We examine the use of personal protective equipment (PPE) in two interdisciplinary medical settings to inform the design of just-in-time alerts and reminders for correcting PPE noncompliance. We reviewed videos of 26 pediatric resuscitations occurring over the course of the COVID-19 pandemic at an urban pediatric teaching hospital. Through video review, we identified causes for PPE noncompliance, activities that were frequently performed without PPE, instances in which PPE was intentionally removed, and mechanisms by which healthcare providers corrected PPE noncompliance. We also interviewed 18 registered nurses working in the hospital's emergency department (ED) and intensive care unit (ICU) to better understand observed PPE behaviors and practices. Our results suggest that alert design will require considering the urgency of correcting PPE noncompliance against the urgency of tasks being performed. We discuss our findings through the lens of the COM-B framework and conclude by exploring design opportunities for just-in-time alerts and reminders for prompting PPE noncompliance corrections in dynamic medical work.

Hemorrhage, or severe blood loss due to injury, is a leading cause of preventable deaths after injury. This study uses and extends activity theory to understand the dynamics of team-based hemorrhage control during trauma resuscitation and to explore potential computerized mechanisms to support this time- and safety-critical process. We reviewed videos of 25 resuscitation cases and analyzed hemorrhage control activities using nine activity theory prompts, including a new prompt-speech intention-a critical but underexplored dimension of teamwork in prior activity theory analyses. Through this process, we identified the most common delay-causing activities and developed routine and non-routine activity models for each. A comparison of these models showed that variations from the routine models emerged due to changes in the division of labor, instruments, community, and speech intentions. We contribute to research on designing socio-technical systems by (1) identifying needs and opportunities for computerized support that address delays in complex medical teamwork and (2) examining how an intervention changes an activity model. We also show how adding detailed speech data aids in identifying contradictions between elements in an activity model.

Almost half of the preventable deaths in emergency care can be associated with a medical delay. Understanding how clinicians experience delays can lead to improved alert designs to increase delay awareness and mitigation. In this paper, we present the findings from an iterative user-centered design process involving 48 clinicians to develop a prototype alert system for supporting delay awareness in complex medical teamwork such as trauma resuscitation. We used semi-structured interviews and card-sorting workshops to identify the most common delays and elicit design requirements for the prototype alert system. We then conducted a survey to refine the alert designs, followed by near-live, video-guided simulations to investigate clinicians' reactions to the alerts. We contribute to CSCW by designing a prototype alert system to support delay awareness in time-critical, complex teamwork and identifying four mechanisms through which teams mitigate delays.

Designing computerized approaches to support complex teamwork requires an understanding of how activity-related information is relayed among team members. In this paper, we focus on verbal communication and describe a speech-based model that we developed for tracking activity progression during time-critical teamwork. We situated our study in the emergency medical domain of trauma resuscitation and transcribed speech from 104 audio recordings of actual resuscitations. Using the transcripts, we first studied the nature of speech during 34 clinically relevant activities. From this analysis, we identified 11 communicative events across three different stages of activity performance-before, during, and after. For each activity, we created sequential ordering of the communicative events using the concept of narrative schemas. The final speech-based model emerged by extracting and aggregating generalized aspects of the 34 schemas. We evaluated the model performance by using 17 new transcripts and found that the model reliably recognized an activity stage in 98% of activity-related conversation instances. We conclude by discussing these results, their implications for designing computerized approaches that support complex teamwork, and their generalizability to other safety-critical domains.