Abstract
We share lessons learned from a collaborative in situ simulation of Ebola preparedness for a large health region. The lessons are to use proactive analysis, undertake in situ simulation, and have professionals in infection prevention and control and those in human factors collaborate. These lessons are applicable as generalizable concepts, not only to Ebola preparedness but also to other infectious diseases, including the “next Ebola.” Implementing these concepts will help contribute to improvements in both patient and provider safety.
Keywords
Analysis of simulated infection scenarios by human factors and infection control and prevention professionals highlights the need for infection disease preparedness.
Compounding the infectivity of Ebola and related diseases is the global mobility of both human and animal populations. Such mobility has increased at unprecedented rates and is a major driver in the rapid dissemination of communicable disease–related public health threats. These threats include many emerging infectious diseases, such as influenza, Ebola, Zika virus, and drug-resistant organisms (MacPherson et al., 2009). It has been estimated that each year, approximately 3 billion individuals travel over large geographic distances, with half crossing international boundaries (World Bank Group, 2016). For example, at Heathrow Airport alone, an estimated 70 passengers land every minute, any of whom could be infected with the “next Ebola.”
We faced such a possibility in 2014, when an airline passenger presented at a hospital in our city with a travel history and symptoms consistent with potential Ebola (“Alberta Health Services Says,” 2014). Although an Ebola diagnosis was ruled out within 24 hr, this event highlighted the importance of our health system’s (Alberta Health Services) preparedness for Ebola.
We reflect on our experiences from preparing for Ebola in 2014 to identify a broadly applicable health systems approach for the “next Ebola.” We focus on three areas: proactive analysis, in situ simulation, and collaboration between the disciplines of human factors and infection prevention and control (IPC).
Our Experience
Part of our health system’s Ebola preparations involved simulating patient care, including the transport, triage, and treatment of Ebola patients. These standardized patients presented with a history compatible with having Ebola, including vomiting and diarrhea, thus requiring wet contact precautions; that is, the need for specific PPE for health care workers (see Figure 1). The simulations were conducted in situ; that is, at acute care sites in real time amid ongoing patient care at six hospitals, with patients presenting at home or at an acute care site. Each simulation then followed one of six scenarios, involving the triage, treatment, and transport of patients as realistically as possible, using all necessary equipment (PPE, instructional aids for donning and doffing, carts in which PPE were stocked) and in standard environments (emergency department isolation rooms, hallways, intensive care units).
A health care professional in full personal protective equipment, preparing to enter a patient’s home.
The simulations presented an opportunity for us as a human factors research team (W21C, n.d.) to video and photograph donning and doffing activities for PPE, which are vital in the care and treatment of patients with suspected Ebola (see Hallihan et al., 2015).
The project met the criteria for Quality Assurance/Program Evaluation activities by the University of Calgary’s Health Research Ethics Board.
We obtained copies of donning and doffing procedural aids and various types of PPE and photographed relevant equipment and environments. To avoid recording actual patients or interfering with the operation of the simulations, we could not always collect uninterrupted video recordings. However, we did not miss key activities, such as doffing. In total, we collected 13 hr and 355 GB of video.
We imported video recordings into a commercial software program designed for systematic analysis of observational data. Thus we could track and code what each health care professional (HCP) did; that is, to describe and quantify specific HCP actions and behaviors as well as the context in which they occurred.
We used an informal task analysis to initially code for actions and behaviors that could lead to potential contamination of HCPs with Ebola during donning and doffing PPE, as determined during the planning of the project. We then consulted an expert advisory group of IPC personnel to identify problematic events. Coding was further influenced by review of IPC and human factors literature about highly contagious diseases and HCP behaviors.
After a discussion with a doctor who had been assigned to a Red Cross Ebola clinic in Sierra Leone, we again reviewed and revised our coding. This iterative coding process facilitated collaboration between IPC specialists, doctors, and human factors specialists and was key to the success of the project.
The coding taxonomy had two major categories: doffing procedure and PPE disposal. With the former, we looked for out-of-sequence actions and checklist deviations. With the latter, we coded for PPE correctly placed into containers, PPE overflow from containers, and PPE on the floor as well as actions that placed HCP at high risk of potentially being contaminated with the Ebola virus, such as HCPs touching their facial mucous membranes or eyes with potentially contaminated body parts (hand or forearm; see Figure 2; Hallihan, Baers, et al., 2015).
