Kinometrix

The model is the entire product. Kinometrix has no hardware, no interface of its own and no services layer: it is a headless machine learning system that reads the electronic health record, computes a risk profile and writes it back. Apply the standard test and nothing remains if the model is removed. This is the cleanest centrality case among the harm prevention vendors in this category, and the direct contrast with VirtuSense, which predicts the same outcome from in room sensing rather than from record data.

Nothing is auto actioned clinically. The system computes a risk profile, writes it to the record and prompts the care team with interventions matched to that profile, and a nurse decides what to do. TWO PROPERTIES EARN THE B RATHER THAN A LOWER GRADE. The nurse's own expert assessment is an INPUT to the model rather than something the model replaces, which is the same structural property that distinguishes Luminare in this category: the clinician is inside the computation rather than downstream of it. And the output carries specific risk drivers rather than an opaque score, so a clinician can disagree with a driver rather than only with a number. Held below A because no threshold, false negative rate or abstention behaviour is published, and because writing risk profiles into the record automatically raises an unaddressed question about what happens when the model's assessment and the nurse's documented assessment diverge.

The model is described only as a proprietary predictive machine learning model. No model class, no feature list, no training population, no development or validation methodology, no calibration and no operating characteristics are published. Two partial disclosures are credited without lifting the grade: the input space is characterised at a high level as existing EHR data including the nurse's own assessment, and the product surfaces SPECIFIC RISK DRIVERS alongside each prediction rather than a bare score, which is a real interpretability property since a clinician can see what is driving the number and act on that rather than on the number alone. That driver level output is more than most competitors offer and would support a higher grade if the underlying model were described at all.

Every claim is vendor published without derivation, and the headline figure is the weakest kind. Accuracy of around 98 percent is stated for an outcome the company itself describes as affecting roughly one million patients a year across US hospitals, which is an incidence of a few percent of admissions, so predicting that no patient will fall would already produce accuracy in the high nineties. Accuracy is the wrong metric for this problem and its use here obscures rather than demonstrates performance. Elimination of false low risk assessments, reduction of preventable falls to virtually zero, superiority to all other fall risk prediction tools and a 6.5 to 1 return on investment are all asserted with no denominator, comparison group, time period or method. No peer reviewed evaluation, external validation or named customer result was located. AN INTERNAL INCONSISTENCY IS RECORDED ON THE SAME BASIS APPLIED TO AFFINEON AND ETIOMETRY: the annual US cost of inpatient falls is given as 6 billion dollars on one page and 7 billion on another.

No privacy policy, data processing statement, retention period or training use disclosure was located. Not Rated means no public evidence was found rather than evidence of weakness. The architecture raises the question in a specific form worth putting to the vendor: a headless system that continuously reads the record and writes back to it needs broad and persistent record access, so establish what data leaves the institution, whether processing occurs in the hospital environment or the vendor's, and whether customer data contributes to model development.

No public statement on business associate agreements, execution terms, cost or subprocessor disclosure was located. Not Rated means no public evidence was found. No named hospital customer was identified either, so this posture appears untested in public.

No SOC 2 of any type, HITRUST, ISO 27001, trust centre or report request path was located. Not Rated means no public evidence was found. For a product requiring continuous read and write access to the electronic health record, the absence of any published security posture is a material gap rather than a formality, and should be the first question in any evaluation.

No FDA clearance was located and none is claimed. A fall risk score that surfaces its own risk drivers to a clinician who retains the decision has a reasonable clinical decision support exclusion argument available, and the driver level transparency strengthens it, since the exclusion turns on whether a clinician can independently review the basis for a recommendation. But the company does not make that argument publicly and nothing states the regulatory basis on which the product operates. The pressure injury product warrants the same question separately, since it predicts a condition rather than a safety event.

No subgroup performance, calibration or fairness analysis is published. THE EXPOSURE SPECIFIC TO FALL PREDICTION IS LABEL QUALITY AND IT IS SERIOUS. A model trained on recorded inpatient falls learns from an outcome that is known to be inconsistently captured: unwitnessed falls, near misses and falls without injury are variably documented, and documentation practice differs by unit, by shift and by staffing level. A model trained on that record therefore learns where falls get REPORTED as much as where they occur. The same problem this index records for Birth Model on obstetric risk and for Healthplus.ai on action defined labels applies here directly. A second exposure follows from the design: because the nurse's assessment is an input, whatever variation exists in that assessment is carried into the model rather than corrected by it. Nothing published addresses either, and no performance breakdown by age, mobility status, cognitive impairment, unit type or staffing level exists.

The headless architecture is the substantive claim on this axis and it is a genuine design position rather than a slogan. Carrying no interface of its own means the product can integrate with any EHR and render inside existing workflow, and predictions are written back into the record automatically rather than held in a parallel dashboard, which is the property that determines whether a risk score is actually seen. The company also emphasises that it requires no additional documentation from nurses, explicitly contrasting itself with EHR integrated tools that add fields to complete, and in a category whose failure mode is clinician burden that is a meaningful differentiator. Held at B because no EHR vendor is named anywhere, no marketplace listing, Showroom entry or partner certification was located, and no FHIR or interface specification is published, so the any EHR claim is unverified.

No named hospital deployment, customer count or installed base was identified anywhere. No implementation timeline, resourcing requirement, hosting architecture or data residency commitment is published. What can be said is confined to the architecture: a headless system reading and writing the record, configurable per organisation, with no hardware and no additional clinician documentation, which implies a light implementation but nothing published confirms it. A CURRENCY CAVEAT IS RECORDED RATHER THAN LEFT IMPLICIT: the retrieved material dates largely from 2023 and no more recent activity was identified, so establish current commercial status and reference customers directly before evaluating.

No pricing published at any level: no rate card, no unit of pricing, no band and no implementation fee. THE ASYMMETRY RECORDED AGAINST DROXI AND HEALTHPLUS.AI APPEARS AGAIN AND IS BECOMING A PATTERN IN THIS CATEGORY: the company publishes a specific customer side return, 6.5 to 1 on investment, while gating its own price entirely, so a buyer is handed one side of the equation and asked to trust the other. The ratio has no stated basis, cost model or time horizon.

Narrow and early. Coverage is confined to two hospital acquired conditions, inpatient falls as the first product and pressure injuries as the second, within general hospital inpatient settings. No specialty depth, no unit specific configuration, no paediatric or obstetric application and no population limits are described. The company frames a broader ambition to address hospital acquired conditions generally, but only the two are documented. Narrow scope is a reasonable position for an early stage vendor and the falls problem is genuinely large, but this axis measures coverage and the coverage is two conditions.