Wavital

A hospital integrates Wavital’s real-time data platform to stream vital signs directly from bedside monitors. Using the Wavital Marketplace, they access AI algorithms that are initially trained on global datasets, then refined and personalized using the hospital’s own patient data. This continuous local tuning improves prediction accuracy for critical events like sepsis, cardiac arrest, and respiratory failure, delivering instant, tailored alerts to clinicians — all with minimal IT burden.

A children's hospital uses Wavital’s platform to continuously monitor young patients' vital signs in real time. AI algorithms, trained on pediatric-specific datasets and further refined using the hospital’s own patient data, predict early signs of conditions like sepsis, respiratory failure, or neurological decline. Clinicians receive real-time, patient-specific alerts, allowing them to intervene earlier, reduce ICU stays, and deliver safer, more personalized care to their most vulnerable patients.

Primary users are frontline clinicians — including physicians, nurses, respiratory therapists, and intensivists — who act on real-time AI alerts to prevent patient deterioration. Secondary users include hospital administrators, clinical informatics teams, and IT staff who manage system deployment, data governance, and compliance. Additionally, researchers and AI developers use the platform to create, refine, and deploy new predictive algorithms based on real-world clinical data.

Unlike traditional EHR systems that primarily document patient information for retrospective review, Wavital delivers real-time, proactive insights. EHRs typically rely on manual charting, delayed vital updates, and rule-based alerts; Wavital continuously streams high-fidelity vitals directly from bedside monitors, uses predictive AI instead of static thresholds, and generates actionable alerts within seconds — not hours.

Wavital is patient-level, device-agnostic, and cloud-native, making it faster to deploy, easier to scale, and far more effective at detecting early signs of clinical deterioration — without needing massive customization or "bolt-on" EHR modules.