Left: A health care professional (HCP) with coveralls with a tear over the left forearm. Right: The same HCP then touching the face near the nose and mouth with the left forearm.
Figure 2 provides an example of coded HCP actions and behaviors. Additional details of coding and recommendations can be found in Hallihan, Davies, et al. (2015).
We applied a time stamp to each recorded event, coded a total of 1,309 events, and analyzed each one based on its frequency and duration. We then evaluated the results of coding using the Winnipeg model as a framework (Davies, 2000; Duchscherer & Davies, 2012). This human factors model is based on Donabedian’s structure, process, and outcome (Donabedian, 1966) and has five components: patients, personnel, environment/equipment, organization, and regulatory agencies. The model provided a framework for the context in which the actions and behaviors occurred.
This evaluation enabled us to provide 123 evidence-based recommendations for changes that our health system could make (see final report, Hallihan et al., 2015) to reduce the potential contamination of HCPs. IPC within Alberta Health Services adopted 88% of the department-specific recommendations.
Proactive analysis
The goal of proactive analysis is to seek out hazards and mitigate or eliminate them before anyone is harmed. Even well-resourced health care settings are likely to harbor many unforeseen hazards, which can be found in any part of the health care system (Reason, 2000). If not identified and dealt with, these hazards can contribute to harm to patients and staff. Hidden or latent hazards can be especially problematic when considering infectious diseases and how quickly they can spread.
Our project underlined the value of proactive analysis with respect to Ebola preparation. For example, we observed an HCP doffing her PPE into a biohazard bin overflowing with discarded PPE from the two previous HCPs (Hallihan, Davies, et al., 2015). The HCP decided to deal with the overflowing bin by compressing the PPE with her gloved hands. Unknown to her, the two previous HCPs had put the scissors used to remove their boot covers into the same bin. The two HCPs were not able to discard the scissors in the sharps bin, as it was fixed to the wall, out of reach in the designated clean zone. Both the hazard (scissors in the bin) and the system deficiency (design of the room and its equipment) were identified through proactive analysis while we were providing simulated care, rather than finding these issues while treating real Ebola patients.
The lesson learned from this project is not to wait for the “next Ebola”; rather, we should take action now. Although taking action is not without cost, “investment in prevention and preparation is worth much more than spending on response” (Sands, Mundaca-Shah, & Dzau, 2016). Although high-stakes outbreaks are infrequent, early identification of hazards, through simulation, can enhance an organization’s readiness for dealing with potential epidemics.
In situ simulation
Simulation has been a part of health care for more than four decades (Aggarwal et al., 2010) and has been found to be of value in preparing for infrequent events. Simulation plays a role in education and can also be used for quality and safety improvements (Salas, Paige, & Rosen, 2013), although the latter has tended to be on a reactive or retrospective basis. When simulation has been used as a proactive tool, the focus has generally been on team preparation rather than on the system.
In situ simulations are defined as those that occur in clinical environments amid ongoing patient care involving staff who work in their usual teams when performing their usual duties. Thus, in situ simulation provides the opportunity to identify hazards in the real world. We found in situ simulation to be particularly helpful. Instead of conducting observational studies of donning and doffing in the laboratory, we observed simulations in the environments in which HCPs normally work, with real and simulated components. This method enabled us to look for factors influencing HCPs and their actions and behaviors as the HCPs performed tasks and provided care. These factors would not have been apparent if the actions and behaviors were not looked at in the context of care.
For example, running our simulations amid real patient care exposed some unforeseen problems. First, more than once we observed a simulated Ebola patient vomiting as she was transported down a hallway where real patients were sitting. Second, involving all types of health care providers facilitated our observing support services (often not part of medical simulation exercises; see Jolly et al., 2012), such as housekeeping and security. Housekeepers were noted to be unfamiliar with the donning-and-doffing process, and security staff were uncertain as to whether or not they needed to don PPE. Third, we were able to observe HCPs in various settings. For example, paramedics responding to a house call had to set up a tent outside the patient’s home to don PPE, due to space restrictions in the ambulance and the weather (snow on the ground, gusty winds).
