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Hospitals can synchronize a real-time capacity board with an electronic health record (EHR) by connecting the systems through supported HL7 v2 admission-discharge-transfer (ADT) feeds, FHIR R4 APIs, or a combination of both. Reliable integration requires more than turning on an interface: the hospital must define the decisions the board supports, map patients and locations, assign ownership of every status, test operational edge cases, and monitor stale or failed updates.
This guide presents a practical implementation sequence, a bed-state model, interface choices, testing scenarios, governance controls, and questions to ask vendors. Product capabilities and outcome figures described below are vendor statements and must be verified in the hospital’s own EHR version and deployment.
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Start with the operational decisions, not the interface
Capacity software is useful only when its data changes a real decision. Before selecting an interface, identify the work that should improve and the people who will act on the information.
Define the decisions the live view must support
- Bed placement: Which patient should receive which available bed, and what clinical or isolation constraints apply?
- Transfers: When should a patient move between units, and who confirms that the destination is ready?
- Expected discharges: Which patients are likely to leave today, and what downstream tasks depend on that estimate?
- Emergency-department boarding: How long have admitted patients waited for an inpatient bed, and where is escalation needed?
- Capacity escalation: When should leaders open surge space, defer elective activity, or activate a command-center response?
- Cross-unit coordination: How do nursing, environmental services, transport, registration, and bed management share one current status?
“Capacity management” may cover several views. GE HealthCare describes capacity, patient-management, flow, and boarding views, illustrating why a project should name each workflow instead of accepting a generic promise of “EHR integration.”
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Define who needs the view and what they can do
List the roles that need live information—bed managers, charge nurses, patient-placement staff, registration, physicians, environmental-services teams, transport, executives, and interface support. For each role, document whether the user only reads status, changes an operational field, acknowledges an exception, or initiates a transaction. This distinction determines access controls, write-back requirements, training, and audit needs.
Choose the interface path your installed systems actually support
HL7 and FHIR are standards families, not guarantees that two products will interpret data identically. Confirm the EHR edition, enabled modules, interface engine, message profiles, FHIR version, authentication method, and permitted fields with both vendors.
| Integration path | Typical information | Advantages | Questions to verify |
|---|---|---|---|
| HL7 v2 ADT | Admission, discharge, transfer, registration, patient and encounter updates, and location changes | Widely used for event-driven feeds and mature interface-engine support | Which ADT trigger events and segments are sent? Are cancellations, corrections, merges, and acknowledgements supported? Is transport MLLP or another method? |
| FHIR R4 APIs | Resources such as Patient, Encounter, Location, and related operational data, subject to the implementation profile | API-based retrieval and, where enabled, more granular reads or writes | Which resources, profiles, search parameters, scopes, rate limits, and write operations are enabled in this hospital’s tenant? |
| Hybrid HL7/FHIR | Real-time ADT events combined with API lookups or updates | Can pair rapid event notification with richer context or controlled write-back | Which system wins when an event and an API response disagree? How are latency, retries, and duplicate updates reconciled? |
Dualis TrackER says it uses FHIR R4 for named EHRs and HL7 v2 over MLLP for other ADT-capable EHRs. That is a vendor-described capability, not proof that a particular hospital’s configuration is compatible. Bonami Hospital Operations AI likewise describes connectivity through FHIR APIs and HL7 v2 ADT. Require a written interface matrix for your exact versions rather than relying on a standards label.
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A live board fails when two systems use different identifiers or disagree about who can change a field. Create a data dictionary and an ownership decision for every value.
| Data domain | Mapping to document | Ownership decision | Edge cases to test |
|---|---|---|---|
| Patient identity | Enterprise patient identifier, medical-record number, name, date of birth, and any local aliases | EHR or master-patient index is authoritative | Duplicate registration, identity merge, correction after an ADT message |
| Encounter | Visit identifier, encounter class, admit/discharge timestamps, and status | EHR registration/ADT source | Observation-to-inpatient conversion, canceled admission, late discharge |
| Unit, room, and bed | Facility, campus, nursing unit, room, bed code, and hierarchy | Agree whether the EHR location master or capacity system controls the operational map | Renamed units, temporary surge spaces, retired beds, duplicate local codes |
| Patient location event | Prior location, new location, effective time, event time, and sending application | ADT event stream, with reconciliation to the EHR record | Out-of-order events, transfer reversal, patient physically moved before documentation |
| Operational bed state | Occupied, assigned, unassigned, blocked, dirty/being cleaned, ready, or other local states | Usually a bed-management or capacity workflow; document whether any value writes back to the EHR | Isolation hold, maintenance block, bed reserved for a service, cleaning completion without assignment |
GE HealthCare describes a capacity view with occupied, assigned, unassigned, and blocked beds. Those labels are useful starting points, not a universal standard. Define local meanings in plain language—for example, whether “assigned” means a patient has been selected, whether “blocked” includes infection-control holds, and whether a cleaned bed is immediately available or still requires a release step.
