AITRICS VitalCare
AITRICS is a Korean medical AI company whose AITRICS-VC (VitalCare) predicts patient deterioration from electronic medical record data. It is the first vendor in this category focused on the GENERAL WARD rather than the ICU, which is a materially different detection problem: ward patients are observed intermittently rather than monitored continuously, so the model must work from sparse, irregularly timed measurements. As marketed, VitalCare runs two deep learning models built on a bidirectional long short term memory architecture. VC-MAES predicts clinical deterioration events, defined as unplanned ICU transfer, cardiac arrest or in hospital death, within six hours. VC-SEPS predicts sepsis onset within four hours. The company also markets cardiac arrest prediction within 24 hours and, in the ICU, mortality prediction within six hours. Inputs are 19 parameters drawn from the EMR: six vital signs, 11 blood test results, level of consciousness and age. The development and validation work is published as a medRxiv preprint. Models were derived on 357,009 adult general ward admissions at Yonsei Severance Hospital between 2013 and 2017, then externally validated on 22,073 admissions at National Health Insurance Service Ilsan Hospital. In external validation VC-MAES reached an AUROC of 0.918 against 0.834 for MEWS and 0.883 for NEWS, and VC-SEPS reached 0.941 against 0.559 for SOFA, 0.687 for qSOFA and 0.767 for NEWS. Both models held AUROC above 0.86 across all age and sex categories, which is the only published subgroup performance reporting located for any vendor in this category. Regulatory footprint spans several jurisdictions, and the scopes differ in ways a buyer must read carefully. Korea's MFDS approved the deterioration prediction product for both general wards and intensive care. Hong Kong's Medical Device Division and Vietnam's Ministry of Health followed. The US clearance is materially narrower: 510(k) K240756, granted 23 July 2024 under 21 CFR 870.2300, describes software performing rule based calculation of conventional early warning scores including NEWS, MEWS and qSOFA, screening patients against predefined thresholds and displaying them on a dashboard. It is indicated solely for the general ward and is expressly not indicated for the ICU or operating rooms, and the submission required no clinical data. VitalCare is reported in use at more than 60 hospitals in Korea. Pricing is not published.
Capability Axes
The product AITRICS builds, publishes on and sells is a pair of deep learning models, and there is no ambiguity about it: the bidirectional LSTM architecture is named in the literature and the models are the entire value proposition. Strip them out and nothing saleable remains. ONE CAVEAT BELONGS HERE RATHER THAN BURIED, because it changes what a US buyer is actually getting: the device description in the US 510(k) K240756 describes software performing RULE BASED CALCULATION of conventional early warning scores, not the deep learning models. In its US cleared configuration as described to the FDA, the centrality of machine learning is much lower than this grade implies. See the FDA axis.
The US cleared indications for use carry two explicit published constraints: the software is not intended to replace bedside patient monitors or clinicians' clinical decision making, and it is restricted to the general ward and expressly not indicated for the ICU or operating rooms. A setting restriction written into the label is a stronger form of scope discipline than most vendors accept. Nothing is auto actioned and there is no de escalation or low risk output of the kind that creates the invisible failure mode elsewhere in this category. Held below A because no operating threshold is published, no positive predictive value at that threshold is stated, no abstention or uncertainty behaviour is described, and nothing addresses what a clinician should do when the model and the conventional early warning score disagree, which is the practical question given the product displays both.
Strong disclosure of the kind this market rarely offers. THE ARCHITECTURE IS NAMED, a bidirectional long short term memory network, which almost no vendor in this index does. Inputs are enumerated at 19 EMR parameters covering six vital signs, 11 blood tests, level of consciousness and age, with the published work specifying age, vital signs, laboratory results and Glasgow Coma Scale. Derivation and external validation cohorts are named with institutions, sizes and date ranges, and performance is reported with 95 percent confidence intervals and benchmarked against four conventional scores. Held at B rather than A for three reasons: the study is a preprint and has not completed peer review, no calibration curves or feature attribution are published, and the company nowhere explains the gap between the deep learning models it publishes on and the rule based device described in its US clearance.
