Vitalchat
Vitalchat, based in Raleigh, North Carolina, sells a software led virtual care platform for hospitals. Built on real time audio and video, it connects virtual nurses, specialists and family to patient rooms and runs on standard hardware the hospital already owns or sources, rather than on proprietary room devices, integrating with telemetry, ambient monitoring and clinical workflow tools. It supports virtual nursing, observation, emergency department and perioperative support, and care at home, and Vitalchat is a founding member of the Rural Health Transformation Alliance.
Its trust center describes AI that analyzes video, audio and sensor data from inpatient rooms in real time to identify trends, predict risks and flag anomalies as alerts and automated actions for clinical teams. University Hospitals piloted the platform on four hospitals' medical surgical, intensive care and pediatric units, then covered all 274 inpatient beds and the emergency department at UH Lake West, and reports an estimated return of more than $10 million in six months; North Mississippi Health Services is a second named customer.
The company runs a trust center on Secureframe with a SOC 2 Type 2 report available on request. It publishes no price, no named record system integration and no accuracy figure for its AI.
Capability Axes
An AI Health Index grade measures what a buyer can verify from public sources on the date shown. It is not a rating of how good the product is. A vendor can build an excellent system and grade low on an axis because it publishes nothing an outsider can check. How grades read
Vitalchat now sells a virtual care collaboration platform, and AI is a feature within it. Its home page describes an open, software led platform built on real time audio and video that runs on hospitals' existing hardware and integrates with telemetry, ambient monitoring and workflow tools, for virtual nursing, observation, emergency and perioperative support and care at home. The AI appears in the trust center, which says the technology analyzes video, audio and sensor data from the inpatient setting in real time to identify trends, predict risks and flag anomalies as alerts and automated actions, and in the University Hospitals case study, which lists an AI enabled platform supporting real time insights and alerts. Recognizing events in video and audio is a learned function by what it does.
The platform's core value, virtual nurses reaching every room, stands without the learned layer.
Automation is claimed and its limits are not described. The trust center says the AI turns its analysis into alerts and automated actions that support clinical teams; the University Hospitals case study describes virtual nurses in a central command center handling admissions, discharges, observation, fall prevention and urgent interventions with in room teams. Nothing says which actions the system takes on its own, what a person approves first, or what thresholds raise an alert.
What the AI does is described in general terms and nothing is identified. The trust center says the technology performs continuous real time analysis of video, audio and sensor data in the inpatient setting to identify trends, predict risks and flag anomalies, turned into alerts and automated actions. No model type, training data, version, update practice or accuracy figure is published.
Which AI is Vitalchat's own and which a partner's cannot be told from what is published. Vitalchat describes a software platform on the hospital's own off the shelf hardware, integrating third party telemetry, ambient monitoring and workflow tools, with AI analysis of room video and audio; the trust center lists a vendor risk management policy. No cloud provider, model provider or subprocessor list is published.
Named deployments with a vendor estimate and no method. The University Hospitals case study describes a pilot across four hospitals and five units (medical surgical, intensive care and pediatric), then full deployment at UH Lake West across all 274 inpatient beds and the emergency department, and reports an estimated return of more than $10 million in six months; a UH quality executive is quoted on the savings. North Mississippi Health Services is a second named case study. No baseline, measure, period definition or method stands behind the $10 million estimate, and nothing measures the AI alerts separately from the virtual nursing model.
General controls are published and the AI questions are not answered. The trust center, monitored through Secureframe, states that production data is not used in development and testing environments except when needed to debug customer issues, that service data is encrypted at rest, and that a data retention and disposal policy governs customer data, without publishing its terms. The privacy notice excludes the services Vitalchat provides as a business associate for health care organizations.
No retention period for patient video or audio, statement on whether captured material trains the models, or de identification method is published, and nothing is published on safety engineering. The capture reaches intensive care units and operating rooms, where patients often cannot be told or object.
The business associate role is stated and no service privacy document is published. The privacy notice says it does not apply to the services Vitalchat offers as a business associate on behalf of health care organizations, which states the role, and the trust center lists HIPAA among the frameworks it monitors. No business associate agreement, tier or service privacy document is published.
A trust center with a SOC 2 Type 2 report available on request. trust.vitalchat.com, linked in the footer and run on Secureframe, shows continuous control monitoring and lists controls across change management, secure development, business continuity, incident response with tested plans, risk assessment, vendor risk management, access control and encryption at rest. It offers the SOC 2 report on request, and lists SOC 2 Type 2, ISO 27001, HITRUST, HIPAA, NIST and FedRAMP among the frameworks it tracks; no ISO 27001 or HITRUST certificate is shown.