Our simulations were supported at the highest level of the organization, and the planning group included all the necessary simulation experts. But not everyone will be able to carry out a similar project. At the very minimum, what others can do is conduct “walk-through” exercises with a human factors expert. These exercises should involve at least one representative of each type of HCP and support worker and ensure that all necessary equipment is in place in all the relevant care environments. In addition, photographing and/or videoing HCPs interacting with each other, the equipment, and their environments facilitates later review with appropriate content experts to help identify hazards and areas for improvement.
Collaboration between human factors and IPC
As a discipline, IPC arose from the struggle within the medical community against infectious disease and epidemics, such as the worldwide spread of penicillin-resistant Staphylococcus aureus in the 1950s. IPC methods traditionally have been based on persuading individuals to take certain safety measures (Forder, 2007), for example, “wash your hands” and “cover your cough” (Anderson, Gosbee, Bessesen, & Williams, 2010). Thus, when faced with emerging infectious diseases such as Ebola, IPC improvement efforts continue to be aimed at individual HCPs and patients. When three HCPs in Texas became infected with Ebola, reviews focused on them, asking them to recall how they might have failed to adhere to a protocol (“Ebola Test on Texas Health-Care Worker,” 2014).
This focus on the individual HCP ties into the common belief that people are at fault when things go wrong (Leape & Berwick, 2005; Reason, 2000). By its nature, IPC takes an approach that focuses on the actions and behaviors of HCPs, which are not always linked to the context in which they are carried out (Gurses, Ozok, & Pronovost, 2012). In addition, describing one “cause” can lead to prescribing corrective action for that single factor, despite the fact that other factors could be identified in a systematic investigation. Although such individually aimed efforts are to some extent helpful, “changing within systems” is known to be less effective at creating improvement than is “changing systems” (Berwick, 1996). Reminders and training, although “cheap and quick to apply” (Reason, 2002), do not provide the same advantages as “forcing functions” and other engineering constraints that do not allow HCPs to carry out potentially harmful actions.
Anderson et al. (2010) asked, “Why are IPC-related practices so difficult to follow?” The implication of this question is “Why can’t a specific individual HCP follow IPC practices?” However, instead of asking why individuals find practices difficult to follow, the better question to ask is which design factors contribute to these difficulties (Anderson et al., 2010).
In contrast to IPC, human factors generally takes a systems-level approach rather than a person-specific approach. Human factors provides information about human limitations (e.g., vision, memory, physical reach), individual differences (e.g., age, training), and the influence that those characteristics exert on an individual’s interaction with other people, tasks, equipment, and environments (Wickens, Hollands, Banbury, & Parasuraman, 2013). Design improvements are aimed at hazards and system deficiencies contributing to problems. For example, rather than relying on health care workers to remember the correct order in which they don PPE, supply carts should be organized logically, to match the order of a well-designed guide to facilitate donning of PPE (Alvarado, 2012).
Conclusion
Our project demonstrated how IPC and human factors can work collaboratively, although this is not the first time this has occurred (Anderson et al., 2010; Gurses et al., 2012; Storr, Wigglesworth, & Kilpatrick, 2013). We believe a collaborative approach is the optimal one, with IPC and human factors specialists each bringing their own complementary expertise to focus on infectious disease preparedness.
In our project, IPC clinicians contributed the necessary medical context that many human factors experts lack, including the rigor of infection and prevention and control methodology aimed at individual HCPs. Human factors specialists also looked at individual HCPs but through the lens of a systematic approach to the context in which actions and behaviors occurred. This collaborative examination was made possible through the recording and systematic analysis of coded audiovisual data. The collaboration of IPC and human factors specialists was fundamental to this process of identifying hazards leading to potential contamination of HCPs with Ebola.
Each of these lessons – to be proactive, to use in situ simulation, and to collaboratively combine the strengths of IPC and human factors – is applicable to addressing many emerging infectious diseases. We must be prepared for the “next Ebola,” the arrival of which is unpredictable but inevitable. Applying these lessons will help to ensure that patients receive the safest possible care and that those who provide care are able to do so safely.
Footnotes
Acknowledgements
We thank the many volunteers who participated in the original quality improvement study on which this commentary is based. We thank all the individuals and departments across Alberta Health Services who contributed their expertise and resources to the original quality improvement initiative on which this commentary is based. We particularly acknowledge the teams involved in coordinating the simulation exercises, including critical care, emergency disaster management, emergency medical services patient care simulation, eSIM, and infection prevention and control.