Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach

2026-02-26 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Healthcare Optimization · Depth: Advanced, long

Summary

A study proposes and evaluates a Genetic Algorithm (GA) framework for optimizing complex medical appointment scheduling across multiple healthcare facilities, addressing clinical safety protocols and patient logistics. Using a synthetic dataset of 50 medical acts across four facilities, two GA variants (Pre-Ordered and Unordered) were compared against First-Come, First-Served (FCFS) and Random Choice baselines. The GA framework achieved a 100% constraint fulfillment rate, resolving temporal overlaps and clinical incompatibilities that FCFS failed to address in 60% and 40% of cases, respectively. GA variants also showed statistically significant improvements ($p<0.001$) in patient-centric metrics, achieving an Idle Time Ratio (ITR) frequently below 0.4 and reducing inter-healthcenter trips. Both GA variants converged to comparable global optima by the 100th generation, suggesting superior efficiency over manual scheduling.

Key takeaway

For healthcare administrators and IT teams managing multi-center scheduling, adopting a Genetic Algorithm approach can significantly enhance operational efficiency and patient satisfaction. This automated framework ensures 100% adherence to complex clinical constraints and drastically reduces patient wait times and logistical burdens, moving beyond the limitations of manual or simple deterministic methods. Consider piloting a GA-based system to streamline complex patient journeys and reduce administrative overhead.

Key insights

Genetic Algorithms effectively optimize complex medical appointment scheduling, ensuring clinical safety and improving patient experience.

Principles

• Stochastic global search heuristics excel in NP-hard optimization.
• Constraint-aware initialization can improve initial search locus.
• High penalties effectively prune non-viable solutions.

Method

The GA framework defines individuals as binary arrays of selected slots, uses a fitness function penalizing overlaps and trips, applies tournament selection, single-point crossover, and a 10% mutation rate over 200 generations.

In practice

• Implement GAs for multi-center healthcare scheduling.
• Prioritize patient-centric metrics like ITR and facility changes.
• Incorporate clinical incompatibility rules into scheduling algorithms.

Topics

• Genetic Algorithms
• Healthcare Scheduling
• Appointment Optimization
• Constraint Satisfaction
• Patient Logistics

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.