Bed management
Recommend placements that match acuity and isolation while relieving ED boarding.
Operations · Capacity
Hospital Resource Management AI
An agent that turns live capacity signals into concrete recommendations for beds, staffing, and OR scheduling, surfacing the next best move to bed managers, nurse leaders, and OR coordinators for approval instead of running blind.
Recommend, don't auto-actHIPAA-ReadyAudit LoggedHL7 v2
Trusted by global innovators
Capacity decisions made on stale data
Bed managers, charge nurses, and OR coordinators make high-stakes calls all day (which patient goes where, whether staffing matches acuity, how to sequence the OR) often from whiteboards, phone calls, and a census that's already out of date. Bottlenecks in the ED, mismatched staffing, and idle OR blocks are the visible cost of decisions made without a live, connected picture.
Architecture
Reference architecture for how this agent connects to your EHR and data systems.
Key Capabilities
Concrete capacity recommendations (beds, staffing, and OR) grounded in live operational data.
Live capacity picture
Ingests HL7 ADT feeds, bed-management status, staffing rosters, and the OR board into one continuously updated operational view.
Demand forecasting
Projects near-term admissions, discharges, and unit-level census so managers can see building pressure before it becomes a bottleneck.
Bed-assignment recommendations
Suggests placements that match acuity, isolation, and unit constraints, ranking options and flagging conflicts for the bed manager to approve.
Staffing optimization
Compares forecast demand and acuity against the roster and recommends shifts, float moves, or flex staff to close gaps.
Who runs it
Where the resource agent runs
Capacity command centers
Feed forecasts and next-best-move suggestions into the command-center view.
Nurse staffing
Align shift staffing to forecast demand and acuity across units.
OR scheduling
Surface block-utilization gaps and sequencing improvements for coordinators.
Command center
What does a hospital command center need from software?
A hospital command center needs one live view built from feeds each team already produces: ADT events, transfer requests, cleaning jobs, discharge orders and the OR schedule. Agnotic Technologies builds the integrations and screens, and every suggested move goes to the person who owns it.
| Function | Feed it needs | Who acts on it |
|---|---|---|
| Bed board | HL7 v2 ADT admit, transfer and discharge events (A01, A02, A03) and patient updates (A08), plus bed status from the bed management system | Bed managers and house supervisors |
| Transfer center intake | Transfer requests from referring hospitals, taken by phone or web form, with diagnosis, level of care and the accepting physician | Transfer center nurses and the accepting physician |
| ED boarding view | Bed requests for ED patients with an admit decision, matched against open beds by unit, level of care and isolation needs | ED charge nurse and bed managers |
| EVS bed turnaround | Cleaning job events from the environmental services system (requested, started, completed), linked to each bed | EVS supervisors, with bed status updated for bed managers |
| Discharge pipeline | Expected discharge dates, discharge orders and open tasks such as transport, medications and follow-up appointments | Case managers and unit charge nurses |
| OR schedule status | Case schedule and status updates from the surgical scheduling system, often as HL7 v2 SIU messages | OR coordinators, with post-surgery bed needs passed to bed managers |
Feeds and owners differ between hospitals, so we map yours before any screen is designed.
What resource optimization changes
Directional impact, validated per deployment rather than guaranteed.
Real-time
Beds, staffing, and OR in one live view
Forecast
Pressure seen before it becomes a bottleneck
Advisory
Every move approved by a manager
Compliance-First Healthcare App Development Services Backed by Global Standards
15-Minute Scoping Call01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Standards
What it is built against
HIPAASOC 2HL7FHIRHITRUST
How It Works
From live signals to approved moves
Four stages: capture operational feeds, forecast and optimize under an advisory guardrail, present ranked recommendations, and write approved decisions back.
1.
Capture operational feeds
HL7 ADT events, bed status, staffing rosters, and the OR schedule stream in to form a real-time picture of capacity and demand.
ADT + roster capture
2.
Forecast + optimize
Demand forecasting and optimization models weigh constraints (acuity, isolation, block time) to generate feasible options, never a single opaque answer.
Model + guardrails
3.
Recommend to managers
Ranked recommendations with rationale go to the bed manager, nurse leader, or OR coordinator, who approves, adjusts, or overrides.
Human decision
4.
Write back the decision
Approved moves post to the bed-management, staffing, and OR systems over HL7 v2 and APIs, and the decision and its rationale are logged.
