ArticleHuman Factors Evaluation of the Universal Anaesthesia Machine: Assessing Equipment with High-Fidelity Simulation Prior to Deployment in a Resource-Constrained Environment
Introduction
Approximately 5 billion people worldwide lack access to safe and affordable surgical and anesthesia care. In 2010, an estimated 16.9 million deaths (32.9% of all deaths worldwide) were due to conditions that required surgical care.1 In high-income countries (HICs), perioperative and anesthetic-related mortality rates have significantly decreased despite the need to care for ever-sicker patients. Patients continue to have a high risk of anesthesia-related mortality in low- and middle-income countries (LMICs).2 Consequently, improving surgical outcomes in LMICs requires improvements to perioperative anesthesia care.3 The anesthesia machine, is an essential life support device that is particularly vulnerable to dysfunction in resource-constrained environments.
Global health disparities exist in anesthesia care, which limit access to quality surgical care in many LMICs. Examples include Liberia, which has zero anesthesia physicians and 60 nurse anesthetists, as well as Sierra Leone with 2 anesthesia physicians nationally (personal communication Ministry of Health of Liberia and Ministry of Health and Sanitation of Sierra Leone). Anesthesia care providers in LMICs face many challenges, including insufficient numbers of trained personnel, inadequate supply of essential medications, unreliable infrastructure, and equipment scarcity. In a recent study of 590 facilities in 22 LMICs, 35% of facilities lacked access to oxygen and 40% lacked functioning anesthesia machines.4 Often, anesthesia equipment donated or purchased for LMIC hospitals is not suited for the environment and will not function properly (if at all). Estimates indicate that 70%–90% of devices designed for industrialized countries will not function in LMICs.5 Factors that make an anesthesia machine unsuited to the local environment include machines that require compressed oxygen sources but are deployed in clinical locations without consistent availability; machines that are vulnerable to failure in the presence of an unstable or inadequate power grid but are placed where power failures are frequent; and machines placed where there is a mismatch between the biomedical maintenance support requirements and availability of parts and/or adequately-trained personnel. In these challenging locations, an anesthesia machine that is optimized for the low-resource environment is needed. Other challenging in-situ factors include environments with typically high heat and humidity as well as often mismatches between consumable requirements and consumable availability.
There have been repeated calls for device manufactures to develop technology that is adaptable enough to reach populations in Sub-Saharan Africa. The World Federation of Societies of Anaesthesiologists (WFSA) is participating in the development of an ISO standard (8835-7:2011) for anesthetic delivery systems in areas with limited access to anesthetic gases and inconsistent electrical power, and the WFSA Safety and Quality of Practice Committee has provided guidance to support anesthetic machines in different regions worldwide.6 Some equipment manufacturers have designed systems for these environments such as the Glostavent®, a low-resource optimized anesthesia machine. However, the need for machines which are reliably functional in these locations remains great. Currently, an overarching method for designing new equipment to support anesthesia care delivery in these settings is largely absent.
The Universal Anaesthesia Machine® (Gradian Health Systems, Inc.) is a low-resource optimized anesthesia machine specifically designed to be used in the austere clinical environment. Use of this machine in a controlled environment was examined to explore machine-user issues and to ascertain ease of use prior to the development of a curriculum for use and deployment on a large scale in a low-resource environment. We postulated that a simulation-based human factors evaluation could provide a safe, efficient, low-cost and complementary approach to traditional methods of testing equipment and devices intended for deployment to vulnerable populations in low-resource settings. While medical simulation cannot fully replicate the low-resource environment (e.g. high heat and humidity), targeted challenges within the environment (e.g. user issues, symbology, visual/aural cues during use) can be replicated so that the device performance can be assessed.
In this study, we report the results of a simulation-based human factors evaluation of the Universal Anaesthesia Machine (UAM®; Gradian Health Systems Inc. New York, NY). Study objectives were twofold. First, we assessed the usability of the UAM® across provider groups with differing levels of experience under normal and challenging clinical and environmental conditions. Second, feedback was gathered on available UAM training material and usability issues in order to maximally prepare Sierra Leone-based anesthesia providers (locally trained nurse anesthetists) still in-training on how to use the UAM®. Since the curriculum that we plan to develop is simulation based, information like that displayed in Appendix table C was useful in understanding what features of training require greater emphasis. We discuss implications of study results for a broader approach to meeting the equipment needs of care providers in LMIC settings.
Section snippets
Materials and methods
This study was approved by the Institutional Review Board of the Johns Hopkins University School of Medicine (JHSOM). Data collection occurred between September 2012 and July 2013 at the JHSOM Simulation Center. Concurrent with this study, the research team was conducting an observational study of anesthesia delivery in two tertiary-care hospitals in Freetown, Sierra Leone.7 Simulation scenarios were designed to reflect challenges found in this austere environment.6, 8, 9
Results
Table 1 summarizes demographics for the 35 anesthesia providers who participated in the study. Ages ranged from 25 to 59 years. Fourteen participants (40%) had international anesthesia mission trip experience prior to the study. Eight (22.9%) had previous experience administering halothane. Only two (5.71%) had previous clinical experience with the administration of draw-over inhalational anesthesia. All participants had clinical experience with multiple anesthesia delivery systems, and 22
Discussion
In this study, we evaluated the usability of the UAM, workload levels with scenarios and feedback for the development of training material by using high fidelity medical simulation as a method for human factors device evaluation. We also used our advanced simulation equipment to confirm that this unique device was safe to use on a vulnerable population (economically disadvantaged), prior to introducing it to the two most critical institutions in Sierra Leone. With this approach, we examined a
Conclusion
Though this study uncovered potential opportunities to improve UAM® usability, our findings suggest that the device is well designed for overcoming challenges in anesthesia functionality that can cause even the most high-end anesthesia machines to fail in the resource-constrained environment. The study also showed that the UAM® has a high ease of use and good clarity of instruction. The ability of the UAM® to function well during power failures and oxygen shortages allowed Western users,
Author's contributions
John Sampson helped to design the study and write the manuscript and provided critical review.
Benjamin Lee helped to design the study and write the manuscript and provided critical review.
Rahul Koka helped to design the study, analyze and interpret the data, and write the manuscript. He attests to the integrity of the original data and the analysis.
Adaora Chima helped to design the study, collect the data, analyze and interpret the data, and write the manuscript. She attests to the integrity of
Conflict of interest
All authors discloses that the research described in this manuscript was supported by an unrestricted grant from Gradian Health Inc. and that the curriculum described within the manuscript was later used for international training projects performed by the co-authors.
Attestation: All authors approved the final manuscript.
Funding
Funding was provided via an unrestricted grant from Gradian Health Systems, Inc.
Acknowledgements
This work was supported through a grant from Gradian Health Systems, Inc. We would also like to thank the support team from the Johns Hopkins ACCM Global Alliance of Perioperative Professionals (GAPP).
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IRB: Research was approved by the Johns Hopkins University Institutional Review Board. The IRB can be contacted at [email protected] or via phone at 410-955-3008.