HospitalOPD Bottleneck Labelled dataset

Description

The dataset contains 10,000 event records representing patient journeys through a hospital Outpatient Department (OPD). The dataset was developed to support research in healthcare process mining, workflow analytics, bottleneck detection, and predictive process monitoring. It captures the sequence of activities performed during patient visits, including consultations, laboratory investigations, medical imaging services, and pharmacy interactions. The dataset is structured as an event log where each patient visit is represented as a process instance and each healthcare activity is recorded as an event with associated timestamps. These timestamps enable the reconstruction of complete patient pathways and the calculation of workflow performance indicators such as waiting times, processing durations, throughput times, and delay propagation. In addition to traditional event log attributes, the dataset includes process mining features describing workflow behavior, resource utilization indicators reflecting workload conditions, and graph-based structural features derived from workflow graph analysis. Centrality measures such as degree, betweenness, closeness, eigenvector, and eccentricity were computed to quantify the structural importance of activities within the healthcare workflow network. A binary bottleneck label is also included, identifying events associated with workflow congestion and excessive delays. This allows the dataset to be used for supervised machine learning tasks related to predictive bottleneck identification. The dataset provides a comprehensive benchmark for studying patient flow dynamics, healthcare workflow optimization, process-aware machine learning, workflow graph analytics, and predictive process monitoring. By integrating temporal, behavioral, resource-related, and structural workflow characteristics, it enables researchers to investigate how workflow structure and operational performance jointly influence bottleneck formation in healthcare systems.

Institutions

• Telekom Malaysia Berhad (Malaysia)

  Kuala Lumpur, Kuala Lumpur
• Riphah International University

  Punjab, Rawalpindi
• Multimedia University

  Melaka, Malacca

Categories

Licence