Substantial development and external validation, published with full conflict disclosure. Kim Y, Hahn S, Kim KJ et al., medRxiv 2025, doi 10.1101/2025.08.20.25334022, posted 24 August 2025. Derivation on 357,009 adult general ward admissions at Yonsei Severance Hospital 2013 to 2017; external validation on 22,073 admissions at NHIS Ilsan Hospital. VC-MAES AUROC 0.918 (95 percent CI 0.909 to 0.927) against MEWS 0.834 and NEWS 0.883; VC-SEPS AUROC 0.941 (0.934 to 0.947) against SOFA 0.559, qSOFA 0.687 and NEWS 0.767. Conflicts are stated plainly: seven authors are AITRICS employees and two are founders serving as chief executive and chief research officer. THE GAP IS THE ONE THIS CATEGORY EXISTS TO NAME. The paper's own introduction identifies high false alarm rates and alarm fatigue as a principal barrier to clinical adoption, and then reports AUROC as its primary metric. AUROC IS PREVALENCE INDEPENDENT AND TELLS A NURSE NOTHING ABOUT HOW MANY FALSE ALARMS THEY WILL SEE PER SHIFT. No positive predictive value, no alert burden per patient day and no median lead time are reported. Also held at B because the work is a preprint, the external validation is single centre, and both cohorts are Korean with data now eight to thirteen years old.
No privacy policy, data processing statement, retention policy or training use disclosure was located. Not Rated means no public evidence was found rather than evidence of weakness. What is known comes from the regulatory filing rather than customer facing material: input arrives from the EHR over an HL7 feed and the product is a browser delivered display. Open and unaddressed, and material given the company is Korean with deployments in Korea, Hong Kong, Vietnam and clearance for the US: where processing occurs, whether customer data crosses borders, and whether deployment data is used for model retraining.
No statement on business associate agreements, execution terms, cost or subprocessor disclosure was located. Not Rated means no public evidence was found. Worth noting for a US buyer that the company holds a US clearance but no US deployment was identified, so its HIPAA 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. The US 510(k) records conformance to IEC 62304 for software life cycle, IEC 62366-1 for usability engineering and IEC 60601-1-8 for alarm systems, but these are safety and quality standards rather than information security attestations, the same distinction this index applies to ISO 13485 and to 510(k) clearance generally.
GENUINE MULTI JURISDICTION BREADTH, AND A US CLEARANCE THAT DOES NOT COVER THE MARKETED PRODUCT. Korea's MFDS approved the deterioration prediction system for both general wards and intensive care. Hong Kong's Medical Device Division and Vietnam's Ministry of Health followed, with Indonesia and Malaysia stated as in progress. THE US CLEARANCE MUST BE READ IN FULL. K240756, decision 23 July 2024, 21 CFR 870.2300 Cardiac Monitor, Class II, product code PLB, predicate K213335 Capsule Surveillance System. The filing's own device description states that AITRICS-VC receives vital signs and blood test results from the EHR and CONDUCTS RULE BASED CALCULATIONS FOR CONVENTIONAL EARLY WARNING SCORES including NEWS, MEWS and qSOFA, screening patients against predefined thresholds and displaying them on a dashboard. The indications restrict it solely to the general ward and state expressly that it is NOT indicated for the ICU or operating rooms. The performance section states the device does not require clinical data, and none was submitted. So the deep learning deterioration and sepsis prediction the company publishes on and sells elsewhere is not what the FDA cleared. Clearing a narrow predicate matched claim to enter a market and expanding later is ordinary and legitimate regulatory strategy, and this note is not an allegation of impropriety. It is a warning that FDA CLEARANCE HERE IS A CLAIM ABOUT A RULE BASED WARD DASHBOARD, NOT ABOUT THE ML MODELS. Graded B rather than A for that mismatch and the absence of clinical data in the US submission, and not lower because the Korean approval of the ML product is substantive.