System write-back
Rule-aware scheduling
How does healthcare staffing scheduling AI follow labor rules?
Healthcare staffing scheduling AI follows labor rules by treating them as hard limits the optimizer cannot break, then ranking the schedules that pass. Agnotic Technologies stores each rule with its owner and effective date, so a nurse leader sees why each shift was assigned.
| Rule type | What it limits | How the scheduler applies it |
|---|---|---|
| Licensure and competencies | Who may work a unit or role: RN, LPN, charge nurse, specialty competencies | Staff with an expired license or a missing competency are removed before ranking, and upcoming expiry dates raise a reminder |
| Skill mix and RN cover | How many RNs, LPNs and aides each unit needs per shift for its census and acuity | Nursing leadership sets the targets per unit; the AI fills to them and flags any shift where an RN would not be immediately available |
| Overtime basis | Whether overtime is counted per 40-hour workweek or on an agreed 14-day 8 and 80 period | Each employee's basis is stored on their record, and a shift's cost is worked out on that basis before it is offered |
| Union and contract terms | Seniority order for extra shifts, mandatory overtime limits, float rules and rest between shifts | Clauses are entered as versioned rules with effective dates, so a new agreement changes the scheduler on its start date |
| State staffing rules | Ratios or staffing plans where a state requires them | Loaded per state and unit by your compliance team; the AI never infers them |
| Fatigue and preferences | Consecutive shifts, night rotations, requested days off and leave | Hard limits block a shift; preferences only change the ranking, and both are shown to the manager |
Every offered shift lists the rules it passed, and a manager approves the schedule before it is published. The agent recommends; it never posts shifts on its own.
Facts and sources
Which rules shape staffing scheduling AI?
Short facts we design around, each linked to its primary source.
Under the Medicare hospital conditions of participation, the nursing service must have adequate numbers of registered nurses, licensed practical nurses and other personnel to care for all patients as needed, with a registered nurse immediately available for any patient when needed.
Source: 42 CFR 482.23(b) (eCFR)A hospital nursing service must have a procedure to ensure that nursing personnel for whom licensure is required have valid and current licensure.
Source: 42 CFR 482.23(b)(2) (eCFR)Under section 7(j) of the Fair Labor Standards Act, a hospital and an employee may agree before the work is done to compute overtime over a 14-day period instead of a workweek, with at least one and one-half times the regular rate for hours over 8 in a workday and over 80 in the period.
Source: 29 CFR 778.601 (eCFR)
Related proof
Read Case StudyLera Health: compliant women's health platform
Related delivery proof from our compliant, AI-assisted healthcare work, not this exact agent.
Compliance & Guardrails
15-Minute Scoping CallGuardrails on every recommendation
In healthcare, the guardrails matter as much as the capability.
PHI handling
Operational and patient data run under a BAA on HIPAA-ready infrastructure, encrypted throughout, with access scoped to capacity-management roles.
Human-in-the-loop
The agent is advisory by design, bed managers, nurse leaders, and OR coordinators approve every move; nothing changes a schedule automatically.
Explainability
Each recommendation shows the constraints, forecast, and trade-offs behind it, so a manager can see why an option was ranked first.
Audit logging
Every recommendation, the manager's decision, and the resulting write-back are logged for operational review and accountability.
Frequently Asked Questions
Yes. Operational and patient data run under a BAA on HIPAA-ready infrastructure, encrypted in transit and at rest, with access scoped to capacity-management roles. Every recommendation, decision, and write-back is logged for review.
No. It is advisory by design. It gives bed managers, nurse leaders, and OR coordinators a live picture and ranked recommendations, but they make every call: the agent never moves a patient or changes a schedule on its own.
It isn't conversational. It's an optimization agent that continuously ingests ADT, staffing, and OR data, forecasts demand, and produces ranked, explained capacity recommendations for managers to approve.
We start with a single domain such as bed management, typically running in advisory mode within a few weeks of connecting the operational feeds. We calibrate against your historical data before extending to staffing and OR.
No. Ratios, skill mix targets and any state staffing plan are set by nursing leadership and your compliance team, then loaded as rules. The AI fills shifts to those targets and shows the manager every shift it cannot fill without breaking one.
Give your capacity decisions a live picture
Tell us your workflow and systems, and we'll return an architecture review and a phased rollout plan.
contact@agnotic.com
Partnerships
contact@agnotic.com