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Practice Operations

Surgical Coordination Calls in an Eye Practice

Cataract and retina surgery generate a chain of calls around one procedure. How surgical coordination runs when every step is booked and tracked in athenaOne.

9 min read

Surgical coordination in an eye practice is not one appointment. It is a sequence of six or seven contacts around a procedure, and every one of them is a phone call somebody has to make.

A cataract patient generates a measurement visit, a clearance requirement, a pre-operative confirmation, the surgery itself, a next-day check, a follow-up week, and very often a second eye that repeats the entire chain a month later. The surgery is scheduled. Almost nothing else in that list is, and the practice finds out which pieces were missed by getting a call.

The schedule shows the surgery and hides the sequence

Look at an eye practice schedule and you see procedures. What you do not see is the work that has to happen on either side of each one.

That work is real and it is mostly telephone work. Confirming the patient completed their pre-operative measurements. Chasing a clearance from another practice. Confirming the patient understands what time to arrive and what to stop taking. Booking the day-after check before the patient leaves. Booking the week-after visit. Then starting again for the second eye.

None of those steps is difficult. The difficulty is that they are dependencies, and a dependency that lives only in a coordinator’s memory or a shared spreadsheet fails silently. The surgery date arrives and one input is missing, and the practice discovers it on the morning of, when the cost of discovering it is highest.

The failure mode is well known in multi-stage scheduling generally: one leg gets booked and the other does not, and the second half of the workflow does not exist until a human notices. Eye surgery is that pattern repeated several times per patient.

Linked appointments are convention, not configuration

The structural reason this keeps happening is that the scheduling template does not know the sequence exists.

The same thing happens in other procedure-heavy specialties. At one vascular group roughly 90% of visits are an imaging study plus a provider visit, booked as a pair with specific spacing, and nothing in the template enforces the pairing. It exists as convention among the people who do the booking, while the patient just calls asking to see the doctor.

An eye practice has the same shape with more legs. The measurement visit has to precede the surgery by enough time for the lens to be ordered. The post-operative checks have to follow at defined intervals. The second eye has to sit far enough after the first. Those are firm operational rules and none of them are expressed anywhere the schedule can read.

What closes the gap is treating the sequence as the unit of work rather than the appointment. Reading the patient’s encounter history through GET /chart/{patientid}/encounters and their existing bookings through GET /appointments/booked shows which legs exist and which are missing. That comparison is what turns a vague sense that follow-up is inconsistent into a specific list of patients with a gap.

The automation books the missing administrative legs and confirms the ones that exist. It does not decide the intervals. Those come from the surgeon and are configured once.

The complication: reminders fire on the first appointment only

This one is small, entirely mechanical, and responsible for a surprising number of missed post-operative visits.

Native reminders in most systems fire on the chronologically first appointment. A patient with a 9:30 imaging slot and a 10:00 provider visit gets reminded about one of them and misses the other. In an eye practice this shows up when a patient has a measurement visit and a consultation on the same morning, or a post-operative check and an optical fitting.

The patient is not being careless. They received a reminder, they attended what the reminder said, and they left. From the practice’s side it reads as a no-show on the second appointment, which sends it into a no-show recovery process that will not fix the actual cause.

An automation working the day-before confirmation call handles this by confirming the patient’s full day rather than a single appointment. It reads everything booked for that patient on that date, states them together in order, and confirms the whole block. That is a small change with a direct effect on post-operative attendance.

Where it hands off is anything the patient raises about their eye. A confirmation call frequently surfaces a patient describing discomfort, vision changes, or a question about drops. None of that is a scheduling matter. It goes to clinical staff immediately, and the automation’s job is to route it, not to assess it or to reassure the patient about it.

Clearance chasing is paperwork, and it can be worked as a queue

The step that most often delays an eye surgery is a document that has to come from somewhere else.

