Medical imaging fleet management
When an organisation runs more than one piece of medical imaging equipment, managing the fleet stops being about maintaining individual consoles. It becomes an operational problem: where each unit is, how it is, what patterns cross models and sites, and where money is being lost to lack of visibility.
A fleet is more than the sum of its units
A mid-size clinical group can have 20 to 100 imaging units across 3 to 15 sites. Each site with its own technical staff, its own maintenance provider, its own manufacturers and its own incident history. The sum of individual consoles doesn't produce a fleet view — it produces a collection of silos.
What's lost in that silo collection is exactly what costs the most: cross-equipment patterns. The same subsystem error repeating across three different MRIs almost always points to site infrastructure. Anomalous coldhead behaviour appearing across several units of the same model can be a factory issue the manufacturer hasn't communicated yet. Without cross-correlation, you don't see it.
For maintenance companies running fleets across multiple clients, the problem multiplies. The ability to detect patterns, anticipate incidents and prove SLAs is directly proportional to the quality of fleet observability.
Five things a fleet view has to do
It's not just "see all units on one screen". A useful fleet management layer covers five dimensions that individual consoles, by design, can't.
Single consolidated view
Real-time state of the entire fleet, groupable by site, manufacturer, model or client. Filterable, exportable, contextualisable.
Cross-incident correlation
Detection of patterns crossing units, sites or models. What is noise in one piece of equipment is often a signal across three — but only if the data is normalised.
Structured history per unit and per fleet
Traceability for internal audits, vendor warranties, external client SLAs. No more searching raw logs six months later.
Real operational metrics
Real uptime (not contractual), mean time between failures, incident distribution by root cause, operational cost per unit. Data to decide with, not for reports.
Multi-tenant for maintenance companies
Per-organisation isolation for companies managing fleets across multiple hospitals or clinical groups. Client view, global operations view, role-based access.
Argus, designed as a fleet layer from day one
Argus is Nexure AI's platform for multi-vendor, multi-site medical imaging fleets. It is multi-tenant from the architecture — designed for both clinical groups running their own equipment and maintenance companies managing fleets across multiple clients. Compatible with the main manufacturers and designed by a team with biomedical engineering profile.
Explore ArgusQuestions on fleet management
From how many units does a fleet layer start to make sense?
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The practical threshold is 5+ units or 2+ sites. Below that, operational complexity still fits in individual consoles. Above it, cross patterns start to have real value and consolidated traceability is practically mandatory.
Is it for maintenance companies, not just hospitals?
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Yes. The multi-tenant architecture lets a maintenance company run fleets across multiple clients with full tenant isolation and its own global view. This is one of the natural use cases for the platform.
Does it work with mixed fleets across manufacturers?
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Compatible with the main manufacturers in the sector — Philips, Siemens, GE Healthcare and Hologic. Mixed fleets are the norm in the sector and are exactly the case the platform is designed for.
Does it replace the hospital's CMMS or ERP?
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No. Argus operates in the technical observability layer of the equipment. If your organisation already has a CMMS or maintenance ERP, Argus integrates with them when it adds real value — but does not replace them.
Running a multi-site fleet?
If you manage medical imaging equipment across multiple centres — as a clinical group or a maintenance company — and want to see what this would look like in real operation, let's talk.