Fluency for Imaging vs. Change Healthcare Workflow Intelligence

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.

A robust and adaptable rules engine for medical imaging workflows targets radiologists who seek to streamline interpretation and quality control efforts within both singular and multi-facility environments, thus improving visibility and enhancing communication. This system effectively matches service demands with physician availability by automatically prioritizing and allocating cases to the most qualified and accessible doctors. By leveraging a sophisticated worklist that integrates interpretation and quality tasks, the solution encourages teamwork across the organization. The implementation of optimal workflows boosts performance, resulting in faster turnaround times and reduced patient stay durations. Furthermore, the platform emphasizes the prompt handling of exams flagged by AI for potential abnormalities, expediting urgent studies through cutting-edge artificial intelligence techniques. The quality of evaluations is further elevated by incorporating both bespoke and standardized workflows for diverse reviews, which encompass anonymous peer assessments, evaluations from technologists, interactions between Emergency Departments and Radiology, as well as the analysis of mammography images, among others. Overall, this pioneering strategy not only enhances operational efficiency but also raises the quality of care delivered to patients, ensuring that they receive timely and accurate assessments. In a healthcare landscape that increasingly relies on technology, such innovations are essential for improving patient outcomes and fostering a collaborative environment.