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A six-step implementation sequence
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Write the use-case and success criteria
Choose an initial workflow and a bounded population, such as inpatient placement on one campus. Specify the decisions, users, source fields, acceptable freshness, escalation owner, and measurable outcomes. Examples include the percentage of beds with a current state, time from ADT event to board update, or number of unresolved location exceptions.
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Inventory every system and interface
Document the EHR, ADT source, bed-management application, interface engine, identity services, and related staffing, environmental-services, transport, or command-center systems. Ask each vendor for supported message types, FHIR resources and profiles, transport, authentication, sandbox access, monitoring, and version dependencies. TrackER advertises EHR write-back and integration diagnostics; require a demonstration in a representative test environment before treating those functions as available.
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Approve the data dictionary and ownership matrix
Map local patient, encounter, unit, room, and bed identifiers. Define which timestamp is displayed, how time zones and daylight-saving changes are handled, and how the system treats a missing or stale event. Establish deterministic rules for duplicate messages, retries, canceled transactions, late-arriving events, and conflicting edits. If the capacity application can write back, assign one authoritative owner per field and specify conflict resolution.
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Configure the supported connection
For HL7, select the required ADT trigger events and segments, configure acknowledgements, and set transport and retry behavior. For FHIR, register the application, restrict scopes, select resources and search parameters, and define polling or subscription behavior where supported. Record message and resource examples, expected response codes, timeout thresholds, and who receives alerts. Keep production credentials and protected health information out of development environments unless the hospital’s security process explicitly permits them.
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Test clinical and operational scenarios with frontline users
Use representative test patients and beds to exercise admission, transfer, discharge, reassignment, cleaning, blocking, unblocking, canceled or corrected events, and interface downtime followed by recovery. Compare the EHR, capacity board, and any write-back result after every scenario. Verify that timestamps, locations, bed states, user permissions, audit entries, and stale-data warnings are correct. Include bed managers, nursing, registration, IT/interface staff, and other affected roles in scenario review. The vendor pages do not publish a universal test protocol, so the hospital should approve its own acceptance criteria.
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Roll out in phases and monitor exceptions
Start with a defined unit, campus, or workflow. Review rejected messages, unmapped locations, duplicate events, stale records, and user feedback daily during stabilization. Expand only after the initial workflow meets its acceptance criteria. Bonami describes module-by-module activation, while GE HealthCare describes modular capabilities; these are deployment options, not guarantees of a particular implementation speed or result.
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Design freshness, reconciliation, and downtime behavior
Make stale data visible
Every board record should carry an event time and a received or processed time. Display a clear age indicator and define when a record becomes stale. A bed that was “available” several hours ago should not look equivalent to one confirmed moments ago. Set an escalation path for stale feeds instead of silently retaining the last value.
Make messages idempotent and traceable
Store a message or resource identifier, source application, event time, processing result, and correlation identifier. Reprocessing the same event should not create a second patient, transfer, or bed assignment. Preserve rejected messages for investigation, with access limited to authorized support staff.
Plan for downtime and replay
Define what staff do when the EHR feed, interface engine, network, or capacity application is unavailable. Provide a documented manual process, identify the temporary source of truth, and state who reconciles changes after recovery. Test replay of queued events and verify that an old event cannot overwrite a newer location or bed state.
Security, privacy, and governance requirements
Limit data to what the capacity workflow needs. Use role-based access, strong service-account controls, encryption in transit and at rest according to hospital policy, and an audit trail for reads, edits, acknowledgements, and write-back. Separate test and production environments, rotate credentials, and review vendor subprocessors, hosting location, retention, incident response, and support access during procurement.
