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Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach

Ana Rodrigues, Rui Rego

TL;DR

It is suggested that transitioning from manual, human-mediated scheduling to an automated metaheuristic approach enhances clinical integrity, reduces administrative overhead, and significantly improves the patient experience by minimizing wait times and logistical burdens.

Abstract

The optimization of complex medical appointment scheduling remains a significant operational challenge in multi-center healthcare environments, where clinical safety protocols and patient logistics must be reconciled. This study proposes and evaluates a Genetic Algorithm (GA) framework designed to automate the scheduling of multiple medical acts while adhering to rigorous inter-procedural incompatibility rules. Using a synthetic dataset encompassing 50 medical acts across four healthcare facilities, we compared two GA variants, Pre-Ordered and Unordered, against deterministic First-Come, First-Served (FCFS) and Random Choice baselines. Our results demonstrate that the GA framework achieved a 100% constraint fulfillment rate, effectively resolving temporal overlaps and clinical incompatibilities that the FCFS baseline failed to address in 60% and 40% of cases, respectively. Furthermore, the GA variants demonstrated 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. While the GA (Ordered) variant provided a superior initial search locus, both evolutionary models converged to comparable global optima by the 100th generation. These findings suggest that transitioning from manual, human-mediated scheduling to an automated metaheuristic approach enhances clinical integrity, reduces administrative overhead, and significantly improves the patient experience by minimizing wait times and logistical burdens.

Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach

TL;DR

It is suggested that transitioning from manual, human-mediated scheduling to an automated metaheuristic approach enhances clinical integrity, reduces administrative overhead, and significantly improves the patient experience by minimizing wait times and logistical burdens.

Abstract

The optimization of complex medical appointment scheduling remains a significant operational challenge in multi-center healthcare environments, where clinical safety protocols and patient logistics must be reconciled. This study proposes and evaluates a Genetic Algorithm (GA) framework designed to automate the scheduling of multiple medical acts while adhering to rigorous inter-procedural incompatibility rules. Using a synthetic dataset encompassing 50 medical acts across four healthcare facilities, we compared two GA variants, Pre-Ordered and Unordered, against deterministic First-Come, First-Served (FCFS) and Random Choice baselines. Our results demonstrate that the GA framework achieved a 100% constraint fulfillment rate, effectively resolving temporal overlaps and clinical incompatibilities that the FCFS baseline failed to address in 60% and 40% of cases, respectively. Furthermore, the GA variants demonstrated 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. While the GA (Ordered) variant provided a superior initial search locus, both evolutionary models converged to comparable global optima by the 100th generation. These findings suggest that transitioning from manual, human-mediated scheduling to an automated metaheuristic approach enhances clinical integrity, reduces administrative overhead, and significantly improves the patient experience by minimizing wait times and logistical burdens.
Paper Structure (16 sections, 1 equation, 4 figures)

This paper contains 16 sections, 1 equation, 4 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Synthetic Data Generation
  4. The Genetic Algorithm
  5. Defining the search space
  6. Creating the initial population
  7. Fitness evaluation
  8. Selection
  9. Crossover
  10. Mutation
  11. Baseline algorithms
  12. Performance Metrics
  13. Results
  14. Discussion
  15. Conclusion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Mean Fitness Convergence Profiles across 200 Generations. This plot illustrates the evolutionary progression of the GA (Ordered) and GA (Unordered) variants compared to the FCFS and Random Choice baselines.
  • Figure 2: Constraint Fulfillment Rates. A categorical breakdown of the percentage of solutions satisfying the three primary scheduling constraints: 1) temporal non-overlap, 2) clinical compatibility (exam gaps), and 3) transit feasibility ($\ge 3$h).
  • Figure 3: Statistical Distribution of the Idle Time Ratio (ITR). Violin plots representing the density and distribution of the ITR for each tested algorithm. Statistical comparison performed with a Mann-Whitney U test. ns: $p > 0.05$ (not significant); *: $p < 0.05$; **: $p < 0.01$; ***: $p < 0.001$
  • Figure 4: Frequency of Inter-Health Center Transitions. Distribution of the total number of required facility changes per patient journey. Statistical comparison performed with a Mann-Whitney U test. ns: $p > 0.05$ (not significant); *: $p < 0.05$; **: $p < 0.01$; ***: $p < 0.001$