Content area
Full text
Abstract
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.
Keywords
Work measurement, data analytics, ambulance, fair day's work, cumulative work time.
1.Introduction
Emergency medical services (EMS) consist of processes intended to address emerging medical needs from the general population, often materialized as calls to 911. Such calls are random in nature and their response needs can range from well-defined transportation requests to unanticipated life-saving procedures that have been deemed "impossible to predict" [27]. Ambulance crew's job involves evaluating and stabilizing patients' condition prior to reaching a hospital to receive medical attention [1]. Given the flexibility required, EMS systems are human-centered and require a varied set of skills at different stages of the emergency response process. These characteristics make most EMS work systems complex systems that can only be understood in hindsight, and for which capacity planning and evaluation can represent a challenge.
Today, most EMS systems are equipped with networks of computing systems interacting directly with personnel as well as indirectly with other pieces of equipment and tools during the process. Such information...