The platform is sold as virtual care collaboration, with no device claim: video and audio links for virtual nurses and specialists, with AI alerts to clinical teams. The trust center's description of predicting risks from video, audio and sensor data moves toward clinical decision support, and no position is published for it.
How the AI's analysis of patient video and audio is evaluated or governed is not published. The trust center lists security and privacy policies (risk assessment, change management, secure development) that govern the company's information security, not model behavior, and nothing describes validation, monitoring, subgroup results or a responsible AI commitment for the alerts. Patients under continuous observation are often sedated, confused or at high risk, which is where detection is hardest.
What happens when the AI is wrong is not described. The trust center says its analysis produces alerts and automated actions for clinical teams, and does not describe which actions happen without a person, how an alert is checked, or what follows a missed event. No accuracy, false alert rate, warranty, indemnity or correction route is published.
Integration is claimed and no system is named. The home page says the platform integrates with telemetry, ambient monitoring and clinical workflow platforms and is built as an open, modular architecture that connects best of breed tools. No record system, interface standard, marketplace listing or named integration is published, and the University Hospitals case study does not say how the platform connects to UH's record.
A software service on the hospital's own hardware, with no location committed. The home page describes a software led platform that runs on standard, off the shelf equipment the hospital sources and manages, and the trust center lists FedRAMP among the frameworks it tracks. No cloud provider, region, residency commitment or tenancy model is published, and nothing says where video and audio are processed or stored.
No price, unit of charge or pricing basis is published; the site routes buyers to contact the company. It says running on existing, off the shelf hardware lowers total cost of ownership, which describes a cost structure without a figure.
Coverage is named across hospital settings, with one system's deployment behind part of it. Vitalchat names emergency, isolation, pediatric, post acute, behavioral health, medical surgical, intensive care, operating room and home settings, and joins the Rural Health Transformation Alliance as a founding member. It sells virtual nursing as added workforce capacity and emergency department support for triage and throughput. University Hospitals piloted it on medical surgical, intensive care and pediatric units in four hospitals and then covered every inpatient bed and the emergency department at UH Lake West.
The other named settings carry no published deployment.
Alternatives to Vitalchat
Vendors in the same categories as Vitalchat whose graded profiles look most alike, with a line on what each product does.
Similar is not the same, so check that each one does the job you need. No vendor pays for placement.
care.ai makes virtual care and ambient monitoring software for hospital inpatient units.
Biobeat sells wearable vital sign monitors and a cloud platform for hospitals, long term care and home programs.
Caregility, based in Wall, New Jersey, runs a virtual care platform for hospitals and other care settings.
Corsano Health, based in The Hague, makes the CardioWatch, a certified medical wrist wearable that measures up to 10 vital parameters continuously, including heart rate, respiratory rate…
Sickbay, from Medical Informatics Corp (MIC) in Houston, is a vendor neutral platform that captures physiologic data and waveforms from bedside monitors, ventilators and other devices…
VSTOne is VirtuSense's acute care product, a single sensor device per hospital room that predicts when a patient is about to leave the bed or chair unassisted and alerts staff before it happens.
Ordered by how closely the graded profiles match across the fifteen axes, inside the same categories. See how each axis is graded in the methodology.
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.
| Entry Price | Pricing Basis | BAA Tier | Implementation | Source |
|---|---|---|---|---|
|
Not published
|
Not disclosed. No unit of charge is described anywhere, and unusually for this segment the product shape gives no hint, because a platform that ships no hardware has no device to price against. Whether charging follows the room, the bed, the monitored patient, the concurrent observer, the application or the enterprise is entirely unstated, as is whether the virtual sitting, procedural support, workflow automation and family engagement applications are licensed together or separately. | The privacy notice states that Vitalchat provides services as a business associate for health care organizations and excludes them from the notice; no agreement template or terms are published. The trust center lists HIPAA among its monitored frameworks. Ask for the agreement, what the platform captures in operating rooms and intensive care, and where Vitalchat's processing begins on hospital owned hardware. | Not published. The platform runs on hospital sourced hardware, so there is no device installation, but no statement covers minimum equipment and network requirements, integration, clinical program design or training, or whether they are charged. University Hospitals' rollout moved from a four hospital pilot to full deployment at one hospital. | Vendor Published |
No price, unit of charge, range, tiering or trial terms are published; the site routes buyers to contact the company. The one cost statement is structural: the platform runs on standard, off the shelf hardware the hospital sources and manages, which the company says avoids single vendor hardware and lowers total cost of ownership. With no device to count, the unit of charge cannot be inferred.
The University Hospitals case study reports an estimated return of more than $10 million in six months with no calculation shown. Ask for the unit of charge, whether virtual nursing, observation and the AI alerts are licensed together, the annual cost for a representative unit, and the minimum term.