Refining mass casualty plans with simulation-based iterative learning

Adopting human factors in early phase and experimental medicine research: A nested pilot study observing controlled human infection with SARS-CoV-2

AIMS

The influence of human factors on safety in healthcare settings is well established, with targeted interventions reducing risk and enhancing team performance. In experimental and early phase clinical research participant safety is paramount and safeguarded by guidelines, protocolized care and staff training; however, the real-world interaction and implementation of these risk-mitigating measures has never been subjected to formal system-based assessment.

METHODS

Independent structured observations, systematic review of study documents, and interviews and focus groups were used to collate data on three key tasks undertaken in a clinical research facility (CRF) during a SARS CoV-2 controlled human infection model (CHIM) study. The Systems Engineering Initiative for Patient Safety (SEIPS) was employed to analyse and categorize findings, and develop recommendations for safety interventions.

RESULTS

High levels of team functioning and a clear focus on participant safety were evident throughout the study. Despite this, latent risks in both study-specific and CRF work systems were identified in all four SEIPS domains (people, environment, tasks and tools). Fourteen actionable recommendations were generated collaboratively. These included inter-organization and inter-study standardization, optimized checklists for safety critical tasks, and use of simulation for team training and exploration of work systems.

CONCLUSIONS

This pioneering application of human factors techniques to analyse work systems during the conduct of research in a CRF revealed risks unidentified by routine review and appraisal, and despite international guideline adherence. SEIPS may aid categorization of system problems and the formulation of recommendations that reduce risk and mitigate potential harm applicable across a trials portfolio.

Medical maximization: The effect of personality on triage decision-making

Mass Casualty Incidents (MCIs) rapidly overwhelm the ability of local medical resources to deliver comprehensive and definitive medical care and they have been occurring more frequently in recent decades and affect countries of all socioeconomic backgrounds (Hart et al., 2018). As such, it is important to understand how individuals make such decisions in these events and what factors can hinder or help the process. In this study we focused on the critical role of maximization within MCI triage. Triaging an MCI requires juggling the demand and supply of resources, time, and focus, likely leading to various decisions involving compromise/sacrifice. In a vignette study, hosted on Amazon Mturk (n = 235, Mean age = 38.05, 51.49% self-identified as male), which involved triaging over 100 patients we found that trait differences maximization impacted the willingness to use a "black tag". Furthermore, maximization also impacted how much information an individual needed about the patient before being willing to use a black tag. Overall, this research demonstrates the importance of understanding factors that create individual differences in how people make decisions during MCI events, especially those decisions that involve the use of potentially lifesaving treatments.

Hospital Resource Planning for Mass Casualty Incidents: Limitations for Coping with Multiple Injured Patients

Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity-in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances.

Major Incident Hospital Simulations in Hospital Based Health Care: A Scoping Review

Major incidents are occurring in increasing frequency, and place significant stress on existing health-care systems. Simulation is often used to evaluate and improve the capacity of health systems to respond to these incidents, although this is difficult to evaluate. A scoping review was performed, searching 2 databases (PubMed, CINAHL) following PRISMA guidelines. The eligibility criteria included studies addressing whole hospital simulation, published in English after 2000, and interventional or observational research. Exclusion criteria included studies limited to single departments or prehospital conditions, pure computer modelling and dissimilar health systems to Australia. After exclusions, 11 relevant studies were included. These studies assessed various types of simulation, from tabletop exercises to multihospital events, with various outcome measures. The studies were highly heterogenous and assessed as representing variable levels of evidence. In general, all articles had positive conclusions with respect to the use of major incidence simulations. Several benefits were identified, and areas of improvement for the future were highlighted. Benefits included improved understanding of existing Major Incident Response Plans and familiarity with the necessary paradigm shifts of resource management in such events. However, overall this scoping review was unable to make definitive conclusions due to a low level of evidence and lack of validated evaluation.

Five Influential Factors for Clinical Team Performance in Urgent, Emergency Care Contexts

INTRODUCTION

In deployed contexts, military medical care is provided through the coordinated efforts of multiple interdisciplinary teams that work across and between a continuum of widely distributed role theaters. The forms these teams take, and functional demands, vary by roles of care, location, and mission requirements. Understanding the requirements for optimal performance of these teams to provide emergency, urgent, and trauma care for multiple patients simultaneously is critical. A team's collective ability to function is dependent on the clinical expertise (knowledge and skills), authority, experience, and affective management capabilities of the team members. Identifying the relative impacts of multiple performance factors on the accuracy of care provided by interdisciplinary clinical teams will inform targeted development requirements.

MATERIALS AND METHODS

A regression study design determined the extent to which factors known to influence team performance impacted the effectiveness of small, six to eight people, interdisciplinary teams tasked with concurrently caring for multiple patients with urgent, emergency care needs. Linear regression analysis was used to distinguish which of the 11 identified predictors individually and collectively contributed to the clinical accuracy of team performance in simulated emergency care contexts.

RESULTS

All data met the assumptions for regression analyses. Stepwise linear regression analysis of the 11 predictors on team performance yielded a model of five predictors accounting for 82.30% of the variance. The five predictors of team performance include (1) clinical skills, (2) team size, (3) authority profile, (4) clinical knowledge, and (5) familiarity with team members. The analysis of variance confirmed a significant linear relationship between team performance and the five predictors, F(5, 240) = 218.34, P < .001.

CONCLUSIONS

The outcomes of this study demonstrate that the collective knowledge, skills, and abilities within an urgent, emergency care team must be developed to the extent that each team member is able to competently perform their role functions and that smaller teams benefit by being composed of clinical authorities who are familiar with each other. Ideally, smaller, forward-deployed military teams will be an expert team of individual experts, with the collective expertise and abilities required for their patients. This expertise and familiarity are advantageous for collective consideration of significant clinical details, potential alternatives for treatment, decision-making, and effective implementation of clinical skills during patient care. Identifying the most influential team performance factors narrows the focus of team development strategies to precisely what is needed for a team to optimally perform.

Mass casualty medicine: time for a 21st century refresh

Mass casualty events are on the rise globally, as we face increasing pressures from scarcity of resources, population growth, systemic inequalities, geopolitical instabilities, and polarised discourse. Although they are rare events for an individual practitioner, they are going to happen to someone, somewhere, this week, this month, this year. And whilst they are often the last consideration for healthcare systems under constant pressures from daily routine work, individuals, departments, hospitals, and systems have to step up effectively in times of crisis. Failure to do so can lead to suboptimal outcomes for casualties, and even perceived failures can have crippling consequences on staff, families, and communities for years.