patient-flow

Final Capstone Project for General Assembly Data Science Immersive 👩🏻‍⚕️

Emergency Department (ED) Simulation is a Java-based project designed to model and optimize patient flow, resource management, and triage prioritization in hospital emergency departments. Using OOP principles and probabilistic models, this simulation aims to improve efficiency and decision-making in ED operations.

Healthcare operations intelligence case study focused on emergency department optimization, patient flow redesign, and avoidable ED utilization using large-scale encounter, provider, and SDOH analytics.

Individual final-year dissertation project (80%) at King’s College London: a Java agent-based model of Emergency Department patient flow and operations.

Predictive healthcare analytics project focused on patient flow optimization, operational KPIs, and hospital resource utilization.

End-to-end Emergency Department demand and patient flow analysis to identify peak pressure periods and highlight predictable demand patterns, waiting-time risks, and staffing pressure points, enabling evidence-based resource planning and operational decision-making.

BedBoard helps emergency and ward teams answer one operational question in seconds: Which beds are available now, who is assigned, and what is the next patient action?

Hospital operations analysis of care-unit duration, long-stay capacity burden, and discharge destination using MIMIC-IV v3.1 and BigQuery.

Sistema web para gestão de atendimento em saúde, com recepção, triagem, filas, chamadas e relatórios em tempo real.

AI-driven hospital bed management and patient flow optimization system. Predicts bed availability and recommends optimal placement to reduce bottlenecks and wait times.

Enterprise Power BI dashboard for Emergency Department (ED) analytics featuring patient flow, throughput, wait times, clinical quality, resource utilization, operational KPIs, and executive healthcare reporting.

Operational command center for care-pathway coordination, handoff risk, and follow-up escalation across patient journeys.

Research-grade Python healthcare digital twin for MRI demand forecasting, discrete-event simulation, patient-flow modelling, capacity planning, queue analysis, healthcare operations research, and transparent staffing optimisation.