The Queensland Government is aiming to integrate Artificial Intelligence (AI) into its Medical Imaging and Radiology services across the Gold Coast Hospital and Health Service (GCHHS).
Queensland health is facing issues with medical imaging diagnosis, including backlogs due to rising image volumes, slow and error-prone manual data entry and interpretation of medical scans, and inefficient traditional skeletal age assessment methods. To address these issues, they have initiated the Artificial Intelligence in Medical Imaging (AIMI) project, which aims to improve efficiency, enhance accuracy, and decrease delays in clinical settings, ultimately improving patient outcomes.
The agency is seeking applications for Expression of Interest (EOI) for AI-powered solutions that utilise Machine Learning (ML) to analyse a patient’s medical data, from medical notes to diagnostic scans. This system would offer real-time insights and recommendations to physicians when they request and analyse radiology exams.
The agency is seeking responses regarding AI solutions for nine clinical use cases. The tender aims to address challenges in healthcare including unnecessary imaging, diagnosis delays, and manual data entry errors.
The use cases include:
- Clinical decision support: AI applications in radiology to assist in clinical decision-making, providing insights and recommendations to prevent unnecessary scans and delays.
- Worklist triage: Enhancing the efficiency and accuracy of case triaging, allowing radiologists to prioritise reports with urgent findings, enhancing workflow efficiency and potentially saving lives.
- Critical Results Notifications: Providing radiologists with notifications for critical or unexpected findings, facilitating quicker intervention and potentially improving patient outcomes.
- Pathology Detection: Detecting a broad spectrum of abnormalities in Chest X-rays, Chest CT scans, and head CT scans, preventing delays in diagnosis and improving efficiency
- Bone age assessment: AI-powered tool for accurate and timely bone age assessments, with applications in endocrinology, orthopaedics, and paediatrics.
- Workflow streamlining: Automating administrative tasks and improving communication to enhance efficiency and reduce diagnosis time.
- Quality assurance: Enhancing medical imaging processes and quality assurance through comprehensive patient data for informed decision making and improvement of efficiency
The AI solutions must comply with policies and standards from multiple groups, including the TGA, RANZCR, QGEA, and must prioritise data privacy, security, and interoperability,
There will be a supplier briefing session on Thursday the 20th of June via Microsoft Teams.
The closing time and date for responses will remain open until the 12 April 2024.
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