US 20260237496 A1Patent application publicationUnited States

AI-Driven Hospital Resource Planning

Official patent title

Method and system for intelligent patient planning and scheduling in dynamic healthcare environment

Arabic title: طريقة ونظام للتخطيط والجدولة الذكية للمرضى في بيئة رعاية صحية ديناميكية

Invention

Problem

Hospitals often plan patient flow, clinicians, operating rooms, materials and equipment maintenance in separate schedules. Changing demand and resource availability can make those plans inefficient, increasing the risk of delays, idle capacity, bottlenecks and overtime.

Why it matters

Coordinating patient demand with clinicians, beds, operating rooms, supplies, and equipment is central to timely care and efficient use of hospital capacity.

Approach

The disclosed system combines historical and real-time healthcare data with machine-learning forecasts and constrained optimization. It identifies critical resources and creates synchronized patient, workforce, material-release and equipment-maintenance plans across multiple planning levels, with feedback that can revise patient placement between periods.

Who may benefit

Potential beneficiaries include hospitals, health systems, clinical operations teams, healthcare software providers, and organizations managing complex patient and resource schedules.

Potential value

The approach may help healthcare operators coordinate dependent resources, improve utilization, anticipate bottlenecks and align patient schedules with staffing, supplies and equipment availability.

Background

Healthcare facilities must align patient flow with multiple dependent resources whose availability changes over time. Separate planning processes can create bottlenecks, idle capacity, overtime, and schedule disruption. The disclosed approach addresses this coordination problem through predictive models, simulation, and constrained optimization.

Technology overview

Independent claims 1 and 11 cover system and method forms of an integrated healthcare planning platform. Multiple processors train and update machine-learning models for provider availability, material demand, machinery availability or failure, and patient-health variables using historical and real-time data. An advanced planning and scheduling engine evaluates constrained simulation models at facility, resource and within-resource planning levels, identifies critical resources, and searches for feasible and optimal plans. The output synchronizes patient flow with workforce, material requirement and release, and equipment-maintenance schedules; push-pull rules and feedback move patients between periods when capacity changes. Dependent claims add cloud storage, multi-device outputs, real-time execution, operating-room optimization, human-resource availability forecasts and predictive machinery maintenance.

Potential applications

Hospital operating-room and surgery scheduling
Patient-flow, bed and ward capacity planning
Healthcare workforce planning
Medical equipment availability and maintenance planning
Clinical materials and supply-release planning

Evidence-supported advantages

Coordinates patient, workforce, material and equipment plans in one scheduling framework
Uses historical and real-time data to anticipate changing resource constraints
Supports multi-level planning and feedback-based schedule adjustment
Integrates equipment maintenance with patient and resource schedules

Development stage

U.S. patent application publication; implementation maturity and operational validation were not independently verified.

Commercial opportunity

The platform may support licensing or co-development with hospital information-system providers, scheduling-software vendors, health systems, and digital-health operators. Deployment would require integration, cybersecurity, workflow validation, and performance testing using representative operational data.

Patent classifications

WIPO IPC

G16H 40/20Healthcare ICT for scheduling or resource allocation

CPC

G16H 40/20Healthcare ICT for scheduling or resource allocation

Inventors

First inventorAïssa Rezzoug
InventorSaif Ullah
InventorAisha Tayyab
InventorNashmi H. Alrasheedi
InventorMirza Jahanzaib
InventorWasim Ahmad
InventorSalman Hussain

Keywords

patient scheduling
healthcare resource optimization
machine learning
hospital operations
predictive maintenance
resource allocation
operating-room planning
cloud computing

Patent document and drawings

The patent publication is mapped to this record. Patent drawings remain within that publication; no separately cleared public media package has been supplied.