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Abstract #0954

Smart MRI Departments: Intelligent Patient Scheduling Using Real-Time Appointment Duration Metrics and an FNN to Predict Lateness

Oscar Lally1, Alina Schneider1, Molly Buckley1, and Simon Shah1
1Medical Physics and Clinical Engineering, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom

Synopsis

Keywords: Software Tools, MR Value, Productivity, Sustainability, Optimisation, Deep-Learning, Efficiency, Cost-Effective MRI

Motivation: A significant challenge in MRI departments is accurately scheduling patient appointments to maximise efficiency while minimising patient backlog and delays.

Goal(s): To develop an AI-driven tool that uses patient demographic data, scanner utilisation metrics (for real-time appointment duration), and RFID information to create a comprehensive patient booking system.

Approach: 40,000 sets of patient demographic data were processed to train an FNN-based network that estimates patient punctuality. Scanner log files were analysed to ascertain MRI metrics such as accurate protocol lengths.

Results: The FNN outperforms current techniques when predicting patient arrival times and we can extract accurate protocol times for training our next model.

Impact: By increasing patient throughput, our work will be a crucial step towards a smart, sustainable MRI department. More patients will be scanned with less scan idle-time, improving patient outcomes in the long-term, whilst contributing to a greener and cost-effective service.

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