Q/ris 3000 Workflow vs. Fluency for Imaging

A sophisticated online scheduling and room management system has been developed to enhance patient booking efficiency, providing a smooth and nearly perfect experience. It incorporates automated scheduling for complex segmented procedures and enables fully digital patient registration along with effective document management. This system allows for real-time online insurance verification and supports the simultaneous management of multiple facilities. Additionally, it includes a built-in Clinical Decision Support (CDS) tool that significantly improves workflow efficiency. The Q/ris technologist workflow module captures DICOM Structured Reports (SR) automatically and provides a range of tools to help technologists deliver data-rich studies, which ultimately conserves valuable time for radiologists. It also generates electronic worksheets for technologist notes and annotations specific to each exam, enabling real-time edits, modifications, or rescheduling. Furthermore, the system features automatic charge capture and interactive supply tracking, while ensuring compatibility with MIPPS DICOM modality worklists for seamless data integration. This holistic approach guarantees that all operations are optimized for maximum performance, paving the way for a more efficient healthcare delivery system. The integration of these advanced functionalities ultimately leads to improved patient satisfaction and increased operational effectiveness.

Fluency for Imaging is an AI-powered radiology reporting platform developed by Jacobian to support faster, more accurate diagnostic reporting in medical imaging workflows. Radiology departments face increasing workloads and complex reporting requirements, which often lead to documentation inefficiencies and cognitive overload for clinicians. Fluency for Imaging addresses these challenges by combining advanced speech recognition technology, AI-driven automation, and structured reporting capabilities into a unified reporting environment. Radiologists can dictate reports using natural speech while the platform automatically organizes the narrative into structured clinical documentation. The system integrates directly with PACS platforms to transfer measurement data, imaging findings, and AI-generated insights directly into the report, eliminating manual transcription and reducing the potential for errors. Fluency for Imaging also incorporates large language model–based ambient reporting features that can listen to dictations, understand clinical context, and generate structured impressions automatically. Built-in guideline support and intelligent nudging help ensure reports follow established medical standards such as BI-RADS, PI-RADS, and TNM classifications. The platform also enables ontology-based coding using clinical standards including RadLex, SNOMED CT, and ICD-10, transforming unstructured text into structured, data-rich reports suitable for analytics, research, and clinical decision support. Multimedia reporting features allow radiologists to include annotated images, tables, and visual diagrams to improve report clarity for referring physicians. Additionally, Fluency for Imaging supports interoperability through standards such as HL7, FHIR, and FHIRcast, enabling seamless communication between imaging systems, hospital information systems, and analytics platforms.