The Washington Post recently reported that “somehow, this messed-up country [Lebanon], teetering on the brink of economic ruin and political chaos, has done something right when it comes to the coronavirus.” [1]. How has a country amid crippling protests since October 2019, soaring food prices, lack of healthcare resources, and a currency in free-fall managed to nearly plateau the COVID-19 curve? The latest COVID-19 numbers for Lebanon show 1466 confirmed cases and 32 deaths [2]. Comparatively, other countries with similar populations (6 million) such as Singapore (> 40,800 cases) and Norway (> 8600 cases) have not fared as well [3].
People did not trust the Lebanese government would take the necessary measures to control the spread of the virus, so they were proactive in taking extreme precautions from early on—the government soon followed by implementing strict curfews and closing down public gathering spaces. In addition, many had already begun working from home during the 2019 protests or had lost their jobs and were more likely to be home already. Therefore, the gradual spread of the infection in Lebanon allowed healthcare facilities time to prepare and expand hospital capacity, to the point that there are now more beds available than patients to fill them [4].
One of the most practical ways to help hospitals and healthcare personnel have prepared for the pandemic is simulation [5,6,7] as has been shown during previous healthcare crises such as severe acute respiratory syndrome (SARS), Ebola, and influenza A [8,9,10,11]. Healthcare is already a high-risk, high-stress setting prone to errors, even more so when the stakes are higher for both patients and the healthcare professionals themselves and when the guidelines are uncertain [12]. Therefore, it is imperative that medical teams have the opportunity to familiarize themselves with potential clinical scenarios, be situationally aware and cognizant of their environment, and demonstrate effective teamwork behavior by practicing key crisis resource management elements including closed-loop clear communication, distribution of workload, efficient role assignment, and setting priorities dynamically [13, 14]. Alongside training personnel, simulation can also be useful for troubleshooting the system to discover latent safety threats which may have gone unrecognized and proven devastating to patient safety [15].
Institutions around the world have shifted their perceptions of simulation as a “backburner” training tool to a “first choice” strategy for ensuring staff and system readiness in the face of COVID-19 [16], yet we cannot disregard the practical constraints of performing simulations, especially in situ during the pandemic, such as the need for physical distancing, rigorous infection control for the simulators, the equipment, and the participants [17]. Within this dichotomy, we are given the opportunity to assess the staff’s and the system’s flexibility to adapt their day-to-day activities to suit the uncertainties caused by the COVID-19 pandemic. Most would define safety as the absence of accidents and/or incidents, assuring minimal to acceptable risk to the patient. Hollnagel describes two approaches to analyzing safety in healthcare: safety I presumes that if an incident were to occur, then it is due to clear and identifiable failures or malfunctions of technology, procedures, personnel, or the organization; these threats must be identified and either eliminated or resolved [18]. However, given the uncertainty and complexity of healthcare work, the surprise is not that things occasionally go wrong but that they actually go right more often. Therefore, instead of focusing on what went wrong, perhaps we should start looking at factors which contribute to successful outcomes—this is the safety II perspective discovering the system’s adaptability to varying conditions [19], focusing on exploring how successful performances are produced via adaptive mechanisms on the part of personnel or the system itself in the midst of uncertainty [20]. According to safety II, the reason why things go right is the performance variability observed every day [work-as-done] in order to respond to complex, challenging, and varying conditions [21]. Therefore, prior to the implementation of change, determining how success is achieved normally (work-as-done) should focus not only on best practice but also on the various adjustments and trade-offs healthcare workers make to achieve success under the conditions they face, including where resources are limited. The implication is that when flexibility is proven successful, protocols should allow variability [22].
As Patterson et al. explain, surprise is inevitable in healthcare, yet the “ability to recover from that surprise depends on what capacities are already present that can be deployed to address the unexpected” (p. 70, 2019) [23]. In situ simulation—though controlled—offers the perfect opportunity to test permutations of surprises coming as close to “work-as-done” without compromising actual patient safety [23]. Although we had not simulated infection control scenarios in the past, we believe technical and non-technical skills gained from pre-COVID-19 simulations would reflect in the management of COVID-19 simulated patients. Moreover, as Hollnagel describes it, the basis of resilient performance in healthcare [24,25,26,27,28] is the ability to respond, monitor, learn, and anticipate—these are transferable abilities towards a safety II management approach [25].
In an effort to prepare for the challenges of COVID-19, we adopted Hollnagel’s Safety Model to glean latent safety threats by simulating COVID-19 scenarios in various departments within our academic tertiary hospital.
