Patient no-shows are a symptom. Practices need to find the underlying cause

Practice administrators who have already tried reminder calls, texts and other quick fixes for no-shows may be skipping the most important step: figuring out why patients miss appointments in the first place.

That's the view of A.G. Ghanim, M.B.B.Ch., a healthcare senior manager at the University of Michigan who oversees clinical operations across multiple sites. He specializes in workforce optimization, capacity planning and performance improvement, and is founder of ZLinQ Consulting, with a six-step process to define, diagnose, prioritize, design, implement and optimize solutions for patient no-shows. His presentation, "No-Shows Are Not Inevitable: Operational Playbooks Medical Practices Are Using to Protect Access," was part the Medical Group Management Association (MGMA) 2026 Annual Conference.

In a video interview, Ghanim described no-shows as an operational design issue rather than a patient behavior problem. He walked through the costs that rarely appear in a revenue report, including hours of scheduling and prior authorization work that must be redone when a patient doesn't arrive.

"It's really hard to measure, but you can feel it and you can see it if you are on the ground," he said.

He outlined the first steps of his approach: Define the problem, diagnose it and identify the main drivers before trying any solution. He also warned against blaming demographics, such as a clinic's location in a low-income area, which he called a symptom rather than the cause. Because no-shows rarely have a single cause, he ranks the drivers and helps organizations decide how many to tackle with the resources they have. Some clinics have gone from a 19% no-show rate to 7% or 5%. For thin-margin services such as sleep labs, he said, even a 1% or 2% change can affect income for the whole year.

Ghanim compared the challenges facing small independent practices and large health systems. Small practices can act quickly but often lack data and reporting tools, while large systems have the data but must work through layers of approval to change workflows such as centralized scheduling.

"It's very hard for you to understand. But it's very easy for you to execute," he said of small practices.