Setky App

Managing patient flow in a healthcare environment is a complex balancing act where arrival surges, staffing levels, and intake efficiency determine whether a clinic operates smoothly or faces overwhelming bottlenecks. Hospital administrators often struggle to predict how small changes in staffing can ripple through the entire patient journey. Hospital Patient Intake Flow Lab is designed to bring analytical clarity to these challenges by allowing users to model clinical operations and simulate how different configurations impact patient waiting times and station utilization. The experience provides a structured environment for modeling the patient intake process, from front-desk registration to triage and doctor consultations. Users can define simulation parameters such as peak arrival patterns, service time distributions, and staffing counts for each stage of care. Instead of relying on static spreadsheets, the app allows users to immediately visualize how different scenarios such as adding a triage nurse during a morning peak reduce congestion and improve overall flow round by round. The focus of the app is operational analysis and educational modeling. Through discrete-event simulations and visual data summaries, users can explore the dynamics of queue growth and station utilization. By adjusting arrival variability or modifying staffing thresholds, it becomes easier to identify bottlenecks, understand the impact of service-time fluctuations, and find the optimal balance between resource allocation and patient throughput. All analysis is based entirely on the mathematical parameters and operational variables entered by the user. The app does not utilise live hospital data, integrate with medical records, or provide clinical guidance. Instead, it functions as a dedicated sandbox for exploring the foundational concepts of queuing theory and healthcare operations management. Core Features: Flow Simulation Engine - Create structured, round-based simulations using Poisson arrival processes to model realistic clinical demand cycles. Intake Stage Configuration - Define and adjust station types from registration to consultation to test the impact of staffing changes on patient journey times. Arrival Pattern Modelling - Configure peak and off-peak rates to explore how varying patient volumes throughout a 24-hour cycle stress-test clinical capacity. Wait Time Analytics - View in-depth metric summaries, including average wait times and 95th-percentile benchmarks, to evaluate service-level performance. Queue Visualisation - Inspect interactive charts that track queue growth in real-time, helping to pinpoint exactly when and where congestion occurs. Staffing Optimisation - Access automated recommendations to reach target wait times with the most efficient resource allocation based on your unique parameters. Educational Purpose - The app is designed to support learning and experimentation around healthcare operations research and industrial engineering. By allowing users to build and analyse their own flow models, it helps illustrate how arrival rates, service times, and staffing levels relate to one another within a controlled analytical environment. All insights generated by the app are based solely on user-entered data and simplified operational calculations. They are intended for educational and informational exploration and should not be interpreted as medical, clinical, staffing, or professional healthcare advice.