Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach

• URL: http://arxiv.org/abs/2602.21995v1
• Date: Wed, 25 Feb 2026 15:15:57 GMT
• Title: Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach
• Abstract summary: This study proposes and evaluates a Genetic Algorithm framework designed to automate the scheduling of medical acts.<n>Using a synthetic dataset encompassing 50 medical acts, we compared two GA variants, Pre-Ordered and Unordered, against deterministic First-Come, First-Served (F) and Random Choice baselines.
• Score: 0.19575541492160772
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Predicting Future Organ Dysfunction in ICU Patients Using Temporal Convolutional Networks on MIMIC-IV Data [3.007949058551534]
  We investigate the extent to which a Temporal Convolutional Net work (TCN) can predict next-day SOFA scores from ICU time-series data.<n>A residual TCN trained on three-day sliding windows achieved a five-fold cross-validation R2 of 0.740 +- 0.013 and MAE of 1.431 +- 0.022.<n>We conclude that TCNs can extract meaningful predictive signal from ICU physiological data, but that short input windows and complete-case selection bias currently limit their clinical utility.
  arXiv  Detail & Related papers  (2026-08-29T14:40:13Z)
• Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data [0.9786690381850356]
  Alzheimer's disease (AD) requires accurate and scalable tools for assessing disease severity.<n>We propose an attention-enhanced machine learning framework with ordinal regression for automated and interpretable AD severity staging.
  arXiv  Detail & Related papers  (2026-06-10T08:26:11Z)
• How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark [44.60966248233107]
  We introduce LiFS, a large-scale dataset and benchmark derived from the MICCAI 2025 CARE-Liver challenge.<n>LiFS is the first benchmark providing complete gadoxetic acid-enhanced sequences with histopathology-confirmed annotations from diverse real-world scanners.
  arXiv  Detail & Related papers  (2026-05-25T08:47:16Z)
• Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment [56.62016795093786]
  Cerebra is an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.<n>It supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete.<n>Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation.
  arXiv  Detail & Related papers  (2026-03-23T05:46:45Z)
• MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization [46.65200216642429]
  We introduce MedAD-38K, the first large-scale, multi-modal, and multi-center benchmark for MedAD featuring diagnostic Chain-of-Thought (CoT) annotations alongside structured Visual Question-Answering (VQA) pairs.<n>Our proposed model, MedAD-R1, achieves state-of-the-art (SOTA) performance on the MedAD-38K benchmark, outperforming strong baselines by more than 10%.
  arXiv  Detail & Related papers  (2026-02-01T07:56:10Z)
• Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction [45.67071315035565]
  Intraoperative hypotension poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability.<n>We propose CSA-TTA, a novel Cross-Sample Augmented Test-Time Adaptation framework that enhances training by incorporating hypotension events from other individuals.<n>We evaluate the proposed CSA-TTA on both the VitalDB dataset and a real-world in-hospital dataset by integrating it with state-of-the-art time series forecasting models.
  arXiv  Detail & Related papers  (2025-12-12T08:02:37Z)
• The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality [12.724499570706051]
  Using more than 8,000 infertility treatment records, we evaluate four alignment strategies.<n> GRPO achieves the highest algorithmic accuracy across multiple decision layers.<n>Clinicians consistently prefer the SFT model, citing clearer reasoning processes.
  arXiv  Detail & Related papers  (2025-11-22T14:48:54Z)
• Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution [42.85462513661566]
  We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay.<n>A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes.<n>On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model while maintaining well-calibrated predictions.
  arXiv  Detail & Related papers  (2025-11-19T20:11:49Z)
• A Hybrid Enumeration Framework for Optimal Counterfactual Generation in Post-Acute COVID-19 Heart Failure [1.9794615806637272]
  We present a counterfactual inference framework for individualized risk estimation and intervention analysis.<n>The framework combines exact enumeration with optimization-based methods, including the Nearest Instance Counterfactual Explanations (NICE) and Multi-Objective Counterfactuals (MOC) algorithms.
  arXiv  Detail & Related papers  (2025-10-21T17:35:12Z)
• Medical priority fusion: achieving dual optimization of sensitivity and interpretability in nipt anomaly detection [0.0]
  Clinical machine learning faces a critical dilemma in high-stakes medical applications.<n>A paradox becomes particularly acute in non-invasive prenatal testing (NIPT), where missed chromosomal abnormalities carry profound clinical consequences.<n>We introduce Medical Priority Fusion (MPF), a constrained multi-objective optimization framework that resolves this fundamental trade-off.
  arXiv  Detail & Related papers  (2025-09-22T15:49:20Z)
• Primary Care Diagnoses as a Reliable Predictor for Orthopedic Surgical Interventions [0.10624941710159722]
  Referral workflow inefficiencies contribute to suboptimal patient outcomes and higher healthcare costs.<n>In this study, we investigated the possibility of predicting procedural needs based on primary care diagnostic entries.
  arXiv  Detail & Related papers  (2025-02-06T17:15:12Z)
• Biomimicry in Radiation Therapy: Optimizing Patient Scheduling for Improved Treatment Outcomes [0.0]
  This study delves into the integration of biomimicry principles within the domain of Radiation Therapy (RT) to optimize patient scheduling. Three bio-inspired algorithms are employed for optimization to tackle the complex online scheduling problem. The results of this study unveil the effectiveness of applied bio-inspired algorithms in optimizing patient scheduling for RT.
  arXiv  Detail & Related papers  (2024-01-16T15:37:23Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
  Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. BaBSim.Hospital is a tool for capacity planning based on discrete event simulation. We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
  arXiv  Detail & Related papers  (2021-05-16T12:38:35Z)
• Collaborative residual learners for automatic icd10 prediction using prescribed medications [45.82374977939355]
  We propose a novel collaborative residual learning based model to automatically predict ICD10 codes employing only prescriptions data. We obtain multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57 and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:07:27Z)
• Ensemble model for pre-discharge icd10 coding prediction [45.82374977939355]
  We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We obtain multi-label classification accuracies of 0.73 and 0.58 for average precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:02:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.