Eniax, the Chilean AI reclaiming lost medical appointments: the real value of digital health is logistics

By EntrepreNerd · November 11, 2026.

Eniax, a Chilean company founded in 2015 by Milan Didara, develops an artificial intelligence platform for managing outpatient care: scheduling, confirmations, reminders, rescheduling, waiting lists and follow-up on pending tests and check-ups. According to the outlet, it works with more than 650 healthcare centers in seven countries and handles more than 90 million appointments a year.

One of its components is Patricia, an AI system that, according to the source, draws on millions of previous interactions to recognize behavioral patterns and tailor communication to each patient. Besides reducing no-shows, it identifies people who tried to get an appointment and failed, and keeps that request alive so they can be contacted when a slot opens up.

The article offers only one specific result: in the first week of October 2026, at Clínica INDISA, 16% of patients who had abandoned the scheduling process ended up getting an appointment thanks to this system. The figure comes from a single center and a single week, and it appears in a promotional piece, so it is a sign of potential, not proof of broad impact. Key data is missing: how much no-shows fall overall and how the system compares with previous methods.

The project began with support from Corfo (Chile's economic development agency) and early work with Clínica Dávila. It grew out of the discovery that the problem went beyond booking: attendance had to be confirmed, check-ups had to be remembered, and canceled appointments had to be reassigned to whoever was still waiting. Eniax was also one of nine startups selected by WOM Empresas to take part in EtMday, a gathering of entrepreneurs and investors.

Our take: AI in healthcare tends to be associated with spectacular diagnoses, but much of patients' everyday suffering is logistical. An appointment lost to an unannounced no-show is medical capacity that exists and goes to waste while someone else waits. Optimizing that flow cures nothing on its own, but it increases the care actually delivered without building a single new operating room.

It is also a case where AI has a clear advantage: a repetitive task, abundant data and low clinical risk, because the system does not decide on treatments. The strong point here is scale. The issues to watch are the privacy of patient data and whether personalized messaging leaves out people with less digital access.

Overall, the shift toward AI-assisted healthcare will arrive through these administrative layers before it arrives through autonomous medicine. In the short term, it means less call-center and administrative work at clinics, and that should be acknowledged. In the long term, the same efficiency logic lays the foundation for health systems that serve more people with the same resources, a modest but real step toward the abundance of care we hold up as the goal.