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Governance should include a clinical owner, capacity or operations owner, EHR owner, interface-engine owner, security and privacy reviewers, and a vendor contact. Approve changes through the hospital’s normal change-control process. A new unit code, bed type, EHR upgrade, or interface-profile change can invalidate mappings even when the connection itself remains technically up.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate products and proposals
Ask vendors to answer these questions in writing and demonstrate them with your data model:
- Which EHR products, versions, interface engines, and deployment environments are supported today?
- Which HL7 v2 ADT message types, segments, trigger events, and FHIR R4 resources or profiles are implemented?
- Can the system map enterprise patient IDs, encounters, campuses, units, rooms, beds, and local status codes without custom code?
- Is the integration read-only, or can it write back? Which fields can be written, and how are conflicts, reversals, and unauthorized edits handled?
- How are freshness, queue depth, rejected messages, retries, reconciliation, and downtime exposed to hospital support staff?
- Can the workflow distinguish occupied, assigned, unassigned, blocked, dirty, reserved, and ready states according to local policy?
- What authentication, authorization, audit, hosting, retention, and incident-response controls are provided?
- What implementation services, training, testing support, upgrade obligations, and ongoing support model are included?
- What evidence supports claimed outcomes, and can the vendor provide methodology, baseline, dates, sample size, and independent validation?
What vendor examples do—and do not—establish
GE HealthCare Command Center
GE HealthCare describes integrated source systems and real-time capacity insights, including a view of occupied, assigned, unassigned, and blocked beds. Its product page says the platform has been used across nearly 500 hospitals and medical facilities and more than 55,000 beds. Those are GE HealthCare’s reported deployment figures, not independent validation of effectiveness or proof that the same footprint exists in every region or edition.
The same page displays customer-reported figures: 19,000 additional patients served annually at Providence Swedish; a 1.07-day reduction in length of stay at The Queen’s Health Systems; $40 million in labor-cost savings at Duke Health; and an 80% reduction in delayed admissions and 90% reduction in deferred admissions through the Transfer Center at Children’s Mercy Kansas City. Treat each as a vendor-displayed customer outcome. Before using one in a business case, request the measurement period, baseline, denominator, attribution, sample, and study design.
Lindsey Powell, MBA, BSN, RN, Director of Patient Placement & Capacity at Providence Swedish, is quoted by GE HealthCare: “Our command center helps us get the right patient in the right bed with the right caregiver—the first time.” The statement describes an experience at that organization; it is not a guarantee for another hospital.
Dualis TrackER
TrackER says it supports FHIR R4 for named EHRs and HL7 v2 over MLLP for other ADT-capable EHRs. It also describes live bed tracking, EHR write-back, and integration diagnostics. Verify the exact EHR connector, resource and message scope, write permissions, error handling, and diagnostic visibility in the hospital’s environment.
Bonami Hospital Operations AI
Bonami describes FHIR API and HL7 v2 ADT connectivity, existing-system integration, and phased module activation. These descriptions indicate possible implementation patterns, not independent evidence that a particular hospital will achieve a stated timeline, cost, or outcome.
Costs, timelines, and evidence you should demand
There is no established independent figure here for integration cost, typical duration, or causal hospital-wide improvement. Those variables depend on the EHR edition, interface-engine capacity, number of campuses and beds, data quality, security review, custom mapping, testing depth, training, and whether write-back is required. Request a written scope that separates one-time interface work, implementation services, licenses, environment setup, testing, training, support, upgrades, and any usage-based charges.
Likewise, do not turn a vendor case-study number into a forecast. Ask for a dated baseline, precise metric definition, comparison period, exclusions, confidence or uncertainty information where available, and an explanation of what other operational changes occurred during measurement.
Operational metrics after go-live
Monitor technical and clinical-operational signals together:
- Percentage of ADT messages or FHIR transactions accepted, rejected, retried, and resolved.
- Median and worst-case time from source event to capacity-board update.
- Number and age of unmapped patients, encounters, units, rooms, and beds.
- Count of stale records and duration of each stale interval.
- Differences between EHR location and capacity-board location found during reconciliation.
- Manual corrections, write-back conflicts, and unauthorized-edit attempts.
- Staff adoption by role and unresolved workflow exceptions.
- Operational measures chosen at the outset, such as boarding time or time to assign a ready bed, reported with the same definitions and period used for the baseline.
Review these measures in a regular governance meeting. A technically healthy interface that produces a confusing or distrusted board is not a successful integration; conversely, a useful workflow may require refining local definitions even when message delivery is perfect.
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