Tracks referrals out and in, chases the ones that stall, and flags the patients who have fallen between two providers.
An AI agent for patient referral coordination closes a loop that is open in almost every practice. A referral goes out, and then one of several things happens: the specialist accepts and books, the patient never makes the appointment, the referral is rejected and nobody notices, or the appointment happens and no letter comes back. Only the first is fine, and the practice usually cannot tell which occurred without somebody checking. Patients fall through this gap routinely and the practice finds out when they return months later, still unseen. The agent tracks each referral from the moment it is sent, chases at the points where they typically stall, and flags the ones where the loop has not closed. None of the tracking requires any medical judgment, which is what makes the gap so persistent: it is work that is unmistakably somebody's responsibility in principle and nobody's in practice. Built on Agentplace: the agent runs on its own page, so a visitor finishes the whole request in the conversation.
Tracks the loop and flags where it broke.
Tracks every referral sent and its current state
Confirms whether the receiving provider accepted it
Checks whether the patient actually booked
Chases outstanding referrals at the usual stall points
Flags referrals where no report has come back
Routes clinical questions to the referring clinician
The referring clinician has done their part when the referral goes out, the receiving provider's responsibility starts when they accept it, and the space in between belongs to nobody. That is where patients are lost — not through anybody's error, but because there is no point in the process at which somebody is supposed to notice that a referral sent six weeks ago has produced nothing. Tracking the loop is administrative work with a clinical consequence, which is an unusual and important combination: the task requires no medical judgment at all, and failing to do it means patients go unseen. The flag that matters most is the quiet one — a referral accepted, an appointment made, and no report received — because that is the case where everybody assumes it went fine.
Track it out, check each stage, flag what stalls.
Every referral, with the receiving provider and the date it went out.
Accepted or rejected, booked or not booked, seen or not seen, report received or outstanding.
Chase the stages that stall, and flag anything where the loop has not closed to the referring clinician.
Three referrals, three different failures.
Scenario: a practice ran a referral audit and found patients who had been referred more than four months earlier with no record of any outcome. The agent begins tracking. Of three referrals sent in one week, one is accepted and booked and needs nothing. The second is rejected by the receiving service eight days later for an administrative reason, which under the old process would have sat unnoticed in an inbox — the agent flags it to the referring clinician, who resubmits it correctly the same day. The third is accepted, but four weeks on the agent finds no record that the patient ever booked. It contacts the patient, who says they never received the appointment letter. The appointment is arranged. None of the three required clinical judgment from the agent, and two of the three would have failed silently.
Anybody who cannot say what happened to a referral.
Patients lost in referrals are a safety issue, not an admin one.
Nobody is responsible for the middle of the process.
You do not find out that a referral was rejected.
Unclosed referral loops are an auditable risk.
The queue is too large to track by memory.
The same gaps exist on the inbound side.
Where referrals are tracked and who is told.
Holds every referral, its stage, dates and the receiving provider.
Chases receiving providers and records their responses.
Contacts patients who have not booked their appointment.
Flags rejections and unclosed loops to the referring clinician.
Stores reports as they come back against the referral.
Reports loop closure rate and where referrals stall.
The referral failures worth catching.
Questions about closing the referral loop.
An AI agent for patient referral coordination tracks every referral from the moment it is sent, checks whether it was accepted, whether the patient booked and whether a report came back, chases the stages that stall, and flags unclosed loops to the referring clinician.
The quiet one. A referral accepted, an appointment apparently made, and no report received looks like success to everybody involved, and it is the case where a patient can go unseen for months.
None. Every step is administrative — sent, accepted, booked, seen, reported. That is precisely why it is worth automating and why it is so consistently left undone.
For booking status, yes, with the practice's agreement. A patient who did not receive a letter is the most common single cause of a stalled referral and a text resolves it.
The same tracking applies inbound and it is often worse, because the receiving side has less visibility of what the referrer expected. Both directions are worth closing.
Unclosed referral loops are auditable in most systems and are a recognized patient safety concern. That framing usually matters more internally than the administrative time saved.
Loop closure rate and where referrals stall. Practices are usually surprised by both, and the stall points cluster in two or three places that can be addressed directly.
It runs on Agentplace. Agentplace is an AI agent platform where the agent gets its own page, talks to your visitors there, and carries the request through to the end instead of handing it to a form. You can open this template and change any step before you publish it.
Tracks referrals out and in, chases the ones that stall, and flags the patients who have fallen between two providers. Open it in Agentplace and change any step before you publish.