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The business of medicine once again finds itself in the throes of rapidly escalating costs, concerns about quality of care, and demands for efficiency while simultaneously enhancing quality. Considerable effort has already been spent in trying to improve costs, quality, and patient satisfaction. The apparent failure to do so may be the result of a fundamental misunderstanding of the salient features of clinical practice and the misapplication of quality improvement techniques. This article explores some of the significant issues and offers potential new directions.

Demands to optimize productivity and quality require a patient scheduling system that can balance patient demand and clinic resources. The consequences of unscheduled and late patient arrivals on operational efficiencies have been documented. Less understood is the impact of unscheduled and late arrivals on the quality of service each receives. This article examines the impact of unscheduled and late patient arrivals on operational, clinical and administrative outcomes that affect quality of care of children potentially eligible for immunizations. An unexpected finding was the generally better and faster levels of service for late arrivals.

Tighter competition and rationed resources place a premium on health clinic management of patient arrival times to maximize smooth workflow dynamics and consistency in patient processes. Early efforts to analyze patient arrival characteristics relied on assumptions that may have been too simplistic. For instance, it was assumed that a scheduled patient's arrival was likely to fit a bell-shaped curve in terms of being early, late, or on time and that any one patient's likelihood of being "on time" was purely a random event. However, our analysis of patient arrival times, obtained from detailed workflow observations in nine community clinics, indicates that the likelihood of a patient arriving early, late, or on time is neither entirely random nor does the pattern of arrivals fit a bell-shaped curve. Rather, patients tend to arrive in "clumps," possibly due to factors such as traffic patterns and parking availability. These findings are important with respect to 1) clinic practice management, 2) scheduling optimization strategies, and 3) computer simulation and analysis of clinic processes.