Videos
Video: patient onboarding through digital ticket triage
Andrew Novage and George Williams on automating healthcare workflows safely — patient onboarding, vaccine scheduling, claims registration, and digital support — without black-box AI risk.
About this webinar
A 51-minute Kognitos webinar focused on what it actually takes to put AI automation into a regulated healthcare environment — for both payers and providers.
Speakers
- Andrew Novage — leads the Kognitos sales team in the West. Comes from a family of physicians.
- George Williams — Solution Engineering at Kognitos. Leads the live demo of patient-onboarding and ticket-triage automations.
The three reasons healthcare has been slow to adopt AI
- Lack of trust: ask ChatGPT, Grok, or Gemini the same question ten times and you can get nine different answers. Inconsistency is a feature of how LLMs work — but it's a liability when a wrong answer becomes a patient complication, a missed payment, or a wrongly denied claim.
- Lack of control: no guard rails, no way to audit exactly what the AI is doing — and HIPAA data privacy makes this concern especially sharp.
- Investment risk: pilots that don't go to production are widely visible. Healthcare executives want predictable ROI before committing.
How Kognitos addresses each concern
- Trust: the LLM is used for understanding and creativity; the runtime is deterministic English-as-code. The same input always produces the same answer.
- Control and audit: every step of every run is logged as English “facts.” A compliance officer can read what happened without parsing developer logs. Sensitive data flows can be scoped per department.
- HIPAA-grade deployment: tenancy and data handling are designed for regulated industries up front, not retrofitted.
Use cases shown in the demo
- Patient onboarding — extract patient identifiers from new-patient forms, validate across documents, file to SharePoint, and email the intake team.
- Vaccine scheduling — high-volume, repetitive scheduling work prone to manual errors.
- Claims registration — get from FNOL to a structured claim faster, with the audit trail regulators expect.
- Digital support ticket triage — classify inbound tickets and route to the right team without dropping context.
Questions answered in this video
Why has healthcare been slow to adopt AI according to this webinar?
Three reasons: lack of trust (LLMs give inconsistent answers, and in healthcare a wrong answer becomes a patient complication or a wrongly denied claim), lack of control (no guard rails or audit trail, plus HIPAA data privacy concerns), and investment risk (pilots that don't make it to production).
How does Kognitos make AI safe enough for HIPAA-regulated workloads?
By separating the creative LLM layer from the deterministic execution layer. The LLM helps with understanding and extraction; the runtime executes English-as-code deterministically and logs every step as English “facts” for audit. Data handling and tenancy are designed for regulated industries from the start.
Which healthcare workflows does the demo cover?
Patient onboarding (extract and validate patient identifiers, file to SharePoint, email the team), vaccine scheduling (high-volume scheduling automation), claims registration (FNOL to structured claim), and digital support ticket triage (classify and route inbound tickets).
Why won't a black-box LLM work for claims and patient data?
Because the answers aren't reproducible — the same question can return different answers on different runs. In healthcare, that translates directly to clinical or financial harm. Kognitos's deterministic English-as-code runtime ensures the same input always yields the same output, which is the bar regulators and compliance officers actually need.