IntelyCare

The models carry the economics. Matching a nurse to a shift is a preference learning problem across two sides, and the company adds price optimisation on top, learning from every interaction and from a two way rating system. Anticipating a facility's need before it is posted is a forecasting claim, not a scheduling feature.

Held at B because the other asset is the supply. A two sided marketplace works because enough nurses are on it, and a competitor with equally good models and no pool would fill nothing. That is the moat is the network case this index applies elsewhere, and here the network and the model reinforce each other rather than one substituting for the other.

Both parties retain a veto and the system decides what they are choosing between. A nurse sees the shifts the model surfaces and accepts or declines; a facility posts need and receives matches. Nothing is assigned over anyone's objection.

The autonomy sits upstream of those choices. The model determines which shifts a given nurse is shown, at what wage, and by the company's own account creates shifts before a facility has recognised its need. A nurse choosing freely among options selected for them by a system whose objective is fill rate is exercising a narrower discretion than the framing suggests.

Held at B rather than lower because the human decisions are real and the consequences are commercial rather than clinical. Nothing published describes what a nurse is told about why a shift is offered to them at a given rate.

The mechanism is described at a useful level even though the models are not. The company states that the system learns from preferences on both sides, that a rating system feeds back into matching, that pricing responds to fill difficulty, and that hours are validated by geofencing. That is more operational detail than most marketplaces publish.

What is missing is any number behind the claims. A fill rate around three times the industry average and a claim to the highest fill rate in the market are both stated without a denominator, a comparison method or a period. No description of the pricing model exists beyond the direction it moves.

The mechanism is described more openly than most marketplaces manage and no party in the chain is named. The vendor states that the system learns from preferences on both sides, that a rating system feeds back into matching, that pricing responds to fill difficulty, and that hours are validated by geofencing, which tells a buyer what the system consumes and what it produces even though no model, provider or hosting arrangement is identified and no sub processor list was located.

The data profile is worth stating because it changes which regime governs and therefore which questions matter. The sensitive material here is largely about workers rather than patients, covering licences and credentials, work history, shift preferences, ratings, pay and location captured at check in and check out.

That sits outside health privacy law and inside employment and state privacy law, so the usual business associate framing does not apply and a buyer cannot assume the protections it implies. The location question needs a direct answer rather than an inference: whether geofencing captures position only at check in and check out, or continuously through a shift, is a materially different thing and public material does not say. Ask that, ask for retention on location and rating history, and ask for a sub processor list.

Commercial scale is substantial and independently corroborated by funding and by an acquisition: roughly 174 million dollars raised, a valuation above 1.1 billion, and the purchase of an acute care competitor in January 2026.

The operational claims are not measured. Fill rate at three times the industry average is the central assertion and it carries no baseline definition, no cohort and no period. Nothing published addresses the outcomes that would matter to a buyer beyond fill: whether shifts filled through the platform are associated with different retention, different agency spend over time, or different care quality than shifts filled otherwise. This index does not accept scale as evidence of benefit.

Graded on an honest basis, with a distinction worth stating because it changes the shape of the risk.

The sensitive data here is largely about workers rather than patients: licences and credentials, work history, shift preferences, ratings, pay, and location captured by geofencing at check in and check out. That is employment and location data on clinical staff, which sits outside health privacy law and inside employment and state privacy law instead.

No retention schedule, encryption detail or position on secondary use was located. The location question deserves a direct answer: whether geofencing captures position only at check in and out, or continuously during a shift, is a materially different thing and public material does not say.

Graded on an honest basis, and the frame fits loosely. A staffing platform employing nurses and placing them into facilities is not primarily handling patient information, so the instrument that usually governs this axis is not the central one; employment agreements, facility contracts and state staffing regulation carry more of the weight.

That changes at the edges, since scheduling and credentialing systems inside a facility touch operational data. No compliance documentation was located either way.

Recorded honestly and provisionally: the dedicated trust and security search this index requires was not run in this pass, and no attestation was encountered incidentally.

The platform holds nurse licence and credential records for a large workforce, which is identity grade data, and any assessment of how it is protected was not retrieved.

No device pathway applies and none is claimed, and this record is a useful reminder that the relevant regulator is not always a health regulator.

