Wilson K, Sullivan M. Preventing medication errors with smart infusion technology. American Journal of Health-System Pharmacy. 2004;61(2):177–183. 

12. Maddox RR, Oglesby H, Williams CK, Fields M, Danello S. Continuous Respiratory Monitoring and a “Smart” Infusion System Improve Safety of Patient-Controlled Analgesia in the Postoperative Period. In: Henriksen K, Battles JB, Keyes MA, Grady ML, eds. Advances in Patient Safety: New Directions and Alternative Approaches (Vol. 4: Technology and Medication Safety). Rockville, MD: Agency for Healthcare Research and Quality (US); August 2008. 

13. Scanlon M. The Role of “Smart” Infusion Pumps in Patient Safety. Pediatric Clinics. 2012;59(6):1257–1267. 

14. Ohashi K, Dalleur O, Dykes PC, Bates DW. Benefits and risks of using smart pumps to reduce medication error rates: A systematic review. Drug Safety. 2014;37(12):1011–1020.