JMIR Publications

ABSTRACT

Background:

Diagnostic exam scheduling is a recurrent operational challenge in healthcare because limited daily capacity must be allocated across heterogeneous requests with different urgency and follow-up timing requirements. In this context, optimization methods can support more timely and efficient scheduling decisions.

Objective:

In collaboration with Unidade Local de Saúde de Santo António (ULSSA), an optimization-based approach was proposed to improve the management of outpatient exam scheduling.

Methods:

Anonymous operational data from ULSSA, corresponding to 2025, were analysed to model the scheduling process over a 4-week (28-day) horizon comprising 2460 exam requests, including 156 exams with follow-up requirements. Two optimization approaches, CP-SAT and an NCO-inspired constructive approach, were evaluated under the same daily capacity structure, prioritization logic, and penalty-based framework, confirmed across 10 independent runs under four solver configurations, with operational outcomes remaining unchanged. The best-performing approach was then compared with the hospital scheduling solution for the same period.

Results:

CP-SAT achieved the best overall performance among the 2 optimization approaches. It scheduled 1400 exams versus 1391 for the NCO-inspired approach, reduced free time from 42 to 3 minutes, and achieved a substantially lower total penalty (49,790.78 vs 83,652.10). Both methods achieved 0 unscheduled due exams, 0 delayed scheduled exams, and 0 delayed unscheduled exams. The NCO-inspired approach required less runtime than CP-SAT (17.2 vs 214.6 seconds). When compared with the hospital scheduling solution, CP-SAT scheduled more exams (1400 vs 1147) and eliminated all unscheduled due exams (0 vs 80), delayed scheduled exams (0 vs 23), and delayed unscheduled exams (0 vs 40).

Conclusions:

Both optimization approaches generated feasible schedules under the same evaluation framework, but CP-SAT produced the best overall results in the tested setting. The findings suggest that optimization-based scheduling can improve the timely allocation of diagnostic exams and reduce follow-up-related delays when compared with routine hospital scheduling practice.