Tivara | AI for Patient Engagement

What

Tivara is an AI patient engagement platform focused on automating phone-based workflows for healthcare organizations. It is designed for practices and care teams that need to manage high volumes of routine patient calls across scheduling, refills, intake, reminders, and after-hours support.

The product appears positioned as a healthcare-specific voice automation layer that works with existing telephony, EMR, and practice management systems. Its core workflow is to answer patient calls, complete common administrative tasks autonomously, and escalate to staff when requests are out of scope, low confidence, or require clinical judgment.

Features

• 24/7 AI phone agents: Handles inbound patient calls at any time, which can reduce missed calls and improve access outside normal office hours.
• Appointment scheduling: Verifies provider availability in real time and books appointments directly into the EMR, helping reduce scheduling friction.
• Refill, lab, and order request capture: Collects required details and surfaces requests within the patient chart, which can streamline routine follow-up work.
• After-hours triage support: Follows practice protocols and escalates urgent cases appropriately, supporting safer call handling when staff are unavailable.
• Patient intake automation: Gathers demographics, insurance, and diagnosis information before the first visit and can create a chart via the PMS when needed.
• Integration with EMR and PM systems: Connects to leading EMR and practice management systems through APIs and HL7 interfaces, supporting end-to-end phone workflows without replacing the existing phone system.

Helpful Tips

• Validate escalation design early: For healthcare voice automation, the quality of live transfer rules, on-call routing, and low-confidence fallback paths is as important as the automation itself.
• Start with high-volume, low-variance workflows: Scheduling, reminders, and refill intake are often the best first use cases because they are repetitive and easier to standardize.
• Review workflow fit by specialty: Tivara mentions specialty-specific agents, so buyers should confirm how well scripts, protocols, and intake logic match their specific practice operations.
• Check integration depth, not just connectivity: The page states EMR and PM integration, but implementation teams should verify exactly which write-back actions, chart updates, and task creation flows are supported in their environment.
• Plan staff adoption alongside deployment: Operational gains usually depend on front-desk and clinical teams understanding when the AI resolves requests independently and when it hands off work.