AI Integration for Doxy.me

AI integration for Doxy.me focuses on augmenting the pre-visit and post-visit workflows that surround the secure video session. The primary surfaces for integration are the platform's Custom Fields, Webhooks, and API for patient data, which allow you to inject intelligence into intake, follow-up, and administrative tasks. Key data objects include patient profiles, appointment records, and custom form submissions, which serve as triggers for AI-driven actions.

Implementation typically involves setting up a middleware layer that listens for Doxy.me webhook events—like appointment.scheduled or appointment.completed—and routes relevant data to AI agents. For example, upon receiving an appointment.scheduled event, an agent can analyze the patient's intake form from custom fields, generate a personalized pre-visit summary for the clinician, and push it back into the appointment notes via API. Post-visit, another agent can draft a visit summary and care instructions, then trigger an automated SMS or email to the patient through connected comms platforms, all while logging actions for audit.

Rollout should be phased, starting with a single, high-impact workflow like automated intake summarization to prove value and ensure data privacy. Governance is critical; all AI interactions must be designed with HIPAA compliance in mind, ensuring no PHI is stored in unapproved LLM environments and that all data flows are logged. A successful integration turns Doxy.me from a simple video conduit into an intelligent care coordination hub, reducing manual data entry for staff and improving the patient experience with timely, personalized touchpoints. For related architectural patterns, see our guides on AI Intake Workflows for Virtual Care Platforms and AI Visit Summarization for Telehealth Platforms.

The integration surface for Doxy.me centers on its webhook events and custom patient fields. Key architectural touchpoints include the room.started and room.ended webhooks, which signal when a virtual waiting room is active and when a visit concludes. These events trigger AI workflows. Patient data, collected via Doxy.me's intake forms, can be extended using custom fields to store AI-generated outputs like a pre-visit summary or post-engagement action items, making them visible to providers directly within the Doxy.me interface.

A production implementation typically involves a middleware service (often deployed as a secure cloud function) that subscribes to Doxy.me webhooks. When a room.ended event is received, the service calls Doxy.me's API to retrieve the visit's transcript (if recorded) and associated patient intake data. This payload is sent to a governed LLM—like a HIPAA-aligned Azure OpenAI instance—prompted to generate a structured SOAP note draft and a patient-friendly follow-up summary. The results are written back to the patient's record via Doxy.me's API into designated custom fields and simultaneously queued for optional clinician review before finalization.

Rollout requires a phased approach: start with non-clinical workflows like automated post-visit satisfaction surveys or FAQ bots triggered from the waiting room. For clinical documentation, implement a human-in-the-loop approval step where the AI-generated note is presented to the clinician in a separate dashboard (or within Doxy.me via an embedded iframe) for review and sign-off before being committed to the patient record. Governance must address audit trails for all AI-generated content, prompt versioning, and strict access controls to ensure only authorized roles can trigger or modify AI workflows. This architecture augments Doxy.me's core simplicity without disrupting the provider's established workflow.

Our integration architecture treats Doxy.me as the system of record, with AI agents operating as stateless, event-driven services. We connect via Doxy.me's webhooks (e.g., visit.started, visit.ended) and REST APIs for custom field updates. AI processing occurs in a secure Inference Systems environment, with all Protected Health Information (PHI) encrypted in transit and at rest. We never store raw visit transcripts or patient data; we process, summarize, and write structured outputs back to designated custom fields or via secure API calls, maintaining a full audit trail of all AI actions linked to the Doxy.me visit ID.

A phased rollout is critical for clinician adoption and risk management. We recommend starting with a single, high-ROI workflow in a pilot group, such as AI-driven post-visit summary generation. This allows us to:

• Validate data flows and accuracy thresholds with real visit data.
• Establish a human-in-the-loop review step where clinicians approve or edit AI-generated notes before they are saved to the patient record.
• Refine prompts and output formats based on clinician feedback.
• Monitor system performance and API usage before scaling.

Governance is built into the workflow design. Each AI agent action is permission-scoped, requiring explicit role-based access control (RBAC) checks against Doxy.me user roles before writing data. We implement prompt versioning and output logging to trace any summary or recommendation back to the exact AI model and instructions used. For compliance, our integration supports configurable data retention policies for AI-generated artifacts and can be designed to operate within specific geographic boundaries for data residency requirements.