BackgroundSeveral of the many emergency department (ED) interventions intended to address the complex problem of (over)crowding are based on the principle of streaming: directing different groups of patients to different processes of care. Although the theoretical basis of streaming is robust, evidence on the effectiveness of these interventions remains inconclusive.MethodsThis qualitative research, grounded in the population-capacity-process model, sought to determine how, why and under what conditions streaming interventions may be effective. Data came from a broader study exploring patient flow strategies across Western Canada through in-depth interviews with managers at all levels. We undertook realist analysis of interview data from the 98 participants who discussed relevant interventions (fast-track/minor treatment areas, rapid assessment zones, diverse short-stay units), focusing on their explanations of initiatives’ perceived outcomes.ResultsEssential features of streaming interventions included separation of designated populations (population), provision of dedicated space and resources (capacity) and rapid cycle time (process). These features supported key mechanisms of impact: patients wait only for services they need; patient variability is reduced; lag time between steps is eliminated; and provider attitude change promotes prompt discharge. Conversely, reported failures usually involved neglect of one of these dimensions during intervention design and/or implementation. Participants also identified important contextual barriers to success, notably lack of outflow sites and demand outstripping capacity. Nonetheless, failure was more commonly attributed to intervention flaws than to context factors.ConclusionsWhile streaming interventions have the potential to reduce crowding, a theory-based intervention relies on its implementers’ adherence to the theory. Streaming interventions cannot be expected to yield the desired results if operationalised in a manner incongruent with the theory on which they are supposedly based.