During the vaccine rollout, an opened vial gave a clinic six to twelve hours to use every dose. State and local health authorities told sites to keep standby lists so unused doses could be given before they expired.
A canceled appointment carries a deadline of its own, printed in the schedule: the appointment time. The room and the clinician are there for that hour whether or not a patient fills it, and somewhere a patient is waiting for an earlier slot.
Most practices now have software meant to handle this, and the best evidence on it points to a gap. It fills a minority of the slots it offers and works better for some patients than others, and what’s left looks like a job for a person working a list against the clock.
Even well-run waitlist software fills fewer than half the slots it offers
A multisite evaluation of automated waitlists, tools that notify waiting patients when an earlier appointment opens, put numbers on the results across U.S. health systems. The median share of offered slots that ended up filled was 25 percent. Systems the authors classed as high-performing reported about 39 percent.
Real physician voices, twice a week
Free, and one click to unsubscribe.
UCSF’s published numbers sit inside that range. Over about nine months its tool sent 60,660 offers for 21,978 open appointments, with accepted offers for 30.5 percent of the appointments. Patients accepted 11 percent of the offers, and those who took an earlier slot were seen a median of 14 days sooner.
The multisite authors attribute the spread in fill rates mainly to implementation and organizational setting, and the high performers ran their waitlist batches a median of 4.5 times a day against 3.0 across all systems. They conclude that the strongest results come when a waitlist is integrated into a system’s scheduling infrastructure instead of running as a “stand-alone technical solution.”
The offers worked better for some patients than others
Two health systems found the same pattern in who said yes. At UCSF, patients 65 and older and Chinese-speaking patients had lower odds of accepting an offer (odds ratios 0.86 and 0.62), and among patients who accepted, those on Medicaid or Medicare were less likely to complete the visit.
At Sutter Health, a 2020 analysis of 177,311 offers found acceptance lower among older patients and among those with more comorbidities or visits for chronic conditions. Both studied the same EHR module, so the pattern may reflect how that kind of offer reaches patients more than anything about the patients themselves.
The tools did shorten waits for the patients they reached. Sutter’s patients who accepted saw a doctor 15 days sooner in primary care and 24 sooner in specialty care. Neither paper suggests anyone designed an offer to leave patients out. An offer that arrives as a text or email and has to be accepted through a portal login suits some patients’ lives better than others, and the UCSF authors warned that tools like this “may exacerbate inequities in access.”
Neither study set out to test whether a phone call would reach more of the patients an offer misses.
Reaching the patient who never opens the app
Take a cancellation at 2:40 for a 4:00 visit, 80 minutes out. The high-performing systems in the multisite study reported a median minimum look-ahead of about four hours when searching for slots to offer, so an opening this late falls outside the window their waitlists search.
A scheduler with a short list can still work it, starting with whoever has waited longest. The next name has never logged into the portal and speaks Spanish, so the call happens in Spanish. By a quarter past three, the slot is filled, and a patient who was told three weeks is in the exam room that afternoon.
The scene is illustrative, since no study measured a staff-run version. The nearest evidence is a 2010 randomized trial of appointment reminders. Among 9,835 patients in an academic practice, no-show rates were 13.6 percent after a reminder call from front-desk staff, 17.3 percent after an automated reminder, and 23.1 percent with no reminder. A staff call outperformed an automated message at getting patients to their appointments, though the trial can’t say whether staff would out-fill a tool.
The UCSF authors call manual waitlist work impractical at busy medical centers, and at a system handling 1.7 million outpatient visits a year that’s a fair judgment. A small practice has fewer slots to fill and fewer names to work, so the question there is whether anyone is assigned to it. The tools earn their place. What they leave is the patients the offers miss and the slots that open too late for an offer.
A waitlist is only as fast as the person working it
What the tools leave comes down to who works the list, and how fast. Handing it to the front desk for the gaps between check-ins runs into the clock, since cancellations arrive on their own schedule and a slot that waits for a quiet moment at 4:30 has already passed. Overbooking manages expected no-shows while a patient who has waited weeks for an earlier appointment goes on waiting.
A tool also settles who gets a slot by who responds first. Offers can go to as many as ten patients at once, and anyone slower finds the slot gone. The UCSF authors call for a system that offers earlier appointments based on clinical urgency, which is a ranking a person can apply and a first-to-respond offer doesn’t.
The standby list points to a better model, one that works alongside a tool instead of replacing one. The tool makes the first pass. A person works what’s left, calling down a ranked list, starting with the longest waits and the patients a text is least likely to reach, in a patient’s own language wherever the practice has staff who speak it, and booking directly into the schedule.
The work is small in volume and strict in timing, which makes it hard to staff. It’s usually too little for a full-time hire at a small practice and too urgent for the gaps in a front desk’s day. The role is defined by the clock: The work starts the moment a slot opens.
Next time a visit cancels with less than 48 hours’ notice, write down when the cancellation came in and when a new patient was booked into the slot. The gap between the two is your practice’s recovery time. If the slot stayed empty, that’s the finding.
Karan Kanwar is the CEO of Central AI and Wing Assistant. Central AI provides an AI operating system for appointment-driven medical practices, handling calls, texts, web chat, and messages around the clock, qualifying patients and booking appointments directly into a practice’s existing systems. It is HIPAA compliant and designed to complement the tools practices already use.
Wing Assistant pairs that technology with skilled virtual assistants who take on the administrative work that pulls teams away from patient care. Kanwar writes on where automation and human support meet in health care operations, and how practice leaders can adopt both effectively. He shares updates on LinkedIn.
