Healthcare AI Glossary

Autonomous, goal-directed clinical processes that adapt to patient needs while maintaining human oversight. Unlike rigid chatbots, agentic workflows can make decisions, escalate issues, and follow multi-step care protocols.

Predefined checkpoints in a patient care journey that ensure critical information is collected and key interventions occur at the right time.

Human-readable markdown files containing clinical workflows with metadata for triggers, versioning, and patient-specific adaptations.

A safety mechanism where AI decisions are reviewed by qualified healthcare professionals before execution, ensuring clinical accuracy and patient safety.

Multi-factor algorithm that quantifies the AI's certainty about responses, considering pattern matches, context completeness, and clinical risk factors.

Atomic, reusable safety checks that detect high-risk situations, medication issues, or mental health crises requiring immediate intervention.

A secondary AI model that evaluates primary AI responses against clinical safety criteria before patient delivery, acting as an automated quality check.

System design that fundamentally separates personally identifiable information (PII) from AI processing, ensuring compliance by architecture rather than policy.

Patient interactions stripped of identifying information, allowing AI processing while maintaining complete privacy protection.

Tamper-proof record of all AI decisions, human reviews, and patient interactions required for regulatory compliance and clinical quality assurance.

Modular AI instruction framework separating medical safety guidelines, clinic policies, and conversation style into manageable, version-controlled components.

Template-based content personalization using clinic-specific information, automatically replacing placeholders with actual values at runtime.

Regex-based system for identifying clinical concerns, using weighted patterns to detect symptoms, medication mentions, and risk factors.

Empowering clinical teams to create, modify, and deploy care workflows without programming knowledge or IT dependencies.

Prioritized workflow for human review of AI interactions requiring clinical judgment, organized by severity and wait time.

Technology-enabled standardization of evidence-based care protocols, ensuring consistent delivery while adapting to individual patient needs.

The ability to rapidly adapt clinical workflows, add new treatment protocols, and scale patient capacity without technical constraints.

Secure, isolated environments for multiple healthcare organizations on a shared infrastructure, each with custom workflows and data separation.

Explainable AI feature showing the reasoning behind every recommendation, enabling rapid clinical validation and building provider trust.

Automated coordination of patient touchpoints, follow-ups, and interventions based on care plans and real-time patient data.