Problem

Avoca Systems addresses the access and operational bottlenecks that prevent radiology clinics from converting patient demand into completed scans. More than 30% of calls into radiology centres can go unanswered, while referrals arrive through disconnected channels and staff must manually interpret documents, contact patients, and coordinate appointments across multiple systems. The economic pain is lost bookings and underused scanner capacity, combined with pressure to hire and retain more clerical staff.

The killer use case is completing a clinically appropriate imaging appointment despite radiology’s complexity. A booking may depend on the modality, body region, contrast requirements, patient history, preparation instructions, equipment availability, staff qualifications, and referring-provider details. Avoca is designed to prevent an unanswered call, stalled referral, or failed handoff from becoming a lost scan.

Product / Service

Avoca is an AI patient-access and operational-intelligence platform for radiology networks. It integrates with existing radiology systems and automates inbound and outbound calls, online scheduling, fax and e-referral intake, rescheduling, cancellations, preparation questions, reminders, results, and follow-up. Its central capability is encoding each network’s booking rules, rosters, resource constraints, and operational knowledge so patients can be matched to appropriate appointments in real time.

The platform consolidates calls, online bookings, and referrals into one workflow and writes confirmed appointments back to the RIS, reducing manual data entry and double handling. It operates continuously across patient-access channels, while routing exceptions to staff with the relevant context. The intended benefit is more captured demand and completed scans without proportionally increasing headcount, alongside a smoother patient experience.

Market

Avoca competes in healthcare IT and enterprise software, specifically AI-enabled patient access, scheduling, referral management, and operational automation for outpatient radiology networks. Its positioning is against generic voice and scheduling tools, as well as the fragmented manual workflows built around legacy systems, spreadsheets, and receptionist knowledge. The reviewed materials do not identify a named direct competitor; the company’s differentiation is its radiology-specific handling of clinical, operational, and resource constraints rather than generic appointment booking.

The company has meaningful early commercial traction rather than being merely pre-product: its July 2026 launch materials say it was live across more than 250 clinics, including networks operating 150-plus locations, and report deployments that reduced call abandonment from roughly 30% to 2%, achieved more than 80% booking conversion for eligible calls, and averaged 4.6/5 patient satisfaction. Its website cites an 86% call-resolution rate and customers including Partnered Health and Jobfit. Public materials reviewed do not disclose revenue or pricing, so it is best characterized as an early-stage company with live deployments, not confidently classified as pre-revenue.