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Objective Present a collection of papers focusing on improving healthcare practice through the implementation of human factors and ergonomics principles that were presented at the International Ergonomics Association (IEA) 2021 international conference. Background The mission of the IEA is to elaborate and advance ergonomics science and practice and to expand its scope of application. Method We reviewed papers that were submitted for presentation at the IEA 2021 international conference and focused on improving healthcare practice through the implementation of human factors and ergonomics principles. Results The eight papers that are included in this special issue cover varied aspects of human factors application and implementation. Conclusion This special issue provides clear evidence that the science of human factors is relevant and is continuing to grow and so is its implementation in healthcare. Application This special issue offers a selection of applied works, providing a wide scope of human factors guidelines, methods, and theories in healthcare work environments.

Descriptions of a dynamic warning system usually assign the system certain diagnostic values. Operators should adjust their responses to these values when considering the output of the system for their decisions. This approach is not necessarily appropriate for complex systems that are controlled by human operators. Here the operators' actions are likely to change the frequency of events that should trigger a warning. Consequently the operator will change the predictive value of a warning system that is imperfectly correlated with the monitored events. In general its diagnostic value decreases for better operators. This phenomenon is demonstrated on the example of binary warnings about binary events, and an empirical demonstration of the phenomenon is provided in an experimental study of a process control task. Actual or potential applications of this research include improved understanding of the determinants of operators'responses to warnings, which should help in the design of better warning systems.

Objective To develop a new model to quantify information management dynamically and to identify factors that lead to information gaps. Background Information management is a core task for emergency medical service (EMS) team leaders during the prehospital phase of a mass-casualty incident (MCI). Lessons learned from past MCIs indicate that poor information management can lead to increased mortality. Various instruments are used to evaluate information management during MCI training simulations, but the challenge of measuring and improving team leaders' abilities to manage information remains. Method The Dynamic Communication Quantification (DCQ) model was developed based on the knowledge representation typology. Using multi point-of-view synchronized video, the model quantifies and visualizes information management. It was applied to six MCI simulations between 2014 and 2019, to identify factors that led to information gaps, and compared with other evaluation methods. Results Out of the three methods applied, only the DCQ model revealed two factors that led to information gaps: first, consolidation of numerous casualties from different areas, and second, tracking of casualty arrivals to the medical treatment area and departures from the MCI site. Conclusion The DCQ model allows information management to be objectively quantified. Thus, it reveals a new layer of knowledge, presenting information gaps during an MCI. Because the model is applicable to all MCI team leaders, it can make MCI simulations more effective. Application This DCQ model quantifies information management dynamically during MCI training simulations.

Objective: To explore cognitive strategies clinicians apply while performing a medication reconciliation task, handling incomplete and conflicting information. Background: Medication reconciliation is a method clinicians apply to find and resolve inconsistencies in patients' medications and medical conditions lists. The cognitive strategies clinicians use during reconciliation are unclear. Controlled lab experiments can explore how clinicians make sense of uncertain, missing, or conflicting information and therefore support the development of a human performance model. We hypothesize that clinicians apply varied cognitive strategies to handle this task and that profession and experience affect these strategies. Method: 130 clinicians participated in a tablet-based experiment conducted in a large American teaching hospital. They were asked to simulate medication reconciliation using a card sorting task (CaST) to organize medication and medical condition lists of a specific clinical case. Later on, they were presented with new information and were asked to add it to their arrangements. We quantitatively and qualitatively analyzed the ways clinicians arranged patient information. Results: Four distinct cognitive strategies were identified ("Conditions first": n = 76 clinicians, "Medications first": n = 7, "Crossover": n = 17, and "Alternating": n = 10). The strategy clinicians applied was affected by their experience ( p = .02) but not by their profession. At the appearance of new information, clinicians moved medication cards more frequently (75.2 movements vs. 49.6 movements, p < .001), suggesting that they match medications to medical conditions. Conclusion: Clinicians apply various cognitive strategies while reconciling medications and medical conditions. Application: Clinical information systems should support multiple cognitive strategies, allowing flexibility in organizing information.