An AI assistant constantly scans patient records and automatically takes action to close care gaps—like ordering overdue labs or scheduling screenings—so nothing falls through the cracks.

Beacon Health deploys agentic AI that continuously monitors patient panels against quality measure benchmarks (e.g., HEDIS, CMS Stars). When a gap is detected—such as a missed A1C test for a diabetic patient or an overdue colorectal cancer screening—the agent autonomously initiates the appropriate workflow within the EHR: placing lab orders, generating patient outreach messages, or flagging the chart for the next visit. Unlike traditional analytics dashboards that surface gaps for humans to act on, Beacon's agents execute the multi-step remediation process end-to-end, mimicking the workflow a care coordinator would follow. This is critical for value-based care organizations where quality scores directly determine shared savings and bonus payments. The system leverages clinical NLP to parse unstructured notes and confirm whether a gap is truly open (avoiding false positives from data buried in free text), and uses reinforcement learning signals from successful closures to optimize outreach timing and channel selection over time.

It's like having a tireless medical assistant who never forgets a single patient's overdue checkup and books it before anyone even asks.

An AI assistant constantly scans patient records and automatically takes action to close care gaps—like ordering overdue labs or scheduling screenings—so nothing falls through the cracks.

Beacon Health deploys agentic AI that continuously monitors patient panels against quality measure benchmarks (e.g., HEDIS, CMS Stars). When a gap is detected—such as a missed A1C test for a diabetic patient or an overdue colorectal cancer screening—the agent autonomously initiates the appropriate workflow within the EHR: placing lab orders, generating patient outreach messages, or flagging the chart for the next visit. Unlike traditional analytics dashboards that surface gaps for humans to act on, Beacon's agents execute the multi-step remediation process end-to-end, mimicking the workflow a care coordinator would follow. This is critical for value-based care organizations where quality scores directly determine shared savings and bonus payments. The system leverages clinical NLP to parse unstructured notes and confirm whether a gap is truly open (avoiding false positives from data buried in free text), and uses reinforcement learning signals from successful closures to optimize outreach timing and channel selection over time.

It's like having a tireless medical assistant who never forgets a single patient's overdue checkup and books it before anyone even asks.

An AI nurse-line that never sleeps—it answers patient questions after hours, tells them if they need the ER or just a next-day appointment, and follows up automatically.

Primary care practices in value-based care arrangements bear financial risk for unnecessary emergency department visits, yet most lack the staffing to provide robust after-hours coverage. Beacon Health's agentic AI addresses this by deploying a conversational triage agent that engages patients via secure messaging, SMS, or voice when the practice is closed. The agent conducts structured symptom assessments using clinically validated triage protocols (e.g., Schmitt-Thompson), asks clarifying questions, and determines the appropriate level of care: self-care instructions, next-day appointment scheduling, urgent care referral, or 911 escalation. For non-urgent cases, the agent can autonomously schedule follow-up appointments, send educational materials, or place the patient on a nurse callback list for the morning. The system integrates with the patient's EHR record to contextualize responses—for example, recognizing that chest pain in a patient with known GERD and recent normal cardiac workup warrants different triage than in a patient with coronary artery disease risk factors. The conversational AI uses a retrieval-augmented generation (RAG) architecture grounded in clinical triage guidelines to minimize hallucination risk, and all interactions are logged in the EHR for provider review. This reduces after-hours staffing costs, prevents avoidable ER visits (saving shared savings dollars), and improves patient satisfaction and access.

It's like a calm, knowledgeable nurse who picks up the phone at 2 AM, checks your chart, and tells you exactly what to do—without ever yawning.