Hospital Bed Management

Part of the worlds-biggest-software-project initiative.

An AI-native, open platform for real-time hospital bed tracking, capacity planning, and patient flow optimisation.

Hospital Bed Management is a candidate project to build a vendor-neutral, standards-based bed management system for hospitals and health systems. It targets bed control teams, charge nurses, and capacity planners who today rely on proprietary EHR modules or expensive standalone platforms with descriptive analytics rather than predictive AI. The goal is to combine real-time ADT/FHIR-driven bed status with machine-learning discharge prediction and demand forecasting in a deployable, interoperable package.

Why Hospital Bed Management?

Incumbents like TeleTracking, Epic BedTracker, and Oracle Health (Cerner) are fully proprietary and expensive — large hospital systems pay $500K to $3M+ in total cost of ownership, with implementations often taking 6–18 months.
Epic BedTracker is locked to Epic-only sites and offers functional rather than specialised bed operations; non-Epic hospitals have no comparable embedded option.
Most established platforms provide primarily descriptive analytics; predictive AI is concentrated in newer commercial entrants such as LeanTaaS and BigBear.ai with their own custom pricing.
There are no production-grade open-source bed management platforms today, despite open standards (HL7 FHIR R4/R5, SANER IG, HAPI FHIR) being mature enough to support one.
Public-health bed capacity reporting (CDC/NHSN, FHIR SANER IG) is mandatory in surge events but poorly served outside enterprise EHR contracts.

Key Features

Real-Time Bed Status & Workflow

Real-time bed status board covering occupied, dirty, clean, reserved, out-of-service, and isolation states
Bed request, assignment, and confirmation workflow for admitting staff and bed coordinators
Housekeeping task dispatch with dirty-to-clean turnaround time tracking and automated alerts
Multi-unit census view with isolation and acuity flags
Mobile-accessible charge nurse and house supervisor views

Predictive Patient Flow

AI-powered discharge time prediction at patient level with 4-hour precision, trained on diagnosis, orders, and care plan data
Proactive bed reservation based on discharge predictions, enabling parallel housekeeping preparation
Bed demand forecasting 24–72 hours ahead by unit and specialty
Discharge barrier checklist with structured escalation and timestamps for delayed discharge management

Capacity Command & Reporting

Multi-facility systemwide capacity command centre view
Throughput dashboard: occupancy rate, bed turnaround time, LOS by unit
HL7 FHIR SANER-compliant capacity reporting for public health and regulatory requirements
Surge-protocol alerts with configurable escalation thresholds

Interoperability & Sensors

HL7 v2 ADT ingestion (A01/A02/A03/A11/A12) for automatic status updates
HL7 FHIR R4 Location, Group, and MeasureReport resources for bed data exchange
RTLS integration layer for hardware-agnostic automatic bed occupancy detection
Connectors for major EHRs (Epic, Oracle Health, Meditech) and EVS dispatch systems

Advanced AI Capabilities (Backlog)

Autonomous bed assignment as a constraint-satisfaction engine considering acuity, isolation, gender, nurse ratio, and proximity
External signal integration for surge forecasting (flu surveillance, ED arrival trends, community data)
Transport orchestration coordinating patient movement alongside bed assignment
Natural language query interface for house supervisors and administrators
Patient and family estimated room readiness notifications via SMS/app

AI-Native Advantage

AI is not bolted on — it drives the core flow. Discharge prediction at admission enables proactive bed reservation and housekeeping scheduling with demonstrated 87%+ accuracy in industry benchmarks. Machine learning trained on historical ADT patterns, seasonal variation, and external surveillance signals can forecast bed demand 24–72 hours ahead, reducing diversion events and ED boarding. Real-time autonomous bed assignment frames placement as a constraint satisfaction problem, simultaneously matching acuity, isolation, proximity, and staffing constraints in ways static rules cannot.

Tech Stack & Deployment

The platform is designed around open standards: HL7 v2 ADT for legacy EHR integration, HL7 FHIR R4/R5 for modern interoperability, the FHIR SANER Implementation Guide for public-health capacity reporting, and IHE PCD profiles for bed sensor and IoT integration. HAPI FHIR (Apache 2.0) and OpenHIE components can underpin the data layer without licence concerns. Expected deployment modes include self-hosted for hospitals with strict data-residency requirements and a managed cloud option for smaller facilities. Mobile-first interfaces target charge nurses and house supervisors, with command-centre views for multi-facility systems.

Market Context

The global hospital bed management systems market was valued at approximately $2.15–2.5B in 2025 and is projected to reach $3.8–5.9B by 2030–2035 at a CAGR of 6.6–10.3% (Grand View Research; Market.us; DataBridge Market Research). Incumbent pricing ranges from $500K–$3M+ in TCO for large enterprise deployments, $100K–$500K/year for mid-market platforms, and $50K–$200K/year per facility for AI-driven SaaS. Primary buyers are hospital COOs, patient flow coordinators, chief nursing officers, ED directors, and multi-facility health system executives.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.