Many procedures require a clearance from the patient’s primary care practice or a specialist, which means the eye practice is waiting on a third party who has no stake in its schedule. Someone has to call, confirm the request was received, call again, and finally get the document routed to the right place. When nobody is assigned to that, it surfaces as a cancellation close to the surgery date.

Run as a queue instead of as a memory, it is tractable. Sort scheduled procedures by date, identify the ones with an outstanding clearance, and work the oldest gap first. The calls themselves are highly repetitive: a request for status, a confirmation of where to send it, a note recording what was said.

The distinction that keeps this administrative is worth stating plainly. The automation chases the document, confirms receipt, and routes it to the right work queue. It does not read the clearance, does not judge whether its contents are sufficient, and does not tell anyone the patient is cleared. A clinician makes that call after reading it, every time.

The phone burden is where the business case lives

Practices usually accept that this coordination work is necessary and assume it simply requires staff. The volume data suggests how much staff.

In a March 10, 2026, MGMA Stat poll of practice leaders, the most time-intensive phone tasks were eligibility and prior authorization at 45%, scheduling at 31%, intake at 9%, and prescription refills at 6%, with an other category at 9%. The poll had 294 applicable responses.

Surgical coordination straddles the top two lines. Every procedure carries an eligibility and authorization question, and every procedure carries several scheduling contacts. A practice doing a meaningful volume of cataract surgery is running that combined burden many times a week, per surgeon.

The part worth automating is the repetitive part: confirming, booking, chasing, and recording. The part worth protecting is the coordinator’s time with the patients whose situation is genuinely complicated, which is where an experienced surgical coordinator earns their salary and where they currently have the least time to spend.

Measure completion of the sequence, not activity

Coordination is unusually easy to measure badly, because the volume of calls made goes up whether or not the work is landing.

The number that reflects the goal is the share of completed procedures whose full sequence was booked before the patient left the building. Measured that way, most practices find their post-operative booking rate is lower than they believed, and that the gap concentrates in specific clinic sessions rather than being spread evenly.

Two more are worth watching. Clearance lead time, measured as days between the request going out and the document arriving, tells you whether the queue is being worked or just generated. And second-eye conversion, measured as the share of first-eye patients whose second procedure gets scheduled within the practice’s intended window, is the one with revenue attached and the one most often left to whether the patient remembers to call back.

Key Takeaways

  • Treat the surgical sequence as the unit of work, not the individual appointment. The schedule shows procedures and hides the six contacts around each one.
  • Compare encounter history against existing bookings to find missing legs. A dependency that lives in a coordinator’s memory fails without any signal.
  • Confirm the patient’s whole day, not one appointment. Reminders that fire only on the chronologically first booking are a common cause of missed post-operative visits.
  • Work outstanding clearances as a dated queue sorted by procedure date, rather than relying on someone to remember which ones are pending.
  • Chase the clearance document, never interpret it. The automation confirms receipt and routes it; a clinician decides whether the patient is cleared.
  • Route anything a patient says about their eye to clinical staff immediately. Confirmation calls surface symptoms, and a symptom is never a scheduling matter.
  • Book the second-eye procedure as part of the sequence instead of leaving it to the patient to call back. It is the step with the most revenue attached to it.
  • Measure the share of procedures whose full sequence was booked before the patient left, plus clearance lead time. Calls made will rise regardless of whether any of this improved.

Eye surgery is one of the most protocol-driven things a practice does, which is exactly why the coordination around it should not depend on memory. The intervals are known, the required documents are known, and the sequence repeats for nearly every patient. What is missing is a system that reads which legs of that sequence already exist and works the rest without being asked. An AI team inside athenaOne can compare a patient’s encounters against their bookings to find the gap and confirm the full day rather than a single slot. It chases clearances as a queue sorted by surgery date, books the post-operative visits and the second eye while the patient is still engaged, and routes every clinical question to the people licensed to answer it.

Sources

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Written by Kevin Henrikson