A Novel Pipeline to Automatically Harness Log File Data for Enhanced MRI Parameter Analysis, Patient Care and Radiology Utilisation

Motivation: A deeper understanding of how information in log files can contribute to service improvement, for example reducing patient backlog and improving patient safety. Goal(s): To construct an automated data pipeline that analyses and processes log files, returning quantitative results and graphical visualisations that can be interpreted in a clinically useful way. Approach: We obtained log files from our scanner fleet and constructed a Python codebase to read, analyse and interpret data pertaining to time utilisation and patient exposure. Results: We have demonstrated the code’s capability to provide detailed exposure monitoring as well as insights into how productively we use time on our scanners. Impact: Our work will help us to optimise the patient pathway, improve patient safety and investigate how more obscure parameters can affect our service delivery. Our code will be open source, enabling others to benefit and contribute, improving services more widely.