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AI Referral Automation via Kailo Medical’s KailoHub and KailoFlow

See how Kailo’s AI-enabled referral automation accelerates orders to report, empowering imaging teams with seamless workflow and smarter decision-making.

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Overview

Radiology providers globally manage thousands of referrals each day from general practitioners, specialists, and patients. These referrals may arrive by email in a wide range of formats. Formats provided ranged from structured electronic documents to scanned handwritten notes and mobile phone images which created complex and time consuming administrative workload for intake teams.

Challenge

The traditional referral intake process at radiology practices relied heavily on manual handling. Staff scanned referral documents into the Radiology Information System (RIS) and manually transcribed key details such as patient information, referring doctor, and clinical notes. This labor-intensive process consumed valuable administrative time, introduced potential for human error, and created bottlenecks that delayed triage and appointment scheduling. The administrative labour needed to be reduced and streamlined in order to create more accurate referral readings, and faster triage.

Kailo Medical Solution

Kailo Medical developed a solution to streamline and modernize its referral workflow using KailoHub and KailoFlow. Kailo Medical developed a connected AI‑powered automation platform designed to enhance accuracy and efficiency in healthcare data processing.

  • KailoHub monitors incoming referral inboxes and employs advanced AI models to extract relevant data fields from documents of varying types and formats.This formats included scanned typed documents, hand written referrals and mobile phone photos.
  • The system also provides a confident estimation for extracted data, indicating where human review may be necessary.
  • KailoFlow serves as an intuitive review interface, presenting staff with a referral viewer and prefilled data fields that have been extracted from the referral itself. Fields with lower confidence are clearly highlighted, allowing users to verify or correct them efficiently before sending the completed referral to the RIS.
  • Both the structured data and the original referral document are securely stored and accessible within the RIS, ensuring traceability and auditability.

Impact

  • Substantial reduction in manual data entry and administrative overhead.
  • Faster referral triage and scheduling, streamlining patient access to care.
  • Improved data accuracy through AI‑assisted extraction and quality validation.
  • Scalable architecture supporting regional rollout, with national expansion underway.

Conclusion

By implementing Kailo Medical’s AI‑powered referral automation with KailoHub and KailoFlow, Radiologists will replace manual, error‑prone processes with a streamlined, intelligent workflow. The solution enhances accuracy, efficiency, and operational scalability—laying the groundwork for a nationwide digital transformation in radiology referral management.

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