The work of ambulance crew members is highly variable and unpredictable. Decisions made based on an oversimplified understanding of the workday may result in crewmembers struggling to meet unrealistic demands and subsequently lead to fatigue and burnout. Manually collecting work measurement data using traditional techniques is costly and time-consuming, while the resulting estimates may quickly become obsolete. Fortunately, most of today's ambulance service systems involve technologies that capture data throughout operations. This research capitalizes on such data to support the quantification and visualization of the workday of ambulance crews in real time. Physical data collection efforts focus on filling the gaps between available process data and work measurement, to the extent allowed by cost constraints. Using such data, we characterized the workday of a crew as the amount of work time incurred up to a specific point in time, given observed process data as well as information on work dynamics and process variability. Cumulative work time curves with uncertainty bands are proposed to visualize and analyze the workday in terms of expectations as well as human capabilities and limitations. Such information will be useful for the support of real-time operational decisions, retrospective analysis of workload, and prospective analysis of decisions in terms of their impact on the workers' wellbeing.