What governs this business is employment and labour law. The company employs its nurses as W2 workers rather than contractors, which is a classification question that has produced significant litigation across gig platforms and which this company has answered in the more expensive direction. Wage and hour rules apply to shifts validated by geofencing. State staffing agency licensure applies in many jurisdictions, several states have moved to regulate healthcare staffing agency rates directly, and nurse licensure verification is a legal obligation the platform assumes on the facility's behalf.

A buyer should treat this as a regulated labour supplier rather than as software.

Nothing published on evaluation, monitoring or fairness, and this record has the most direct exposure to a worker of anything in this index.

The model sets what a nurse is paid. Pricing a shift by how hard it is to fill means two nurses doing identical work in the same unit can be paid differently according to when they accepted and how desperate the facility was, and the same logic implies a nurse who reliably accepts is worth offering less. That is algorithmic wage determination for clinical labour, and it has an established literature and active regulatory attention behind it in other sectors.

The matching side carries the same question in a different form. If the model learns from ratings, and ratings carry the biases of the people giving them, then which nurses see which shifts inherits those biases and compounds them, because fewer offers means fewer ratings. No analysis by race, age, licence type or any other characteristic was located, nor any statement that pay outcomes are monitored for disparity.

Credit where due: employing nurses as W2 staff with benefits removes a whole class of harm that contractor platforms create, and the company positions on it openly.

Two passes located no accuracy figure, no evaluation methodology, no published limitations and no warranty, indemnity or remediation commitment, and the affected party here is a worker rather than a patient, which is the second record in this index with that shape. The system decides two things about a nurse: whether a shift is offered to them, and what it pays.

The mechanism is described at a useful level, with learning from preferences on both sides, a rating system feeding back into matching, and pricing that responds to fill difficulty, but no description of the pricing model exists beyond the direction it moves. So an algorithm sets what a clinician earns for a shift and nothing published states what it weighs, what floor exists, or whether a nurse can see why an offer was priced as it was.

The performance claims are equally unanchored: a fill rate around three times the industry average and a claim to the highest fill rate in the market are stated without a denominator, a comparison method or a period. Two further mechanisms decide access to work and have no published governance. A rating feeds matching, so a poor rating reduces future offers, and nothing describes how a rating is challenged or corrected.

And hours are validated by geofencing, so a location reading can determine whether a shift is paid. Ask what the pricing model weighs, how a rating is appealed, and what happens when geofencing is wrong.

Not an electronic health record story and correctly so. The systems that matter here are facility scheduling, timekeeping, credentialing and payroll, and the combined platform now claims to span permanent staff, internal resource pools and contingent labour in one place, which implies integration with a facility's own scheduling rather than sitting beside it.

No named integration, interface standard or scheduling platform partnership was located, and that is the gap a facility should press on, because a contingent labour tool that cannot see the permanent roster cannot reason about the whole staffing picture.

A hosted platform with a mobile application for the workforce and a facility facing interface. No hosting model, region or retention position was located.

The question specific to this product is retention of location and shift history for former workers, since a nurse who leaves the platform has left behind a detailed record of where they worked and when. Nothing published addresses it.

More mechanism published than most, and no rate. The company states that facilities pay flat rates for the platform and that the wage attached to a shift moves with how difficult that shift is to fill. Publishing the direction the price moves is a real disclosure, and it lets a facility understand that its cost per shift is dynamic rather than fixed.

What is absent is the number, the spread and who captures it. A facility should establish the platform fee separately from the wage component, ask what the observed range of shift rates has been for comparable units, and ask how the split between the nurse's pay and the company's margin behaves when a shift becomes hard to fill. That last question is the one that determines whether surge pricing is passing money to the nurse or to the platform.

Broadened materially in January 2026. The company began in post acute settings, skilled nursing and long term care, where the staffing shortage is most acute and the technology least present, and the acquisition of an acute care platform extended it into hospitals.

The combined proposition now covers three distinct labour types, permanent staff, internal resource pools and contingent shifts, which is a wider span than a pure per diem marketplace and is what makes it interesting to a health system rather than only to a facility manager. Coverage is national across the United States. Held at B because the workforce is nursing and nursing assistance rather than the full clinical workforce, and nothing extends to physicians or allied health.