An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times.

ABSTRACT: Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rathe

SUBMITTER: Feng H 

PROVIDER: S-EPMC11557935 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature