HealthLeap

The model is the product. No hardware, no separate application, no services layer: the platform reads the chart, computes a risk score and writes it into the workflow. Remove the model and nothing remains. The company's own framing is consistent with this, positioning malnutrition as the first condition its screening engine addresses rather than as the product itself, which implies the engine rather than any one indication is the asset.

A dedicated clinical use and safety statement, which almost nothing in this category publishes, and its content is genuinely constraining rather than decorative. The software is stated not to be a diagnostic tool, not to provide treatment recommendations, not to be used as the sole basis for determining nutritional risk or the presence of malnutrition, and to support rather than direct clinical decision making, with explicit instruction to use it alongside clinical assessment, physical assessment and laboratory findings. That is a complete adjunctive position published in one place where a clinician can find it. Nothing is auto actioned and there is no low risk or de escalation output. Held below A because no threshold, false negative rate or abstention behaviour is published, and because screening 100 percent of inpatients daily generates alert volume that nothing published quantifies, in a category whose defining failure is alerts being ignored.

The input space is enumerated properly rather than gestured at: clinical notes, laboratory results, vital signs, active medication orders and problem lists, analysed daily across the whole admission. THE TECHNICAL BOUNDARY STATEMENT IS THE UNUSUAL PART and it is credited: the company states that the software uses only existing EHR data and evidence based risk factors, and expressly that it does NOT acquire or process medical images, waveforms or device signals. Publishing what a system does not do is rare in this market. Precision on what kind of scope statement this is: it bounds the DATA SOURCES rather than telling a clinician what the absence of a flag does not rule out, which is the more valuable form this index credits in Eko Health. Held at B because the model class, features, training population, validation methodology and operating characteristics are all undisclosed, and evidence based risk factors is not a specification.

The deployment is substantial and specific: a systemwide rollout across Houston Methodist screening more than 150,000 inpatients annually, with a named executive sponsor, and stated intent to track clinical, operational and financial outcomes including length of stay and documentation accuracy. That is a real enterprise commitment at a major academic health system rather than a pilot. THE EVIDENCE CLAIM ITSELF COULD NOT BE VERIFIED AND THAT IS RECORDED RATHER THAN ASSUMED: the company describes itself as the only commercially available peer reviewed validated platform of its kind and the only validated AI malnutrition screening tool, but no publication was retrieved in this pass, so the claim is noted as made rather than confirmed. Nothing published gives sensitivity, positive predictive value, alert burden or a comparison against the admission questionnaire the product replaces. Flag for the refresh pass: locate the peer reviewed validation and grade it directly, since if it exists and is sound this axis moves materially.

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. One architectural fact narrows the exposure and is credited in the note rather than the grade: the platform processes only data already present in the record and ingests no device signals or images. The open questions are where processing occurs, whether chart data including free text clinical notes leaves the institution, and whether customer data contributes to model development, which matters more here than usual because the system reads unstructured notes across an entire health system's inpatient population.

No public statement on business associate agreements, execution terms, cost or subprocessor disclosure was located. Not Rated means no public evidence was found. A systemwide deployment at a major health system means agreements plainly exist and were negotiated at enterprise scale, but nothing about them is published.

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 platform with continuous read access to complete inpatient charts including free text notes across an entire health system, the absence of a published security posture is a material gap and should be an early question in any evaluation, particularly given an enterprise customer would ordinarily have required attestations that are simply not public.

No FDA clearance was located and none is claimed. The clinical use and safety statement is evidently written with the clinical decision support exclusion in mind and does the work carefully: not diagnostic, no treatment recommendations, not the sole basis for a determination, supports rather than directs decision making, used alongside independent clinical and laboratory assessment. That is a deliberately constructed exclusion posture rather than an accidental one, and it is stronger than most in this category because the underlying risk factors are described as evidence based and the clinician retains an independent assessment path. The company does not make the regulatory argument explicitly, so the basis is inferred from the language rather than stated, and a buyer should ask for it directly.

No subgroup performance, calibration or fairness analysis is published. THE STRUCTURAL EXPOSURE HERE IS THE REVENUE LOOP AND IT IS SHARPER THAN THE COMPARABLE CASE IN THIS CATEGORY. Malnutrition is a documented comorbidity that raises the recorded severity of illness and the associated payment weight, and the company markets the platform on exactly that: aligning frontline care with hospital coding, ensuring conditions get coded, optimising reimbursement accuracy and defending against retrospective payer claim denials. So a single engine both identifies the condition and drives the documentation that captures the revenue from it, with no independent check between the two. Identifying genuinely undiagnosed malnutrition is a real clinical good and being paid appropriately for care delivered is legitimate, so this is recorded as a structure rather than an accusation, consistent with how this index treats OmniMD, Droxi Unlock and Luminare. IT IS SHARPER HERE FOR A SPECIFIC REASON: malnutrition coding is an established area of payer scrutiny, and a product marketed partly as audit defence for a diagnosis it helped generate sits in an unusually tight loop. A second, unaddressed exposure: a model reading free text notes inherits whatever variation exists in how thoroughly different clinicians document nutritional status, which differs by unit, staffing and patient.

Workflow native by design, which is the property that determines whether a risk score is ever seen. Scores and prioritisation alerts are pushed into the interfaces nurses and dietitians already use rather than requiring a separate application or login, and the platform requires no additional documentation from clinicians, which matters in a category whose failure mode is clinician burden. Reading unstructured clinical notes alongside structured labs, vitals, orders and problem lists implies a deeper integration than a discrete data feed. Held at B because no EHR vendor is named in retrieved material, no marketplace listing, Showroom entry or partner certification was located, and no FHIR or interface specification is published, so the depth is described by effect rather than by mechanism.

One named deployment but a serious one. Houston Methodist has rolled the platform out systemwide, covering more than 150,000 inpatients annually with 100 percent of adult inpatient charts screened daily from admission through discharge, and the health system's associate chief innovation officer is named publicly in support. An enterprise wide commitment at a large academic health system is a materially stronger reference than the pilot deployments common at this stage. Held at B because it is a single named customer, no implementation timeline or resourcing requirement is published, no hosting architecture or data residency commitment is stated, and no second reference site was identified.

No pricing published at any level: no rate card, no unit of pricing such as per bed, per admission or per screened patient, no band and no implementation fee. The same asymmetry recorded against Droxi, Healthplus.ai and Kinometrix applies and is more pronounced here, because the company quantifies several categories of customer upside, reduced length of stay, increased revenue, improved reimbursement accuracy and reduced claim denials, while publishing nothing about cost. A buyer evaluating a return on investment case built entirely on the vendor's own revenue arithmetic should ask for the denominator.

Narrow and early. Coverage is a single condition, malnutrition risk, across adult inpatients in general hospital settings. The company frames this as the first condition rather than the only one and positions the underlying engine as a general screening safety net, but no second condition is documented and no timeline is published. No paediatric, obstetric or critical care specific configuration is described, and no population exclusions are stated beyond the adult inpatient scope. Narrow focus is reasonable for an early stage vendor with one enterprise customer, but this axis measures coverage and the coverage is one condition.