THE FIRST PUBLISHED SUBGROUP PERFORMANCE REPORTING LOCATED FOR ANY VENDOR IN THIS CATEGORY, and it should be the benchmark others are asked to match. The validation work reports that both models maintained AUROC above 0.86 across all age and sex categories, which is a quantified subgroup floor rather than a statement of intent. Every other vendor assessed in this lane publishes nothing of the kind. THE LARGER EXPOSURE IS TRANSPORTABILITY, and it is the sharpest version of this problem the index has encountered. Both models were derived and externally validated entirely in Korean cohorts on data from 2013 to 2017, and the product now holds US clearance with no published validation in any US population. The Epic Sepsis Model record established that the same model's sensitivity fell from 33 percent at a Michigan academic centre to 14.7 percent at county emergency departments within one country. A shift from Korean tertiary hospitals to the US care system, with different case mix, coding practice, laboratory conventions and ward staffing ratios, is a larger distributional change than that. Subgroups are also age and sex only, with no race or ethnicity breakdown, which is understandable in a single country cohort and precisely why it does not transfer. No calibration by subgroup is reported.
Ingest is confirmed in the regulatory filing as EHR data over an HL7 feed, delivered to clinicians through a web browser, which is a verified fact rather than a marketing claim. Beyond that the disclosure is thin: no EHR vendor is named anywhere, there is no FHIR support described, no SMART on FHIR launch, no marketplace listing or partner review credential, and no outbound path for pushing scores back into other systems. An Azure Marketplace listing exists. The counterweight, credited on the deployment axis rather than here, is that integration plainly works at scale in Korean hospitals; but nothing published tells a US buyer what integrating with Epic, Oracle Health or Meditech would involve.
Real scale in one market. VitalCare is reported in use at more than 60 hospitals in Korea, and named institutions appear consistently across independent symposium and conference reporting, including Seoul St Mary's, Gangnam Severance, Seoul National University Bundang, Yonsei Severance, NHIS Ilsan and Hallym University Chuncheon Sacred Heart. Delivery is browser based with an Azure Marketplace listing. Held at B because no US deployment was identified despite the US clearance, no implementation timeline, resourcing or data feed prerequisite is published, and no data residency commitment is stated, which is a live question for a Korean vendor selling across Korea, Hong Kong, Vietnam and now the US.
No pricing published at any level: no rate card, no unit of pricing, no indicative band, and no implementation or integration fee. An Azure Marketplace listing exists but carries no published price. Standard for this category and behind the published ladders that earn a Commercial A elsewhere in this index.
THE GENERAL WARD FOCUS IS A GENUINE AND USEFUL POSITION IN A CATEGORY THAT IS OTHERWISE ICU HEAVY. Ward deterioration is a harder detection problem than ICU deterioration for a reason worth stating: ICU patients are monitored continuously, while ward patients are measured intermittently, often every four to eight hours, so a ward model must work from sparse and irregularly timed observations. Every other vendor assessed in this lane is built around continuous critical care data streams. Coverage is published with explicit limits, which is credited: adults, general ward, with ICU mortality prediction marketed under the Korean approval but expressly excluded from the US clearance. Held at B because the ICU and ward claims differ by jurisdiction in a way the company does not clearly signpost, and because no paediatric or neonatal indication exists.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
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No pricing published at any level. No rate card, no stated unit of pricing such as per bed, per ward or per monitored patient day, and no published implementation, integration or training fee. An Azure Marketplace listing exists but carries no published price. The most important non price question for a US buyer is a scope question rather than a cost one, and it should be settled before any commercial discussion: the US 510(k) covers a rule based early warning score dashboard for the general ward only, expressly not the ICU, so establish in writing which product configuration is being quoted, whether the deep learning deterioration and sepsis models are included in a US deployment, and under what regulatory basis they would run.