All use cases

Prevent / Care

Doctor copilot — pre-drafted clinical reply with citations

A GLP-1 patient asks the doctor a question, and the reply is already waiting — assembled from everything the patient has said across every channel, with the source behind every line. The clinician edits and approves.

Close the loopSystem triggerBehavioral triggerEmailChatIn-appGLP-1 & PeptidesHealth & Telehealth

What it does

When a GLP-1 patient sends a clinical question, the clinician usually has to reconstruct the context first — scroll the chat history, find the last dose, recall the side-effect the patient mentioned weeks ago — before writing a word. That reconstruction is slow, it is repeated for every message, and a reply written without the full picture risks missing something. The agent does the assembly: it pulls the patient's full cross-channel history and the relevant knowledge-base passages, pre-drafts a reply with a citation behind each clinical claim, and hands it to the clinician to edit and approve. The clinician keeps every decision; the typing and the context-hunt are gone, and the cited trail is auditable.

How it works

  1. 1
    Trigger. a clinical question from a GLP-1 patient lands in the doctor's reply queue.
  2. 2
    Decision. the agent assembles the patient's full cross-channel history (current dose, program stage, side-effect log, prior threads) and retrieves the relevant knowledge-base passages, drafting a reply with a source attached to every clinical claim; the draft passes quality gates before the clinician sees it, and anything uncertain is posed as a question rather than asserted.
  3. 3
    Action. the pre-drafted reply appears inside the clinician's tool with the citations alongside and approve, edit, and ask-rework controls; one click sends it to the patient.
  4. 4
    Follow-through. clinician edits feed back as a quality signal; the approved reply, its sources, and the outcome write back to the patient's memory, so the next draft starts from the full picture and the citation trail stays auditable.
Example
Agent (draft for review)Mild nausea around this point in titration is common and usually eases over the following weeks — taking the dose with food and easing portion sizes tends to help. *Drafted from her last two check-ins and the side-effect log; each line cited to the program's GLP-1 nausea guidance.*
ClinicianGood, but she also flagged reflux on Tuesday — fold that in and keep it to one paragraph.
AgentUpdated: the reply now addresses the reflux note from Tuesday and is condensed to one paragraph, citations retained. Ready to approve.

Configuration

How the agent is wired for this use case.

Triggera clinical question from a GLP-1 patient landing in the doctor's reply queue.
Tools & actions
  • Clinical/EHR system · read the patient's full cross-channel history (current dose, program stage, side-effect log, prior threads) to assemble the draft
  • Knowledge base · retrieve the relevant passages and attach a source to every clinical claim
  • Clinical queue · surface the pre-drafted reply with citations alongside and approve, edit, and ask-rework controls
  • Clinical queue · send the approved reply to the patient on one click
  • Memory store · write the approved reply, its sources, and the outcome back to the patient's memory
Autonomyassembly, retrieval, citation, and quality gating run unattended; nothing reaches a patient without explicit clinician sign-off — a copied error is catastrophic in a clinical setting, so every draft passes quality gates and anything uncertain is posed as a question, not an assertion. The agent never sets a dose, diagnoses, or sends without the clinician.
Channelsemail · chat · in-app
Escalationthe clinician owns every send — approve, edit, or ask for a rework; an uncertain draft is posed as a question for the clinician rather than asserted to the patient.

What you need

The inputs this use case runs on. Your channels stay yours; the agent supplies the judgment.

Signals

new clinical-question events into the doctor's reply queue; program-stage and side-effect events that should shape a draft.

Data

the patient's cross-channel history and memory (current dose, program stage, side-effect log, prior threads); GLP-1 clinical protocols and knowledge sources with citable passages.

Guardrails

nothing patient-bound without explicit clinician approval; quality gates on every draft; a source attached to every clinical claim; a full audit log of drafts, edits, and approvals; the agent never advises a dose or diagnoses.

Metrics it moves

  • clinician-hours-savedup, as the context-hunt and the typing collapse into review-and-approve and scarce clinical hours return to care.
  • time-to-resolutiondown, because the patient gets a clinician-approved answer in minutes of review time rather than hours of writing time.
  • csatup, since replies arrive faster and grounded in the patient's actual cross-channel history rather than a cold read of one thread.

Related use cases

Prevent / Care

Clinician copilot — approve instead of writing

the cross-industry approve-queue this specialises to GLP-1 consults

Prevent / Care

Doctor copilot — in-tool conversational draft revision

the editing-loop step the clinician runs on this pre-draft

Convert

Warm referral from free user to paid clinical consult

the consult this copilot answers, where the question originates

See it on your own customer journey

Bring one drop-off, one churn cliff, or one silent segment. We will show you what a proactive agent with memory and judgment does with it.

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