This study aims to develop a heuristic for an outpatient appointment system considering patient classification. The proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied. Having Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46% and 5.94% among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session. The single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts The proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

Marcelo Oleskovicz

OUTPATIENT APPOINTMENT SYSTEMS: A NEW HEURISTIC WITH PATIENT CLASSIFICATION

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Journal Info

Category	Quartile
HEALTH CARE SCIENCES & SERVICES	3